diff --git "a/4842.jsonl" "b/4842.jsonl" new file mode 100644--- /dev/null +++ "b/4842.jsonl" @@ -0,0 +1,715 @@ +{"seq_id":"394157836","text":"import cv2\nimport argparse\nimport imutils\n\nap = argparse.ArgumentParser()\nap.add_argument(\"-i\", \"--image\", required=True, help=\"path to the input image\")\nargs = vars(ap.parse_args())\n\nimage = cv2.imread(args[\"image\"])\n\ncrop1 = image[85:250, 85:220]\ncrop2 = image[173:235, 13:81]\ncrop3 = image[90:450, 0:290]\ncrop4 = image[124:212, 225:380]\n\ncv2.imshow(\"crop1\", crop1)\ncv2.imshow(\"crop2\", crop2)\ncv2.imshow(\"crop3\", crop3)\ncv2.imshow(\"crop4\", crop4)\n\ncv2.waitKey(0)","sub_path":"CV/01-computer-vision-basics/crop.py","file_name":"crop.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"598785419","text":"# pylint: disable=E1101\nimport time\nimport turtle\n\n\n# constants for all draw functions for a nice and consistent look\nLENGTH = 6\nANGLE = 35\n\ndef draw_tree(height):\n \"\"\"\n Draws a fractal tree with `height` repetitions.\n \n `height` defines the overall height of the tree is also responsible for\n the length of each branch in every iteration\n \"\"\"\n if height == 0:\n return\n # left branch\n turtle.forward(LENGTH * height)\n turtle.left(ANGLE)\n draw_tree(height - 1) # drawing the little tree at the end of the branch\n # right branch\n turtle.right(2 * ANGLE)\n draw_tree(height - 1) # drawing the little tree at the end of the branch\n turtle.left(ANGLE)\n turtle.backward(LENGTH * height)\n\n\ndef draw_house():\n \"\"\"This draws a nice and simple house!\"\"\"\n # the dimensions of our house\n height = 5 * LENGTH\n width = 7 * LENGTH\n roofside = (width ** 2 / 2) ** (1 / 2)\n\n # left wall\n turtle.forward(height)\n # roof\n turtle.right(45)\n turtle.forward(roofside)\n turtle.right(90)\n turtle.forward(roofside)\n turtle.right(45)\n # right wall\n turtle.forward(height)\n turtle.right(90)\n # bottom line\n turtle.forward(width)\n turtle.right(90)\n\n\ndef draw_world(curvature_step=0):\n \"\"\"\n This draws a turtle world.\n \n The curvature step is relevant for drawing a round world.\n The higher the curvature step is, the smaller our circle will be.\n \n Each village will consist of one house and 3 trees, with one being taller.\n \"\"\"\n if curvature_step > 0: # this ensures we are going full circle\n villages = 360 // 4 // curvature_step\n else: # 5 villages for our flat world\n villages = 5\n\n # the _ is called an anonymous variable, since we don't use it anyway\n # we don't need to give it a name. It just acts as a counter.\n for _ in range(villages):\n prepare_drawing()\n draw_house()\n finish_drawing()\n\n turtle.right(curvature_step)\n turtle.forward(LENGTH * 11)\n\n # and draw the three trees\n for j in range(3):\n prepare_drawing()\n # the middle one will be 5 high, since we iterate over 0,1,2\n # and only for 1 modulo 2 is 1 returned.\n draw_tree(3 + j % 2 * 2)\n finish_drawing()\n\n turtle.right(curvature_step)\n turtle.forward(LENGTH * 3)\n\n turtle.forward(LENGTH)\n\n\ndef init():\n \"\"\"set up the turtle parameters\"\"\"\n turtle.reset()\n turtle.shape('turtle')\n turtle.speed('fastest')\n turtle.up()\n\n\ndef prepare_drawing():\n \"\"\"move the pen down to actually draw and make turtle upright\"\"\"\n turtle.down()\n turtle.left(90)\n\n\ndef finish_drawing():\n \"\"\"move pen up to stop drawing and return turtle to axis\"\"\"\n turtle.right(90)\n turtle.up()\n\n\ndef draw_flat_world():\n \"\"\"wrapper to start drawing a flat world with 0 curvature\"\"\"\n init()\n turtle.goto(-300, 0)\n\n draw_world()\n turtle.goto(0,0)\n\n\ndef draw_round_world(curvature_step=5):\n \"\"\"wrapper to draw a curved world with a default curvature step of 5\"\"\"\n init()\n turtle.goto(0, 300)\n draw_world(curvature_step)\n turtle.goto(0,0)\n\n\ndef draw():\n \"\"\"Draw the flat world. Rest shortly to marvel at it. Draw round world.\"\"\"\n draw_flat_world()\n time.sleep(3)\n draw_round_world()\n turtle.done()\n\n# Start the party!\ndraw()\n","sub_path":"2018/03/turtle_world.py","file_name":"turtle_world.py","file_ext":"py","file_size_in_byte":3398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"501475990","text":"import cv2\nimport os\n\nos.chdir( r'C:\\Users\\xkh\\Desktop\\20210721_162126\\img')\nfilename = r'C:\\Users\\xkh\\Desktop\\20210721_162126\\20210721_162126_VIS_H264.MOV'\n\nvideoCapture1 = cv2.VideoCapture(filename)\n\nstatus, frame = videoCapture1.read()\nindex = 0\nwhile True:\n status, frame = videoCapture1.read()\n if not status:\n print('video is all read')\n break\n\n cv2.imwrite('img_'+str(index)+'.jpg', frame)\n index += 1\n\n","sub_path":"Video_2_Pictures.py","file_name":"Video_2_Pictures.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"92919668","text":"#!/usr/bin/env python3\nimport os\nimport sys\nimport gzip\n\nfilename_family_fa = sys.argv[1]\ndirname_output = sys.argv[2]\n\nf_fa = open(filename_family_fa, 'r')\nif filename_family_fa.endswith('.gz'):\n f_fa = gzip.open(filename_family_fa, 'rt')\n\nfamily2seq = dict()\nfamily_seq_list = dict()\nfor line in f_fa:\n if line.startswith('>'):\n seq_h = line.strip().lstrip('>')\n family_seq_list[seq_h] = ''\n\n tmp_family_id = seq_h.split('|')[0]\n if tmp_family_id not in family2seq:\n family2seq[tmp_family_id] = []\n family2seq[tmp_family_id].append(seq_h)\n else:\n family_seq_list[seq_h] += line.strip()\nf_fa.close()\n\nfor tmp_family_id, tmp_h_list in family2seq.items():\n dirname_output_sub = os.path.join(dirname_output, tmp_family_id[-1])\n if not os.access(dirname_output_sub, os.W_OK):\n os.mkdir(dirname_output_sub)\n\n dirname_output_gt100 = os.path.join(dirname_output, 'gt100')\n if not os.access(dirname_output_gt100, os.W_OK):\n os.mkdir(dirname_output_gt100)\n\n dirname_output_lt3 = os.path.join(dirname_output, 'lt3')\n if not os.access(dirname_output_lt3, os.W_OK):\n os.mkdir(dirname_output_lt3)\n\n seq2h = dict()\n for tmp_id in sorted(tmp_h_list):\n ## Remove family_id from the sequence for display.\n new_id = tmp_id.replace('%s|' % tmp_family_id, '')\n tmp_seq = ''.join(family_seq_list[tmp_id]).replace('-', '')\n if tmp_seq not in seq2h:\n seq2h[tmp_seq] = []\n seq2h[tmp_seq].append(new_id)\n \n out_list = []\n for tmp_seq, tmp_h_list in seq2h.items():\n if len(tmp_h_list) == 1:\n out_list.append('>%s\\n%s' % (tmp_h_list[0], tmp_seq))\n else:\n gencode_h_list = [x for x in tmp_h_list if x.endswith('-GENCODE')]\n if len(gencode_h_list) == 1:\n out_list.append('>%s\\n%s' % (gencode_h_list[0], tmp_seq))\n for tmp_h in tmp_h_list:\n if tmp_h == gencode_h_list[0]:\n continue\n sys.stderr.write('Replace %s --> %s (identical)\\n' % (tmp_h, gencode_h_list[0]))\n else:\n out_list.append('>%s\\n%s' % (tmp_h_list[0], tmp_seq))\n for tmp_h in tmp_h_list:\n if tmp_h == tmp_h_list[0]:\n continue\n sys.stderr.write('Replace %s --> %s (identical)\\n' % (tmp_h, tmp_h_list[0]))\n\n \n if len(out_list) > 100 * 2:\n filename_output = os.path.join(dirname_output_gt100,\n '%s.msa_in.fa' % tmp_family_id)\n elif len(out_list) < 3 * 2:\n filename_output = os.path.join(dirname_output_lt3,\n '%s.msa_in.fa' % tmp_family_id)\n else:\n filename_output = os.path.join(dirname_output_sub,\n '%s.msa_in.fa' % tmp_family_id)\n\n sys.stderr.write('Write %s\\n' % filename_output)\n f_out = open(filename_output, 'w')\n f_out.write('\\n'.join(out_list) + \"\\n\")\n f_out.close()\n","sub_path":"MODtree/prepare-MODtree-msa.py","file_name":"prepare-MODtree-msa.py","file_ext":"py","file_size_in_byte":3068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"225123675","text":"import asyncio\nimport requests\n\nasync def request():\n url = 'https://www.baidu.com'\n status = requests.get(url)\n return status\ndef callback(task):\n print('Status',task.result())\n\ncoroutine = request()\n# task = asyncio.ensure_future(coroutine)\n# task.add_done_callback(callback) # 添加回调\n# loop = asyncio.get_event_loop()\n# loop.run_until_complete(task)\n\"\"\"\n callback() 方法传递给了封装好的 task 对象，这样当 task 执行完毕之后就可以调用 callback() 方法了，同时 task 对象还会作为参数传递给 callback() 方法，调用 task 对象的 result() 方法就可以获取返回结果了。\n\"\"\"\n# 不用回调方法，直接在 task 运行完毕之后也可以直接调用 result() 方法获取结果\ntask = asyncio.ensure_future(coroutine)\nloop = asyncio.get_event_loop()\nloop.run_until_complete(task)\nprint('Task Result:',task.result())","sub_path":"Learn-python/python编程/13-asyncio并发编程/02-绑定回调.py","file_name":"02-绑定回调.py","file_ext":"py","file_size_in_byte":889,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"36768782","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[251]:\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\n\n\n# In[252]:\n\n\ndef f(x):\n return (3*x*x +2*x - 1)\n\n\n# In[253]:\n\n\ndef fprime(x):\n return(6*x + 2)\n\n\n# In[254]:\n\n\nepsilon_threshold = .0001\nlearning_rate = .01\n\n\n# In[255]:\n\n\ndef grad_descent_ep(x=0): # termination by change in epsilon\n epsilon_values = []\n current_epsilon = x\n while current_epsilon > epsilon_threshold:\n oldX = x\n x = x - learning_rate*fprime(x)\n current_epsilon = abs(f(oldX) - f(x))\n epsilon_values.append(current_epsilon)\n \n return (epsilon_values,x)\n\n\n# In[256]:\n\n\ndef grad_descent_it(iterations=100): # termination by iterations\n epsilon_values = []\n count = 0\n x = random.randint(-100,100)\n while count < iterations:\n oldX = x\n x = x - learning_rate*fprime(x)\n epsilon_values.append(abs( (f(oldX) - f(x)) ))\n count = count + 1\n \n return (epsilon_values,x)\n\n\n# In[257]:\n\n\ndef grad_descent_bare(iterations=100): # barebones gradient descent, no error tracking, uses iteration termination\n count = 0\n x = random.randint(-100,100)\n while count < iterations:\n x = x - learning_rate*fprime(x)\n count = count + 1 \n return (x)\n\n\n# In[258]:\n\n\nplot_epsilon_values,minVal = grad_descent_it()\nprint(minVal)\nprint(f(minVal))\n\n\n# In[259]:\n\n\nepsilon_to_plot = np.array(plot_epsilon_values)\nplt.plot(epsilon_to_plot)\n\n\n# In[260]:\n\n\nprint(grad_descent_bare())\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"Gradient Descent.py","file_name":"Gradient Descent.py","file_ext":"py","file_size_in_byte":1522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"94901311","text":"import numpy as np\nimport cv2\n\ncap = cv2.VideoCapture(1)\n\nwhile(1):\n #获取每一帧\n ret, frame = cap.read()\n\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n\n #设定蓝色阙值\n lower_blue = np.array([110,50,50])\n upper_blue = np.array([130,255,255])\n #根据􏹈值构建掩模\n mask = cv2.inRange(hsv,lower_blue,upper_blue)\n # 对原图像和掩模进行位运算\n res = cv2.bitwise_and(frame,frame,mask=mask)\n\n cv2.imshow('frame', frame)\n cv2.imshow('mask', mask)\n cv2.imshow('res', res)\n\n k=cv2.waitKey(5)&0xFF\n if k==27:\n break\ncap.release()\ncv2.destroyAllWindows()","sub_path":"face/trace_color.py","file_name":"trace_color.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"105229460","text":"\"\"\"Code that handles CLI commands to upload\"\"\"\nimport datetime as dt\nimport logging\nimport sys\nimport traceback\n\nimport click\n\nfrom cg.apps import coverage as coverage_app\nfrom cg.apps import gt, hk, lims, madeline, scoutapi, tb\nfrom cg.cli.workflow.mip_dna.deliver import CASE_TAGS, SAMPLE_TAGS\nfrom cg.exc import AnalysisUploadError\nfrom cg.meta.deliver import DeliverAPI\nfrom cg.meta.report.api import ReportAPI\nfrom cg.meta.upload.scoutapi import UploadScoutAPI\nfrom cg.meta.workflow.mip_dna import AnalysisAPI\nfrom cg.store import Store\n\nfrom .beacon import beacon\nfrom .coverage import coverage\nfrom .delivery_report import delivery_report, delivery_report_to_scout, delivery_reports\nfrom .genotype import genotypes\nfrom .mutacc import process_solved, processed_solved\nfrom .observations import observations\nfrom .scout import scout, upload_case_to_scout\nfrom .utils import _suggest_cases_to_upload\nfrom .validate import validate\n\nLOG = logging.getLogger(__name__)\n\n\n@click.group(invoke_without_command=True)\n@click.option(\"-f\", \"--family\", \"family_id\", help=\"Upload to all apps\")\n@click.option(\n \"-r\",\n \"--restart\",\n \"force_restart\",\n is_flag=True,\n help=\"Force upload of analysis \" \"marked as started\",\n)\n@click.pass_context\ndef upload(context, family_id, force_restart):\n \"\"\"Upload results from analyses.\"\"\"\n\n click.echo(click.style(\"----------------- UPLOAD ----------------------\"))\n\n context.obj[\"status\"] = Store(context.obj[\"database\"])\n\n if family_id:\n family_obj = context.obj[\"status\"].family(family_id)\n if not family_obj:\n message = f\"family not found: {family_id}\"\n click.echo(click.style(message, fg=\"red\"))\n context.abort()\n\n if not family_obj.analyses:\n message = f\"no analysis exists for family: {family_id}\"\n click.echo(click.style(message, fg=\"red\"))\n context.abort()\n\n analysis_obj = family_obj.analyses[0]\n\n if analysis_obj.uploaded_at is not None:\n message = f\"analysis already uploaded: {analysis_obj.uploaded_at.date()}\"\n click.echo(click.style(message, fg=\"red\"))\n context.abort()\n\n if not force_restart and analysis_obj.upload_started_at is not None:\n if dt.datetime.now() - analysis_obj.upload_started_at > dt.timedelta(hours=24):\n raise AnalysisUploadError(\n f\"The upload started at {analysis_obj.upload_started_at} \"\n f\"something went wrong, restart it with the --restart flag\"\n )\n\n message = f\"analysis upload already started: {analysis_obj.upload_started_at.date()}\"\n click.echo(click.style(message, fg=\"yellow\"))\n return\n\n context.obj[\"housekeeper_api\"] = hk.HousekeeperAPI(context.obj)\n\n context.obj[\"madeline_api\"] = madeline.api.MadelineAPI(context.obj)\n context.obj[\"genotype_api\"] = gt.GenotypeAPI(context.obj)\n context.obj[\"lims_api\"] = lims.LimsAPI(context.obj)\n context.obj[\"tb_api\"] = tb.TrailblazerAPI(context.obj)\n context.obj[\"chanjo_api\"] = coverage_app.ChanjoAPI(context.obj)\n context.obj[\"deliver_api\"] = DeliverAPI(\n context.obj,\n hk_api=context.obj[\"housekeeper_api\"],\n lims_api=context.obj[\"lims_api\"],\n case_tags=CASE_TAGS,\n sample_tags=SAMPLE_TAGS,\n )\n context.obj[\"scout_api\"] = scoutapi.ScoutAPI(context.obj)\n context.obj[\"analysis_api\"] = AnalysisAPI(\n context.obj,\n hk_api=context.obj[\"housekeeper_api\"],\n scout_api=context.obj[\"scout_api\"],\n tb_api=context.obj[\"tb_api\"],\n lims_api=context.obj[\"lims_api\"],\n deliver_api=context.obj[\"deliver_api\"],\n )\n context.obj[\"report_api\"] = ReportAPI(\n store=context.obj[\"status\"],\n lims_api=context.obj[\"lims_api\"],\n chanjo_api=context.obj[\"chanjo_api\"],\n analysis_api=context.obj[\"analysis_api\"],\n scout_api=context.obj[\"scout_api\"],\n )\n\n context.obj[\"scout_upload_api\"] = UploadScoutAPI(\n hk_api=context.obj[\"housekeeper_api\"],\n scout_api=context.obj[\"scout_api\"],\n madeline_api=context.obj[\"madeline_api\"],\n analysis_api=context.obj[\"analysis_api\"],\n lims_api=context.obj[\"lims_api\"],\n )\n\n if context.invoked_subcommand is not None:\n return\n\n if not family_id:\n _suggest_cases_to_upload(context)\n context.abort()\n\n family_obj = context.obj[\"status\"].family(family_id)\n analysis_obj = family_obj.analyses[0]\n if analysis_obj.uploaded_at is not None:\n message = f\"analysis already uploaded: {analysis_obj.uploaded_at.date()}\"\n click.echo(click.style(message, fg=\"yellow\"))\n else:\n analysis_obj.upload_started_at = dt.datetime.now()\n context.obj[\"status\"].commit()\n context.invoke(coverage, re_upload=True, family_id=family_id)\n context.invoke(validate, family_id=family_id)\n context.invoke(genotypes, re_upload=False, family_id=family_id)\n context.invoke(observations, case_id=family_id)\n context.invoke(scout, case_id=family_id)\n analysis_obj.uploaded_at = dt.datetime.now()\n context.obj[\"status\"].commit()\n click.echo(click.style(f\"{family_id}: analysis uploaded!\", fg=\"green\"))\n\n\n@upload.command()\n@click.pass_context\ndef auto(context):\n \"\"\"Upload all completed analyses.\"\"\"\n\n click.echo(click.style(\"----------------- AUTO ------------------------\"))\n\n exit_code = 0\n for analysis_obj in context.obj[\"status\"].analyses_to_upload():\n\n if analysis_obj.family.analyses[0].uploaded_at is not None:\n LOG.warning(\n \"Newer analysis already uploaded for %s, skipping\", analysis_obj.family.internal_id\n )\n continue\n\n internal_id = analysis_obj.family.internal_id\n LOG.info(\"uploading family: %s\", internal_id)\n try:\n context.invoke(upload, family_id=internal_id)\n except Exception:\n\n LOG.error(\"uploading family failed: %s\", internal_id)\n LOG.error(traceback.format_exc())\n exit_code = 1\n\n sys.exit(exit_code)\n\n\nupload.add_command(process_solved)\nupload.add_command(processed_solved)\nupload.add_command(validate)\nupload.add_command(beacon)\nupload.add_command(scout)\nupload.add_command(upload_case_to_scout)\nupload.add_command(observations)\nupload.add_command(genotypes)\nupload.add_command(coverage)\nupload.add_command(delivery_report)\nupload.add_command(delivery_reports)\nupload.add_command(delivery_report_to_scout)\n","sub_path":"cg/cli/upload/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":6554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"206637840","text":"#!/usr/bin/env python3\n\nimport pickle\nimport requests\nimport time\nfrom conf import conf\n\nclass request(conf):\n def __init__(self):\n super().__init__()\n\n self.cache_file = \"cache.pickle\"\n self.session = requests.session()\n\n\n def get(self, url):\n # Fetch and return specified source\n\n width = 50\n print(\"#\" + url[:width].ljust(width), end=\" \", flush=True)\n\n # build headers\n cache = self._returnCacheData()\n if url in cache:\n header = {\n \"If-None-Match\" : cache[url][\"ETag\"],\n #\"If-Modified-Since\" : self.cache[url][\"cachedate\"],\n \"Accept-Language\" : \"en-us\",\n \"User-Agent\" : 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12) AppleWebKit/602.1.39 (KHTML, like Gecko) Version/10.0 Safari/602.1.38',\n \"Accept\" : \"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8\",\n }\n else:\n header = {}\n\n try:\n # Fetch\n r = self.session.get(url, headers=header)\n except requests.exceptions.ConnectionError as e:\n print(\"Failed\")\n return \"\"\n\n # if modified\n if r.status_code.real == 200:\n\n # save as a cache\n self._updateCache(r)\n print(\"Done\")\n\n return r.text\n\n # if not modified\n elif r.status_code.real == 304:\n print(\"Not modified\")\n return cache[url][\"data\"]\n\n else:\n print(\"Unknown %d\" % r.status_code.real)\n return \"\"\n\n\n def _returnCacheData(self):\n\n try:\n with open(self.cache_file, mode='rb') as f:\n cache = pickle.load(f)\n except:\n cache = dict()\n\n return cache\n\n def _updateCache(self, response):\n\n # open the cache file\n cache = self._returnCacheData()\n\n timestamp = time.gmtime(time.time())\n timestamp = time.strftime(\"%a, %d %b %Y %H:%M:%S GMT\", timestamp)\n etag = response.headers[\"Etag\"] if \"ETag\" in response.headers else \"\"\n cache.update(\n {\n response.url:\n {\n \"data\": response.text,\n \"cachedate\": timestamp,\n \"ETag\": etag\n }\n })\n\n with open(self.cache_file, mode=\"wb\") as f:\n pickle.dump(cache, f)\n\n return\n\nif __name__ == '__main__':\n req = request()\n resp = req.get('https://www.google.com')\n\n print(resp[:100])\n","sub_path":"request.py","file_name":"request.py","file_ext":"py","file_size_in_byte":2521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"169590279","text":"\"\"\"\nGiven a string, print the number of alphabets present in the string.\n\nInput:\n\nThe first line of input contains an integer T denoting the number of test cases. The description of T test cases follows.Each test case contains a single string.\n\n\nOutput:\n\nPrint the number of alphabets present in the string.\n\nConstraints:\n\n1<=T<=30\n\n1<=size of string <=100\n\n\nExample:\n\nInput:\n\n2\nadjfjh23\nnjnfn_+-jf\n\nOutput:\n\n6\n7\n\"\"\"\n\n\ndef count_alphabets(str):\n count = 0\n for ch in str:\n if ch.isalpha():\n count += 1\n return count\n\n\nif __name__ == '__main__':\n t = int(input())\n for i in range(t):\n str = input()\n print(count_alphabets(str))","sub_path":"practice/school/count_alphabets.py","file_name":"count_alphabets.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"333605570","text":"import objsocket\nimport queue\nimport threading\nimport pickle\n\nclass Controller:\n '''\n Это класс написанный для примера, в реальности на его месте\n скорее всего будет программа на VB.net, которая будет подбирать\n параметры. Она должна будет посылать по сокетам то же, что и этот\n класс.\n '''\n\n def _init__(self, ip, task_port, result_port):\n self.task_socket = objsocket.objsocket()\n self.result_socket = objsocket.objsocket()\n\n self.task_socket.connect(ip, task_port)\n self.result_socket.connect(ip, result_port)\n\n def main(self):\n tasks = ['spam', 'eggs', 'ham']\n for i in range(3):\n task_socket.send_obj(tasks.pop())\n answer = result_socket.recv_obj()\n print(answer)\n\nclass Master:\n '''\n Класс, который получает задачи от чего-нибудь вроде Controller,\n распределяет их по Node-ам, получает результаты и посылает их\n обратно в Controller.\n '''\n\n def __init__(self, task_port, result_port, node_addresses):\n self.tasks = queue.Queue()\n self.results = queue.Queue()\n self.active_task_stream = False\n self.controller = _ControllerConnection(task_port, result_port, self)\n self.nodes = [_NodeConnection(ip, port, self) for ip, port in node_addresses]\n\n def start(self):\n self.controller.start()\n for node in self.nodes:\n node.start()\n\n def grab_result(self, result):\n '''Эту функцию будет вызывать _NodeConnection чтобы передать результаты в Master'''\n self.results.put(result)\n self.tasks.task_done()\n\n def give_task(self):\n '''Эту функцию будет вызывать _NodeConnection чтобы взять задание из Master'''\n return self.tasks.get()\n\n def put_task(self, task):\n '''Эту функцию будет вызывать _ControllerConnection чтобы положить задание в очередь Master'''\n self.tasks.put(task)\n\n def get_result(self):\n '''Эту функцию будет вызывать _ControllerConnection чтобы взять результат из очереди Master'''\n try:\n return self.results.get(timeout=1)\n except queue.Empty:\n return None\n\n\nclass _ControllerConnection:\n def __init__(self, task_port, result_port, master):\n self.master = master\n self.thread = threading.Thread(target=self.main)\n\n self.task_socket = objsocket.objsocket()\n self.result_socket = objsocket.objsocket()\n\n self.task_socket.bind_and_listen(task_port)\n self.result_socket.bind_and_listen(result_port)\n\n def start(self):\n self.thread.start()\n\n def main(self):\n while True:\n task_connection, task_adress = self.task_socket.accept()\n self.master.active_task_stream = True\n print('Connected to task_socket {}'.format(task_adress))\n result_connection, result_adress = self.result_socket.accept()\n print('Connected to result_socket {}'.format(result_adress))\n\n task_thread = threading.Thread(target=self._task_stream, args=(task_connection, ))\n result_thread = threading.Thread(target=self._result_stream, args=(result_connection, ))\n\n task_thread.start()\n result_thread.start()\n\n task_thread.join()\n result_thread.join()\n\n def _task_stream(self, task_connection):\n while True:\n try:\n task = task_connection.recv_obj()\n except objsocket.EndOfInputException:\n task_connection.close()\n self.master.active_task_stream = False\n print('Disconnected from task_socket')\n break\n print('Got task from cont {}'.format(task))\n self.master.put_task(task)\n\n def _result_stream(self, result_connection):\n while True:\n result = self.master.get_result()\n if result == None:\n pass\n if not self.master.active_task_stream:\n result_connection.close()\n print('Disconnected from result_socket and closed connection to cont')\n break\n else:\n result_connection.send_obj(result)\n print('Sent result to cont')\n\nclass _NodeConnection:\n def __init__(self, ip, port, master):\n self.ip = ip\n self.port = port\n self.master = master\n self.sock = objsocket.objsocket()\n self.thread = threading.Thread(target=self.main)\n\n def start(self):\n self.thread.start()\n\n def main(self):\n sucsessful = self.sock.connect(self.ip, self.port)\n if sucsessful:\n print('Connected to node {ip}:{port}.'.format(ip=self.ip, port=self.port))\n else:\n print('Node {ip}:{port} is unavailible.'.format(ip=self.ip, port=self.port))\n return\n\n while True:\n task = self.master.give_task()\n self.sock.send_obj(task)\n print('Sent task {}'.format(task))\n result = self.sock.recv_obj()\n print('Got result')\n self.master.grab_result(result)\n\n\nclass Node:\n '''\n Класс, который получает задания от Master и возвращает ответы.\n '''\n\n def __init__(self, function, port):\n self.sock = objsocket.objsocket()\n self.sock.bind_and_listen(port)\n self.function = function\n\n def start(self):\n while True:\n connection, address = self.sock.accept()\n print('Connected to master {}'.format(address))\n\n while True:\n try:\n task = connection.recv_data()\n except objsocket.EndOfInputException:\n print('Disconnected from master {}'.format(address))\n connection.close()\n break\n\n task = pickle.loads(task)\n print('Got task {}'.format(task))\n connection.send_obj(self.function(task))\n print('Sent result')\n","sub_path":"multiserver_old_versions/multiserver_alternative/master/multiserver.py","file_name":"multiserver.py","file_ext":"py","file_size_in_byte":6482,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"116488270","text":"import urllib.request\r\nimport jsonpath\r\nimport json\r\nimport urllib.error\r\nimport csv\r\nimport threading\r\nout = open('C:/4月12日查询5月3日.csv', 'a', newline='')\r\ncsv_writer = csv.writer(out, dialect='excel')\r\n\r\ncsv_writer.writerow(\r\n ['序号', '行程','日期','航班号', '航空公司', '出发机场', '到达机场', '起飞时间', '到达时间', '飞行时长', '飞行里程', '执飞机型', '价格', '折扣', '餐食'])\r\ndef init():\r\n header=('User-Agent','Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.101 Safari/537.36')\r\n opener=urllib.request.build_opener()\r\n opener.addheaders=[header]\r\n urllib.request.install_opener(opener)\r\ndef getINFO(url='',start='',arrive=''):\r\n try:\r\n data=urllib.request.urlopen(url).read().decode('utf-8','ignore')\r\n except urllib.error.URLError as e:\r\n print(\"查询失败\")\r\n return\r\n js=json.loads(data)\r\n info_list = jsonpath.jsonpath(js, '$..binfo')\r\n if (info_list == False):\r\n print('无航班记录\\n')\r\n return\r\n\r\n aircode_list=jsonpath.jsonpath(js,'$..airCode')\r\n depairport_list=jsonpath.jsonpath(js,'$..depAirport')\r\n arrairport_list=jsonpath.jsonpath(js,'$..arrAirport')\r\n deptime_list=jsonpath.jsonpath(js,'$..depTime')\r\n arrtime_list=jsonpath.jsonpath(js,'$..arrTime')\r\n distance_list=jsonpath.jsonpath(js,'$..distance')\r\n time=jsonpath.jsonpath(js,'$..flightTime')\r\n fullname_list=jsonpath.jsonpath(js,'$..fullName')\r\n maincarrier_list=jsonpath.jsonpath(js,'$..mainCarrierFullName')\r\n\r\n mode_list=jsonpath.jsonpath(js,'$..planeFullType')\r\n price_list=jsonpath.jsonpath(js,'$..minPrice')\r\n discount_list=jsonpath.jsonpath(js,'$..discountStr')\r\n share_list=jsonpath.jsonpath(js,'$..codeShare')\r\n stops_list=jsonpath.jsonpath(js,'$..stops')\r\n mealDesc_list=jsonpath.jsonpath(js,'$..mealDesc')\r\n\r\n print('查询到'+str(len(info_list))+'条航班记录，已写入文件\\n')\r\n\r\n for i in range(0,len(info_list)):\r\n if(share_list[i]==False):\r\n if(stops_list[i]==False):\r\n '''print('航班号：'+aircode_list[i]+' 航空公司：'+maincarrier_list[i]+' 出发机场：'+depairport_list[i]\r\n +' 到达机场：'+arrairport_list[i]+' 起飞时间：'+deptime_list[i]+\" 到达时间：\"+arrtime_list[i]\r\n +' 飞行时长：'+time[i]+' 飞行里程：'+str(distance_list[i])+' 机型：'+mode_list[i]+' 价格：'+str(price_list[i])+' 折扣：'+discount_list[i]+' '+mealDesc_list[i]\r\n )'''\r\n try:\r\n csv_writer.writerow([str(i+1),start+'-'+arrive,date,aircode_list[i],maincarrier_list[i],start+depairport_list[i],arrive+arrairport_list[i],deptime_list[i],arrtime_list[i],\r\n time[i],str(distance_list[i])+'KM',mode_list[i],str(price_list[i]),discount_list[i],mealDesc_list[i]])\r\n except IndexError as i:\r\n print(\"读写错误！\")\r\n return\r\n #csv_writer.writerow([' '])\r\n else:\r\n '''print('航班号：' + aircode_list[i] + ' 航空公司：' + maincarrier_list[i] + ' 出发机场：' + depairport_list[i]+' 经停机场：' + info_list[i]['stopAirports'][0]\r\n + ' 到达机场：' + arrairport_list[i] + '起飞时间：' + deptime_list[i] + \" 到达时间：\" + arrtime_list[i]\r\n + ' 飞行时长：' + time[i] + ' 飞行里程：' + str(distance_list[i]) + ' 机型：' + mode_list[i] + ' 价格：' + str(\r\n price_list[i]) + ' 折扣：' + discount_list[i]+' '+mealDesc_list[i]\r\n )'''\r\n try:\r\n #csv_writer.writerow(['航班号', '航空公司', '出发机场','经停机场','到达机场', '起飞时间', '到达时间', '飞行时长', '飞行里程', '执飞机型', '价格', '折扣', '餐食'])\r\n csv_writer.writerow(\r\n [str(i+1),start+'-'+arrive,date,aircode_list[i], maincarrier_list[i], start+depairport_list[i]+'(经停'+info_list[i]['stopAirports'][0]+')', arrive+arrairport_list[i],deptime_list[i],\r\n arrtime_list[i],\r\n time[i], str(distance_list[i]) + 'KM', mode_list[i], str(price_list[i]), discount_list[i],\r\n mealDesc_list[i]])\r\n except IndexError as i:\r\n print(\"读写错误！\")\r\n return\r\n #csv_writer.writerow(['','','','经停：'+info_list[i]['stopAirports'][0]])\r\n #csv_writer.writerow([\r\n # ' '])\r\n\r\n\r\n pass\r\n else:\r\n if(stops_list[i]==False):\r\n '''print('航班号(共享)：' + aircode_list[i] + ' 航空公司：' + fullname_list[i] + ' 实际乘坐：'+info_list[i]['mainCarrier']+' 实际航空公司：'+maincarrier_list[i]+' 出发机场：' + depairport_list[i]\r\n + ' 到达机场：' + arrairport_list[i] + ' 起飞时间：' + deptime_list[i] + \" 到达时间：\" + arrtime_list[i]\r\n + ' 飞行时长：' + time[i] +' 飞行里程：'+str(distance_list[i])+'KM'+ ' 机型：' + mode_list[i] + ' 价格：' + str(price_list[i]) + ' 折扣：' + discount_list[i]\r\n )'''\r\n #csv_writer.writerow(\r\n #['航班号', '航空公司', '实际乘坐','实际航空公司','出发机场', '到达机场', '起飞时间', '到达时间', '飞行时长', '飞行里程', '执飞机型', '价格', '折扣', '餐食'])\r\n try:\r\n csv_writer.writerow(\r\n [str(i+1),start+'-'+arrive,date,aircode_list[i]+'(共享) 实际乘坐'+info_list[i]['mainCarrier'], fullname_list[i]+'(实际承运'+maincarrier_list[i]+')',start+depairport_list[i], arrive+arrairport_list[i],deptime_list[i],\r\n arrtime_list[i],\r\n time[i], str(distance_list[i]) + 'KM', mode_list[i], str(price_list[i]), discount_list[i],\r\n mealDesc_list[i]])\r\n except IndexError as i:\r\n print(\"读写错误！\")\r\n return\r\n #csv_writer.writerow(['','实际乘坐'+info_list[i]['mainCarrier'],maincarrier_list[i]])\r\n\r\n else:\r\n '''print('航班号(共享)：' + aircode_list[i] + ' 航空公司：' + fullname_list[i] +' 实际乘坐：'+info_list[i]['mainCarrier']+' 实际航空公司：'+maincarrier_list[i]+ ' 出发机场：' + depairport_list[i]\r\n + ' 经停机场：' + info_list[i]['stopAirports'][0] + ' 到达机场：' + arrairport_list[i] + ' 起飞时间：' +\r\n deptime_list[i] + \" 到达时间：\" + arrtime_list[i]\r\n + ' 飞行时长：' + time[i] + ' 飞行里程：' + str(distance_list[i]) + ' 机型：' + mode_list[i] + ' 价格：' + str(\r\n price_list[i]) + ' 折扣：' + discount_list[i] + ' ' + mealDesc_list[i]\r\n )'''\r\n #csv_writer.writerow(\r\n #['航班号', '航空公司', '实际乘坐','实际航空公司','出发机场', '经停机场', '到达机场', '起飞时间', '到达时间', '飞行时长', '飞行里程', '执飞机型', '价格', '折扣', '餐食'])\r\n try:\r\n csv_writer.writerow(\r\n [str(i+1),start+'-'+arrive,date,aircode_list[i] + '(共享) 实际乘坐'+info_list[i]['mainCarrier'], fullname_list[i]+'(实际承运'+maincarrier_list[i]+')',\r\n start+depairport_list[i]+'(经停'+info_list[i]['stopAirports'][0]+')', arrive+arrairport_list[i],deptime_list[i],\r\n arrtime_list[i],\r\n time[i], str(distance_list[i]) + 'KM', mode_list[i], str(price_list[i]), discount_list[i],\r\n mealDesc_list[i]])\r\n except IndexError as e:\r\n print(\"读写错误！\")\r\n return\r\n\r\n pass\r\n\r\ndef main(citylist,date):\r\n for start in citylist:\r\n start_code=urllib.request.quote(start)\r\n\r\n for arrive in citylist:\r\n if(start==arrive):\r\n continue;\r\n else:\r\n \r\n print('正在查询'+start+'飞往'+arrive+'的航班信息...')\r\n arrive_code=urllib.request.quote(arrive)\r\n url='https://flight.qunar.com/touch/api/domestic/wbdflightlist?departureCity='+start_code+'&arrivalCity='+arrive_code+'&departureDate='+date+'&ex_track=&sort=&isNewInterface=true'\r\n getINFO(url=url,start=start,arrive=arrive)\r\n\r\nif __name__ == '__main__':\r\n\r\n date='2018-05-03'\r\n citylist=open('C:/city.txt','r')\r\n citylist=citylist.read()\r\n citylist=citylist.split('-')\r\n print(citylist)\r\n init()\r\n main(citylist,date)\r\n\r\n '''t1=threading.Thread(target=main,args=(citylist,date,),name='thread1')\r\n t2=threading.Thread(target=main,args=(citylist,date,),name='thread2')\r\n t1.start()\r\n t2.start()\r\n t1.join()\r\n t2.join()'''\r\n\r\n\r\n","sub_path":"flight.py","file_name":"flight.py","file_ext":"py","file_size_in_byte":9504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"493607956","text":"#CTI-110\r\n#P3T1 Area of Rectangle\r\n#Sean Mellers\r\n#25 June 2019\r\n\r\n#User provides dimensions of rectangle 1\r\nlength1 = int(input('Enter length of rectangle 1: '))\r\nwidth1 = int(input('Enter width of rectangle 1: '))\r\n\r\n#User provides dimensions of rectangle 2\r\nlength2 = int(input('Enter length of rectangle 2: '))\r\nwidth2 = int(input('Enter width of rectangle 2: '))\r\n\r\n#Calculate dimensions\r\narea1 = length1 * width1\r\narea2 = length2 * width2\r\n\r\n#Confirm results\r\nif area1 > area2:\r\n print('Rectangle1 has the greater area.')\r\nelif area1 < area2:\r\n print('Rectangle2 has the gtreater area.')\r\nelse:\r\n print('Both Rectanlges have the same area.')\r\n","sub_path":"P3T1_AreaofRectangles_Sean Mellers.py","file_name":"P3T1_AreaofRectangles_Sean Mellers.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"47978788","text":"from django.conf.urls import patterns, url\n\nfrom imperavi.views import upload_image, uploaded_images_json, upload_file\n\nurlpatterns = patterns('',\n url(r'^upload-image/(?P.*)', upload_image, name=\"imperavi-upload-image\"),\n url(r'^get-json/(?P.*)', uploaded_images_json, name=\"imperavi-get-json\"),\n url(r'^upload-file/(?P.*)', upload_file, name=\"imperavi-upload-file\"),\n url(r'^upload-link-file/(?P.*)', upload_file, {'upload_link': True}, name=\"imperavi-upload-link-file\"),\n)\n","sub_path":"imperavi/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"354264692","text":"'''\n@Description : This tool helps to test PINDEL\n@Created : 03/23/2017\n@Updated : 03/23/2017\n@author : Ronak H Shah\n\n'''\n\nimport filecmp\nimport os\nfrom subprocess import Popen\nimport shlex\nimport nose\nimport logging\n\ndef setup_module(): \n this_dir, this_filename = os.path.split(__file__)\n new_dir = os.path.dirname(this_dir)\n inputFileVcf = os.path.join(new_dir, \"data\", \"sample_input\", \"PoolTumor2-T_bc52_PINDEL_0.2.5a7.vcf\")\n outFileVcf = os.path.join(new_dir, \"PoolTumor2-T_bc52_PINDEL_0.2.5a7_STDfilter.vcf\")\n outFileTxt = os.path.join(new_dir, \"PoolTumor2-T_bc52_PINDEL_0.2.5a7_STDfilter.txt\")\n cmpFileVcf = os.path.join(new_dir, \"data\", \"sample_output\", \"PoolTumor2-T_bc52_PINDEL_0.2.5a7_STDfilter.vcf\")\n cmpFileTxt = os.path.join(new_dir, \"data\", \"sample_output\", \"PoolTumor2-T_bc52_PINDEL_0.2.5a7_STDfilter.txt\")\n scriptFile = os.path.join(new_dir, \"filter_pindel.py\")\n cmd = \"python \" + scriptFile + \" -v -tsn PoolTumor2-T \" + \"-ivcf \" + inputFileVcf\n args = shlex.split(cmd)\n if(os.path.isfile(outFileTxt) or (os.path.isfile(outFileVcf))):\n os.remove(outFileTxt)\n os.remove(outFileVcf)\n try:\n proc = Popen(args)\n proc.wait()\n retcode = proc.returncode\n if(retcode >= 0):\n pass\n except:\n e = sys.exc_info()[0]\n logging.info(\"Running of python command: %s \\n has failed. The exception produced is %s Thus we will exit\",cmd,e)\n sys.exit(1)\n\ndef teardown_module():\n this_dir, this_filename = os.path.split(__file__)\n new_dir = os.path.dirname(this_dir)\n outFileVcf = os.path.join(new_dir, \"PoolTumor2-T_bc52_PINDEL_0.2.5a7_STDfilter.vcf\")\n outFileTxt = os.path.join(new_dir, \"PoolTumor2-T_bc52_PINDEL_0.2.5a7_STDfilter.txt\")\n if(os.path.isfile(outFileTxt) or (os.path.isfile(outFileVcf))):\n os.remove(outFileTxt)\n os.remove(outFileVcf)\n\ndef test_text_fileSimilarity():\n this_dir, this_filename = os.path.split(__file__)\n new_dir = os.path.dirname(this_dir)\n outFileTxt = os.path.join(new_dir, \"PoolTumor2-T_bc52_PINDEL_0.2.5a7_STDfilter.txt\")\n cmpFileTxt = os.path.join(new_dir, \"data\", \"sample_output\", \"PoolTumor2-T_bc52_PINDEL_0.2.5a7_STDfilter.txt\")\n nose.tools.ok_(filecmp.cmp(outFileTxt, cmpFileTxt), msg=\"The current result text file and the original result text file for PINDEL are not the same\") \n\ndef test_vcf_fileSimilarity():\n this_dir, this_filename = os.path.split(__file__)\n new_dir = os.path.dirname(this_dir)\n outFileVcf = os.path.join(new_dir, \"PoolTumor2-T_bc52_PINDEL_0.2.5a7_STDfilter.vcf\")\n cmpFileVcf = os.path.join(new_dir, \"data\", \"sample_output\", \"PoolTumor2-T_bc52_PINDEL_0.2.5a7_STDfilter.vcf\")\n nose.tools.ok_(filecmp.cmp(outFileVcf, cmpFileVcf), msg=\"The current result vcf file and the original result vcf file for PINDEL are not the same\") \n\nif __name__ == '__main__':\n nose.main()\n","sub_path":"tests/test_pindel.py","file_name":"test_pindel.py","file_ext":"py","file_size_in_byte":2898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"436081137","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\ndef mi_range(a, b):\n\t\"\"\" Genera la lista de números enteros en el rango de a a b-1 \"\"\"\n\ti = a\n\twhile i < b:\n\t\tyield i\n\t\ti += 1\n\nfor n in mi_range(10, 21):\n\tprint(n)\n","sub_path":"Parte-4/hola-generadores.py","file_name":"hola-generadores.py","file_ext":"py","file_size_in_byte":213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"228466757","text":"import sys\n\ndef calc(f, d):\n\tt = 0\n\tfor i in d:\n\t\tt += i % f\n\treturn t\n\nif __name__ == \"__main__\":\n\tf = int(sys.stdin.readline().strip())\n\td = sys.stdin.readline().strip().split()\n\td = [int(i) for i in d]\n\tans = str(calc(f, d))\n\tsys.stdout.write(ans + \"\\n\")\n","sub_path":"ProCo_2010/nov/nov23.py","file_name":"nov23.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"384067636","text":"import numpy as np\n\n\nclass DsaCliqueEnv(object):\n def __init__(self, num_user, num_channel, r_fail, r_succeed, r_idle):\n self.num_user = num_user\n self.num_channel = num_channel\n\n # reward\n self.r_fail = r_fail\n self.r_succeed = r_succeed\n self.r_idle = r_idle\n\n # space\n self.n_action = num_channel + 1\n self.n_observation = num_channel + 2\n\n # timestamp\n self.t = 0\n\n def reset(self):\n self.t = 0\n obs = np.zeros((self.num_user, self.n_observation), dtype=float)\n obs[:, 0] = 1\n return obs\n\n def step(self, action):\n self.t += 1\n in_use = np.zeros(self.num_channel, dtype=int)\n r = np.zeros(self.num_user)\n obs = np.zeros((self.num_user, self.n_observation), dtype=float)\n\n for i in range(self.num_user):\n obs[i, action[i]] = 1\n if action[i] > 0:\n in_use[action[i] - 1] += 1\n for i in range(self.num_user):\n if action[i] > 0:\n if in_use[action[i] - 1] > 1: # conflict\n r[i] = self.r_fail\n else: # succeed\n r[i] = self.r_succeed\n obs[i, -1] = 1\n else:\n r[i] = self.r_idle\n\n return obs, r, False, in_use\n","sub_path":"dsa_env.py","file_name":"dsa_env.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"376430459","text":"#WordPuzzle V1\n#This version plans to implement the ability for the program to select a word from a limited word list\n#The program will then remove the first letter and replace it with an _ and the player can make a guess\n#if correct, the program congratulates. if not then condolensces are sent\n#Functions such as multiple guesses and multiple _ in place of letters will be absent\n\nimport random\n\n\ndef main():\n #This prints the instructions to the game\n instructions_file = open(\"instructions Wordpuzzle.txt\", \"r\")\n file_contents = instructions_file.read()\n instructions_file.close()\n print(file_contents)\n \n #the word list and the method used to delete the first letter\n wordbank = ['Mango', 'Banana', 'Watermelon', 'Kiwi']\n random_word = random.choice(wordbank)\n rest_of_random = random_word[1:]\n guess = '_' + rest_of_random\n \n #prompts user for a guess\n print(\"The answer so far is \" )\n print(guess)\n player_input = input(\"Guess the letter: \")\n \n #method used to remove all but the first letter in order to match player input\n first_of_random = random_word[:1] \n if player_input.lower() == first_of_random.lower():\n print('Good job! You found the word ' + random_word + '!')\n else:\n print('Not quite, the correct word was ' + random_word + '. Better luck next time')\n \n input('Press enter to end the game.')\n\nmain()","sub_path":"CMPUT-174-Fa19/guess-the-word/Guess_The_Word_V1.py","file_name":"Guess_The_Word_V1.py","file_ext":"py","file_size_in_byte":1410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"499937732","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Nov 3 23:28:28 2019\r\n\r\n@author: Aakash\r\n\"\"\"\r\n\r\nimport random\r\nimport numpy as np\r\nimport time\r\nfrom loadData import *\r\nfrom modelsGAN import *\r\nimport cv2\r\n\r\nNphi = 1024\r\nNg = 128\r\nNz = 100\r\nNd = 128\r\nNx = Ng+Nz\r\nMd = 4\r\nMg = 16\r\nNres = 4\r\nbatch_size = 64\r\nepochs = 600\r\nstart_epoch = 0\r\nlearning_rate = 0.0002\r\ngen_loc = 'Generator0_5_2a.h5'\r\ndis_loc = 'Discriminator0_5_2a.h5'\r\nrandom.seed(time.time())\r\nnp.random.seed(int(time.time()+0.5))\r\n\r\ndef KL_loss(y_dummy, musigma):\r\n global Ng\r\n mu = musigma[:,:Ng]\r\n logsigma = musigma[:,Ng:]\r\n loss = -logsigma + 0.5 * (-1 + K.exp(2*logsigma) + K.square(mu))\r\n loss = K.mean(loss)\r\n return loss\r\n \r\nm = start_epoch//100\r\nlearning_rate = 0.0002/(1<= lenX or i3 >lenX:\r\n i2 = 0\r\n i3 = curr_size \r\n x_wrong = X_real[i2:i3,:,:]\r\n wrong_labels = np.zeros((curr_size,1))\r\n \r\n eps = np.random.normal(0, 1, [curr_size, Ng])\r\n z = np.random.normal(0, 1, [curr_size, Nz])\r\n x_false, musigma = gen0.predict([phi_t, eps, z])\r\n \r\n X = np.concatenate([x_real, x_false, x_wrong], axis = 0)\r\n Labels = np.concatenate([real_labels, false_labels, wrong_labels], axis=0)\r\n \r\n #shuff = np.arange(3*curr_size)\r\n #np.random.shuffle(shuff)\r\n #X = X[shuff]\r\n #Phi_t = Phi_t[shuff]\r\n #Labels = Labels[shuff]\r\n \r\n d_loss += dc0.train_on_batch([phi_t, X[0:curr_size]], [Labels[0:curr_size]])\r\n d_loss += dc0.train_on_batch([phi_t, X[curr_size:d_size]], [Labels[curr_size:d_size]])\r\n d_loss += dc0.train_on_batch([phi_t, X[d_size:]], [Labels[d_size:]])\r\n \r\n loss = gan0.train_on_batch([phi_t, eps, z], [real_labels, musigma_dummy])\r\n for k in range(len(loss)):\r\n g_loss[k] += loss[k]\r\n \r\n \r\n if ((j+1) % 30 == 0) or ((j+1) == iterations):\r\n d_loss /= 30\r\n for k in range(len(g_loss)):\r\n g_loss[k] /= 30 \r\n print((i+1), (j+1), d_loss, g_loss)\r\n d_loss = 0\r\n g_loss = [0, 0, 0]\r\n #print('Memory Recollected-',gc.collect())\r\n \r\n gen0.save_weights(gen_loc)\r\n dc0.save_weights(dis_loc)\r\n if (i+1)%100 == 0:\r\n learning_rate /= 2\r\n K.set_value(dc0.optimizer.lr, learning_rate)\r\n K.set_value(gan0.optimizer.lr, learning_rate)\r\n print('The Learning Rate Now is:', K.get_value(dc0.optimizer.lr))\r\n if (i+1)%10 == 0:\r\n emb = testEmb[:,random.randint(0, testEmb.shape[1]-1),:]\r\n eps = np.random.normal(0, 1, [testEmb.shape[0], Ng])\r\n z = np.random.normal(0, 1, [testEmb.shape[0], Nz])\r\n x_test, _ = gen0.predict([emb, eps, z])\r\n x_test = 127.5*(x_test + 1)\r\n for k in range(x_test.shape[0]):\r\n cv2.imwrite('ResultsI_2\\\\'+str(k)+'.jpg', x_test[k])\r\n \r\n","sub_path":"stage1.py","file_name":"stage1.py","file_ext":"py","file_size_in_byte":4733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"98667770","text":"\nimport globals\nimport multiprocessing\nimport numpy as np\nfrom Geometry import *\nimport matplotlib.pyplot as plt\nfrom ParticleProduction import *\nfrom ParalellFunctions import *\nimport numpy.ma as ma\n\n\nclass ParticleList():\n def __init__(self,num_procs):\n self.num_procs = num_procs\n \n for rank in range(num_procs):\n globals.x.append(ma.masked_all(globals.max_number_of_particles))\n globals.y.append(ma.masked_all((globals.max_number_of_particles)))\n globals.z.append(ma.masked_all((globals.max_number_of_particles)))\n globals.vx.append(ma.masked_all((globals.max_number_of_particles)))\n globals.vy.append(ma.masked_all((globals.max_number_of_particles)))\n globals.vz.append(ma.masked_all((globals.max_number_of_particles)))\n globals.species.append(ma.masked_all((globals.max_number_of_particles),dtype = int))\n globals.isw.append(ma.masked_all((globals.max_number_of_particles),dtype = int))\n globals.time_until_collision.append(ma.masked_all((globals.max_number_of_particles)))\n globals.flight_time.append(ma.masked_all((globals.max_number_of_particles)))\n globals.n_of_particles.append(0)\n\n\n #globals.isp.append(ma.masked_all((globals.max_number_of_particles),dtype = int))\n\n def add_particles(self,plist):\n\n for rank in range(globals.number_of_processors):\n self.extend_all(rank,plist[rank])\n\n def initialize_particles(self):\n\n #electron_list = volume_production(globals.sp_e,globals.N_e)\n #self.add_particles(electron_list)\n\n #Hplus_list = volume_production(globals.sp_Hp,globals.N_Hp)\n #self.add_particles(Hplus_list)\n\n Hminus_list = volume_production(globals.sp_Hm,globals.N_Hm)\n self.add_particles(Hminus_list)\n\n Hminus_list = volume_production_2(globals.sp_Hm,globals.N_Hm)\n self.add_particles(Hminus_list)\n\n\n def get_num_particles(self,rank):\n return globals.n_of_particles[rank]\n\n\n def get_x(self, rank):\n return globals.x[rank]\n def get_y(self, rank):\n return globals.y[rank]\n def get_z(self, rank):\n return globals.z[rank]\n def get_vx(self, rank):\n return globals.vx[rank]\n def get_vy(self, rank):\n return globals.vy[rank]\n def get_vz(self, rank):\n return globals.vz[rank]\n def get_species(self, rank):\n return globals.species[rank]\n def get_isw(self, rank):\n return globals.isw[rank]\n def get_time_until_collision(self, rank):\n return globals.time_until_collision[rank]\n def get_flight_time(self, rank):\n return globals.flight_time[rank]\n def get_isp(self,rank):\n return globals.isp[rank]\n def get_charge(self,rank):\n length = globals.x[rank].compressed()\n ip_list = range(length)\n charge = ma.empty((length))\n index = 0\n for ip in ip_list:\n species = globals.species[rank].compressed()[ip]\n charge[index] = globals.species_list[species].get_charge()\n index = index + 1\n return charge\n\n def get_mass(self,rank):\n length = globals.x[rank].compressed()\n ip_list = range(length)\n mass = ma.empty((length))\n index = 0\n for ip in ip_list:\n species = globals.species[rank].compressed()[ip]\n mass[index] = globals.species_list[species].get_mass()\n index = index + 1\n return mass\n\n def get_all(self,rank):\n return globals.x[rank], globals.y[rank], globals.z[rank], globals.vx[rank], globals.vy[rank], globals.vz[rank], globals.species[rank], globals.isw[rank], globals.time_until_collision[rank], globals.flight_time[rank], globals.isp[rank]\n\n def get_all_len(self):\n for rank in range(globals.number_of_processors):\n lx = len(globals.x[rank])\n ly = len(globals.y[rank])\n lz = len(globals.z[rank])\n lvx = len(globals.vx[rank])\n lvy = len(globals.vy[rank])\n lvz = len(globals.vz[rank])\n lisw = len(globals.isw[rank])\n lspc = len(globals.species[rank])\n lft = len(globals.flight_time[rank])\n ltcol = len(globals.time_until_collision[rank])\n lisp = len(globals.isp[rank])\n print(lx,ly,lz,lvx,lvy,lvz,lisw,lspc,lft,ltcol,lisp)\n def get_number_of_active_things(self,str):\n for rank in range(globals.number_of_processors):\n lx = len(globals.x[rank].compressed())\n ly = len(globals.y[rank].compressed())\n lz = len(globals.z[rank].compressed())\n lvx = len(globals.vx[rank].compressed())\n lvy = len(globals.vy[rank].compressed())\n lvz = len(globals.vz[rank].compressed())\n lisw = len(globals.isw[rank].compressed())\n lspc = len(globals.species[rank].compressed())\n lft = len(globals.flight_time[rank].compressed())\n ltcol = len(globals.time_until_collision[rank].compressed())\n lisp = len(globals.isp[rank].compressed())\n print(str,lx,ly,lz,lvx,lvy,lvz,lisw,lspc,lft,ltcol,lisp)\n\n\n def extend_all(self,rank,all):\n\n l = len(all[0])\n if l > 0:\n\n n_start = globals.n_of_particles[rank]\n n_end = n_start + l\n\n #for i in range(9):\n #print(i,l,len(globals.vx[rank]),len(globals.vx[rank][n_start:n_end]),len(all[i]))\n\n globals.x[rank][n_start:n_end] = ma.array(all[0])\n globals.y[rank][n_start:n_end] = ma.array(all[1])\n globals.z[rank][n_start:n_end] = ma.array(all[2])\n globals.vx[rank][n_start:n_end] = ma.array(all[3])\n globals.vy[rank][n_start:n_end] = ma.array(all[4])\n globals.vz[rank][n_start:n_end] = ma.array(all[5])\n globals.species[rank][n_start:n_end] = ma.array(all[6])\n globals.isw[rank][n_start:n_end] = ma.array(all[7])\n globals.flight_time[rank][n_start:n_end] = ma.array(all[8])\n globals.time_until_collision[rank][n_start:n_end] = ma.array(all[9])\n globals.isp[rank].mask[n_start:n_end] = False\n globals.n_of_particles[rank] = n_end\n\n #self.set_all_flag_false(rank,n_start,n_end)\n\n\n def set_all_flag_false(self,rank,n_start,n_end):\n for i in range(n_start,n_end):\n globals.x[rank][i].mask = False\n globals.y[rank][i].mask = False\n globals.z[rank][i].mask = False\n globals.vx[rank][i].mask = False\n globals.vy[rank][i].mask = False\n globals.vz[rank][i].mask = False\n globals.species[rank][i].mask = False\n globals.isw[rank][i].mask = False\n globals.flight_time[rank][i].mask = False\n globals.time_until_collision[rank][i].mask = False\n globals.isp[rank][i].mask = False\n\n\n def set_x(self,rank,x_new):\n n = len(x_new)\n globals.x[rank][:n] = x_new\n\n def set_y(self,rank,y_new):\n n = len(y_new)\n globals.y[rank][:n] = y_new\n\n def set_z(self,rank,z_new):\n n = len(z_new)\n globals.z[rank][:n] = z_new\n\n def set_vx(self,rank,vx_new):\n n = len(vx_new)\n globals.vx[rank][:n] = vx_new\n\n def set_vy(self,rank,vy_new):\n n = len(vy_new)\n globals.vy[rank][:n] = vy_new\n\n def set_vz(self,rank,vz_new):\n n = len(vz_new)\n globals.vz[rank][:n] = vz_new\n\n def set_species(self,rank,species_new):\n n = len(species_new)\n globals.species[rank][:n] = species_new\n\n def set_isw(self,rank,isw_new):\n n = len(isw_new)\n globals.isw[rank][:n] = isw_new\n\n\n def set_flight_time(self,rank,flight_time_new):\n n = len(flight_time_new)\n globals.flight_time[rank][:n] = flight_time_new\n\n def set_time_until_collision(self,rank,time_until_collision_new):\n n = len(time_until_collision_new)\n globals.time_until_collision[rank][:n] = time_until_collision_new\n\n def set_isp(self,rank,isp_new):\n n = len(isp_new)\n globals.isp[rank][:n] = isp_new\n def set_num_particles(self,rank,n):\n globals.n_of_particles[rank] = n\n\n def set_all(self,rank,all):\n n = len(all[0])\n globals.x[rank][:n] = all[0]\n globals.y[rank][:n] = all[1]\n globals.z[rank][:n] = all[2]\n globals.vx[rank][:n] = all[3]\n globals.vy[rank][:n] = all[4]\n globals.vz[rank][:n] = all[5]\n globals.species[rank][:n] = all[6]\n globals.isw[rank][:n] = all[7]\n globals.flight_time[rank][:n] = all[8]\n globals.time_until_collision[rank][:n] = all[9]\n #globals.isp[rank][:n] = all[10]\n\n\n\n\n\n\n\n def move(self):\n manager = multiprocessing.Manager() # No clue what this does lol.\n return_dict_move = manager.dict() # List where outputs for each process will be saved\n jobs = []\n\n\n for rank in range(globals.number_of_processors):\n p = multiprocessing.Process(target=move_parallell, args=(rank,return_dict_move),processes=4)\n jobs.append(p)\n p.start()\n\n # Don't pass this loop until all jobs are finished.\n for proc in jobs:\n proc.join()\n\n\n for rank in range(globals.number_of_processors):\n\n indices = np.where(globals.x[rank].mask == False)\n if len(indices[0]) == 0:\n continue\n\n for index in range(len(indices[0])):\n ip = indices[0][index]\n\n globals.x[rank].__setitem__(ip,return_dict_move[rank][0][index])\n globals.y[rank].__setitem__(ip,return_dict_move[rank][1][index])\n globals.z[rank].__setitem__(ip,return_dict_move[rank][2][index])\n globals.flight_time[rank].__setitem__(ip,return_dict_move[rank][3][index])\n\n #globals.x[rank].compressed() = return_dict_move[rank][0]\n #globals.y[rank].compressed() = return_dict_move[rank][1]\n #globals.z[rank].compressed() = return_dict_move[rank][2]\n #globals.flight_time[rank].compressed() = return_dict_move[rank][3]\n\n def delete_elements(self,del_list,rank):\n #d = np.zeros((globals.max_number_of_particles))\n\n for ip in del_list:\n\n\n globals.x[rank].mask[ip] = True\n globals.y[rank].mask[ip] = True\n globals.z[rank].mask[ip] = True\n globals.vx[rank].mask[ip] = True\n globals.vy[rank].mask[ip] = True\n globals.vz[rank].mask[ip] = True\n globals.species[rank].mask[ip] = True\n globals.isw[rank].mask[ip] = True\n globals.time_until_collision[rank].mask[ip] = True\n globals.flight_time[rank].mask[ip] = True\n globals.isp[rank].mask[ip] = True\n\n\n def check_boundary(self,t):\n man = multiprocessing.Manager() # No clue what this does lol.\n send_dict = man.dict()\n del_dict = man.dict()\n jobs = []\n for rank in range(globals.number_of_processors):\n send_dict[rank] = [[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[]]\n del_dict[rank] = []\n for rank in range(globals.number_of_processors):\n #n_particles = globals.n_of_particles[rank]\n p = multiprocessing.Process(target=check_boundary_parallell, args=(rank,send_dict,del_dict))\n jobs.append(p)\n p.start()\n #time.sleep(0.01)\n\n # Don't pass this loop until all jobs are finished.\n for proc in jobs:\n proc.join()\n\n jobs = []\n\n\n for old_rank in range(globals.number_of_processors):\n del_list = []\n for new_rank in range(globals.number_of_processors):\n try:\n # [old_rank][new_rank][particle number]\n\n n_end = n_start + len(ip)\n for ip in send_dict[old_rank][new_rank]:\n n_start = globals.n_of_particles[new_rank]\n\n globals.x[new_rank][n_start] = globals.x[old_rank][ip]\n globals.y[new_rank][n_start] = globals.y[old_rank][ip]\n globals.z[new_rank][n_start] = globals.z[old_rank][ip]\n globals.vx[new_rank][n_start] = globals.vx[old_rank][ip]\n globals.vy[new_rank][n_start] = globals.vy[old_rank][ip]\n globals.vz[new_rank][n_start] = globals.vz[old_rank][ip]\n globals.species[new_rank][n_start] = globals.species[old_rank][ip]\n globals.isw[new_rank][n_start] = globals.isw[old_rank][ip]\n globals.time_until_collision[new_rank][n_start] = globals.time_until_collision[old_rank][ip]\n globals.flight_time[new_rank][n_start] = globals.flight_time[old_rank][ip]\n #globals.isp[new_rank][n_start] = globals.isp[old_rank][ip]\n\n globals.n_of_particles[new_rank] = globals.n_of_particles[new_rank] + 1\n\n except:\n continue\n #if(len(del_dict[old_rank]) == 0):\n # continue\n #print(\"START DELETING\",len(del_dict[old_rank]),del_dict[old_rank])\n for old_rank in range(globals.number_of_processors):\n self.delete_elements(del_dict[old_rank],old_rank)\n\n\n\n\n\n def get_density(self):\n charge_density = np.zeros((128,196,196))\n # for particle_list in plist:\n # This should be paralellized\n\n\n for rank in range(globals.number_of_processors):\n\n n = globals.n_of_particles[rank]\n xi = globals.x[rank].compressed()\n yi = globals.y[rank].compressed()\n zi = globals.z[rank].compressed()\n\n\n\n\n if(len(xi) == 0):\n continue\n # This is stupid and should be fixed.\n\n\n\n \"\"\"\n xi = xi[yi<194]\n yi = yi[yi<194]\n zi = zi[yi<194]\n xi = xi[zi<194]\n yi = yi[zi<194]\n zi = zi[zi<194]\n \"\"\"\n #print(len(xi),len(yi),len(zi),len(globals.vx[rank]),len(globals.vy[rank]),len(globals.vz[rank]))\n\n xm = xi.astype(int)\n ym = yi.astype(int)\n zm = zi.astype(int)\n\n xp = xm + 1\n yp = ym + 1\n zp = zm + 1\n\n\n\n charge = self.get_charge(len(xi),rank)\n\n V111 = np.multiply((xi - xm),(yi - ym),(zi - zm))\n\n V112 = np.multiply((xi - xm),(yi - ym), (zp - zi))\n V121 = np.multiply((xi - xm), (yp - yi), (zi - zm))\n V211 = np.multiply((xp - xi), (yi - ym), (zi - zm))\n\n V122 = np.multiply((xi - xm), (yp - yi), (zp - zi))\n V212 = np.multiply((xp - xi), (yi - ym), (zp - zi))\n V221 = np.multiply((xp - xi), (yp - yi), (zi - zm))\n\n V222 = np.multiply((xp - xi), (yp - yi), (zp - zi))\n\n # Total volume\n V_tot = np.multiply((xp - xm), (yp - ym), (zp - zm))\n V_tot[V_tot < 0.001] = 1\n\n for ip in range(n):\n #print(ip)\n #print(xi.shape,yi.shape,zi.shape)\n if(globals.y[rank][ip]>196 or globals.y[rank][ip]>196):\n print(\"Muslims:\",globals.y[rank][ip],globals.z[rank][ip])\n\n charge_density[xm,ym,zm] = charge_density[xm,ym,zm] + np.divide(np.multiply(charge,V222),V_tot)\n\n charge_density[xm,ym,zp] = charge_density[xm,ym,zp] + np.divide(np.multiply(charge, V221), V_tot)\n charge_density[xm,yp,zm] = charge_density[xm,yp,zm] + np.divide(np.multiply(charge, V212), V_tot)\n charge_density[xp,ym,zm] = charge_density[xp,ym,zm] + np.divide(np.multiply(charge, V122), V_tot)\n\n charge_density[xm,yp,zp] = charge_density[xm,yp,zp] + np.divide(np.multiply(charge, V211), V_tot)\n charge_density[xp,yp,zm] = charge_density[xp,yp,zm] + np.divide(np.multiply(charge, V112), V_tot)\n charge_density[xp,ym,zp] = charge_density[xp,ym,zp] + np.divide(np.multiply(charge, V121), V_tot)\n\n charge_density[xp,yp,zp] = charge_density[xp,yp,zp] + np.divide(np.multiply(charge, V111), V_tot)\n\n return charge_density\n\n\n def calvel_new(self,E_field,B_field):\n\n ## read all 16 ranks:\n manager = multiprocessing.Manager() # No clue what this does lol.\n velocity_dict = manager.dict() # List where outputs for each process will be saved\n edit_vel_dict = manager.dict()\n for rank in range(globals.number_of_processors):\n velocity_dict[rank] = []\n jobs = []\n # Loop over each domain\n for rank in range(globals.number_of_processors):\n n_particles = self.get_num_particles(rank)\n p = multiprocessing.Process(target=calvel_parallell, args=(self,rank,n_particles,E_field,B_field,velocity_dict))\n jobs.append(p)\n p.start()\n #time.sleep(0.5)\n\n # Don't pass this loop until all jobs are finished.\n for proc in jobs:\n proc.join()\n\n\n # m * (v_new - v_old) / dt = q * (E + cross(v,B))\n # v_new = v_old + q / m * (E + cross(v,B)) * dt\n for rank in range(globals.number_of_processors):\n\n if(velocity_dict[rank] == []):\n continue\n\n indices = np.where(globals.vx[rank].mask == False)\n\n if len(indices) <= 1:\n continue\n\n for index in range(len(indices[0])):\n ip = indices[0][index]\n globals.vx[rank].__setitem__(ip,velocity_dict[rank][0,index])\n globals.vy[rank].__setitem__(ip,velocity_dict[rank][1,index])\n globals.vz[rank].__setitem__(ip,velocity_dict[rank][2,index])\n\n def track_positions(self,time):\n\n ## read all 16 ranks:\n manager = multiprocessing.Manager() # No clue what this does lol.\n return_dict = manager.dict() # List where outputs for each process will be saved\n jobs = []\n # Loop over each domain\n for rank in range(globals.number_of_processors):\n p = multiprocessing.Process(target=save_npy_paralell, args=(time,rank))\n jobs.append(p)\n p.start()\n\n # Don't pass this loop until all jobs are finished.\n for proc in jobs:\n proc.join()\n\n\n def plot_particle_distributions(self,time):\n\n # Velocity distribution\n fig = plt.figure()\n num_bins = 50\n vx_vec = []\n vy_vec = []\n vz_vec = []\n for v in globals.vx:\n vx_vec.extend(v)\n for v in globals.vy:\n vy_vec.extend(v)\n for v in globals.vz:\n vz_vec.extend(v)\n plt.subplot(1,3,1)\n plt.hist(vx_vec, num_bins, range = (-10,10), facecolor='blue', alpha=0.5)\n plt.subplot(1,3,2)\n plt.hist(vy_vec, num_bins, range = (-10,10), facecolor='blue', alpha=0.5)\n plt.subplot(1,3,3)\n plt.hist(vz_vec, num_bins, range = (-10,10), facecolor='blue', alpha=0.5)\n plt.savefig(\"Figures/Histograms/Velocity/velocity_histogram at t = \" + str(time) + \".png\")\n plt.close()\n\n # Position distribution\n fig = plt.figure()\n num_bins = 50\n x_vec = []\n y_vec = []\n z_vec = []\n for xi in globals.x:\n x_vec.extend(xi)\n for yi in globals.y:\n y_vec.extend(yi)\n for zi in globals.z:\n z_vec.extend(zi)\n plt.subplot(1,3,1)\n plt.hist(x_vec, num_bins, facecolor='blue', alpha=0.5)\n plt.subplot(1,3,2)\n plt.hist(y_vec, num_bins, facecolor='blue', alpha=0.5)\n plt.subplot(1,3,3)\n plt.hist(z_vec, num_bins, facecolor='blue', alpha=0.5)\n plt.savefig(\"Figures/Histograms/Position/position_histogram at t = \" + str(time) + \".png\")\n plt.close()\n\n # Energy distribution\n fig = plt.figure()\n num_bins = 100\n E_vec = []\n for rank in range(globals.number_of_processors):\n for i in range(self.get_num_particles(rank)):\n species = globals.species_list[globals.species[rank][i]]\n E = species.get_energy(globals.vx[rank][i],globals.vy[rank][i],globals.vz[rank][i])\n E_vec.append(E)\n\n plt.hist(E_vec, num_bins,range = (0,10000), facecolor='blue', alpha=0.5)\n plt.savefig(\"Figures/Histograms/Energy/energy_histogram at t = \" + str(time) + \".png\")\n plt.close()\n","sub_path":"PYPIC/classes/ParticleList.py","file_name":"ParticleList.py","file_ext":"py","file_size_in_byte":20539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"135115384","text":"\"\"\"\nDONE:\n2. Model different behaviours around choosing what to work on (ignore priorities) (DONE)\n3. Implement case priorities or business value (DONE)\n4. Add a release phase, either when a fixed number of cases are done or when a certain amount of time has passed. The idea is to see\nhow released value varies accordingly. (DONE)\n5. Allow the varying of any parameter in the model so we can do \"hyperparameter tuning\" (or equivalent), e.g., vary the number of QAs\nto see what the impact on value delivered is\n6. Remove the clunky connection between current state and the pile that a case is on to control who can pick it up next. Feels\nclumsy and that we should only need to use one state variable to control this.\n7. Factor in intangibles - expensive tasks that generate value at an increasing rate over time, so are effectively long-term\ninvestments\n8. Agents can learn over time from overall metrics, or from other agents (other agents broadcast their states). If we mimic an actual\nKanban process then all agents have perfect information of the workloads of other agents and can alter their behaviour accordingly. If\n(as in reality) agents' (bounded) rationality is to maximise their own business, then we should be able to see the effect of this.\n9. Monitor blocking and starving metrics for each queue.\n\"\"\"\nimport sys\nimport simpy\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport logging\nimport copy\n\nfrom tqdm import tqdm\nfrom pubsub import pub\nfrom models import Run, Case, Workflow, Work\nfrom agents import BA, QA, Releaser, Developer\nfrom workflows import WorkTypes as wt\nfrom workflows.standard import StandardWorkflowFactory\n\nRANDOM_SEED = 42\nSIM_TIME = 1000 # Simulation time in days\nNUM_DEVELOPERS = 8\nNUM_QA = 2\nNUM_BA = 1\nNUM_RUNS = 200\n#NUM_RUNS = 1\nMAX_QA_PILE = 10\nMAX_DEV_PILE = 20\nMAX_REVIEW_PILE = 20\n\nNUM_SOURCE_CASES = 300\nNUM_INITIAL_CASES = 100\n#NUM_SOURCE_CASES = 20\n#NUM_INITIAL_CASES = 10\n\nNEW_CASE_INTERVAL = 5\nANALYSIS_PILE_MIN = 5\n\nlogging.basicConfig(level=logging.ERROR,\n format='%(message)s',)\n\n\ndef monitor(env, finisher):\n while True:\n if len(released_pile.items) == len(standard_cases):\n # Trigger the finisher event to succeed which will end the sim\n finisher.succeed()\n\n # Calculate the total value of all cases that are in the flow.\n total_value = 0\n for case in progress_pile.items:\n total_value += case.get_current_value()\n\n total_values.append(total_value)\n\n # Track numbers of work items in different states (which is not the same as cases in different workflow\n # states but we'll add that next)\n # todo: track cases in each workflow state. Tricky thing is a case can potentially be in many \"states\"\n # todo: if there are multiple work items in action at once. Might be easier to count unique combinations of\n # todo: case and workflow type.\n analysis_size.append(len(work_piles[wt.WORK_ANALYSIS].items))\n dev_size.append(len(work_piles[wt.WORK_DEV].items))\n dev_put_queue_size.append(len(work_piles[wt.WORK_DEV].put_queue))\n qa_size.append(len(work_piles[wt.WORK_QA].items))\n merge_size.append(len(work_piles[wt.WORK_MERGE].items))\n done_size.append(len(done_pile.items))\n release_size.append(len(released_pile.items))\n\n yield env.timeout(1)\n\ndef sourcer(name, env):\n \"\"\"\n The job of the sourcer is just to generate new cases by adding them from the hidden backlog periodically.\n \"\"\"\n while True:\n # Get a case off the Source backlog\n if len(work_piles[wt.WORK_ANALYSIS].items) <= ANALYSIS_PILE_MIN:\n logging.debug('%s fetching a source case at %s' % (name, env.now))\n case = yield source_pile.get()\n\n # Wait the specified time before adding it to the dispatch pile (for work allocation) and the progress pile\n # (for tracking)\n yield env.timeout(NEW_CASE_INTERVAL)\n\n # Add work items for the new case to the dispatch pile\n dispatch_new_case_work(case)\n logging.info('%s dispatching %s at %s' % (name, case, env.now))\n else:\n yield env.timeout(1)\n\n\ndef dispatch_new_case_work(case):\n \"\"\"\n Initialises work queues with work items for a new case\n :param case:\n :return:\n \"\"\"\n # Track the case on the progress pile\n progress_pile.put(case)\n\n # Add the cases first work items to the dispatch pile\n items = case.workflow.get_next_step()\n if items is not None:\n for item in items:\n dispatch_work_item(item)\n\n\ndef dispatch_work_item(item):\n \"\"\"\n Add a work item to the relevant queue depending on whether it is a Workflow or Work item\n :param item:\n :return:\n \"\"\"\n if item is not None:\n if type(item) is Workflow:\n for work in item.work_items:\n dispatch_pile.append(work)\n elif type(item) is Work:\n assign_work_to_pile(item)\n else:\n raise Exception(\"Incorrect work item type: %s\" % type(item))\n\n\ndef dispatcher(name, env):\n \"\"\"\n This actor distributes cases to different piles when the current assignee has relinquished it.\n This way an individual actor doesn't need to know what happens to the case next after they've finished with it\n :param name:\n :param env:\n :return: void\n \"\"\"\n while True:\n # Get all work items off the dispatch backlog, work out what needs to happen the associated case next and\n # add the relevant items to the relevant piles.\n for item in dispatch_pile:\n dispatch_work_item(item)\n logging.debug('%s dispatched work for work item %s at %s' % (name, item, env.now))\n\n # Clear the dispatch pile\n dispatch_pile.clear()\n\n # Do this once a day\n yield env.timeout(1)\n\n\ndef case_done_listener(case=None):\n # Move the case to the done_pile pile\n assert case is not None, \"case_done_listener: No case provided\"\n done_pile.put(case)\n\n\ndef dispatch_work_listener(work=None):\n # Move the case to the done_pile pile\n assert work is not None, \"dispatch_work_listener: No work provided\"\n dispatch_pile.append(work)\n\n\npub.subscribe(case_done_listener, 'case_is_done')\npub.subscribe(dispatch_work_listener, 'dispatch_work')\n\n\ndef assign_work_to_pile(work):\n \"\"\"\n Assign a work item to the relevant pile depending on the type of the work\n :param work:\n :return:\n \"\"\"\n if work.name in work_piles.keys():\n logging.debug('Assigning work item %s to pile %s at %s' % (work, work.name, env.now))\n work_piles[work.name].put(work)\n\n\ndef plot_ecdf(data, color='black'):\n _ = plt.plot(np.sort(data), np.arange(1, len(data) + 1) / len(data), marker='.', linestyle='none', alpha=0.01, color=color)\n return _\n\n\n# Create a repeatable backlog of cases that have binomially distributed sizes and normally distributed values\nlogging.info('Creating standard cases')\nnp.random.seed(RANDOM_SEED)\n\nstandard_cases = []\nsizes = np.random.gamma(10, size=NUM_SOURCE_CASES).astype(np.int64)\nvalues = (np.random.normal(0, 1, size=NUM_SOURCE_CASES)) * 10\nfor idx, size in enumerate(sizes):\n standard_cases.append(Case(size=max(size, 1), value=values[idx], name='%d' % idx,\n workflow=StandardWorkflowFactory.make_workflow(dev_size=size))) # value = size\n\n# Do multiple executions of this model where behaviour of models varies\nruns = []\nreleases = []\ncases = []\ncurrent_release = None # a global resource, can only be one release active at a time\nsourcers = []\ndispatchers = []\nfinisher = None\n\nfor run in tqdm(range(NUM_RUNS)):\n logging.debug('---- Run %s -----' % run)\n # Create environment and start processes\n analysis_size = []\n dev_size = []\n review_size = []\n dev_put_queue_size = []\n qa_size = []\n done_size = []\n release_size = []\n merge_size = []\n cycle_times = []\n total_values = []\n\n # Choose a random strategy for each run\n dev_strategy = Developer.random_strategy()\n ba_strategy = BA.random_strategy()\n qa_strategy = QA.random_strategy()\n dev_review_choice_strategy = np.random.randint(3)\n\n env = simpy.Environment()\n\n # The dispatch pile is just a list, as it gets processed entirely each day\n dispatch_pile = []\n\n # Create standard piles\n source_pile = simpy.Store(env, len(sizes))\n done_pile = simpy.Store(env)\n released_pile = simpy.Store(env)\n progress_pile = simpy.Store(env)\n\n # Create named piles for specific work types, i.e., work that needs to be done to get the case to the Done pile\n work_piles = {\n wt.WORK_ANALYSIS: simpy.Store(env, len(sizes)),\n wt.WORK_QA: simpy.Store(env, MAX_QA_PILE),\n wt.WORK_DEV: simpy.Store(env, MAX_DEV_PILE),\n wt.WORK_REVIEW: simpy.FilterStore(env, MAX_REVIEW_PILE),\n wt.WORK_MERGE: simpy.FilterStore(env)\n }\n\n # Create a Run to store params\n run = Run(params={\n 'dev_strategy': dev_strategy,\n 'ba_strategy': ba_strategy,\n 'dev_review_choice_strategy': dev_review_choice_strategy\n })\n\n # Create a pile of all remaining source cases\n for case in standard_cases[NUM_INITIAL_CASES:len(standard_cases)]:\n _case = copy.deepcopy(case)\n _case.set_env(env)\n source_pile.put(_case)\n\n # Create a pile of cases to get the system to a steady state. We will start by putting the first workflow step\n # for each case on the dispatcher's pile and let them allocate work based on the workflow step properties.\n for case in standard_cases[:NUM_INITIAL_CASES]:\n _case = copy.deepcopy(case)\n _case.set_env(env)\n dispatch_new_case_work(_case)\n\n # Create some developers (who will do both development and reviews) with a random strategy\n developers = []\n for i in range(NUM_DEVELOPERS):\n developers.append(env.process(Developer('Developer %d' % i,\n env,\n strategy=dev_strategy,\n dev_pile=work_piles[wt.WORK_DEV],\n review_pile=work_piles[wt.WORK_REVIEW],\n merge_pile=work_piles[wt.WORK_MERGE]\n ).run()))\n\n # Create some QAs\n qas = []\n for i in range(NUM_QA):\n qas.append(env.process(QA('QA %d' % i, env, qa_strategy, work_piles[wt.WORK_QA], current_release).run()))\n\n # Create some BAs\n bas = []\n for i in range(NUM_BA):\n bas.append(env.process(BA('BA %d' % i, env, ba_strategy, work_piles[wt.WORK_ANALYSIS]).run()))\n\n # Create a Releaser\n releasers = [env.process(Releaser('Releaser 0', env, current_release, done_pile, releases, released_pile,\n standard_cases).run())]\n\n # Create a Dispatcher\n dispatchers = [env.process(dispatcher('Dispatcher 0', env))]\n\n # Create a case Sourcer\n sourcers = [env.process(sourcer('Sourcer 0', env))]\n\n # add monitoring, which will also terminate the sim on completion of all work\n finisher = simpy.Event(env)\n env.process(monitor(env, finisher))\n\n # Execute!\n env.run(until=finisher)\n\n # Store cycle time values\n for case in released_pile.items:\n cycle_times.append(case.cycle_time())\n\n # Store stats for this run\n run.data = {\n wt.WORK_ANALYSIS: analysis_size,\n wt.WORK_DEV: dev_size,\n wt.WORK_REVIEW: review_size,\n wt.WORK_QA: qa_size,\n wt.WORK_MERGE: merge_size,\n 'done': done_size,\n 'release': release_size,\n 'cycle_time': cycle_times,\n 'total_value': total_values\n }\n\n runs.append(run)\n\n#sys.exit(0)\n\n# Counts in each state\nfor i, data in enumerate([x.run_data(wt.WORK_ANALYSIS) for x in runs]):\n _ = plt.plot(data, alpha=0.1, color='blue')\nfor i, data in enumerate([x.run_data(wt.WORK_DEV) for x in runs]):\n _ = plt.plot(data, alpha=0.1, color='red')\nfor i, data in enumerate([x.run_data(wt.WORK_QA) for x in runs]):\n _ = plt.plot(data, alpha=0.1, color='green')\nfor i, data in enumerate([x.run_data(wt.WORK_REVIEW) for x in runs]):\n _ = plt.plot(data, alpha=0.1, color='green')\nfor i, data in enumerate([x.run_data('done') for x in runs]):\n _ = plt.plot(data, alpha=0.1, color='orange')\nfor i, data in enumerate([x.run_data('release') for x in runs]):\n _ = plt.plot(data, alpha=0.1, color='black')\nplt.show()\n\n# Cycle time ecdf\ncolors = ['red', 'pink', 'green', 'blue', 'orange', 'black']\nline_styles = ['-', '--', '-.', ':'] # solid, dash, dash-dot, dot\n\nfor run in runs:\n color = colors[run.params['strategy']]\n _ = plot_ecdf(run.run_data('cycle_time'), color=color)\nplt.show()\n\n# Total value plot\nfor run in runs:\n color = colors[run.params['strategy']]\n line_style = line_styles[run.params['ba_strategy']]\n _ = plt.plot(run.run_data('total_value'), alpha=0.1, color=color, linestyle=line_style)\nplt.show()","sub_path":"kanbansim/kanban.py","file_name":"kanban.py","file_ext":"py","file_size_in_byte":13090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"590927784","text":"from flask_restplus import Api, Resource, fields\nfrom app import api,db\nfrom ..models import patientstudy as ez\n\nns = api.namespace('PatientStudies', description='PatientStudy operations')\n\n\nstudy = api.model('PatientStudy', {\n 'id': fields.Integer(readOnly=True, description='The study unique identifier'),\n 'entity_id': fields.Integer(required=True, description='Entity associated with the study'),\n 'patient_id': fields.Integer(required=True, description='Patient associated with the study'),\n 'strip_id': fields.Integer(required=True, description='Strip associated with the study'),\n 'trial_id': fields.Integer(required=True, description='Trial associated with the study'),\n 'guid': fields.String(readOnly=True,required=False, description='Unique generated ID of the study'),\n 'entity_name': fields.String(readOnly=True,required=False, description='Entity Name'),\n 'patient_name': fields.String(readOnly=True,required=False, description='Patient Name'),\n 'strip_name': fields.String(readOnly=True,required=False, description='Strip Name'),\n 'trial_name': fields.String(readOnly=True,required=False, description='Trial Name')\n\n})\n\n\nclass PatientStudyDAO(object):\n def __init__(self):\n pass\n \n def displayList(self,entityID):\n e2 = ez.PatientStudy()\n return e2.displayList(entityID)\n\n def list(self,entityID):\n if entityID!=0:\n return ez.PatientStudy.query.filter_by(entity_id=entityID).all()\n else:\n return ez.PatientStudy.query.all()\n\n\n def get(self, id):\n obj = ez.PatientStudy.query.get(id)\n if obj is not None:\n return obj\n api.abort(404, \"PatientStudy {} doesn't exist\".format(id))\n\n def create(self, data):\n e2 = ez.PatientStudy()\n\n e2.entity_id = data.get(\"entity_id\", None)\n e2.patient_id = data.get(\"patient_id\", None)\n e2.strip_id = data.get(\"strip_id\", None)\n e2.trial_id = data.get(\"trial_id\", None)\n\n\n db.session.add(e2)\n db.session.commit()\n return e2\n\n def update(self, id, data):\n e2 = self.get(id)\n\n e2.entity_id = data.get(\"entity_id\", None)\n e2.patient_id = data.get(\"patient_id\", None)\n e2.strip_id = data.get(\"strip_id\", None)\n e2.trial_id = data.get(\"trial_id\", None)\n\n #study.save()\n db.session.commit()\n return e2\n\n def delete(self, id):\n study = self.get(id)\n db.session.delete(study)\n db.session.commit()\n\n\nDAO = PatientStudyDAO()\n\n\n@ns.route('/display/')\nclass PatientStudyDisplayList(Resource):\n '''Shows a list of all entities - Internal'''\n @ns.doc('list_entities')\n @ns.marshal_list_with(study)\n def get(self,entityID):\n '''List all names'''\n return DAO.displayList(entityID)\n\n@ns.route('/list/')\nclass PatientStudyList(Resource):\n '''Shows a list of all entities, and lets you POST to add new names'''\n @ns.doc('list_entities')\n @ns.marshal_list_with(study)\n def get(self,entityID):\n '''List all names'''\n return DAO.list(entityID)\n\n@ns.route('/')\nclass PatientStudyList2(Resource):\n @ns.doc('create_study')\n @ns.expect(study)\n @ns.marshal_with(study, code=201)\n def post(self):\n '''Create a new name'''\n return DAO.create(api.payload), 201\n\n\n@ns.route('/')\n@ns.response(404, 'PatientStudy not found')\n@ns.param('id', 'The name identifier')\nclass PatientStudy(Resource):\n '''Show a single PatientStudy item and lets you delete them'''\n @ns.doc('get_entity')\n @ns.marshal_with(study)\n def get(self, id):\n '''Fetch a given resource'''\n return DAO.get(id)\n\n @ns.doc('delete_study')\n @ns.response(204, 'PatientStudy deleted')\n def delete(self, id):\n '''Delete a name given its identifier'''\n DAO.delete(id)\n return '', 204\n\n @ns.expect(study)\n @ns.marshal_with(study)\n def put(self, id):\n '''Update a name given its identifier'''\n return DAO.update(id, api.payload)","sub_path":"src/main/python/rdsiapp/app/apis/patientstudies.py","file_name":"patientstudies.py","file_ext":"py","file_size_in_byte":4064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"187410239","text":"# 'inplace'-version of 2-sum\n\nfrom hashtable import Hashtable\nimport time\n\ndef two_sum(data, hashtable, c=10000):\n \"\"\"\n 2-SUM algorithm via hash table and grouping.\n\n O(n) time with a small constant compared to the straightforward way.\n \"\"\"\n global sums\n for value in data:\n group = Hashtable.group(value)\n\n # ===== nearest to zero =====\n if -c <= value <= c:\n keys = [group, 2*c, -2*c]\n else:\n keys = [-group, -group - c, -group + c]\n for key in keys:\n if key in hashtable:\n for i in hashtable[key]:\n if -c <= value + i <= c:\n sums[value + i] = True\n\n\nif __name__ == \"__main__\":\n start_time = time.time()\n file = open(\"data_2sum.txt\", \"r\")\n data = file.read().split(\"\\n\") # # file.readlines() # [line.split(\"\\n\") for line in file.readlines()]\n data = list(map(int, data[:-1]))\n hashtable = Hashtable(data)\n sums = {}\n two_sum(data, hashtable.hashtable)\n print(len(sums))\n print(\"--- %s seconds ---\" % (time.time() - start_time))","sub_path":"Two_sum/tsum_hash_v01d.py","file_name":"tsum_hash_v01d.py","file_ext":"py","file_size_in_byte":1100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"23475023","text":"\"\"\"\nFunctions to create an animation of an utterance.\n\nDate: Dec 2017\nAuthor: Aciel Eshky\n\n\"\"\"\n\n# write a function to reduce ultrasound frame rate.\nimport os\nimport shutil\nimport subprocess\n\nimport matplotlib.pyplot as plt\n\nfrom ustools.read_core_files import *\nfrom ustools.transform_ultrasound import transform_raw_ult_to_world_multi_frames\nfrom ustools.reshape_ultrasound import reduce_frame_rate\nfrom ustools.reshape_ultrasound import reshape_ultrasound_array\n\n\ndef write_images_to_disk(ult_3d, directory, title=None):\n \"\"\"\n A function to write the ultrasound frames as images to a directory. The images are generated as plots without axes.\n :param ult_3d: input ultrasound object as a 3d numpy array\n :param directory: the directory to write the images to\n :param title: an optional title for the image\n :return:\n \"\"\"\n print(\"writing image frames to disk...\")\n\n plt.figure(dpi=300)\n\n if title is not None:\n plt.title(title)\n\n c = ult_3d[0]\n im = plt.imshow(c.T, aspect='equal', origin='lower', cmap='gray')\n for i in range(1, ult_3d.shape[0]):\n c = ult_3d[i]\n im.set_data(c.T)\n plt.axis(\"off\")\n plt.savefig(directory + \"/%07d.jpg\" % i, bbox_inches='tight')\n\n\ndef create_video(ult_3d, frame_rate, output_video_file, title=None):\n \"\"\"\n A function to animate an ultrasound object.\n :param ult_3d: input ultrasound object as a 3d numpy array. Can be raw or transformed.\n :param frame_rate: taken from the ultrasound parameter file (unless the ultrasound has been down-sampled)\n :param output_video_file: the path/name of the output video\n :param title: an optional title for the video\n :return:\n \"\"\"\n print(\"creating temporary directory...\")\n directory = '.temp'\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n write_images_to_disk(ult_3d=ult_3d, directory=directory, title=title)\n\n print(\"creating video from images frames using ffmpeg...\")\n subprocess.call(\n [\"ffmpeg\", \"-y\", \"-r\", str(frame_rate),\n \"-i\", directory + \"/%07d.jpg\", \"-vcodec\", \"mpeg4\", \"-qscale\", \"5\", \"-r\",\n str(frame_rate), output_video_file])\n print(\"video saved.\")\n\n shutil.rmtree(directory)\n print(\"image frames files deleted from disk.\")\n\n\ndef crop_audio(audio_start_time, input_audio_file, output_audio_file):\n \"\"\"\n A function to crop the audio. I should consider the cases where audio start time is 0 or negative\n :param audio_start_time: taken from the ultrasound parameter file: 'TimeInSecsOfFirstFrame'\n :param input_audio_file: path/name of input audio\n :param output_audio_file: path/name of output audio\n :return:\n \"\"\"\n print(\"cropping audio...\")\n\n subprocess.call(\n [\"ffmpeg\", \"-ss\", str(audio_start_time), \"-i\", input_audio_file, output_audio_file])\n\n\ndef append_audio_and_video(audio_file, video_file, output_video_file):\n \"\"\"\n Outputs the video file with audio.\n :param audio_file:\n :param video_file:\n :param output_video_file:\n :return:\n \"\"\"\n print(\"appending audio to video...\")\n\n subprocess.call(\n [\"ffmpeg\",\n \"-i\", audio_file,\n \"-i\", video_file,\n \"-codec\", \"copy\", \"-shortest\", output_video_file])\n\n\ndef animate_utterance(prompt_file, wave_file, ult_file, param_file, output_video_file, frame_rate=60,\n background_colour=0):\n \"\"\"\n\n :param prompt_file:\n :param wave_file:\n :param ult_file:\n :param param_file:\n :param output_video_file:\n :param frame_rate:\n :param background_colour:\n :return:\n \"\"\"\n\n # temp file names\n temp_audio_file = \"cropped_audio.wav\"\n temp_video_file = \"video_only.avi\"\n\n # prompt file is used for a video caption\n video_caption = ', '.join(parse_prompt_file(prompt_file))\n\n # read parameter file\n param_df = parse_parameter_file(param_file=param_file)\n\n # use offset parameter to crop audio\n crop_audio(audio_start_time=param_df['TimeInSecsOfFirstFrame'].value,\n input_audio_file=wave_file,\n output_audio_file=temp_audio_file)\n\n # read ultrasound, reshape it, reduce the frame rate for efficiency, and transform it\n ult = read_ultrasound_file(ult_file=ult_file)\n\n ult_3d = reshape_ultrasound_array(ult, output_dim=3,\n number_of_vectors=int(param_df['NumVectors'].value),\n pixels_per_vector=int(param_df['PixPerVector'].value))\n\n x, fps = reduce_frame_rate(ult_3d=ult_3d, input_frame_rate=float(param_df['FramesPerSec'].value),\n output_frame_rate=frame_rate)\n\n print(\"transforming raw ultrasound to world...\")\n y = transform_raw_ult_to_world_multi_frames(x, background_colour=background_colour)\n\n # create video without audio\n create_video(y, fps, temp_video_file, title=video_caption)\n\n # append audio and video\n append_audio_and_video(temp_audio_file, temp_video_file, output_video_file)\n\n # remove temporary files\n os.remove(temp_audio_file)\n os.remove(temp_video_file)\n\n print(\"video creation complete.\")\n\n","sub_path":"ustools/animate_utterance.py","file_name":"animate_utterance.py","file_ext":"py","file_size_in_byte":5131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"162980902","text":"#!/usr/bin/python\n\n\n#########################################################################\n#import and clean up data\nfrom ParseData import parseData\nimport numpy as np\nimport pandas as pd\n\nfrom scipy import stats\nfrom sklearn.feature_selection import SelectPercentile, f_classif\n\nimport pylab\nimport re\n\nfrom sklearn import decomposition\n\n##########################################################################\n\n\ntitanic, titanic_test = parseData()\nprint(titanic.columns.values)\n\n#features = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Title', 'MarriedName']\nfeatures = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'FamilyNumber', 'FamilyMembers', 'Title', 'MarriedName']\n\n\n\n# split into survivors/not\ntitanic_survivors = titanic[titanic[\"Survived\"] == 1]\ntitanic_perished = titanic[titanic[\"Survived\"] == 0]\n\nprint(\"Training count, survived, perished \", len(titanic), len(titanic_survivors), len(titanic_perished))\n\n################################################################################\n\nprint(titanic.describe())\n\n\n###################################################################\nprint(\"Original\")\npvalues = []\nfor i in features:\n\tz_stat, p_val = stats.ranksums(titanic_survivors[i], titanic_perished[i])\n\tpvalues.append(p_val)\n\nscores = zip(pvalues, features)\nscores_list = list(scores)\nsorted_scores_list = sorted(scores_list)\n\nprint(\"\\nUseful things have a p-value < 0.05\\n\")\nfor i, j in sorted_scores_list:\n\tif i < 0.05:\n\t\tprint(i, j)\n\t\t\n\nprint(\"\\n Not useful statistically\")\nfor i, j in sorted_scores_list:\n\tif i >= 0.05:\n\t\tprint(i, j)\n\t\t\n\t\t\n\n\n\n","sub_path":"Data.py","file_name":"Data.py","file_ext":"py","file_size_in_byte":1609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"37481599","text":"# vim: expandtab:ts=4:sw=4\n\n\nclass TrackState:\n \"\"\"\n Enumeration type for the single target track state. Newly created tracks are\n classified as `tentative` until enough evidence has been collected. Then,\n the track state is changed to `confirmed`. Tracks that are no longer alive\n are classified as `deleted` to mark them for removal from the set of active\n tracks.\n\n \"\"\"\n\n Tentative = 1\n Confirmed = 2\n Deleted = 3\n\n\nclass Track:\n \"\"\"\n A single target track with state space `(x, y, a, h)` and associated\n velocities, where `(x, y)` is the center of the bounding box, `a` is the\n aspect ratio and `h` is the height.\n\n Parameters\n ----------\n mean : ndarray\n Mean vector of the initial state distribution.\n covariance : ndarray\n Covariance matrix of the initial state distribution.\n track_id : int\n A unique track identifier.\n n_init : int\n Number of consecutive detections before the track is confirmed. The\n track state is set to `Deleted` if a miss occurs within the first\n `n_init` frames.\n max_age : int\n The maximum number of consecutive misses before the track state is\n set to `Deleted`.\n feature : Optional[ndarray]\n Feature vector of the detection this track originates from. If not None,\n this feature is added to the `features` cache.\n\n Attributes\n ----------\n mean : ndarray\n Mean vector of the initial state distribution.\n covariance : ndarray\n Covariance matrix of the initial state distribution.\n track_id : int\n A unique track identifier.\n hits : int\n Total number of measurement updates.\n age : int\n Total number of frames since first occurance.\n time_since_update : int\n Total number of frames since last measurement update.\n state : TrackState\n The current track state.\n features : List[ndarray]\n A cache of features. On each measurement update, the associated feature\n vector is added to this list.\n\n \"\"\"\n\n def __init__(self, mean, covariance, track_id, n_init, max_age,\n feature=None):\n self.mean = mean\n self.covariance = covariance\n self.track_id = track_id\n self.hits = 1\n self.age = 1\n self.time_since_update = 0\n\n self.state = TrackState.Tentative\n self.features = []\n if feature is not None:\n self.features.append(feature)\n\n self._n_init = n_init\n self._max_age = max_age\n\n def to_tlwh(self):\n \"\"\"Get current position in bounding box format `(top left x, top left y,\n width, height)`.\n\n Returns\n -------\n ndarray\n The bounding box.\n\n \"\"\"\n ret = self.mean[:4].copy()\n ret[2] *= ret[3]\n ret[:2] -= ret[2:] / 2\n return ret\n\n def to_tlbr(self):\n \"\"\"Get current position in bounding box format `(min x, miny, max x,\n max y)`.\n\n Returns\n -------\n ndarray\n The bounding box.\n\n \"\"\"\n ret = self.to_tlwh()\n ret[2:] = ret[:2] + ret[2:]\n return ret\n\n def predict(self, kf):\n \"\"\"Propagate the state distribution to the current time step using a\n Kalman filter prediction step.\n\n Parameters\n ----------\n kf : kalman_filter.KalmanFilter\n The Kalman filter.\n\n \"\"\"\n self.mean, self.covariance = kf.predict(self.mean, self.covariance)\n self.age += 1\n self.time_since_update += 1\n\n def update(self, kf, detection):\n \"\"\"Perform Kalman filter measurement update step and update the feature\n cache.\n\n Parameters\n ----------\n kf : kalman_filter.KalmanFilter\n The Kalman filter.\n detection : Detection\n The associated detection.\n\n \"\"\"\n self.mean, self.covariance = kf.update(\n self.mean, self.covariance, detection.to_xyah())\n self.features.append(detection.feature)\n\n self.hits += 1\n self.time_since_update = 0\n if self.state == TrackState.Tentative and self.hits >= self._n_init:\n self.state = TrackState.Confirmed\n\n def mark_missed(self):\n \"\"\"Mark this track as missed (no association at the current time step).\n \"\"\"\n if self.state == TrackState.Tentative:\n self.state = TrackState.Deleted\n elif self.time_since_update > self._max_age:\n self.state = TrackState.Deleted\n\n def is_tentative(self):\n \"\"\"Returns True if this track is tentative (unconfirmed).\n \"\"\"\n return self.state == TrackState.Tentative\n\n def is_confirmed(self):\n \"\"\"Returns True if this track is confirmed.\"\"\"\n return self.state == TrackState.Confirmed\n\n def is_deleted(self):\n \"\"\"Returns True if this track is dead and should be deleted.\"\"\"\n return self.state == TrackState.Deleted\n","sub_path":"OneStage/yolo/deep_sort_yolov3/deep_sort/track.py","file_name":"track.py","file_ext":"py","file_size_in_byte":4976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"129679717","text":"from typing import List\n\n\ndef numRookCaptures(board: List[List[str]]) -> int:\n # At first find position of rook and save in 'iR' and 'jR' variables\n iR, jR = 0, 0\n for i in range(len(board)):\n for j in range(len(board[i])):\n if board[i][j] == 'R':\n iR = i\n jR = j\n # find all first figures on line and row\n res = []\n for j in range(jR + 1, len(board)):\n if board[iR][j] != '.':\n res.append(board[iR][j])\n break\n for j in range(jR - 1, -1, -1):\n if board[iR][j] != '.':\n res.append(board[iR][j])\n break\n for i in range(iR + 1, len(board)):\n if board[i][jR] != '.':\n res.append(board[i][jR])\n break\n for i in range(iR - 1, -1, -1):\n if board[i][jR] != '.':\n res.append(board[i][jR])\n break\n # calculate how many pawns\n resCount = 0\n for i in range(len(res)):\n if res[i] == 'p':\n resCount += 1\n return resCount\n","sub_path":"src/numRookCaptures/numRookCaptures.py","file_name":"numRookCaptures.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"540917054","text":"import re\nimport time\nimport json\nimport random\n\nfrom base import *\nfrom globals import *\n\n\nscenario_list = []\nsavedata = {}\nplay_on = {}\nbest_player = []\nplaying = False\n\n\ndef sleep(leng):\n time.sleep(1 + leng / 15)\n\n\ndef scn_init():\n global scenario_list\n global best_player\n best_player = [0, [0]]\n\n text = open('火车游戏.txt', 'r', encoding='utf-8').read()\n start = re.search(R'\\[Day\\d+\\]', text).start()\n end = re.search(R'\\[final\\]', text).start() - 1\n text = text[start:end]\n now = re.search(R'\\[Day\\d+\\]', text)\n\n while now != None:\n # 获得时间\n day = int(re.search(R'\\d+', text[now.start():now.end() - 1]).group())\n if len(scenario_list) < day:\n for i in range(day - len(scenario_list)):\n scenario_list.append([])\n text = text[now.end():]\n\n # 获得编号\n now = re.search(R'\\[\\d+\\]', text)\n num = int(re.search(R'\\d+', text[now.start():now.end() - 1]).group())\n text = text[re.search(R'\\[\\d+\\]\\n*', text).end():]\n\n # 获得情景\n now = re.search(R'\\[end\\]\\n*', text)\n story = text[:now.start() - 1]\n text = text[now.end():]\n part = {}\n now = re.search(R'\\[>-?\\d*[,\\d*]*\\]', text)\n nextD = re.search(R'\\[Day\\d+\\]', text)\n if nextD != None:\n today = text[:nextD.start()]\n text = text[nextD.start():]\n else:\n today = text\n\n while now != None:\n # 获取选择项\n choice = today[0:now.start()]\n end = re.search(R'\\n*\\[end\\]\\n*', today).start()\n # 获取后续情景\n after = today[now.end():end]\n # 获取后续下一天故事\n next_num = re.split(R',', today[now.start() + 2:now.end() - 1])\n next_num = [int(i) for i in next_num if i != '']\n part[choice] = [after, next_num]\n today = today[re.search(R'\\n*\\[end\\]\\n*', today).end():]\n now = re.search(R'\\[>-?\\d*[,\\d*]*\\]', today)\n\n scenario_list[day - 1].append((story, part))\n now = re.search(R'\\[Day\\d+\\]', text)\n\n\ndef startgame(cxt):\n global scenario_list\n global savedata\n global play_on\n global best_player\n global playing\n\n if playing == True:\n return\n else:\n playing = True\n\n if len(cxt['groups']) == 2:\n if cxt['groups'][1] == 'help':\n playing = False\n return dict(reply=prompts['gal_help'])\n\n if cxt['groups'][1] == 'deldata':\n if cxt['user_id'] in whitelist:\n bot.send(cxt, message=prompts['gal_deleted'])\n savedata = {}\n play_on = {}\n best_player = [0, [0]]\n with open(\"./gal.json\", 'w', encoding='utf-8') as json_file:\n json.dump(savedata, json_file, ensure_ascii=False)\n else:\n bot.send(cxt,message=prompts['permission_needed'])\n playing = False\n return\n\n # 加载存档\n try:\n with open(\"./gal.json\", 'r', encoding='utf-8') as json_file:\n savedata = json.load(json_file)\n except FileNotFoundError:\n pass\n player = str(cxt['user_id'])\n if '0' in savedata:\n best_player = savedata['0']\n if player in play_on:\n if play_on[player] == -1:\n playing = False\n return\n\n # 如果没有该玩家的存档则创建一个\n if not player in savedata:\n if len(savedata) <= 5:\n level = 0\n count = len(scenario_list[level])\n nextL = [i for i in range(0, count)]\n savedata[player] = [level, nextL]\n else:\n bot.send(cxt, message=prompts['gal_too_many_players'])\n playing = False\n return\n else:\n level = savedata[player][0]\n nextL = savedata[player][1]\n count = len(nextL)\n\n # 随机读取下一个场景\n rdnum = random.randint(0, count - 1)\n rdnum = nextL[rdnum]\n\n # 发送情景\n text = re.split(R'\\[next\\]\\n*', scenario_list[level][rdnum][0])\n bot.send(cxt, message='Day' + str(level + 1))\n sleep(1)\n\n for i in text:\n if i != '':\n bot.send(cxt, message=i)\n sleep(len(i))\n\n # 发送选项\n select = scenario_list[level][rdnum][1]\n i = 1\n sel_str = ''\n for key in select:\n sel_str += str(i) + '.' + key + '\\n'\n i = i + 1\n bot.send(cxt, message=sel_str[:-1])\n play_on[player] = rdnum\n playing = False\n\n\ndef makechoice(cxt):\n global savedata\n global play_on\n global best_player\n global playing\n\n if playing == True:\n return\n else:\n playing = True\n\n player = str(cxt['user_id'])\n # 判断玩家是否正在玩gal，不是则直接返回\n if not player in play_on or not player in savedata:\n playing = False\n return\n if play_on[player] == -1:\n playing = False\n return\n\n level = savedata[player][0]\n rdnum = play_on[player]\n nextL = savedata[player][1]\n s = cxt['message']\n\n # 判断玩家输入的是否为gal选项，不是则返回\n isgal = False\n for choices in scenario_list[level][rdnum][1]:\n ans = re.search(R'「.+」', choices)\n if ans != None:\n ans = choices[ans.start() + 1:ans.end() - 1]\n else:\n ans = choices\n if s == ans or s == choices:\n text = re.split(\n R'\\[next\\]\\n*', scenario_list[level][rdnum][1][choices][0])\n isgal = True\n for i in text:\n if i != '':\n bot.send(cxt, message=i)\n sleep(len(i))\n # 玩家已做出选择，更新游戏存档\n savedata[player] = [level + 1,\n scenario_list[level][rdnum][1][choices][1]]\n nextL = savedata[player][1]\n if savedata[player][0] > best_player[0] and nextL != [-1] and nextL != []:\n best_player = savedata[player]\n savedata['0'] = best_player\n bot.send(cxt, message=prompts['gal_new_best'])\n break\n if not isgal:\n playing = False\n return\n\n # 第二天可能出现的情景\n # 出现[]为good end，[-1] 是定死的bad end，最后一天的非[]代表暂为bad end，可能因为天数的增加转为good end\n if nextL == [-1] or level == len(scenario_list) and nextL != []:\n bot.send(cxt, message='bad end!')\n savedata[player] = best_player\n bot.send(\n cxt, message=prompts['gal_restore_best'].format(best_player[0] + 1))\n elif nextL == []:\n bot.send(cxt, message='good end!')\n bot.send(cxt, message=prompts['gal_all_passed'])\n savedata = {}\n del play_on[player] # 设置成=-1时每人只能玩一次\n # 保存数据\n with open(\"./gal.json\", 'w', encoding='utf-8') as json_file:\n json.dump(savedata, json_file, ensure_ascii=False)\n playing = False\n","sub_path":"gal.py","file_name":"gal.py","file_ext":"py","file_size_in_byte":7012,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"105338849","text":"import sys\nimport random\n\ndef main(argv):\n \n if len(argv) < 3:\n print('Expected input:\\n or \\n ')\n return\n\n numbersToCheck = []\n\n if (len(argv) == 3):\n numbersToCheck.append(int(argv[2]))\n else:\n numbersToCheck = range(int(argv[2]), int(argv[3]) + 1)\n \n with open(argv[1]) as f:\n lines = f.readlines()\n\n logText = []\n\n lines = [x.strip() for x in lines]\n\n productions = []\n \n for line in lines:\n splitted = line.split('->')\n\n # add spaces on the left and right (if needed),\n # also check if tail is epsilon \n head = ' ' + splitted[0] if len(splitted) == 2 else ' ' + splitted[0] + ' '\n tail = splitted[1] + ' ' if len(splitted) == 2 else ''\n\n productions.append((head, tail))\n\n terminals = {'1', 'c', '$'}\n nonTerminals = getNonTerminals(productions, terminals) \n\n logFilePath = argv[1] + '_Log.txt'\n print('Writing log to: ' + logFilePath)\n\n initialWords = []\n results = []\n\n # assume, that result of first 5 types of non deterministic productions\n for i in range(0, len(numbersToCheck)):\n amount = '(1,1) ' * numbersToCheck[i]\n initialWords.append(' 0 (c,c) ' + amount + '($,$) ')\n\n # actual simulation of the grammar\n for i in range(0, len(initialWords)):\n\n current = initialWords[i]\n \n logText.append('Created word: \\\"' + current + '\\\". Processing...\\n')\n\n error = False\n\n # while there are non terminals\n while containsNonTerminal(current, nonTerminals):\n simulated = False\n\n # check each production\n for head, tail in productions:\n (current, wasSimulated) = simulateProduction(current, head, tail)\n simulated = simulated or wasSimulated\n\n if wasSimulated:\n logText.append('Using: ' + head + '->' + tail + ':\\n' + current + '\\n\\n')\n\n # if was simulated, start from the beginning of the production list\n\n # to prevent non-determinism when\n # replacing to epsilon (step 10),\n # we assume that it will be the last production in a list\n break\n\n # if there is no simulation but there are non-terminals\n if not simulated:\n break\n\n # there are no non-terminals but there are no productions to simulate\n printResult(current, numbersToCheck[i], not containsNonTerminal(current, nonTerminals))\n\n logText.append('\\n\\n\\n')\n\n # write log to file\n with open(logFilePath, 'w+') as logFile:\n logFile.writelines(logText)\n\ndef simulateProduction(current, head, tail):\n wasSimulated = False\n\n # while head is contained by current\n while head in current:\n current = current.replace(head, tail, 1)\n wasSimulated = True\n\n #print(current)\n #printTM(current)\n\n return (current, wasSimulated)\n\ndef simulateProductionLimited(current, head, tail, limit):\n '''\n Try to simulate production, but number of replacements of current production is limited.\n Use this to prevent endless replacements.\n '''\n iteration = 0\n while head in current and iteration < limit:\n current = current.replace(head, tail, 1)\n iteration += 1\n #print(current)\n #printTM(current)\n\n return current\n\ndef containsNonTerminal(str, nonTerminals):\n return any(nonTerm in str for nonTerm in nonTerminals)\n\ndef getNonTerminals(productions, terminals):\n nonTerminals = set()\n\n for (head, tail) in productions:\n ws = set(head.split(' '))\n ws = ws.union(tail.split(' '))\n\n nonTerminals = nonTerminals.union(ws)\n\n # ignore epsilon\n if '' in nonTerminals:\n nonTerminals.remove('')\n\n for t in terminals:\n if t in nonTerminals:\n nonTerminals.remove(t)\n\n return nonTerminals\n\ndef printTM(str):\n '''\n Print turing machine's line. \n I.e. prints only second part of tuples in str\n '''\n out = ''\n splitted = str.split(' ')\n for s in splitted:\n if '(' in s:\n s = s.replace(')', '')\n tuple = s.split(',')\n out += tuple[1]\n print(out)\n\ndef printResult(current, number, isPrime):\n '''Removes spaces and prints result'''\n out = current\n print(str(number) + ' is ' + (' PRIME' if isPrime else 'NOT prime') + \n ('. Grammar result:' + out if isPrime else '. Word has non terminals but hasn\\'t needed productions'))\n\n# call main method\nmain(sys.argv)","sub_path":"UG_Generator.py","file_name":"UG_Generator.py","file_ext":"py","file_size_in_byte":4685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"131505759","text":"from pydynet import rewiring\nfrom pydynet.network import PulseOscillatorNetwork\nfrom numpy import sort,all\n\nclass TestRewiring:\n\n def setup(self):\n pass\n\n def test_add_edge(self):\n net = PulseOscillatorNetwork(10,0.3,'fixed degree')\n nEdges = net.number_of_edges()\n edges = net.edges()\n flag = rewiring.add_random_edge(net)\n if flag:\n assert net.number_of_edges() == nEdges + 1, \"ADD: Edge should have been added!\"\n else:\n assert net.edges() == edges, \"ADD: Addition should have failed!\"\n\n def test_remove_edge(self):\n net = PulseOscillatorNetwork(10,0.3,'fixed degree')\n nEdges = net.number_of_edges()\n edges = net.edges()\n flag = rewiring.remove_random_edge(net)\n if flag:\n assert net.number_of_edges() == nEdges - 1, \"REMOVE: Edge should have been removed!\"\n else:\n assert net.edges() == edges, \"REMOVE: Edges should not have changed!\"\n\n def test_move_edge(self):\n net = PulseOscillatorNetwork(10,0.3,'fixed degree')\n nEdges = net.number_of_edges()\n edges = net.edges()\n flag = rewiring.move_random_edge(net)\n if flag:\n assert net.number_of_edges()== nEdges, \"MOVE: Number of edges should not have changed!\"\n else:\n assert net.edges() == edges, \"MOVE: Edges should not have changed!\"\n\n def test_move_edge_cons(self):\n net = PulseOscillatorNetwork(10,0.3,'fixed degree')\n origDegrees = net.degree()\n edges = net.edges()\n flag = rewiring.move_random_edge_cons(net)\n if flag:\n assert all(sort(origDegrees.values()) == sort(G.degree().values())), \"MOVE CONS: Move should not have changed degree distribution!\"\n else:\n assert net.edges() == edges, \"MOVE CONS: Edges should not have changed!\"\n\n def test_swap_edges(self):\n net = PulseOscillatorNetwork(10,0.3,'fixed degree')\n nEdges = net.number_of_edges()\n edges = net.edges()\n flag = rewiring.swap_random_edges(net)\n if flag:\n assert net.number_of_edges() == nEdges, \"SWAP: Number of edges should not have changed!\"\n else:\n assert net.edges() == edges, \"SWAP: Edges should not have changed!\"\n\n def test_move_degree_dist(self):\n from numpy import sort\n accept = 0.0\n net = PulseOscillatorNetwork(10,0.3,'fixed degree')\n nEdges = net.number_of_edges()\n for i in xrange(0,100):\n accept += rewiring.move_random_edge(net)\n assert net.number_of_edges() == nEdges, \"MOVE DEGREE: Number of edges should not have changed!\"\n\n def test_swap_node_degrees(self):\n accept = 0.0\n net = PulseOscillatorNetwork(10,0.3,'fixed degree')\n deg = net.degree()\n for i in xrange(0,100):\n accept += rewiring.swap_random_edges(net)\n assert accept > 0.0, \"SWAP DEGREE: No swaps were performed!\"\n for k in net.degree():\n assert net.degree()[k] == deg[k], \"SWAP DEGREE: Node degrees should not have changed!\"\n\n def test_graph_perturb(self):\n net = PulseOscillatorNetwork(10,0.3,'fixed degree')\n pertNet,R = rewiring.perturb_graph(net)\n assert R > 0.0, \"PERTURB: No graph operations performed!\"\n","sub_path":"build/lib.macosx-10.5-x86_64-2.7/pydynet/tests/test_rewiring.py","file_name":"test_rewiring.py","file_ext":"py","file_size_in_byte":3305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"652496545","text":"#!/usr/bin/env python3\nimport json\nimport sqlite3\nimport os\nimport sys\n\n\ndef id(x):\n return x\n\n\ndef dict_from_row(row):\n return {k: ga_decoders.get(k, json.loads)(v) for k, v in zip(row.keys(), row) if v is not None}\n\n\ndef remove_set_info(dictionary):\n dictionary.pop(\"setName\", None)\n dictionary.pop(\"setCode\", None)\n dictionary.pop(\"setReleaseDate\", None)\n return dictionary\n\n\ndef remove_unneeded(dictionary):\n dictionary.pop(\"artist\", None)\n dictionary.pop(\"border\", None)\n dictionary.pop(\"flavor\", None)\n dictionary.pop(\"foreignNames\", None)\n dictionary.pop(\"id\", None)\n dictionary.pop(\"multiverseid\", None)\n dictionary.pop(\"number\", None)\n dictionary.pop(\"originalText\", None)\n dictionary.pop(\"originalType\", None)\n dictionary.pop(\"rarity\", None)\n dictionary.pop(\"releaseDate\", None)\n dictionary.pop(\"reserved\", None)\n dictionary.pop(\"timeshifted\", None)\n dictionary.pop(\"variations\", None)\n dictionary.pop(\"watermark\", None)\n dictionary.pop(\"imageName\", None)\n dictionary.pop(\"mciNumber\", None)\n dictionary = remove_set_info(dictionary)\n return dictionary\n\n\ndef set_dictionary(row):\n return dict(zip(row.keys(), row))\n\n\ndef db_to_json_all_sets(database_connection):\n database_connection.row_factory = sqlite3.Row # Enable keys for the rows\n cursor = database_connection.cursor()\n cursor.execute(\"SELECT DISTINCT setCode from cards\")\n\n la_main_dict = {}\n las_rows = cursor.fetchall()\n for ls_set_code in las_rows:\n la_return_data = []\n ls_set_code = set_dictionary(ls_set_code)\n cursor.execute(\"SELECT * FROM cards WHERE setCode = ?\", [ls_set_code[\"setCode\"]])\n card_rows = cursor.fetchall()\n\n ls_set_name = None\n ls_set_release_date = None\n for las_row in card_rows:\n las_row = dict_from_row(las_row) # Turn SQL.Row -> Dictionary\n\n # Set temporary variables used for JSON Sorting data for AllSets\n if not ls_set_name or not ls_set_release_date:\n ls_set_name = las_row[\"setName\"]\n ls_set_release_date = las_row[\"setReleaseDate\"]\n\n # Remove temporary variables from the dictionary, as they're unneeded\n las_row = remove_set_info(las_row)\n la_return_data.append(las_row)\n\n # Inset into dictionary the JSON data\n la_main_dict[ls_set_code[\"setCode\"]] = dict(\n zip([\"cards\", \"name\", \"releaseDate\"], [la_return_data, ls_set_name, ls_set_release_date]))\n\n database_connection.close()\n return la_main_dict\n\n\ndef db_to_json_all_cards(database_connection):\n database_connection.row_factory = sqlite3.Row # Enable keys for the row\n cursor = database_connection.cursor()\n\n las_main_dict = {}\n cursor.execute(\"SELECT DISTINCT name from cards ORDER BY name, setReleaseDate ASC\")\n rows = cursor.fetchall()\n\n # This loop will take a while (~5 minutes) to complete. Be patient\n for this_card in rows:\n cursor.execute(\"SELECT * FROM cards WHERE name = ?\", [this_card[\"name\"]])\n card_rows = cursor.fetchall()\n for row in card_rows:\n row = dict_from_row(row)\n row = remove_unneeded(row)\n las_main_dict[json.loads(this_card[\"name\"])] = row\n return las_main_dict\n\n\ndef main():\n if len(sys.argv) != 4:\n print(\"USAGE: %s <'sets' or 'cards'>\" % sys.argv[0])\n exit(1)\n\n ls_db_path = sqlite3.connect(os.path.expanduser(sys.argv[1])) # File location for database\n ls_json_path = os.path.expanduser(sys.argv[2]) # File location for output\n lb_is_sets = sys.argv[3] == \"sets\" # Are we doing sets or cards\n\n if lb_is_sets:\n dictionary = db_to_json_all_sets(ls_db_path)\n else:\n dictionary = db_to_json_all_cards(ls_db_path)\n\n with open(ls_json_path, 'w') as json_f:\n json.dump(dictionary, json_f, sort_keys=True, indent=4)\n\n\nif __name__ == '__main__':\n ga_decoders = {'setName': id, 'setCode': id, 'setReleaseDate': id}\n main()\n","sub_path":"sql_to_json.py","file_name":"sql_to_json.py","file_ext":"py","file_size_in_byte":4060,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"549842820","text":"# -*- coding: utf-8 -*-\n###################################################################################\n#\n# This program is free software: you can modify\n# it under the terms of the GNU Affero General Public License (AGPL) 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###################################################################################\nfrom odoo import models, api, fields\n\n\nclass ProductProduct(models.Model):\n _inherit = 'product.product'\n\n @api.multi\n def open_so_product_append(self):\n context = {\n 'default_product_id': self.id,\n 'default_name': self.display_name,\n 'default_price_unit': self.list_price,\n }\n return {'type': 'ir.actions.act_window',\n 'view_mode': 'form',\n 'view_type': 'form',\n 'res_model': 'product.product.append.so',\n 'target': 'new',\n 'context': context,\n }\n\n\n","sub_path":"myaddons/so_append_product/models/product_product.py","file_name":"product_product.py","file_ext":"py","file_size_in_byte":1469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"144573054","text":"from glob import iglob\nimport os\nimport sys\ntry:\n import unittest2 as unittest\nexcept ImportError:\n import unittest\n\n\ndef main():\n # Remove any files previously created by Boost.Interprocess, as these\n # sometimes cause tests to fail spuriously\n for path in iglob('/tmp/boost_interprocess/mworks_conduit_test_*'):\n if os.path.isfile(path):\n print >>sys.stderr, 'Removing', path\n os.remove(path)\n\n if len(sys.argv) == 1:\n tests = unittest.defaultTestLoader.discover('.')\n else:\n tests = unittest.defaultTestLoader.loadTestsFromNames(sys.argv[1:])\n\n suite = unittest.TestSuite(tests)\n runner = unittest.TextTestRunner()\n result = runner.run(suite)\n sys.exit(not result.wasSuccessful())\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"tools/python/run_tests.py","file_name":"run_tests.py","file_ext":"py","file_size_in_byte":800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"591765704","text":"# =============================================================================\n# #\n# # Created on Thu Sep 19 18:00:56 2019\n# #\n# # CISC684: Group 2\n# #\n# # @author: Eric Allen \n# # Matthew Walter \n# # Murugesan Somasundaram \n# # \n# =============================================================================\nfrom src.calc_Entropy import calculate_entropy\nfrom src.calc_GINI import calculate_GINI\n\ndef ID3(BinaryTree, data, columns,parent_node_class, calculate, target=\"Class\"):\n \"\"\" ID3 Algorithm for either Entropy or GINI\"\"\"\n if parent_node_class == None:\n column_names = list(columns)\n column_names.remove(\"Class\")\n else:\n column_names = list(columns) \n \n P = len(data.loc[data['Class'] == 1])\n N = len(data.loc[data['Class'] == 0])\n \n #If All Negative\n if P == 0:\n return '0'\n \n #If All Positive\n elif N == 0:\n return '1'\n \n #If length Feature Atrtribute is Empty\n elif len(data) == 0 or len(column_names) == 0:\n return data[\"Class\"].unique()\n \n #Grow the tree\n else: \n #for key in column_names:\n if calculate == \"entropy\":\n new_node = calculate_entropy(data, column_names)\n \n elif calculate == \"GINI\":\n new_node = calculate_GINI(data, column_names)\n\n if parent_node_class == None:\n parent_node_class = BinaryTree.setLeft(None,new_node, N, P)\n\n for each in [0,1]:\n if new_node == 0 or new_node == 1:\n new_node = str(new_node)\n if P < N:\n if each == 1:\n BinaryTree.setRight(parent_node_class,new_node,N,P)\n else:\n BinaryTree.setLeft(parent_node_class,new_node,N,P)\n else:\n if each == 1:\n BinaryTree.setRight(parent_node_class,new_node,N,P)\n else:\n BinaryTree.setLeft(parent_node_class,new_node,N,P)\n else:\n rows = data.loc[data[new_node] == each]\n \n if new_node in column_names:\n column_names.remove(new_node)\n if each == 1: \n node = BinaryTree.setRight(parent_node_class,new_node,N,P)\n rightNode = ID3(BinaryTree, rows,list(column_names), node, calculate)\n BinaryTree.setValue(node,rightNode)\n else: \n node = BinaryTree.setLeft(parent_node_class,new_node,N,P)\n leftNode = ID3(BinaryTree, rows,list(column_names), node, calculate)\n BinaryTree.setValue(node,leftNode)\n \n return new_node","sub_path":"src/ID3.py","file_name":"ID3.py","file_ext":"py","file_size_in_byte":2882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"482513632","text":"from flask import Flask, request\r\nimport mysql.connector\r\nimport math\r\nimport requests\r\nimport time\r\nimport datetime\r\nfrom datetime import timedelta\r\nimport math\r\nfrom math import cos, pi,sin,acos \r\nfrom time import sleep\r\nfrom cryptography.fernet import Fernet\r\nfrom Crypto.Cipher import AES\r\nimport json\r\nfrom flask import jsonify\r\nfrom flask import Response\r\n\r\nimport psycopg2\r\ntry: \r\n conn = psycopg2.connect(database=\"platdb\", user=\"postgres\", password=\"bat123\", host=\"localhost\")\r\n print(\"connected\")\r\nexcept:\r\n print (\"I am unable to connect to the database\")\r\nmycursor =conn.cursor()\r\n\r\napp = Flask(__name__)\r\nstatus=\"alive\"\r\nnode_ip =\"127.0.0.1:5001\"\r\n \r\n@app.route('/')\r\ndef index():\r\n return 'Server Works!'\r\n\r\n@app.route('/central', methods=['POST'])\r\ndef central():\r\n\r\n input = request.get_json()\r\n print(input)\r\n try:\r\n vmdID=input['vmdID']\r\n except:\r\n\r\n if vmdID is none:\r\n return \"Please enter VMD ID\"\r\n\r\n return \"Please enter VMD ID\"\r\n try:\r\n username=input['username']\r\n\r\n except:\r\n if username is none:\r\n return \"Please enter username\"\r\n\r\n return \"Please enter username\"\r\n try:\r\n encrypted_password=input['encrypted_password']\r\n\r\n except:\r\n if encrypted_password is none:\r\n return \"Please enter encrypted_password\"\r\n return \"Please enter encrypted password\"\r\n try:\r\n driver_id=input['driver_id']\r\n\r\n except:\r\n if driver_id is none:\r\n return \"Please enter driver id\"\r\n return \"Please enter driver license no\"\r\n try:\r\n latitude=input['latitude'] \r\n\r\n except:\r\n if latitude is none:\r\n return \"Please enter latitude\"\r\n return \"Please enter latitude\"\r\n try:\r\n longitude=input['longitude']\r\n\r\n except:\r\n if longitude is none:\r\n return \"Please enter encrypted_password\"\r\n return \"Please enter longitude\"\r\n print(\"Master central\")\r\n tin = time.time()\r\n mtimestamp = datetime.datetime.fromtimestamp(tin).strftime('%Y-%m-%d %H:%M:%S')\r\n mycursor.execute(\"INSERT into coordinator_alive(node_ip, timestamp,status) values(%s, %s, %s)\", (node_ip, mtimestamp,status))\r\n conn.commit()\r\n\r\n a=1\r\n b=2\r\n #check security module\r\n issueTic=issueTicket(vmdID)\r\n \r\n \r\n dk=issueTic\r\n authentication=authServer(vmdID, username, encrypted_password,driver_id,dk) \r\n if authentication ==100:\r\n return {\"status\":\"driver not permitted to drive this vehicle\"}\r\n if authentication ==150:\r\n return {\"status\":\"re enter password\"}\r\n #check is zone alive\r\n zones_alive=zoneAlive(a)\r\n #check the local zone\r\n local_zone=findLocalZone(latitude,longitude) \r\n \r\n #check the zone capacity\r\n zone_capacity=zoneCapacity(b)\r\n\r\n result1 = mycursor.execute(\"SELECT zone_id,zone_port,zone_name,zone_status FROM zone_alive\")\r\n result1 = mycursor.fetchall()\r\n\r\n for row in result1 :\r\n zone_id=row[0]\r\n \r\n try:\r\n if local_zone ==1 and zone_id == 1:\r\n zone_aloc=1\r\n \r\n elif local_zone ==2 and zone_id == 2:\r\n zone_aloc=2\r\n elif local_zone ==3 and zone_id == 3:\r\n zone_aloc=3\r\n elif local_zone ==4 and zone_id == 4:\r\n zone_aloc=4\r\n except NameError:\r\n return \"No zone allocated\"\r\n\r\n timez = time.time()\r\n ccurrent_timestamped = datetime.datetime.fromtimestamp(timez).strftime('%Y-%m-%d %H:%M:%S') \r\n\r\n sid=mycursor.execute(\"SELECT zone_capacity FROM zone_available where zone_id=%s\",(zone_aloc,))\r\n \r\n sid=mycursor.fetchall()\r\n for row in sid:\r\n zone_capacity=row[0]\r\n \r\n if zone_capacity<3000:\r\n answ = mycursor.execute(\"SELECT zone_port FROM zone_available where zone_id=%s\",(zone_aloc,))\r\n answ = mycursor.fetchall()\r\n\r\n for row in answ :\r\n zone_port=row[0]\r\n\r\n if zone_port==6001:\r\n mycursor.execute(\"INSERT into vmdLocalZone(vmd_id, local_timestamp, zone_port) values(%s, %s,%s)\", (vmdID, ccurrent_timestamped, zone_port,))\r\n conn.commit()\r\n mycursor.execute(\"INSERT into vmd_zone_allocation(vmd_id, zone_alloc_timestamp, zone_port) values(%s, %s,%s)\", (vmdID, ccurrent_timestamped, zone_port,))\r\n conn.commit()\r\n return {\"zone port\":\"6001\"}\r\n\r\n elif zone_port==6002:\r\n mycursor.execute(\"INSERT into vmdLocalZone(vmd_id, local_timestamp, zone_port) values(%s, %s,%s)\", (vmdID, ccurrent_timestamped, zone_port,))\r\n conn.commit()\r\n mycursor.execute(\"INSERT into vmd_zone_allocation(vmd_id, zone_alloc_timestamp, zone_port) values(%s, %s,%s)\", (vmdID, ccurrent_timestamped, zone_port,))\r\n conn.commit()\r\n return {\"zone port\":\"6002\"}\r\n\r\n elif zone_port==6003:\r\n mycursor.execute(\"INSERT into vmdLocalZone(vmd_id, local_timestamp, zone_port) values(%s, %s,%s)\", (vmdID, ccurrent_timestamped, zone_port,))\r\n conn.commit()\r\n mycursor.execute(\"INSERT into vmd_zone_allocation(vmd_id, zone_alloc_timestamp, zone_port) values(%s, %s,%s)\", (vmdID, ccurrent_timestamped, zone_port,))\r\n conn.commit()\r\n return {\"zone port\":\"6003\"}\r\n\r\n elif zone_port==6004:\r\n mycursor.execute(\"INSERT into vmdLocalZone(vmd_id, local_timestamp, zone_port) values(%s, %s,%s)\", (vmdID, ccurrent_timestamped, zone_port,))\r\n conn.commit()\r\n mycursor.execute(\"INSERT into vmd_zone_allocation(vmd_id, zone_alloc_timestamp, zone_port) values(%s, %s,%s)\", (vmdID, ccurrent_timestamped, zone_port,))\r\n conn.commit()\r\n return {\"zone port\":\"6004\"}\r\n try:\r\n zone_port\r\n \r\n except:\r\n \r\n contingency_zone=contingency(b)\r\n ts = time.time()\r\n conti_timestamp = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\r\n\r\n mycursor.execute(\"INSERT into vmdContingencyZone(vmd_id, conti_timestamp, zone_port) values(%s, %s,%s)\", (vmdID,ccurrent_timestamped , contingency_zone,))\r\n conn.commit()\r\n\r\n mycursor.execute(\"INSERT into vmd_zone_allocation(vmd_id, zone_alloc_timestamp, zone_port) values(%s, %s,%s)\", (vmdID, ccurrent_timestamped, contingency_zone,))\r\n conn.commit()\r\n \r\n if(contingency_zone==6001):\r\n return {\"zone port\":\"6001\"}\r\n if(contingency_zone==6002):\r\n return {\"zone port\":\"6002\"}\r\n if(contingency_zone==6003):\r\n return {\"zone port\":\"6003\"}\r\n if(contingency_zone==6004):\r\n return {\"zone port\":\"6004\"}\r\n\r\ndef authServer(vmdID, username, encrypted_password,driver_id,dk):\r\n print(driver_id)\r\n result = mycursor.execute(\"SELECT vmd_id,username FROM auth where vmd_id=%s\",(vmdID,))\r\n result = mycursor.fetchall()\r\n for row in result :\r\n vmdID2=row[0]\r\n username2=row[1]\r\n \r\n try:\r\n vmdID2\r\n \r\n except NameError:\r\n return 'VMD not identified'\r\n\r\n answer = mycursor.execute(\"SELECT driver_vehicle_category FROM driver where driver_id=%s\",(driver_id,))\r\n answer = mycursor.fetchall()\r\n for row in answer :\r\n driver_vehicle_category=row[0]\r\n\r\n sol = mycursor.execute(\"SELECT v_category FROM vehicle where vmd_id=%s\",(vmdID,))\r\n sol = mycursor.fetchall()\r\n for row in sol :\r\n v_category=row[0]\r\n try:\r\n ekey=\"2eEGqWWnclI-W1ILDyG5gXfLAisa7Sc93shTEggZ2CQ=\"\r\n f= Fernet(ekey) \r\n arr = bytes(encrypted_password, 'utf-8')\r\n obj3 = f.decrypt(arr)\r\n usn=obj3.decode(\"utf-8\") \r\n except:\r\n return 150\r\n if username==usn:\r\n credential=1\r\n else:\r\n return 150\r\n # try:\r\n # driver_vehicle_category\r\n # except:\r\n # return 0\r\n \r\n if driver_vehicle_category==v_category:\r\n vehicle_can_drive=1\r\n else:\r\n return 100\r\n\r\n result = mycursor.execute(\"SELECT curr_timestamp,expiry_time FROM vmd_timestamp where vmd_id=%s\",(vmdID,))\r\n result = mycursor.fetchall()\r\n for row in result :\r\n ticexp_time=row[0]\r\n\r\n tims = time.time()\r\n ccurrent_timestamped = datetime.datetime.fromtimestamp(tims).strftime('%Y-%m-%d %H:%M:%S') \r\n ccurrent_timestamped2=datetime.datetime.strptime(ccurrent_timestamped,'%Y-%m-%d %H:%M:%S')\r\n\r\n yc = int(ccurrent_timestamped2.strftime('%Y'))\r\n mc = int(ccurrent_timestamped2.strftime('%m'))\r\n dc = int(ccurrent_timestamped2.strftime('%d'))\r\n hc = int(ccurrent_timestamped2.strftime('%H'))\r\n minc= int(ccurrent_timestamped2.strftime('%M'))\r\n sc = int(ccurrent_timestamped2.strftime('%S'))\r\n\r\n ccurr_timestamped = datetime.datetime(yc, mc, dc, hc, minc,sc)\r\n try:\r\n yz = int(ticexp_time.strftime('%Y'))\r\n mz = int(ticexp_time.strftime('%m'))\r\n dz = int(ticexp_time.strftime('%d'))\r\n hz = int(ticexp_time.strftime('%H'))\r\n minz= int(ticexp_time.strftime('%M'))\r\n sz = int(ticexp_time.strftime('%S'))\r\n\r\n ticexpiry_time = datetime.datetime(yz, mz, dz, hz, minz,sz)\r\n\r\n if ccurr_timestamped.time() > ticexpiry_time.time():\r\n new=1\r\n else:\r\n new=0\r\n except:\r\n new=0\r\n try:\r\n ticexp_time\r\n except:\r\n new=1\r\n\r\n if dk==1 or new==1:\r\n needTicket=1\r\n else:\r\n needTicket=0\r\n \r\n if credential==1 and vehicle_can_drive==1:\r\n authe=1\r\n \r\n else:\r\n authe=0\r\n\r\n if authe==1 and needTicket==1:\r\n \r\n ts = time.time()\r\n timestamp = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\r\n curr_timestamp=timestamp\r\n #curr_timestamp = timestamp.strftime('%Y-%m-%d %H:%M:%S') \r\n \r\n\r\n t = time.localtime()\r\n current_time = time.strftime(\"%H:%M:%S\", t)\r\n hour = time.strftime(\"%H\", t)\r\n \r\n t_hours_from_now = datetime.datetime.now() + datetime.timedelta(hours=3)\r\n expiry_time=t_hours_from_now\r\n exp_time=t_hours_from_now.isoformat()\r\n \r\n ext=t_hours_from_now.replace(microsecond=0)\r\n #expiry_timestamp = t_hours_from_now.strftime('%Y-%m-%d %H:%M:%S') \r\n \r\n\r\n curr_time=datetime.datetime.now().isoformat()\r\n \r\n\r\n\r\n mycursor.execute(\"INSERT into vmd_timestamp(vmd_id, curr_timestamp, expiry_time) values(%s, %s,%s)\", (vmdID, curr_timestamp, expiry_time,))\r\n conn.commit()\r\n\r\n mycursor.execute(\"INSERT into vmd_timestamp_log(vmd_id, curr_timestamp, expiry_time) values(%s, %s,%s)\", (vmdID, curr_timestamp, expiry_time,))\r\n conn.commit()\r\n \r\n return \"connected\"\r\n else:\r\n return 'Service cannot be accessed'\r\n\r\ndef findLocalZone(latitude,longitude):\r\n latitude=latitude\r\n longitude=longitude\r\n zoneA_lat = 7.513617 #Westen Zone\r\n zoneA_longtitude = 80.137133\r\n\r\n zoneB_lat = 6.507628 #Southern Zone\r\n zoneB_longitude = 80.829782\r\n\r\n zoneC_lat = 7.402797 #Central Zone\r\n zoneC_longitude = 81.418508\r\n\r\n zoneD_lat = 9.021270 #Northern Zone\r\n zoneD_longitude = 80.587440\r\n\r\n #Longitude ref value calculation\r\n Ref_zoneA_lat = (90-zoneA_lat)*(pi/180)\r\n Ref_zoneB_lat = (90-zoneB_lat)*(pi/180)\r\n Ref_zoneC_lat = (90-zoneC_lat)*(pi/180)\r\n Ref_zoneD_lat = (90-zoneD_lat)*(pi/180)\r\n\r\n #latitude ref value calculation \r\n Ref_latitude = (90-latitude)*(pi/180)\r\n\r\n #lalilude bitween zone and input calcualtion\r\n Ref_zoneA_longtitude_input = (zoneA_longtitude - longitude)*(pi/180)\r\n Ref_zoneB_latitude = (zoneB_longitude - longitude)*(pi/180)\r\n Ref_zoneC_latitude = (zoneC_longitude - longitude)*(pi/180)\r\n Ref_zoneD_latitude = (zoneD_longitude - longitude)*(pi/180)\r\n\r\n #distace calculation \r\n RefA_X = cos(Ref_zoneA_lat)*cos(Ref_latitude)\r\n RefA_Y = sin(Ref_zoneA_lat)*sin(Ref_latitude)*cos(Ref_zoneA_longtitude_input)\r\n distanceA = 6371*acos(RefA_X + RefA_Y)\r\n\r\n RefB_X = cos(Ref_zoneB_lat)*cos(Ref_latitude)\r\n RefB_Y = sin(Ref_zoneB_lat)*sin(Ref_latitude)*cos(Ref_zoneB_latitude)\r\n distanceB = 6371*acos(RefB_X + RefB_Y)\r\n\r\n RefC_X = cos(Ref_zoneC_lat)*cos(Ref_latitude)\r\n RefC_Y = sin(Ref_zoneC_lat)*sin(Ref_latitude)*cos(Ref_zoneC_latitude)\r\n distanceC = 6371*acos(RefC_X + RefC_Y)\r\n\r\n RefD_X = cos(Ref_zoneD_lat)*cos(Ref_latitude)\r\n RefD_Y = sin(Ref_zoneD_lat)*sin(Ref_latitude)*cos(Ref_zoneD_latitude)\r\n distanceD = 6371*acos(RefD_X + RefD_Y)\r\n\r\n zone1=0\r\n zone2=0\r\n zone3=0\r\n zone4=0\r\n count=0\r\n\r\n if distanceA < 100:\r\n zonez=1\r\n count=count+1\r\n\r\n elif distanceB < 100:\r\n zonez=2\r\n count=count+1\r\n\r\n elif distanceC < 100:\r\n zonez=3\r\n count=count+1\r\n\r\n elif distanceD < 130:\r\n zonez=4\r\n count=count+1\r\n else:\r\n return \"No zone\"\r\n if count>1:\r\n min_dist=min([distanceA, distanceB, distanceC, distanceD])\r\n if min_dist==distanceA:\r\n return 1\r\n elif min_dist==distanceB:\r\n return 2\r\n elif min_dist==distanceC:\r\n return 3\r\n elif min_dist==distanceD:\r\n return 4\r\n elif zonez==1:\r\n return 1\r\n elif zonez==2:\r\n return 2\r\n elif zonez==3:\r\n return 3\r\n elif zonez==4:\r\n return 4\r\n\r\ndef zoneAlive(a):\r\n \r\n result = mycursor.execute(\"SELECT zone_id FROM zone_dead\")\r\n result = mycursor.fetchall()\r\n for row in result :\r\n zone_id=row[0]\r\n \r\n print(zone_id)\r\n mycursor.execute(\"DELETE FROM zone_alive where zone_id=%s\", (zone_id,))\r\n conn.commit()\r\n return \"zones alive\"\r\n\r\ndef zoneCapacity(b):\r\n\r\n zone_maxlimit=3001\r\n mycursor.execute(\"DELETE FROM zone_available where zone_capacity>=%s\", (zone_maxlimit,))\r\n conn.commit()\r\n \r\n return \"zones free capacity\"\r\n\r\ndef contingency(b):\r\n \r\n yid=mycursor.execute(\"SELECT MIN(zone_capacity) AS minimum FROM zone_available\")\r\n yid =mycursor.fetchall()\r\n for row in yid :\r\n min_zone_capacity=row[0]\r\n \r\n ans=mycursor.execute(\"SELECT zone_port FROM zone_available where zone_capacity=%s\", (min_zone_capacity,))\r\n ans =mycursor.fetchall()\r\n for row in ans :\r\n zone_port=row[0]\r\n \r\n return zone_port\r\n\r\ndef issueTicket(vmdID):\r\n\r\n tims = time.time()\r\n zcurrent_timestamped = datetime.datetime.fromtimestamp(tims).strftime('%Y-%m-%d %H:%M:%S') \r\n zcurrent_timestamped2=datetime.datetime.strptime(zcurrent_timestamped,'%Y-%m-%d %H:%M:%S')\r\n\r\n yc = int(zcurrent_timestamped2.strftime('%Y'))\r\n mc = int(zcurrent_timestamped2.strftime('%m'))\r\n dc = int(zcurrent_timestamped2.strftime('%d'))\r\n hc = int(zcurrent_timestamped2.strftime('%H'))\r\n minc= int(zcurrent_timestamped2.strftime('%M'))\r\n sc = int(zcurrent_timestamped2.strftime('%S'))\r\n\r\n zcurr_timestamped = datetime.datetime(yc, mc, dc, hc, minc,sc)\r\n \r\n\r\n answer = mycursor.execute(\"SELECT curr_timestamp, expiry_time FROM vmd_timestamp where vmd_id=%s\",(vmdID,)) #new table might have to introduce\r\n answer = mycursor.fetchall()\r\n for row in answer :\r\n tcurr_timestamp=row[0]\r\n exp_time=row[1]\r\n try:\r\n ye = int(exp_time.strftime('%Y'))\r\n print(ye)\r\n me = int(exp_time.strftime('%m'))\r\n de = int(exp_time.strftime('%d'))\r\n he = int(exp_time.strftime('%H'))\r\n mine= int(exp_time.strftime('%M'))\r\n se = int(exp_time.strftime('%S'))\r\n\r\n expiry_time = datetime.datetime(ye, me, de, he, mine,se)\r\n#-----------ticket expiry---------------------------------\r\n if zcurr_timestamped.time() > expiry_time.time():\r\n jdk=1\r\n \r\n mycursor.execute(\"DELETE FROM vmd_timestamp where vmd_id=%s\", (vmdID,))\r\n conn.commit()\r\n try:\r\n mycursor.execute(\"DELETE FROM vmdLocalZone where vmd_id=%s\", (vmdID,))\r\n conn.commit()\r\n except:\r\n mycursor.execute(\"DELETE FROM vmdContingencyZone where vmd_id=%s\", (vmdID,))\r\n conn.commit()\r\n \r\n else:\r\n jdk=0\r\n except:\r\n jdk=0\r\n \r\n#------------zone dead-------------------------------------\r\n result = mycursor.execute(\"SELECT zone_id,zone_port FROM zone_dead\")\r\n result = mycursor.fetchall()\r\n for row in result :\r\n zone_id=row[0]\r\n zone_port=row[1]\r\n try:\r\n answer2 = mycursor.execute(\"SELECT zone_port FROM vmdLocalZone where vmd_id=%s\",(vmdID,)) #new table might have to introduce\r\n answer2 = mycursor.fetchall()\r\n for row in answer2 :\r\n zone_ported=row[0]\r\n \r\n except:\r\n ans = mycursor.execute(\"SELECT zone_port FROM vmdContingencyZone where vmd_id=%s\",(vmdID,)) #new table might have to introduce\r\n ans = mycursor.fetchall()\r\n for row in ans :\r\n zone_ported=row[0]\r\n \r\n\r\n try:\r\n\r\n if zone_port==zone_ported:\r\n dul=1\r\n \r\n try:\r\n mycursor.execute(\"DELETE FROM vmdLocalZone where vmd_id=%s\", (vmdID,))\r\n conn.commit()\r\n except:\r\n mycursor.execute(\"DELETE FROM vmdContingencyZone where vmd_id=%s\", (vmdID,))\r\n conn.commit()\r\n else:\r\n dul=0\r\n \r\n if jdk==1 or dul==1:\r\n dk=1\r\n else:\r\n dk=0\r\n return dk\r\n except:\r\n \r\n return \"9\"","sub_path":"centralM.py","file_name":"centralM.py","file_ext":"py","file_size_in_byte":17646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"93086428","text":"import time\nfrom pymongo import MongoClient\nimport re\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions as ec\n# creating collection in mongodb\ncon = MongoClient()\ndb = con.coaching.justdial\n# links dictionary of all links to be explore\nlinks = {\"heading\": \"//h2[@class='store-name']/span/a\",\n \"rating\": \"//span[@class='rating']/span[@class='total-rate']/span[@class='value-titles']\",\n \"address\": \"//span/span/span/span[@class='lng_add']\",\n \"justdial\": \"https://www.justdial.com\", \"city\": \"Indore\",\n \"next\": '//*[@id=\"srchpagination\"]/a[12]',\n \"phone\": \"//p[@class='contact-info ']/span/a/b\",\n \"website\": \"//*[@id='comp-contact']/li[5]/span\",\n 'name_person': '//div[@class=\"col-sm-12 allratingM\"]/div[@class=\"allratR\"]/span/span[@class=\"rName lng_commn\"]',\n 'comments': '//div[@class=\"col-sm-12 allratingM\"]/div[@class=\"allratR\"]/div/p[@class=\"rwopinion2 thr lng_commn\"]',\n 'stars': '//div[@class=\"col-sm-12 allratingM\"]/div[@class=\"allratR\"]/span/span[@class=\"star_m\"]'}\n\n\nclass JustDial:\n \"\"\"\n This function load the web driver and enter the city and the title in the search box,\n and also call a method named 'just_dial()' for the collection of all information\n according to the data model.\n \"\"\"\n def __init__(self):\n browser = webdriver.Chrome(executable_path='C:\\\\Users\\\\tej\\\\PycharmProjects\\\\chromedriver.exe')\n browser.get(links[\"justdial\"])\n time.sleep(2)\n citynm = browser.find_element_by_id('city')\n citynm.clear()\n citynm.send_keys(links['city'])\n c = \"//li[@id='\"+links['city']+\"']/a[@id='\"+links['city']+\"']\"\n wait(browser, c)\n browser.find_element_by_xpath(c).click()\n browser.find_element_by_id('srchbx').send_keys('Coaching classes')\n browser.find_element_by_class_name('search-button').click()\n while wait(browser, links[\"next\"]):\n just_dial(browser)\n browser.find_element_by_xpath(links[\"next\"]).click()\n\n\ndef just_dial(browser):\n \"\"\"\n In this method we find all the headings links on a single page and click them one by one,\n collect data from the directed page and came back to previous page\n and the move forward to the next page for the same working.\n Also, when we click on the heading and directed to the next page we collect all the data that would be useful\n for the further use and are according to the data model.\n We also made a dictionary to store all data after making the data into particular format we needed.\n \"\"\"\n detail = {}\n heading = len(browser.find_elements_by_xpath(links[\"heading\"]))\n for i in range(heading):\n head = browser.find_elements_by_xpath(links[\"heading\"])[i]\n detail[\"name\"] = str(head.text)\n phn = browser.find_elements_by_xpath(links[\"phone\"])[i]\n span = phn.find_elements_by_tag_name(\"span\")\n num_list = []\n for num in span:\n num_list.append(num.get_attribute(\"class\"))\n trash_list = get_num(num_list)\n detail[\"phone\"] = [{'ext': trash_list[0],\n 'phone': trash_list[1],\n 'source': 'justdial'}]\n detail['email'] = [{'email': '',\n 'source': 'justdial'}]\n detail['contact_person'] = [{'name': '',\n 'designation': '',\n 'phone': trash_list[1],\n 'email': '',\n 'url': ''}]\n head.click()\n add = browser.find_element_by_xpath(links[\"address\"])\n trash_list = add.text.split(',')\n address = {}\n line1 = trash_list[0] + ', ' + trash_list[1]\n line2 = \",\".join([str(i.strip()) for i in trash_list[2:] if links['city'] not in i])\n address['city'] = links['city']\n address['state'] = \"Madhya Pradesh\"\n address['country'] = \"India\"\n trash_list = [str(i.strip()) for i in trash_list[2:] if links['city'] in i or links['city'].lower() in i]\n pin = trash_list[0].split('-')\n address['pincode'] = pin[1].strip()\n address[\"address line 1\"] = line1\n address['address line 2'] = line2\n address['longitude'] = ''\n address['latitude'] = ''\n detail['address'] = address\n rate = browser.find_element_by_xpath(links[\"rating\"])\n detail[\"rating\"] = [{'rating': str(rate.text),\n 'source': \"justdial\"}]\n\n namerange = len(browser.find_elements_by_xpath(links['name_person']))\n name = []\n comment = []\n rating = []\n for n in range(namerange):\n name.append(browser.find_elements_by_xpath(links['name_person'])[n].text)\n comment.append(browser.find_elements_by_xpath(links['comments'])[n].text)\n stars = browser.find_elements_by_xpath(links['stars'])[n].find_elements_by_tag_name('span')\n star = []\n for s in stars:\n star.append(s.get_attribute('class'))\n rating.append(get_star(star))\n star = []\n for ind in range(len(name)):\n review = {'comment': comment[ind],\n 'name': name[ind],\n 'url': browser.current_url,\n 'id': '',\n 'rating': rating[ind],\n 'source': 'justdial'}\n star.append(review)\n detail['reviews'] = star\n detail['courses'] = []\n detail['tags'] = []\n detail['direction'] = [{'url': '',\n 'source': 'justdial'}]\n db.insert(detail.copy())\n browser.back()\n wait(browser, links[\"heading\"])\n\n\ndef wait(driver, x):\n # method for waiting for an element presence\n WebDriverWait(driver, 20).until(ec.presence_of_element_located((By.XPATH, x)))\n return True\n\n\ndef get_num(list_num):\n # method for generating number from encoded class and return it\n num_dict = {\"mobilesv icon-dc\": \"+\",\n \"mobilesv icon-fe\": \"\",\n \"mobilesv icon-hg\": \"\",\n \"mobilesv icon-ba\": \"-\",\n \"mobilesv icon-acb\": \"0\",\n \"mobilesv icon-yz\": \"1\",\n \"mobilesv icon-wx\": \"2\",\n \"mobilesv icon-vu\": \"3\",\n \"mobilesv icon-ts\": \"4\",\n \"mobilesv icon-rq\": \"5\",\n \"mobilesv icon-po\": \"6\",\n \"mobilesv icon-nm\": \"7\",\n \"mobilesv icon-lk\": \"8\",\n \"mobilesv icon-ji\": \"9\"}\n phno = ''\n for x in list_num:\n phno += num_dict[x]\n return phno.split('-')\n\n\ndef get_star(star):\n # method to encode the rating stars and return it.\n rate = {'ms10': 1,\n 'ms5': 0.5,\n 'ms0': 0}\n rating = 0\n for x in star:\n rating += rate[x]\n return str(rating)\n\n\nif __name__ == \"__main__\":\n # main method and creation of object to call constructor\n js = JustDial()\n","sub_path":"justdial_scrapper.py","file_name":"justdial_scrapper.py","file_ext":"py","file_size_in_byte":7157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"553167305","text":"\r\n# this is python program to extract phonenumber(of any pattern) and email address(of any username and any domain name) \r\n# from any documents of any amount of pages\r\n\r\n# firsty importing all module using in this program\r\nimport pyperclip\r\nimport re\r\n# regular expression (regex) for phone number(phonenumber_regex)\r\nphonenumber_regex=re.compile(r'''\r\n(\\d{3}|\\(\\d{3}\\)|\\<\\d{3}\\>|\\[\\d{3}\\]|\\{\\d{3}\\}) # area code\r\n(-|.|\\s|_|/) # separator \r\n(\\d{3}) # first 3 digits\r\n(-|.|\\s|_|/) # separator\r\n(\\d{4})''',re.VERBOSE) # last 4 digits\r\n\r\n# regular expression (regex) for email address(emailadd_regex)\r\nemailadd_regex=re.compile(r''' \r\n([a-zA-Z0-9%^&*_+-]+) # username \r\n(@) # @ symbol\r\n([a-zA-Z0-9%^&*_+-]+) # domain name\r\n(\\.) # . symbol\r\n([a-zA-Z]+)''',re.VERBOSE) # after dot symbol\r\n\r\n# this is time to import or paste the document into program by using pyperclip.paste() method .\r\n\r\nlong_text=str(pyperclip.paste())\r\n# phone number match object list\r\nphonenumber_mo=phonenumber_regex.findall(long_text)\r\nprint(\"phone number list:\")\r\nfor x in range(len(phonenumber_mo)):\r\n print(\"\".join(list(phonenumber_mo[x])))\r\n# email address match object list\r\nemailadd_mo=emailadd_regex.findall(long_text)\r\nprint(\"\\nemail address list:\")\r\nfor x in range(len(emailadd_mo)):\r\n print(\"\".join(list(emailadd_mo[x])))\r\n","sub_path":"phonenumber_and_emailaddress_extractor.py","file_name":"phonenumber_and_emailaddress_extractor.py","file_ext":"py","file_size_in_byte":1675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"622179834","text":"import pandas as pd\nimport numpy as np\nimport warnings\nimport random\nfrom random import sample \nfrom collections import Counter\nfrom datetime import datetime\n\nwarnings.filterwarnings(\"ignore\")\n\ntrain = pd.read_json(\"/daintlab/data/music_rec/train.json\")\nsong_meta = pd.read_json(\"/daintlab/data/music_rec/song_meta.json\")\nfiltering = 25\n\ndef to_date(x):\n try:\n y = pd.to_datetime(x, format = \"%Y%m%d\")\n except:\n y = None\n return y\n\ntrain_song = train['songs']\nsong_counter = Counter([song for songs in train_song for song in songs])\nsong_dict = {x: song_counter[x] for x in song_counter}\nsong_dict = dict(filter(lambda x : x[1]>=filtering, song_dict.items())) # filtering song\n\nsong_id_sid = dict()\nfor i, song_id in enumerate(song_dict): \n song_id_sid[song_id] = i \n\n\nsong_meta = song_meta[song_meta[\"id\"].notnull()]\nsong_meta[\"id\"] = song_meta[\"id\"].astype(int)\nsong_meta[\"issue_date\"] = song_meta[\"issue_date\"].astype(str)\nsong_meta[\"issue_date\"] = song_meta[\"issue_date\"].apply(lambda x : x[:4] + x[4:6].replace(\"00\",\"01\") + x[6:].replace(\"00\",\"01\"))\nsong_meta[\"issue_date\"] = song_meta[\"issue_date\"].apply(lambda x : to_date(x))\nsong_meta = song_meta[song_meta[\"issue_date\"].notnull()] # 결측치 제거\nsong_meta[\"timestamp\"] = song_meta[\"issue_date\"].apply(lambda x : datetime.timestamp(x))\nsong_meta[\"itemId\"] = song_meta[\"id\"].apply(lambda x : song_id_sid.get(x))\nsong_meta = song_meta[song_meta[\"itemId\"].notnull()]\n\nissue_date = dict(zip(song_meta[\"itemId\"],song_meta[\"timestamp\"]))\n\nitems = set(issue_date.keys())\nprint(max(song_id_sid.values()))\nnum_items = max(issue_date.keys()) + 1\nprint(\"n_items = {}\".format(num_items))\n\ntrain['itemId'] = train['songs'].apply(lambda x: [song_id_sid.get(item) for item in x if song_id_sid.get(item) != None])\ntrain['itemId'] = train['itemId'].apply(lambda x: [item for item in x if issue_date.get(item) != None])\ntrain.loc[:,'num_items'] = train['itemId'].map(len)\ntrain = train[train[\"num_items\"]>1]\nn_data = len(train)\ntrain[\"userId\"] = range(n_data)\n\ntrain[\"timestamp\"] = train[\"itemId\"].apply(lambda x: [issue_date.get(item) for item in x if issue_date.get(item) != None])\ntrain[\"test_index\"] = train[\"timestamp\"].apply(lambda x : np.array(x).argmax())\ntrain[\"test_rating\"] = train.apply(lambda x: x[\"itemId\"][x[\"test_index\"]], axis = 1)\ntrain[\"test\"] = train[\"itemId\"].apply(lambda x : list(items - set(x)))\ntrain[\"test_negative\"] = train[\"test\"].apply(lambda x : random.sample(x,99))\ntrain[\"train_negative\"] = train.apply(lambda x : list(items - set(x[\"itemId\"]) - set(x[\"test_negative\"])), axis = 1)\ntrain.apply(lambda x : x[\"itemId\"].remove(x[\"test_rating\"]), axis = 1)\ntrain.rename(columns = {\"itemId\":\"train_positive\"}, inplace = True)\ntrain = train[[\"userId\",\"train_positive\",\"train_negative\",\"test_rating\",\"test_negative\"]].reset_index()\nprint(train)\n\n#https://towardsdatascience.com/the-best-format-to-save-pandas-data-414dca023e0d\ntrain.to_feather(\"melon_\"+str(num_items)+\".ftr\")","sub_path":"src/split.py","file_name":"split.py","file_ext":"py","file_size_in_byte":2971,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"584585899","text":"import socket\r\nfrom sys import exit\r\n\r\nc = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n\r\n# set default host and port number\r\nHOST = 'localhost'\r\nPORT = 9999\r\nSNAME = \"SERVER\"\r\n\r\n# set server username\r\nc_name = input(\"Enter client nickname (Leave it blank for default): \")\r\nif c_name == \"\" or c_name == None:\r\n c_name = \"CLIENT\"\r\n\r\n# try to connect to server using default values\r\ntry:\r\n c.connect((HOST, PORT))\r\n\r\nexcept Exception as e:\r\n print(e)\r\n exit()\r\nc.settimeout(60)\r\n# exchange usernames and print acknowledgement\r\ntry:\r\n c.send(bytes(c_name, \"utf-8\"))\r\n SNAME = c.recv(1024)\r\n SNAME = SNAME.decode()\r\nexcept:\r\n print(\"USERNAME FETCHING ERROR. DEFAULT USERNAMES TAKEN !!!\")\r\nfinally:\r\n print(\"Connected with {} \\n\".format(SNAME))\r\n\r\ntry:\r\n while True:\r\n msg = input(\"{}: \".format(c_name))\r\n c.send(bytes(msg, \"utf-8\"))\r\n\r\n if msg == \"quit\":\r\n print(\"DISCONNECTED BY CLIENT\")\r\n break\r\n\r\n ser_msg = c.recv(1024)\r\n print(\"{}: {}\".format(SNAME, ser_msg.decode()))\r\n\r\nexcept Exception as e:\r\n print(e)\r\n\r\nfinally:\r\n c.close()\r\n print(\"EXITING !!!\")","sub_path":"client-server application for chat using TCP/tcpclient.py","file_name":"tcpclient.py","file_ext":"py","file_size_in_byte":1155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"410955970","text":"# -*- coding: utf-8 -*-\n# tensorflow version==1.3.0\n#\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport pickle\nimport time\n\n#开始计时\nstart = time.clock()\n\n#解压数据集\ndef unpickle(filename):\n\twith open(filename, 'rb') as f:\n\t\td = pickle.load(f, encoding = 'latin1')\n\t\treturn d\n\n#one-hot 编码\ndef onehot(labels):\n\tn_sample = len(labels)\n\tn_class = max(labels)+ 1\n\tonehot_labels = np.zeros((n_sample, n_class))\n\tonehot_labels[np.arange(n_sample), labels] = 1\n\treturn onehot_labels\n\t\n# 载入训练数据集\ndata1 = unpickle('cifar10/cifar-10-batches-py/data_batch_1')\ndata2 = unpickle('cifar10/cifar-10-batches-py/data_batch_2')\ndata3 = unpickle('cifar10/cifar-10-batches-py/data_batch_3')\ndata4 = unpickle('cifar10/cifar-10-batches-py/data_batch_4')\ndata5 = unpickle('cifar10/cifar-10-batches-py/data_batch_5')\n\nx_train = np.concatenate((data1['data'], data2['data'], data3['data'], data4['data'], data5['data']), axis=0)\ny_train = np.concatenate((data1['labels'], data2['labels'], data3['labels'], data4['labels'], data5['labels']), axis = 0)\ny_train= onehot(y_train)\n\ntest = unpickle('cifar10/cifar-10-batches-py/test_batch')\nx_test = test['data'][:5000,:]\ny_test = onehot(test['labels'])[:5000, :]\n\nprint('Training dataset shape:', x_train.shape)\nprint('Training labels shape:', y_train.shape)\nprint('Testing dataset shape:', x_test.shape)\nprint('Testing labes shape:', y_test.shape)\n\n#参数设置\nlearning_rate = 1e-3\ntrain_iter \t\t= 200\nbatch_size \t\t= 50\nfeature_num \t= 3072 # 32*32*3 = 3072\nclass_num \t\t= 10\nn_fc1 = 384\nn_fc2 = 192\n\ndef conv2d(x, w):\n\treturn tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME')\n\t\ndef pool_avg_2x2(x):\n\treturn tf.nn.avg_pool(x, ksize=[1, 3, 3, 1], strides=[1, 2, 2,1], padding='SAME')\n\t\n\nx = tf.placeholder(tf.float32, shape = [None, feature_num])\ny = tf.placeholder(tf.float32, shape = [None, class_num])\n#将x 转换为image格式，即 32*32*3的矩阵形式\nx_image = tf.reshape(x, [-1, 32, 32, 3])\n\n\nw_parm = {\n\n\t'conv1': tf.Variable(tf.truncated_normal([5, 5, 3, 32], stddev=0.0001)),\n 'conv2': tf.Variable(tf.truncated_normal([5, 5, 32, 64], stddev=0.01)),\n 'fc1': tf.Variable(tf.truncated_normal([8*8*64, n_fc1], stddev=0.1)),\n 'fc2': tf.Variable(tf.truncated_normal([n_fc1, n_fc2], stddev=0.1)),\n 'fc3': tf.Variable(tf.truncated_normal([n_fc2, class_num], stddev=0.1))\n\n}\n\nb_parm = {\n\n\t'conv1': tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[32])),\n 'conv2': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[64])),\n 'fc1': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[n_fc1])),\n 'fc2': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[n_fc2])),\n 'fc3': tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[class_num]))\n\n}\n\n#卷积层1\nconv1 = conv2d(x_image, w_parm['conv1'])\nconv1 = tf.nn.relu(tf.nn.bias_add(conv1, b_parm['conv1']))\n#池化层1\npool1 = pool_avg_2x2(conv1)\n\n#LRN层local response normalization 局部响应归一化\nnorm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)\n\n#卷积层2\nconv2 = conv2d(norm1, w_parm['conv2'])\nconv2 = tf.nn.relu(tf.nn.bias_add(conv2, b_parm['conv2']))\n\n#LRN层\nnorm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)\n\n#池化层2\npool2 = pool_avg_2x2(norm2)\n\npool2_flat =tf.reshape(pool2, [-1,8*8*64])\n\n#全连接层1\n\nfc1 = tf.add( tf.matmul(pool2_flat, w_parm['fc1']), b_parm['fc1'] )\nfc1 = tf.nn.relu(fc1)\n\n#全连接层2\nfc2 = tf.add(tf.matmul(fc1, w_parm['fc2']), b_parm['fc2'])\nfc2 = tf.nn.relu(fc2)\n\n#全连接层3\nfc3 = tf.add(tf.matmul(fc2, w_parm['fc3']), b_parm['fc3'])\nfc3 = tf.nn.softmax(fc3)\n\n#计算loss\nloss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=fc3, labels=y))\n\n#定义优化器\noptimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)\n\ncorrect_predict = tf.equal(tf.argmax(y,1), tf.argmax(fc3, 1))\naccuracy = tf.reduce_mean(tf.cast(correct_predict, tf.float32))\n\ninit = tf.global_variables_initializer()\n\n\n\nwith tf.Session() as sess:\n sess.run(init)\n total_batch = int(x_train.shape[0]/batch_size)\n start_time = time.time()\n for i in range(200):\n for batch in range(total_batch):\n batch_x = x_train[batch*batch_size : (batch+1)*batch_size, :]\n batch_y = y_train[batch*batch_size : (batch+1)*batch_size, :]\n sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})\n test_acc = sess.run(accuracy, feed_dict={x:x_test, y:y_test})\n print('iter_num is {0} test accuracy: {1}'.format(i,test_acc) )\n\n\n\n#计时结束\nend = time.clock()\nsecond = end-start\nminute = int(second /60)\nsecond = int(second - minute*60)\nprint (\"time is {0} minute {1} second \".format(minute, second))\n\n\n\n\n\n\n","sub_path":"06-cifar10-cnn.py","file_name":"06-cifar10-cnn.py","file_ext":"py","file_size_in_byte":4719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"578779359","text":"# -*- coding: utf-8 -*-\n# @Time : 2021-04-03 10:17\n# @Author : zxl\n# @FileName: 084_2.py\n\n\nclass Solution:\n def largestRectangleArea(self, heights) :\n\n from collections import deque\n\n n = len(heights)\n\n r_lst = deque()\n r_arr = [-1 for i in range(n)]\n\n for i in range(n):\n if len(r_lst) == 0 or heights[i]>=heights[r_lst[-1]]:\n r_lst.append(i)\n else:\n while len(r_lst)>0 and heights[i]=heights[l_lst[-1]]:\n l_lst.append(i)\n else:\n while len(l_lst) > 0 and heights[i] 1)\n if not self.mIsOK:\n return\n self.mColumns = [dc_collection.makeColumn(self,\n \"%s_%d\" % (self.getName(), idx), dc_collection.ATOM_DATA_TYPE_INT)\n for idx, member_name in enumerate(self.mFamilyInfo.getMembers())]\n self.mConfig = unit_data.get(\"config\", dict())\n self.mXCondition = None\n labels = AnfisaConfig.configOption(\"zygosity.cases\")\n self.mVariantSet = VariantSet([labels[key]\n for key in (\"homo_recess\", \"x_linked\", \"dominant\", \"compens\")])\n self.getIndex().getCondEnv().addSpecialUnit(self)\n\n def setup(self):\n self.mXCondition = self.getIndex().getCondEnv().parse(\n self.mConfig.get(\"x_cond\",\n ConditionMaker.condEnum(\"Chromosome\", [\"chrX\"])))\n\n def isAtomic(self):\n return False\n\n def isOK(self):\n return self.mIsOK\n\n def fillRecord(self, inp_data, record):\n for col_h in self.mColumns:\n col_h.setValue(record, inp_data.get(col_h.getName()))\n\n def _makeCrit(self, idx, min_v, max_v = None):\n column = self.mColumns[idx]\n if min_v is not None:\n return lambda record: column.recordValue(record) >= min_v\n return lambda record: column.recordValue(record) <= max_v\n\n @staticmethod\n def _joinAnd(seq):\n return lambda record: all([f(record) for f in seq])\n\n def condZHomoRecess(self, problem_group):\n seq = []\n for idx in range(len(self.mFamilyInfo)):\n if idx in problem_group:\n seq.append(self._makeCrit(idx, 2, None))\n else:\n seq.append(self._makeCrit(idx, None, 1))\n return WS_SpecCondition(\"ZHomoRecess\", self._joinAnd(seq))\n\n def _condZDominant(self, problem_group):\n seq = []\n for idx in range(len(self.mFamilyInfo)):\n if idx in problem_group:\n seq.append(self._makeCrit(idx, 1, None))\n else:\n seq.append(self._makeCrit(idx, None, 0))\n return WS_SpecCondition(\"ZDominant\", self._joinAnd(seq))\n\n def condZDominant(self, problem_group):\n return self.mXCondition.negative().addAnd(\n self._condZDominant(problem_group))\n\n def condZXLinked(self, problem_group):\n return self.mXCondition.addAnd(\n self._condZDominant(problem_group))\n\n def conditionZCompens(self, problem_group):\n seq = []\n for idx in range(len(self.mFamilyInfo)):\n if idx in problem_group:\n seq.append(self._makeCrit(idx, None, 0))\n else:\n seq.append(self._makeCrit(idx, 1, None))\n return WS_SpecCondition(\"ZCompens\", self._joinAnd(seq))\n\n def _buildCritSeq(self, p_group):\n return [\n self.condZHomoRecess(p_group),\n self.condZXLinked(p_group),\n self.condZDominant(p_group),\n self.conditionZCompens(p_group)]\n\n def makeStat(self, data_records, repr_context = None):\n ret = self._prepareStat()\n ret[1][\"family\"] = self.mFamilyInfo.getTitles()\n ret[1][\"affected\"] = self.mFamilyInfo.getAffectedGroup()\n\n if repr_context is None or \"problem_group\" not in repr_context:\n p_group = self.mFamilyInfo.getAffectedGroup()\n else:\n p_group = {m_idx if 0 <= m_idx < len(self.mFamilyInfo)\n else None for m_idx in repr_context[\"problem_group\"]}\n if None in p_group:\n p_group.remove(None)\n ret.append(list(p_group))\n if len(p_group) == 0:\n return ret + [None]\n\n stat = EnumStat(self.mVariantSet)\n crit_seq = self._buildCritSeq(p_group)\n for data_rec in data_records:\n idx_set = set()\n for idx, crit in enumerate(crit_seq):\n if crit(data_rec):\n idx_set.add(idx)\n stat.regValues(idx_set)\n return ret + stat.result()\n\n @staticmethod\n def _getIdxSet(crit_seq, record):\n ret = set()\n for idx, crit in enumerate(crit_seq):\n if crit(record):\n ret.add(idx)\n return ret\n\n def parseCondition(self, cond_info):\n assert cond_info[0] == \"zygosity\"\n unit_name, p_group, filter_mode, variants = cond_info[1:]\n\n if not self.mIsOK or not p_group:\n if filter_mode == \"NOT\":\n return WS_All()\n return WS_None()\n assert unit_name == self.getName()\n assert len(variants) > 0\n\n base_idx_set = self.mVariantSet.makeIdxSet(variants)\n filter_func = WS_EnumCondition.enumFilterFunc(\n filter_mode, base_idx_set)\n crit_seq = self._buildCritSeq(p_group)\n return WS_SpecCondition(\"zygosity\", lambda record:\n filter_func(self._getIdxSet(crit_seq, record)))\n\n#===============================================\ndef loadWSFilterUnit(index, dc_collection, unit_data):\n kind = unit_data[\"kind\"]\n if kind == \"zygosity\":\n ret = ZygosityComplexUnit(index, dc_collection, unit_data)\n return ret if ret.isOK() else None\n if kind in (\"long\", \"float\"):\n return NumericValueUnit(index, dc_collection, unit_data)\n assert kind in (\"enum\", \"presence\")\n if kind == \"enum\" and unit_data[\"atomic\"]:\n return StatusUnit(index, dc_collection, unit_data)\n if kind == \"enum\" and unit_data[\"compact\"]:\n return MultiCompactUnit(index, dc_collection, unit_data)\n return MultiSetUnit(index, dc_collection, unit_data)\n","sub_path":"app/search/flt_unit.py","file_name":"flt_unit.py","file_ext":"py","file_size_in_byte":11032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"112367709","text":"import torch.nn.functional as F\nfrom torch import nn\nfrom torchinfo import summary\n\nclass Critic(nn.Module):\n '''\n Critic or Discriminator model.\n '''\n def __init__(self, in_channel=3, sigmoid=True) -> None:\n super().__init__()\n self.sigmoid = sigmoid\n # Start with 3x64x64\n n_channel = 64\n model = [\n nn.Conv2d(in_channel, n_channel, 4, 2, 1),\n nn.LeakyReLU(0.2, inplace=True)\n ]\n for _ in range(3):\n out_channel = n_channel*2\n model.extend([\n nn.Conv2d(n_channel,out_channel, 4, 2, 1),\n nn.BatchNorm2d(out_channel),\n nn.LeakyReLU(0.2, inplace=True),\n ])\n n_channel = out_channel\n \n self.model = nn.Sequential(*model) \n\n self.fc = nn.Sequential(\n nn.Conv2d(n_channel, 1, 4),\n ) # Final output is only 1 element\n\n def forward(self, x):\n x = self.model(x)\n x = self.fc(x)\n if self.sigmoid: # For BCE-GAN\n x = nn.Sigmoid()(x)\n\n return x\n \nclass Generator(nn.Module):\n '''\n Generator model.\n '''\n def __init__(self, letent: int, our_channel=3) -> None:\n super().__init__()\n # Start with 100x1x1\n n_channel = 512\n model = [\n nn.ConvTranspose2d(letent, n_channel, 2),\n nn.BatchNorm2d(n_channel),\n nn.ReLU(inplace=True)\n ]\n for _ in range(4):\n out_channel = int(n_channel/2)\n model.extend([\n nn.ConvTranspose2d(n_channel, out_channel, 4, 2, 1),\n nn.BatchNorm2d(out_channel),\n nn.ReLU(inplace=True)\n ])\n n_channel = out_channel\n model.extend([\n nn.ConvTranspose2d(n_channel, our_channel, 4, 2, 1),\n nn.Tanh()\n ])\n self.model = nn.Sequential(*model) # 3x64x64\n\n def forward(self, x):\n x = self.model(x)\n\n return x\n \ndef initialize_weights(model):\n for m in model.modules():\n if isinstance(m, nn.Conv2d):\n nn.init.normal_(m.weight.data, 0.0, 0.02)\n if isinstance(m, nn.ConvTranspose2d):\n nn.init.normal_(m.weight.data, 0.0, 0.02)\n if isinstance(m, nn.BatchNorm2d):\n nn.init.normal_(m.weight.data, 0.0,0.02)\n\nif __name__ == '__main__':\n print('---------------------------Critic----------------------------------')\n model = Critic()\n num_layers = len(list(model.parameters()))\n print(f\"The number of layers in the model: {num_layers}\")\n summary(model, input_size=(1, 3, 64, 64))\n print('---------------------------Generator-------------------------------')\n letent = 100\n model = Generator(letent)\n num_layers = len(list(model.parameters()))\n print(f\"The number of layers in the model: {num_layers}\")\n summary(model, input_size=(1, letent, 1, 1))","sub_path":"ECE60146/hw7/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":2920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"632850506","text":"#! /usr/bin/env python\n\n#Python3\n\nimport time\nimport scapy.all as scapy\nimport argparse\n\n# set port forwarding: echo 1 > /proc/sys/net/ipv4/ip_forward\n\n\ndef get_arguments():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-t\", \"--target\", dest=\"target\", help=\"IP of the target\")\n parser.add_argument(\"-g\", \"--gateway\", dest=\"gateway\", help=\"IP of the gateway\")\n options = parser.parse_args()\n if not options.target:\n parser.error(\"[-] Please specify ip for the target, use --help for more info.\")\n elif not options.gateway:\n parser.error(\"[-] Please specify ip for the gateway, use --help for more info.\")\n return options\n\n\ndef get_mac(ip):\n arp_request = scapy.ARP(pdst=ip)\n broadcast = scapy.Ether(dst=\"ff:ff:ff:ff:ff:ff\")\n arp_request_broadcast = broadcast/arp_request\n answered_list = scapy.srp(arp_request_broadcast, timeout=1, verbose=False)[0]\n\n return answered_list[0][1].hwsrc\n\n\ndef spoof(target_ip, spoof_ip):\n target_mac = get_mac(target_ip)\n packet = scapy.ARP(op=2, pdst=target_ip, hwdst=target_mac, psrc=spoof_ip)\n scapy.send(packet, verbose=False)\n\n\ndef restore(destination_ip, source_ip):\n destination_mac = get_mac(destination_ip)\n source_mac = get_mac(source_ip)\n packet = scapy.ARP(op=2, pdst=destination_ip, hwdst=destination_mac, psrc=source_ip, hwsrc=source_mac)\n scapy.send(packet, count=4, verbose=False)\n\n\noptions = get_arguments()\n# \"10.0.2.6\"\n# \"10.0.2.1\"\n\ntarget_ip = options.target\ngateway_ip = options.gateway\nprint(\"\\nARP spoofing target IP: \" + options.target)\nprint(\"ARP spoofing gateway IP: \" + options.gateway)\nprint(\"\\n-------------------------------------------------------------\\n\")\n\n\ntry:\n sent_packets_count = 0\n while True:\n spoof(target_ip, gateway_ip)\n spoof(gateway_ip, target_ip)\n sent_packets_count = sent_packets_count + 2\n print(\"\\r[+] Packets sent: \" + str(sent_packets_count), end=\"\")\n time.sleep(2)\nexcept KeyboardInterrupt:\n print(\"\\n\\r[+] Detected CTRL + C ..... Resetting ARP tables, please wait.\\n\")\n restore(target_ip, gateway_ip)\n restore(gateway_ip, target_ip)\n","sub_path":"arp_spoof/arp_spoof 53.py","file_name":"arp_spoof 53.py","file_ext":"py","file_size_in_byte":2150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"85815608","text":"import time\nimport syslog\nimport traceback\nimport socket\nimport json\n\nclass Provider(object):\n def load_configuration(self):\n try:\n with open('conf/{0}.conf'.format(self.__module__)) as f:\n self.configuration = json.loads(f.read())\n except Exception as e:\n print(e)\n\n def identify_myself(self):\n if socket.gethostname().find('.') >= 0:\n return socket.gethostname()[0:socket.gethostname().find('.')]\n else:\n return socket.gethostname()\n\n def get_queue_data(self):\n try:\n data = {}\n final_structure = {'host': self.myhostname, 'plugins': {}}\n while self.queue.qsize():\n queue_data = self.queue.get(False)\n for key in queue_data:\n data[key] = queue_data[key]\n if data:\n final_structure['plugins'] = data\n return final_structure\n else:\n raise\n except:\n raise\n","sub_path":"agent/providers/provider_template.py","file_name":"provider_template.py","file_ext":"py","file_size_in_byte":1025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"448609800","text":"import os.path\nfrom setuptools import setup, find_packages\n\nVERSION = '1.0.0'\nsetup(\n name = 'lo-tools',\n version = VERSION,\n author = 'jannanlo',\n py_modules=['lo-tools.utility'],\n author_email = 'jannanlo@163.com',\n description = ('many functions is userful to use'),\n install_requires = [\n 'pip',\n ],\n platforms='any',\n url = \"http://www.southbright.com/\",\n packages=find_packages(),\n)","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"494731117","text":"\nimport numpy\nfrom sarpy.io.complex.sicd_elements import ImageData\n\nfrom . import generic_construction_test, unittest\n\n\nfull_image_dict = {'NumRows': 10, 'NumCols': 10}\n\nimage_data_dict = {\n 'PixelType': 'AMP8I_PHS8I',\n 'AmpTable': list(numpy.arange(256, dtype=numpy.float64)),\n 'NumRows': 10,\n 'NumCols': 10,\n 'FirstRow': 0,\n 'FirstCol': 0,\n 'FullImage': full_image_dict,\n 'SCPPixel': {'Row': 5, 'Col': 4},\n 'ValidData': [\n {'Row': 0, 'Col': 1, 'index': 0},\n {'Row': 3, 'Col': 1, 'index': 1},\n {'Row': 3, 'Col': 7, 'index': 2},\n {'Row': 0, 'Col': 7, 'index': 3},\n ],\n}\n\n\nclass TestFullImage(unittest.TestCase):\n def test_construction(self):\n the_type = ImageData.FullImageType\n the_dict = full_image_dict\n item1 = generic_construction_test(self, the_type, the_dict)\n\n\nclass TestImageData(unittest.TestCase):\n def test_construction(self):\n the_type = ImageData.ImageDataType\n the_dict = image_data_dict\n item1 = generic_construction_test(self, the_type, the_dict)\n\n def test_validity(self):\n the_type = ImageData.ImageDataType\n the_dict1 = image_data_dict.copy()\n del the_dict1['AmpTable']\n the_dict2 = image_data_dict.copy()\n the_dict2['PixelType'] = 'RE32F_IM32F'\n\n with self.subTest(msg='Test validity'):\n item1 = the_type.from_dict(the_dict1)\n self.assertFalse(item1.is_valid())\n\n with self.subTest(msg='Test validity'):\n item2 = the_type.from_dict(the_dict2)\n self.assertFalse(item2.is_valid())\n\n with self.subTest(msg='Limits on PixelType'):\n self.assertRaises(ValueError, ImageData.ImageDataType, PixelData='bad_value')\n","sub_path":"tests/io/complex/sicd_elements/test_image_data.py","file_name":"test_image_data.py","file_ext":"py","file_size_in_byte":1747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"587906700","text":"import json\nimport re\nfrom pointc import *\nfrom conpix import * \ntri=[point(0,1),point(1,4),point(0,3)]\nsqr=[point(0,0),point(4,0),point(4,4),point(0,4)]\nrect=[point(0,0),point(4,0),point(4,5),point(0,5)]\ndef parserdata(x):\n match1 = re.search(r'TRIANGLE', x)\n match2 = re.search(r'Square', x)\n match3 = re.search(r'Rectangle', x)\n if match1:\n return tri\n elif match2:\n return sqr\n elif match3:\n return rect\n else:\n return conpixtoval(x)\n","sub_path":"proj/dataparser.py","file_name":"dataparser.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"181494071","text":"def parse_fasta_to_df(file_path, content_alias, id_alias = \"ProtID\"):\n from Bio import SeqIO\n import pandas as pd\n with open(file_path) as fasta_file:\n ids = []\n contents = []\n for record in SeqIO.parse(fasta_file, 'fasta'):\n ids.append(record.id)\n contents.append(record.seq)\n df = pd.DataFrame(data = {id_alias: ids, content_alias: contents})\n return df.set_index(id_alias)\n \ndef filter_unique_rownames(df):\n return df[~df.index.duplicated(keep='first')]\n\n\ndef import_protvec(filepath, namescol = \"words\"):\n \"\"\"\n Import data frame of ProtVec 3-grams. \n \n :param filepath: path to a TSV.\n :param namescol: name of a column with row names \n :return: pandas dataframe with 3-grams as rownames.\n \"\"\"\n import pandas as pd\n protvec_df = pd.read_csv(filepath, sep = \"\\t\", header = 0)\n protvec_df_3gramidx = protvec_df.set_index(namescol)\n return(protvec_df_3gramidx)\n\ndef get3gramvec(threegr_df, threegr_name, as_list = False):\n if not threegr_name in threegr_df.index:\n raise ValueError(''.join([\"The supplied ProtVec dataset is not trained for the threegram: \", threegr_name]))\n vec = threegr_df.loc[threegr_name].values\n if (as_list):\n vec = vec.tolist()\n return vec\n \ndef convert_seq_to_protvec(seq, threegr_df, substitute_any_with=\"G\"):\n \"\"\"\n Get ProtVec representation of a given sequence\n \"\"\"\n import numpy as np\n protvec = np.zeros(100)\n for i in range(0, len(seq) - 3):\n this3gram = str(seq[i:i+3])\n this3gram = this3gram.replace(\"X\", substitute_any_with)\n if not this3gram in threegr_df.index:\n # skip untrained 3grams\n continue\n this3gramvec = get3gramvec(threegr_df, this3gram)\n protvec = np.add(protvec, this3gramvec)\n return protvec\n","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"652683652","text":"import talib\nimport getting_data as gd\nimport numpy as np \nimport pandas as pd \n\n\n# Pandas dataframe settiongs\npd.set_option('display.float_format', lambda x: '%.5f' % x)\n# to reset that option: \n# pd.reset_option('display.float_format')\n# Set all values to five decimal places instead of scientific notation\n\n# Supressing SettingWithCopyWarning Message: REVISE LATER\npd.options.mode.chained_assignment = None # default='warn'\n\n\ndef CCI(df):\n data = df[[\"High\", \"Low\", \"Adj Close\"]]\n commodity = talib.CCI(data[\"High\"], data[\"Low\"], data[\"Adj Close\"], timeperiod=14)\n df[\"CCI\"] = pd.Series(commodity, index=df.index)\n\n return df\n\ndef WilliamsR(df):\n data = df[[\"High\", \"Low\", \"Adj Close\"]] \n williams = talib.WILLR(data[\"High\"], data[\"Low\"], data[\"Adj Close\"], timeperiod=14)\n df[\"WilliamsR\"] = pd.Series(williams, index=df.index)\n \n \n return df\n\ndef RSI(df):\n data = df[\"Adj Close\"]\n rsi = talib.RSI(data, timeperiod=14)\n df[\"RSI\"] = pd.Series(rsi, index=df.index)\n\n return df\n\ndef StochRSI(df):\n data = df[\"Adj Close\"]\n fastk, fastd = talib.STOCHRSI(data, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0)\n df[\"StochRSI_fastk\"] = pd.Series(fastk, index=df.index)\n df[\"StochRSI_fastd\"] = pd.Series(fastd, index=df.index)\n \n \n return df\n\n\n\nif __name__ == \"__main__\":\n StochRSI(df=gd.getData(\"TSLA\"))\n\n\n","sub_path":"Momentum_technical_analysis.py","file_name":"Momentum_technical_analysis.py","file_ext":"py","file_size_in_byte":1382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"354397496","text":"from django.contrib import admin\nfrom books.models import Categories, Books, Years, CoverTypes, Publisher, Statuses\n\n# Register your models here.\n\nadmin.site.register(Categories)\nadmin.site.register(Years)\nadmin.site.register(CoverTypes)\nadmin.site.register(Publisher)\nadmin.site.register(Statuses)\n\n\n@admin.register(Books)\nclass BooksAdmin(admin.ModelAdmin):\n class Meta:\n fields = ('title', 'author', 'cover', 'year', 'coverType', 'publisher',\n 'status', 'description', 'category', 'exchange', 'sale', 'price')\n\n\n # user_insert(self, request)\n # list_display = ('title', 'author', 'cover', 'year', 'coverType', 'publisher',\n # 'status', 'description', 'category', 'exchange', 'sale', 'price', 'user')\n # list_display_links = ('title', 'author', 'cover', 'year', 'coverType', 'publisher',\n # 'status', 'description', 'category', 'exchange', 'sale', 'price', 'user')\n # list_editable = ('title', 'author', 'cover', 'year', 'coverType', 'publisher',\n # 'status', 'description', 'category', 'exchange', 'sale', 'price', 'user')\n","sub_path":"books/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":1129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"461197664","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.6 (3379)\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.linux-x86_64/egg/lsga/tests/tournament_selection_test.py\n# Compiled at: 2019-02-10 14:34:46\n# Size of source mod 2**32: 986 bytes\n\"\"\" Test case for built-in Tournament Selection operator.\n\"\"\"\nimport unittest\nfrom lsga.components import Population, BinaryIndividual\nfrom lsga.operators import TournamentSelection\n\nclass TournamentSelectionTest(unittest.TestCase):\n\n def setUp(self):\n self.maxDiff\n\n def fitness(indv):\n x, = indv.solution\n return x ** 3 - 60 * x ** 2 + 900 * x + 100\n\n self.fitness = fitness\n\n def test_selection(self):\n indv = BinaryIndividual(ranges=[(0, 30)])\n p = Population(indv)\n p.init()\n selection = TournamentSelection()\n father, mother = selection.select(p, fitness=(self.fitness))\n self.assertTrue(isinstance(father, BinaryIndividual))\n self.assertTrue(isinstance(mother, BinaryIndividual))\n\n\nif '__main__' == __name__:\n suite = unittest.TestLoader().loadTestsFromTestCase(TournamentSelectionTest)\n unittest.TextTestRunner(verbosity=2).run(suite)","sub_path":"pycfiles/lsga-0.6.0-py3.6/tournament_selection_test.cpython-36.py","file_name":"tournament_selection_test.cpython-36.py","file_ext":"py","file_size_in_byte":1246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"278424267","text":"import math\r\n\r\ndef func(x):\r\n return (x/(1+x**2))\r\ndef deq( x, y ): \r\n\treturn (1/(1+x**2)-2*y**2) \r\n\t\r\n# Function for euler formula \r\ndef euler( x0, y, h, x ): \r\n\ttemp = -0\r\n\tmaxerr=0\r\n\r\n\t# Iterating till the point at which we \r\n\t# need approximation \r\n\twhile x0 < x: \r\n\t\ttemp = y \r\n\t\ty =y+0.5*h*(deq(x0,y)+deq(x0+h,y+h*deq(x0,y)))\r\n\t\tx0=x0+h\r\n\t\tyact = func(x0)\r\n\t\terr=abs((yact-y)/y)*100\r\n\t\tif err>maxerr:\r\n\t\t maxerr=err\r\n\r\n\t# Printing approximation \r\n\tprint(\"Approximate solution at x = \", x, \" is \", \"%.6f\"% y) \r\n\tprint(\"Error: \", maxerr, \"%\")\r\n# Driver Code \r\n# Initial Values \r\nx0 = 0\r\ny0 = 0\r\nh = 0.001\r\n\r\n# Value of x at which we need approximation \r\nx = 10\r\n\r\neuler(x0, y0, h, x) \r\n","sub_path":"trapezoidODEimplicit.py","file_name":"trapezoidODEimplicit.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"143805779","text":"import atexit\nimport inspect\nimport os\nimport re\nimport sys\nfrom .excepts import AlreadyPlanned, BadPlan\n\n\ndef concat(directive, msg, sep=\" \"):\n return \"{0}{1}{2}\".format(directive, sep, msg) if msg else str(directive)\n\n\nclass Block(object):\n\n def __enter__(self):\n Builder.instance.begin_block(self)\n return self\n\n def __exit__(self, *args):\n Builder.instance.end_block()\n\n def describe(self, builder, args):\n builder.diag(*args)\n\n def directive(self):\n return None\n\n def ok(self, builder, test, name):\n ok = \"{0} {1}\".format(\"ok\" if test else \"not ok\", builder.current)\n return concat(ok, name, \" - \")\n\n\nclass Builder(object):\n\n instance = None\n\n # The documentation says this is re.RegexObject, but that's a lie.\n RE_TYPE = type(re.compile(\"\"))\n\n FAILED = \"Looks like you failed {0} test{1} of {2}{3}.\"\n BAD_PLAN = \"Looks like you planned {0} test{1} but ran {2}.\"\n NO_PLAN = \"Tests were run but no plan was declared \" \\\n \"and done_testing() was not seen.\"\n\n\n def __init__(self):\n self.planned = None\n self.current = 0\n self.failed = 0\n self.blocks = [Block()]\n self.out = sys.stdout\n self.err = sys.stderr\n self.active = None\n self.atexit = False\n\n\n def __del__(self, shutdown=False):\n if self.active:\n self.active = False\n code = self.exit_info()\n if shutdown:\n os._exit(code)\n else:\n sys.exit(code)\n\n\n def exit_info(self):\n plan_s = \"\" if self.planned == 1 else \"s\"\n fail_s = \"\" if self.failed == 1 else \"s\"\n\n if not self.planned:\n self.diag(self.NO_PLAN)\n return 255\n\n if self.planned != self.current:\n self.diag(self.BAD_PLAN.format(self.planned, plan_s, self.current))\n if self.failed:\n self.diag(self.FAILED.format(self.failed, fail_s,\n self.current, \" run\"))\n return 255\n\n if self.failed:\n self.diag(self.FAILED.format(self.failed, fail_s, self.current, \"\"))\n\n return min(self.failed, 254)\n\n\n def activate(self):\n if self.active is None:\n self.active = True\n atexit.register(lambda: self.__del__(True))\n\n\n def output(self, stream, message):\n self.activate() # any output activates the builder\n lines = message.splitlines()\n stream.write(lines[0])\n stream.write(\"\\n\")\n for line in lines[1:]:\n stream.write(\"# \")\n stream.write(line)\n stream.write(\"\\n\")\n stream.flush()\n\n\n def printout(self, message):\n self.output(self.out, message)\n\n def printerr(self, message):\n self.output(self.err, message)\n\n\n def block(self):\n return self.blocks[-1]\n\n def begin_block(self, block):\n self.blocks.append(block)\n\n def end_block(self):\n self.blocks.pop()\n\n\n def note(self, *args):\n self.printout(\"# \" + \"\".join(args))\n\n def diag(self, *args):\n self.printerr(\"# \" + \"\".join(args))\n\n def describe(self, *args):\n self.block().describe(self, *args)\n\n\n def plan(self, planned):\n if self.planned:\n raise AlreadyPlanned(self.planned, planned)\n\n if not isinstance(planned, int) or planned < 1:\n raise BadPlan(planned)\n\n self.planned = planned\n self.printout(\"1..{0}\".format(self.planned))\n return self.planned\n\n\n def skip_all(self, reason=None):\n if self.planned:\n raise AlreadyPlanned(self.planned, \"skip_all\")\n\n self.printout(concat(\"1..0 # SKIP\", reason))\n sys.exit(0)\n\n\n def bail_out(self, reason=None):\n self.printout(concat(\"Bail out!\", reason))\n self.active = False\n sys.exit(255)\n\n\n def format_value(self, value, spaces):\n if value is None:\n return \"None\"\n elif isinstance(value, type):\n return value.__name__\n elif isinstance(value, self.RE_TYPE):\n return value.pattern\n else:\n return value\n\n\n def failure_info(self, name, upstack, diagnostics):\n directive = self.block().directive()\n if directive:\n test = \"({0}) test\".format(directive)\n else:\n test = \"test\"\n\n try:\n frm = inspect.stack()[upstack + 1]\n where = \"at {0} in line {1}\".format(os.path.basename(frm[1]),\n frm[2])\n except Exception as e:\n where = \"but can't tell where: {0}\".format(e)\n\n if name:\n self.describe(\"Failed {0} '{1}'\".format(test, name))\n self.describe(where)\n else:\n self.describe(\"Failed {0} {1}\".format(test, where))\n\n if diagnostics:\n indent = len(max(diagnostics.keys(), key=len))\n for key, value in diagnostics.items():\n spaces = \" \" * (indent - len(key))\n formatted = self.format_value(value, spaces)\n self.describe(\" {0}{1}: {2}\".format(spaces, key, formatted))\n\n\n\n def ok(self, test, name=None, upstack=1, diagnostics=None):\n self.current += 1\n self.printout(self.block().ok(self, test, name))\n\n if not test:\n self.failed += 1\n self.failure_info(name, upstack + 1, diagnostics)\n\n return test\n","sub_path":"TestMore/builder.py","file_name":"builder.py","file_ext":"py","file_size_in_byte":5527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"64317196","text":"from models.verdict import Verdict\n\ndef create_verdict(user, article, comment, evaluation=None, rating=None):\n verdict = Verdict()\n verdict.article = article\n verdict.comment = comment\n verdict.evaluation = evaluation\n verdict.rating = rating\n verdict.user = user\n\n return verdict\n","sub_path":"tests/utils/create_verdict.py","file_name":"create_verdict.py","file_ext":"py","file_size_in_byte":302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"129078279","text":"\"\"\"Microsoft SSO Oauth Helper class\"\"\"\n\nfrom typing import Dict\n\nfrom fastapi_sso.sso.base import OpenID, SSOBase\n\n\nclass MicrosoftSSO(SSOBase):\n \"\"\"Class providing login via Microsoft Graph OAuth\"\"\"\n\n provider = \"microsoft\"\n scope = [\"email\", \"openid\", \"profile\"]\n version = \"v1.0\"\n\n @classmethod\n async def get_discovery_document(cls) -> Dict[str, str]:\n \"\"\"Get document containing handy urls\"\"\"\n return {\n \"authorization_endpoint\": \"https://login.microsoftonline.com/common/oauth2/v2.0/authorize\",\n \"token_endpoint\": \"https://login.microsoftonline.com/common/oauth2/v2.0/token\",\n \"userinfo_endpoint\": f\"https://graph.microsoft.com/{cls.version}/me\",\n }\n\n @classmethod\n async def openid_from_response(cls, response: dict) -> OpenID:\n \"\"\"Return OpenID from user information provided by Microsoft Office 365\"\"\"\n return OpenID(\n first_name=response.get(\"givenName\"),\n last_name=response.get(\"surname\"),\n email=response.get(\"userPrincipalName\", \"\"),\n provider=cls.provider,\n id=response.get(\"id\"),\n display_name=response.get(\"displayName\"),\n picture=None,\n )\n","sub_path":"fastapi_sso/sso/microsoft.py","file_name":"microsoft.py","file_ext":"py","file_size_in_byte":1233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"467683997","text":"\"\"\"Toolbox for unbalanced dataset in machine learning.\n\n``UnbalancedDataset`` is a set of python methods to deal with unbalanced\ndatset in machine learning and pattern recognition.\n\nSubpackages\n-----------\ncombine\n Module which provides methods based on over-sampling and under-sampling.\nensemble\n Module which provides methods generating an ensemble of\n under-sampled subsets.\nover_sampling\n Module which provides methods to under-sample a dataset.\nunder-sampling\n Module which provides methods to over-sample a dataset.\nutils\n Module which provides helper methods.\n\"\"\"\n\nfrom .version import _check_module_dependencies, __version__\n\n_check_module_dependencies()\n\n# Boolean controlling whether the joblib caches should be\n# flushed if the version of certain modules changes (eg nibabel, as it\n# does not respect the backward compatibility in some of its internal\n# structures\n# This is used in nilearn._utils.cache_mixin\nCHECK_CACHE_VERSION = True\n\n# list all submodules available in nilearn and version\n__all__ = ['combine',\n 'ensemble',\n 'over_sampling',\n 'under_sampling',\n 'utils']\n","sub_path":"Version0.9/unbalanced_dataset/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"344608945","text":"\"\"\"\nAgilent 33220A Function generator\n\"\"\"\nimport socket\n\nclass FunctionGenerator(object):\n def __init__(self,addr=('192.168.1.135', 5025)):\n self.addr = addr\n \n def set_load_ohms(self,ohms):\n self.send(\"OUTPUT:LOAD %d\" % ohms)\n \n def set_dc_voltage(self,volts):\n self.send(\"APPLY:DC DEF, DEF, %f\" % volts)\n \n def enable_output(self,on):\n if on:\n self.send(\"OUTPUT ON\")\n else:\n self.send(\"OUTPUT OFF\")\n \n def send_get(self,cmd,timeout=1):\n result = None\n try:\n s = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n s.connect(self.addr)\n s.settimeout(timeout)\n s.send(cmd+'\\n')\n result = s.recv(1024)\n finally:\n s.close()\n return result\n \n def send(self,cmd):\n try:\n s = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n s.connect(self.addr)\n s.send(cmd+'\\n')\n finally:\n s.close()\n \n","sub_path":"kid_readout/equipment/agilent_33220.py","file_name":"agilent_33220.py","file_ext":"py","file_size_in_byte":1046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"412971858","text":"import re\nfrom numpy import *\nimport os\n\nfilePath='txt/'\nfile_list = os.listdir(filePath)\n\nfor file in file_list:\n fp = open(\"txt/\"+file, 'r')\n op = open(\"result-nospace.txt\",'a+')\n for eachline in fp.readlines():\n op.write(eachline.split(' ')[0]+'\\n')\n # op.write('\\n')\n \n fp.close()\n op.close()\n","sub_path":"mask-dataset/re-make-dataset/choose_txt.py","file_name":"choose_txt.py","file_ext":"py","file_size_in_byte":325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"203556146","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.http import JsonResponse\nfrom mysite import settings\nimport os\nimport sys\nimport logging\n\n\nif (not (os.path.join(settings.BASE_DIR, \"img_proc/\") in sys.path)):\n sys.path.append(os.path.join(settings.BASE_DIR, \"img_proc/\"))\n\nimport basic_img as bimg\n\n# reference the logger\nlogger = logging.getLogger('django')\n\n\n\ndef index(request):\n return HttpResponse(\"This is image app home page\")\n\n\n# simple download request\ndef download(request, file_name):\n # open the image\n img_data = open(os.path.join(os.path.join(settings.BASE_DIR, \"static/\"), file_name), \"rb\").read()\n\n # get the image type\n fname = str(file_name)\n last_idx = fname.find('.', 0)\n cur_idx = fname.find('.', last_idx + 1)\n while (cur_idx != -1):\n last_idx = cur_idx\n cur_idx = fname.find('.', cur_idx + 1)\n if (last_idx == -1):\n return HttpResponse(\"Wrong format of filename\")\n else:\n img_type = fname[last_idx + 1:]\n\n return HttpResponse(img_data, content_type='image\\\\' + img_type)\n\n\n# update file and image process\ndef upload(request):\n if request.method == \"POST\":\n my_file = request.FILES.get('file', '')\n full_file_name = request.POST.get('file_name', '')\n func = request.POST.get('func', '')\n # if (myFile == '' || file_name == ''):\n # return HttpResponse(\"no files for upload!\")\n\n # write the image into static/\n img_path = os.path.join(os.path.join(settings.BASE_DIR, 'static/'), full_file_name)\n destination = open(img_path, 'wb+')\n for chunk in my_file.chunks():\n destination.write(chunk)\n destination.close()\n\n # image process\n img = bimg.basic_img(os.path.join(settings.BASE_DIR, 'static/'), full_file_name)\n res_file_name = \"\"\n img_number = 0\n if (func == 'color2gray'):\n res_file_name = img.color2gray()\n res_file_name_list = [res_file_name]\n img_number = 1\n\n elif (func == 'drawContour'):\n res_file_name = img.drawContour()\n res_file_name_list = [res_file_name]\n img_number = 1\n\n elif (func == 'splitTo9'):\n res_file_name_list = img.splitTo9Images()\n img_number = 9\n\n # response = HttpResponse()\n # response['file_name_list'] = res_file_name_list\n # response['img_number'] = img_number\n response = JsonResponse({'file_name_list': res_file_name_list, 'img_number': img_number})\n\n logger.error(response.content) \n\n return response\n\n else:\n return HttpResponse(\"Wrong request method. Only deal with POST\")\n\n\ndef form_template(request):\n return render(request, 'image/post_form.html')\n\n\ndef test(request, s):\n return HttpResponse(s)\n","sub_path":"image/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2843,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"307234242","text":"#!/usr/bin/env python\n\"\"\"Provides SolverComparisonExperiment\n\"\"\"\n\nimport time\nimport csv\nimport argparse\n\n\nfrom BasicDPLL import BasicDPLL\nfrom DummyBranchDecision import DummyBranchDecision\nfrom RandomFalseBranchDecision import RandomFalseBranchDecision\n\nfrom dimacs_tools import load_dimacs, load_sudokus\nfrom InMemoryMetrics import InMemoryMetrics\nfrom tools import save_csv\nfrom SudokuRules import SudokuRules\n\n__author__ = \"Meena Alfons\"\n__copyright__ = \"Copyright 2020, Knowledge Representation, SatSolver Project, Group 25\"\n__credits__ = [\"Meena Alfons\"]\n__license__ = \"GPL\"\n__version__ = \"1.0.1\"\n__maintainer__ = \"Meena Alfons\"\n__email__ = \"meena.kerolos@gmail.com\"\n__status__ = \"Development\"\n\nclass DiffConstraintsExperiment:\n def __init__(self, decisionHeuristicFactory):\n self.decisionHeuristicFactory = decisionHeuristicFactory\n self.rulesCreator = SudokuRules(9)\n\n def rulesOf(self, rows, cols, blocks):\n name = \"r{}_c{}_b{}\".format(rows, cols, blocks)\n\n self.rulesCreator.reset()\n if rows > 0:\n self.rulesCreator.add_alldiff_row_cum(rows)\n\n if cols > 0:\n self.rulesCreator.add_alldiff_col_cum(cols)\n\n if blocks > 0:\n self.rulesCreator.add_alldiff_block_cum(blocks)\n\n return name, self.rulesCreator.getRules()\n\n\n def generateRules(self, numOfConstraints):\n rulesDict = {}\n # the following nested loop go throw all the combinations of rows, cols, blocks\n # Where each one of them could have values from 0 to 9\n # However the script only considers combinations which add up to the numOfConstraints\n # Example combinations when numOfConstraints=7:\n # row=0 col=0 blocks=7\n # row=1 col=3 blocks=3\n # row=6 col=0 blocks=1\n for row in range(9+1):\n if row == numOfConstraints:\n name, rules = self.rulesOf(row, 0, 0)\n rulesDict[name] = rules\n break\n for col in range(9+1):\n if row + col == numOfConstraints:\n name, rules = self.rulesOf(row, col, 0)\n rulesDict[name] = rules\n break\n for block in range(9+1):\n if row + col + block == numOfConstraints:\n name, rules = self.rulesOf(row, col, block)\n rulesDict[name] = rules\n break\n\n return rulesDict\n\n def run(self, numOfConstraints, timeout, start = 0, end = 1011):\n rules, numOfVars = load_dimacs('rules/sudoku_rules_9x9.txt')\n sudokus = load_sudokus('sudokus/1000_sudokus_9x9.txt')[start:end]\n data = []\n\n rulesDict = self.generateRules(numOfConstraints)\n\n for i in range(len(sudokus)):\n if i % 10 == 9:\n print(\".\", end='', flush=True)\n sudoku = sudokus[i]\n sudokuID = i + 1\n for name in rulesDict:\n rules = rulesDict[name]\n cnf = rules + sudoku\n instanceMetrics = InMemoryMetrics()\n\n before = time.time()\n solver = BasicDPLL(cnf,\n numOfVars,\n self.decisionHeuristicFactory,\n timeout,\n 5,\n instanceMetrics\n )\n result, _ = solver.solve()\n totalTime = time.time()-before\n counters = instanceMetrics.getCounters()\n if result == \"TIMEOUT\":\n print(\"T\", end='', flush=True)\n data.append((\n sudokuID,\n name,\n numOfConstraints,\n result,\n totalTime,\n counters.get(\"loop\", 0),\n counters.get(\"backtrack\", 0),\n counters.get(\"flip\", 0),\n counters.get(\"unit\", 0),\n ))\n\n # Save output to csv\n header = [\"sudokuID\", \"name\", \"numOfConstraints\", \"result\", \"totalTime\", \"loop\", \"backtrack\", \"flip\", \"unit\"]\n filename=\"constraints_{}_{}_{}.csv\".format(numOfConstraints, start, end)\n save_csv(filename, header, data)\n print(\"\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='Process some integers.')\n parser.add_argument('numOfContraints',\n metavar='numOfContraints',\n type=int,\n help='the number of constraints')\n parser.add_argument('--start',\n metavar='start',\n type=int,\n help='start',\n default=0)\n parser.add_argument('--end',\n metavar='end',\n type=int,\n help='end',\n default=1011)\n parser.add_argument('--timeout',\n metavar='timeout',\n type=float,\n help='timeout',\n default=5)\n args = parser.parse_args()\n numOfContraints = args.numOfContraints\n start = args.start\n end = args.end\n timeout = args.timeout\n print(\"numOfConstraints={}, timeout={}, start={}, end={}\".format(numOfContraints, timeout, start, end))\n\n decisionHeuristicFactory = lambda: DummyBranchDecision()#RandomFalseBranchDecision()\n\n before = time.time()\n experiment = DiffConstraintsExperiment(decisionHeuristicFactory)\n experiment.run(numOfContraints, timeout, start, end)\n print(\"time={} seconds\".format(time.time()-before))","sub_path":"DiffConstraintsExperiment.py","file_name":"DiffConstraintsExperiment.py","file_ext":"py","file_size_in_byte":5661,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"629910420","text":"from datetime import date\nimport sys\nimport json\n\nfrom tokenizer import tokenize, queryKey\nfrom constants import *\nimport schema\n\ndef parseListings(listings):\n if not listings:\n return []\n\n recordedAt = date.today().strftime('%Y-%m-%d')\n posts = listings.split(';')\n\n result = []\n for post in posts:\n buyout, _ = post.split('@')\n result.append(schema.Listing(buyout, recordedAt))\n\n return result\n\ndef addItem(key, value, arr):\n _,buyout,listings = value.split('#')\n listingResult = parseListings(listings)\n\n arr.append(schema.Item(key,buyout,listingResult))\n\ndef main():\n if len(sys.argv) <= 1:\n return\n\n with open(sys.argv[1]) as f:\n content = f.read()\n with open(\"result.json\", 'w') as writer:\n result = queryKey(content, ('Bigglesworth|Alliance', 'history'), addItem)\n if not result:\n return\n writer.write(json.dumps(result, default=vars))\n\nmain()\n","sub_path":"parser/display.py","file_name":"display.py","file_ext":"py","file_size_in_byte":974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"405888001","text":"import argparse\nimport yaml\nimport sys\nfrom typing import List, Optional, Dict, Any, Union\nimport os.path\n\nfrom limit import Limit\n\n\nclass ConfigException(Exception):\n pass\n\n\nclass Course:\n def __init__(self, raw : Dict[str, Any], qdir_root : str):\n if not isinstance(raw, dict):\n raise ConfigException(\"Course must be an object\")\n try:\n self.name = str(raw[\"name\"])\n self.checker = str(raw[\"checker\"])\n self._qdir = raw.get(\"qdir\", self.name)\n self.qdir = os.path.abspath(os.path.join(qdir_root, self._qdir))\n self.isolation = bool(raw.get(\"isolation\", False))\n self.hint = bool(raw.get(\"hint\", False))\n self.authorized : List[str] = raw.get(\"authorized\", [])\n self.path_append : List[str] = raw.get(\"path_append\", [])\n self.extended = bool(raw.get(\"extended\", False))\n self.escape_is = bool(raw.get(\"escape_is\", False))\n except KeyError as ex:\n raise ConfigException(\n f\"Course must set at least 'name' and 'checker': missing {ex}\")\n\n def to_dict(self, expand = False) -> Dict[str, Union[str, bool, List[str]]]:\n res : Dict[str, Union[str, bool, List[str]]] = \\\n {\"name\": self.name,\n \"checker\": self.checker,\n \"isolation\": self.isolation,\n \"hint\": self.hint,\n \"authorized\": self.authorized,\n \"path_append\": self.path_append,\n \"extended\": self.extended,\n \"escape_is\": self.escape_is}\n if expand:\n res[\"qdir\"] = self.qdir\n else:\n res[\"qdir\"] = self._qdir\n return res\n\n def dump(self, stream : Any = None, **kvargs) -> Any:\n return yaml.safe_dump(self.to_dict(**kvargs), stream,\n default_flow_style=False)\n\n\nclass Config:\n def __init__(self, argv : List[str]):\n self.argv = argv\n self.config_file = \"exprtest.yaml\"\n self.socket_fd : Optional[int] = None\n self.socket : Optional[str] = None\n self.port : Optional[int] = None\n self.qdir_root : Optional[str] = None\n self.courses : Dict[str, Course] = {}\n self.max_workers = 4\n self.hint_origin : Optional[str] = None\n self.limit = Limit()\n self._load_from_argv()\n self._load_from_file()\n\n def _load_from_argv(self) -> None:\n parser = argparse.ArgumentParser(\n description=\"ExprTest evaluation service\")\n parser.add_argument(\n '--socket-fd', metavar='FD', dest='socket_fd', type=int,\n help=\"socket file descriptor to be used for UNIX socket server\")\n parser.add_argument(\n '--socket', metavar='FILE', dest='socket', type=str,\n help=\"named socket to be used for UNIX socket server\")\n parser.add_argument(\n '--port', metavar='TPC_PORT', dest='port', type=int,\n help=\"TCP port to be used for HTTP server on localhost\")\n parser.add_argument(\n '--config', metavar='FILE',\n help=\"YAML config file with description of evaluation environment\")\n args = parser.parse_args(self.argv[1:])\n self.socket_fd = args.socket_fd\n self.socket = args.socket\n self.port = args.port\n if args.config is not None:\n self.config_file = args.config\n\n @staticmethod\n def _parse_proc(val : Union[None, str, int, float]) -> Optional[float]:\n if val is None:\n return None\n if isinstance(val, float):\n return val\n if isinstance(val, int):\n return float(val)\n if val[-1:] == '%':\n return int(val[:-1]) / 100\n return float(val)\n\n MEM_MULTIPLIERS = {\"k\": 1024,\n \"M\": 1024 * 1024,\n \"G\": 1024 * 1024 * 1024,\n \"T\": 1024 * 1024 * 1024 * 1024}\n\n @staticmethod\n def _parse_mem(val : Union[None, str, int]) -> Optional[int]:\n if val is None:\n return None\n if isinstance(val, int):\n return val\n mult = Config.MEM_MULTIPLIERS.get(val[-1:])\n if mult is None:\n return int(val)\n return int(val[:-1]) * mult\n\n def _load_from_file(self) -> None:\n try:\n with open(self.config_file, 'r') as fh:\n conf = yaml.safe_load(fh)\n except FileNotFoundError as ex:\n raise ConfigException(\n f\"Config file {self.config_file} not found: {ex}\")\n except yaml.YAMLError as ex:\n raise ConfigException(\n f\"Failed to load config from {self.config_file}: {ex}\")\n\n if not isinstance(conf, dict):\n raise ConfigException(\"Config must be a YAML object\")\n\n self.qdir_root = conf.get(\"qdir_root\")\n self.max_workers = conf.get(\"max_workers\", self.max_workers)\n self.hint_origin = conf.get(\"hint_origin\")\n\n limit_raw = conf.get(\"limit\", {})\n self.limit = Limit(memory = self._parse_mem(limit_raw.get(\"memory\")),\n swap = self._parse_mem(limit_raw.get(\"swap\")),\n cpu = self._parse_proc(limit_raw.get(\"cpu\")))\n\n if self.qdir_root is None:\n raise ConfigException(\"Field 'qdir_root' must be set\")\n courses = conf.get(\"courses\", [])\n if not isinstance(courses, list):\n raise ConfigException(\n \"Courses must be an array of course objects\")\n for c in courses:\n cc = Course(c, self.qdir_root)\n self.courses[cc.name.lower()] = cc\n\n out = len([x for x in [self.socket, self.socket_fd, self.port]\n if x is not None])\n if out == 0:\n self.port = 8080\n if out > 1:\n raise ConfigException(\"At most one of '--socket', '--socket-fd' \"\n \"or '--port' must be used\")\n if len(self.courses) == 0:\n raise ConfigException(\"At least one course must be set\")\n\n def dump(self, stream : Any = None) -> Any:\n return yaml.safe_dump(self.to_dict(), stream, default_flow_style=False)\n\n def to_dict(self) -> Dict[str, Any]:\n return {\"socket_fd\": self.socket_fd,\n \"socket\": self.socket,\n \"port\": self.port,\n \"qdir_root\": self.qdir_root,\n \"max_workers\": self.max_workers,\n \"hint_origin\": self.hint_origin,\n \"limit\": {k: v for k, v in [(\"memory\", self.limit.memory),\n (\"swap\", self.limit.swap),\n (\"cpu\", self.limit.cpu)]\n if v is not None},\n \"courses\": list(map(Course.to_dict, self.courses.values()))}\n\n\ndef parse(argv : List[str]) -> Config:\n return Config(argv)\n\n# vim: colorcolumn=80 expandtab sw=4 ts=4\n","sub_path":"src/core/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":7034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"588661813","text":"import contextlib\n\nfrom tests import utils\n\n\nclass BaseExecutor:\n\n _resource_tmp_dir = None\n\n @contextlib.contextmanager\n def prepare_then_cleanup(self):\n with utils.tmp_dir() as tmp_dirpath:\n self._resource_tmp_dir = tmp_dirpath\n self.prepare()\n\n try:\n yield\n finally:\n self._resource_tmp_dir = None\n\n def prepare(self):\n raise NotImplementedError\n","sub_path":"tests/e2e/executors/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"251730093","text":"def num_to_ordering(number, num_items):\n '''\n Take a number between 0 and num_items!-1\n and then return a permutation of the numbers 1-num_items.\n '''\n # If there is only one item, automatically return 1:\n if num_items == 1: return [1]\n \n # This is the number of permutations of (num_items-1) numbers:\n num_items_minus_1_factorial = math.factorial(num_items-1)\n # This is the first number in our permutation:\n first_number = number//num_items_minus_1_factorial+1\n # Use recursion to figure out the other numbers:\n last_part = num_to_ordering( \\\n number % num_items_minus_1_factorial, num_items-1 \\\n )\n # Make sure there are no conflicts between first_number and last_part:\n for i, num in enumerate(last_part):\n if num >= first_number: last_part[i] += 1\n # Finally, return first_number+last_part:\n return [first_number]+last_part","sub_path":"Python/permutations.py","file_name":"permutations.py","file_ext":"py","file_size_in_byte":896,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"299652690","text":"from django.urls import path\n\nfrom api2 import views\n\nurlpatterns = [\n # path('post/', views.PostListAPIView.as_view(), name='post-list'),\n # path('post//', views.PostRetrieveAPIView.as_view(), name='post-detail'),\n # path('comment/', views.CommentCreateAPIView.as_view(), name='comment-list'),\n # path('post//like/', views.PostLikeAPIView.as_view(), name='post-like'),\n # path('catetag/', views.CateTagAPIView.as_view(), name='catetag'),\n\n path('post/', views.PostViewSet.as_view(actions={\n 'get': 'list',\n }), name='post-list'),\n path('post//', views.PostViewSet.as_view(actions={\n 'get': 'retrieve',\n }), name='post-detail'),\n path('post//like/', views.PostViewSet.as_view(actions={\n 'get': 'like',\n }), name='post-like'),\n\n path('comment/', views.CommentViewSet.as_view(actions={\n 'post': 'create',\n }), name='comment-list'),\n\n path('catetag/', views.CateTagAPIView.as_view(), name='catetag'),\n]","sub_path":"장고 DRF로 변경 연습 코드/result_code/api2/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"547746242","text":"'''\n /*\n * Copyright 2010-2016 Amazon.com, Inc. or its affiliates. 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 * A copy of the License is located at\n *\n * http://aws.amazon.com/apache2.0\n *\n * or in the \"license\" file accompanying this file. This file is distributed\n * on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\n * express or implied. See the License for the specific language governing\n * permissions and limitations under the License.\n */\n'''\n\nfrom AWSIoTPythonSDK.MQTTLib import AWSIoTMQTTShadowClient\nimport sys\nimport logging\nimport time\nimport json\nimport getopt\n\n# Custom Shadow callback\ndef customShadowCallback_Update(payload, responseStatus, token):\n\t# payload is a JSON string ready to be parsed using json.loads(...)\n\t# in both Py2.x and Py3.x\n\tif responseStatus == \"timeout\":\n\t\tprint(\"Update request \" + token + \" time out!\")\n\tif responseStatus == \"accepted\":\n\t\tpayloadDict = json.loads(payload)\n\t\tprint(\"~~~~~~~~~~~~~~~~~~~~~~~\")\n\t\tprint(\"Update request with token: \" + token + \" accepted!\")\n\t\tprint(\"property: \" + str(payloadDict[\"state\"][\"desired\"][\"open\"]))\n\t\tprint(\"~~~~~~~~~~~~~~~~~~~~~~~\\n\\n\")\n\tif responseStatus == \"rejected\":\n\t\tprint(\"Update request \" + token + \" rejected!\")\n\ndef customShadowCallback_Delete(payload, responseStatus, token):\n\tif responseStatus == \"timeout\":\n\t\tprint(\"Delete request \" + token + \" time out!\")\n\tif responseStatus == \"accepted\":\n\t\tprint(\"~~~~~~~~~~~~~~~~~~~~~~~\")\n\t\tprint(\"Delete request with token: \" + token + \" accepted!\")\n\t\tprint(\"~~~~~~~~~~~~~~~~~~~~~~~\\n\\n\")\n\tif responseStatus == \"rejected\":\n\t\tprint(\"Delete request \" + token + \" rejected!\")\n\n# Usage python basicShadowUpdater.py -e a3btdqpnztfelh.iot.us-west-2.amazonaws.com -r root-CA.crt -c RaspberryPi.cert.pem -k RaspberryPi.private.key\n\ndef suscribeToTheThing():\n\thost = \"a3btdqpnztfelh.iot.us-west-2.amazonaws.com\"\n\trootCAPath = \"root-CA.crt\"\n\tcertificatePath = \"IR_Relay_Edison-certificate.pem.crt\"\n\tprivateKeyPath = \"IR_Relay_Edison-private.pem.key\"\n\n\t# Configure logging\n\tlogger = logging.getLogger(\"AWSIoTPythonSDK.core\")\n\tlogger.setLevel(logging.DEBUG)\n\tstreamHandler = logging.StreamHandler()\n\tformatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\tstreamHandler.setFormatter(formatter)\n\tlogger.addHandler(streamHandler)\n\n\t# Init AWSIoTMQTTShadowClient\n\tmyAWSIoTMQTTShadowClient = None\n\tmyAWSIoTMQTTShadowClient = AWSIoTMQTTShadowClient(\"basicShadowUpdater\")\n\tmyAWSIoTMQTTShadowClient.configureEndpoint(host, 8883)\n\tmyAWSIoTMQTTShadowClient.configureCredentials(rootCAPath, privateKeyPath, certificatePath)\n\n\t# AWSIoTMQTTShadowClient configuration\n\tmyAWSIoTMQTTShadowClient.configureAutoReconnectBackoffTime(1, 32, 20)\n\tmyAWSIoTMQTTShadowClient.configureConnectDisconnectTimeout(10) # 10 sec\n\tmyAWSIoTMQTTShadowClient.configureMQTTOperationTimeout(5) # 5 sec\n\n\t# Connect to AWS IoT\n\tmyAWSIoTMQTTShadowClient.connect()\n\n\t# Create a deviceShadow with persistent subscription\n\tBot = myAWSIoTMQTTShadowClient.createShadowHandlerWithName(\"IR_Relay_Edison\", True)\n\treturn Bot\n\ndef updateTheShadow(access, Bot):\n\tJSONPayload = '{\"state\":{\"desired\":{\"open\":' + access + '}}}' #JSON with Shadow state.\n\tBot.shadowUpdate(JSONPayload, customShadowCallback_Update, 5)\n\n","sub_path":"updateShadow.py","file_name":"updateShadow.py","file_ext":"py","file_size_in_byte":3362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"250257134","text":"# Le calcul des expressions composées sans allocation de tableaux intermedaires est possible\n# grâce à la bibliothèque numexpr\n\nimport numpy as np\nrng = np.random.RandomState(42)\nx = rng.rand(1000000)\ny = rng.rand(1000000)\nmask = (x > 0.5) & (y < 0.5)\n\nimport numexpr\nmask_numexrp = numexpr.evaluate('(x > 0.5) & (y < 0.5)')\nnp.allclose(mask, mask_numexrp) # on vérifie que les résultats sont identiques (True)\n\n\n# On peut optimiser le calcul des tableaux en utilisant pndas.eval()\n\nimport pandas as pd\nnrows, ncols = 100000, 100\nrng = np.random.RandomState(42)\ndf1, df2, df3, df4 = (pd.DataFrame(rng.rand(nrows, ncols))\n for i in range(4))\n\n\na = df1 + df2 + df3 + df4 # opération lente\nb = pd.eval(df1 + df2 + df3 + df4) # 2 fois plus rapide que a\n\nnp.allclose(df1 + df2 + df3 + df4,\n pd.eval('df1 + df2 + df3 + df4')) # on vérifie que les résultats sont identiques (True)\n\n# pandas.eval() supporte plusieurs types d'opérations (+, -, &, |, etc...)\n# il supporte aussi l'assignation des variables\ndf = pd.DataFrame(rng.rand(1000, 3), columns=['A', 'B', 'C'])\ndf.head()\ncolumn_mean = df.mean(1)\nresult1 = df['A'] + column_mean\nresult2 = df.eval('A + @column_mean')\nnp.allclose(result1, result2) # return True\n\n# On peut y ajouter des colonnes\ndf.eval('D = (A + B) / C', inplace=True)\ndf.head()\n\n# Le DataFrame a une autre méthode basée sur les chaînes évaluées, appelée la méthode query ()\nresult1 = df[(df.A < 0.5) & (df.B < 0.5)]\nresult2 = pd.eval('df[(df.A < 0.5) & (df.B < 0.5)]')\nnp.allclose(result1, result2)\n\nresult2 = df.query('A < 0.5 and B < 0.5') # utilisation de query() au lieu de eval()\nnp.allclose(result1, result2)\n\n# query() supporte aussi l'assignation des variables locales\nCmean = df['C'].mean()\nresult1 = df[(df.A < Cmean) & (df.B < Cmean)]\nresult2 = df.query('A < @Cmean and B < @Cmean')\nnp.allclose(result1, result2)\n","sub_path":"datascience/scripts/python-scripts/performance-eval-and-query.py","file_name":"performance-eval-and-query.py","file_ext":"py","file_size_in_byte":1889,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"23020499","text":"# -*- coding:utf-8 -*- \r\n\r\nimport http.cookiejar\r\nimport time\r\nimport urllib\r\nimport urllib.request\r\nfrom datetime import date\r\nfrom datetime import datetime\r\nfrom io import BytesIO\r\nfrom urllib.parse import urlparse\r\n\r\nfrom PIL import Image\r\nfrom bs4 import BeautifulSoup\r\n\r\n\r\ndef get_captcha():\r\n data = urllib.request.urlopen('https://curricula.bfsu.edu.cn/academic/getCaptcha.do')\r\n cap1 = Image.open(BytesIO(data.read()))\r\n wide, high = cap1.size\r\n cap1 = cap1.resize((wide * 5, high * 5))\r\n cap1.show()\r\n return\r\n\r\n\r\ndef str_len(string):\r\n row_l = len(string)\r\n gbk_l = len(string.encode('gbk'))\r\n return gbk_l - row_l\r\n\r\n\r\ndef getscore(t):\r\n if len(t) <= 2:\r\n t = date.today()\r\n if 11 >= int(t.strftime('%m')) >= 7:\r\n yearid = str(int(t.strftime('%Y')) - 1980)\r\n termid = '1'\r\n else:\r\n yearid = str(int(t.strftime('%Y')) - 1981)\r\n termid = '2'\r\n # print(yearID,termID)\r\n\r\n else:\r\n yearid = str(int(t[:4]) - 1980)\r\n termid = t[-1].lower()\r\n if termid == 'a':\r\n termid = '2'\r\n elif termid == 's':\r\n termid = '1'\r\n else:\r\n print('Illegal input format.\\n')\r\n return -1\r\n # print(yearID,termID)\r\n print('当前查询时间：' + str(int(yearid) + 1980), '第', termid, '学期')\r\n postdata = {'year': yearid, 'term': termid, 'para': '0', 'sortColumn': '', 'Submit': '查询'}\r\n postdata = urllib.parse.urlencode(postdata).encode('utf-8')\r\n postu = 'https://curricula.bfsu.edu.cn/academic/manager/score/studentOwnScore.do?groupId=&moduleId=2020'\r\n requ = urllib.request.Request(postu, postdata, headers)\r\n # print(request)\r\n re = urllib.request.urlopen(requ)\r\n content = re.read().decode('utf-8')\r\n # print(text)\r\n score = BeautifulSoup(content, 'html.parser')\r\n score = score.find('table', {'class': 'datalist'})\r\n if score is None:\r\n print('无成绩！\\n\\n')\r\n return\r\n # print(score)\r\n print('=' * 92)\r\n print('|{:^2}|{:^2}|{:^5}|{:^25}|{:^3}|{:^4}|{:^3}|{:^2}|{:^4}|{:^4}|'.format('学年', '学期', '课程号', '课程名', '课序号', '总评',\r\n '学分', '学时', '考试性质', '及格标志'))\r\n print('-' * 92)\r\n for tab in score.find_all('tr'):\r\n for i, tdd in enumerate(tab.find_all('td')):\r\n if i == 0:\r\n content = '|{:^4}|'.format(tdd.string.strip())\r\n print(content, end=\"\")\r\n elif i == 1:\r\n content = '{:^3}|'.format(tdd.string.strip())\r\n print(content, end=\"\")\r\n elif i == 2:\r\n content = '{:<8}|'.format(tdd.string.strip())\r\n print(content, end=\"\")\r\n elif i == 3:\r\n # format='{:<15}|'.format(tdd.string.strip())\r\n # print (format,end=\"\")\r\n print('%-*s|' % (int(28 - str_len(tdd.string.strip())), tdd.string.strip()), end=\"\")\r\n elif i == 4:\r\n content = '{:^6}|'.format(tdd.string.strip())\r\n print(content, end=\"\")\r\n elif i == 5:\r\n content = '{:^6}|'.format(tdd.string.strip())\r\n print(content, end=\"\")\r\n elif i == 6:\r\n content = '{:^5}|'.format(tdd.string.strip())\r\n print(content, end=\"\")\r\n elif i == 7:\r\n content = '{:^4}|'.format(tdd.string.strip())\r\n print(content, end=\"\")\r\n elif i == 8:\r\n content = '{:^4}|'.format(tdd.string.strip())\r\n print(content, end=\"\")\r\n elif i == 9:\r\n content = '{:^6}|'.format(tdd.string.strip())\r\n print(content)\r\n else:\r\n # raise\r\n pass\r\n\r\n print('\\nScores have been got.\\n')\r\n return\r\n\r\n\r\ndef loginselect():\r\n header = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:14.0) Gecko/20100101 Firefox/14.0.1',\r\n 'Referer': '''https://curricula.bfsu.edu.cn/\r\n academic/student/selectcoursedb/jumppage.jsp?\r\n groupId=&moduleId=2050'''}\r\n req = urllib.request.Request('https://curricula.bfsu.edu.cn/academic/manager/electcourse/mgspage.do',\r\n headers=header)\r\n while True:\r\n re = urllib.request.urlopen(req)\r\n print(re)\r\n re = re.read().decode('utf-8')\r\n if re.find('选课提示') != -1:\r\n return True\r\n\r\n\r\ndef getuserid():\r\n header = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:14.0) Gecko/20100101 Firefox/14.0.1',\r\n 'Referer': 'https://curricula.bfsu.edu.cn/academic/manager/electcourse/stusced.do'}\r\n req = urllib.request.Request('https://curricula.bfsu.edu.cn/academic/manager/electcourse/stusced.do#fastsc',\r\n headers=header)\r\n res0 = urllib.request.urlopen(req).read()\r\n try:\r\n res=res0.decode()\r\n except:\r\n print('连接失败...尝试获取错误信息...')\r\n res=res0.decode('gbk')\r\n if res.find('ServletException')>=0:\r\n print('选课系统可能已关闭\\n错误信息如下\\n--------------------\\n%s\\n====================' %res0)\r\n return 'ServerError'\r\n #print('res:',res)\r\n html = BeautifulSoup(res, 'html.parser')\r\n user = html.find('input', attrs={'type': 'hidden', 'name': 'checkUserid'})\r\n if user is None:\r\n return None\r\n return user['value']\r\n\r\n\r\ndef quickselect(course, se='1'):\r\n header = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:14.0) Gecko/20100101 Firefox/14.0.1',\r\n 'Referer': 'https://curricula.bfsu.edu.cn/academic/manager/electcourse/stusced.do'}\r\n postdata = {'pcourseid': course, 'seq': se, 'checkUserid': userid, 'Submit': '选课'}\r\n postdata = urllib.parse.urlencode(postdata).encode('utf-8')\r\n req = urllib.request.Request('https://curricula.bfsu.edu.cn/academic/manager/electcourse/scaddaction.do', postdata,\r\n header)\r\n res = urllib.request.urlopen(req).read().decode('utf-8')\r\n # print('\\n\\n\\n',res)\r\n res = BeautifulSoup(res, 'html.parser')\r\n res = res.center.body.find('script').string.strip().split('\\n', 7)\r\n ms = res[4].split(r'\"')[1]\r\n fla = int(res[5].split('=')[1][0])\r\n # print(msg)\r\n return fla, ms\r\n\r\n\r\nhosturl = 'https://jwc.bfsu.edu.cn'\r\nposturl = 'https://curricula.bfsu.edu.cn/academic/j_acegi_security_check '\r\ncj = http.cookiejar.LWPCookieJar()\r\ncookie_support = urllib.request.HTTPCookieProcessor(cj)\r\nopener = urllib.request.build_opener(cookie_support, urllib.request.HTTPHandler)\r\nurllib.request.install_opener(opener)\r\nh = urllib.request.urlopen(hosturl)\r\nheaders = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:14.0) Gecko/20100101 Firefox/14.0.1',\r\n 'Referer': 'https://jwc.bfsu.edu.cn'}\r\nwhile True:\r\n username = input('ID:')\r\n password = input('Password:')\r\n captcha=''\r\n while not captcha:\r\n get_captcha()\r\n captcha = input('请输入图中所示验证码，看不清请直接回车:')\r\n if captcha != '':\r\n postData = dict(groupId='', j_username=username, j_password=password, j_captcha=captcha, button1='登陆')\r\n postData = urllib.parse.urlencode(postData).encode('utf-8')\r\n request = urllib.request.Request(posturl, postData, headers)\r\n # print(request)\r\n response = urllib.request.urlopen(request)\r\n text = response.read().decode('gbk')\r\n # print(response.getheaders())\r\n # print(text)\r\n # print('='*80)\r\n # print(text.decode('gbk'))\r\n # print('='*80)\r\n # print(text.decode('gbk').encode('gbk'))\r\n if text.find('验证码') == -1:\r\n print('Login succeed.\\n等待时间1分钟')\r\n break\r\n else:\r\n print('Login failed.\\n')\r\nwhile True:\r\n comm = input('\\n主菜单_等待指令\\n')\r\n if comm == 'score':\r\n print('\\n----查询成绩')\r\n while True:\r\n print( '输入查询学期，可直接回车查询默认的上学期成绩；\\n否则请输入具体时间格式如\\'2014a\\'（2014年秋季）、\\'2015s\\'（2015年春季）。（输入内容不包括单引号）\\n返回上一层菜单请输入\\'exit\\'')\r\n time = input('查询学期：')\r\n if time == 'exit':\r\n break\r\n getscore(time)\r\n elif comm == 'select':\r\n print('\\n----单次选课')\r\n userid = getuserid()\r\n if userid == 'ServerError':\r\n continue\r\n while True:\r\n courseid = input('请输入课程编号，返回请输入exit ')\r\n if courseid == 'exit':\r\n break\r\n seq = input('若知课序号请输入课程序号，否则请直接回车 ')\r\n if seq == '':\r\n print(quickselect(courseid)[1])\r\n else:\r\n print(quickselect(courseid, seq)[1])\r\n elif comm == 'wait':\r\n print('\\n----循环询问')\r\n userid = getuserid()\r\n if userid == 'ServerError':\r\n continue\r\n courseid = input('请输入课程编号，返回请输入exit ')\r\n if courseid == 'exit':\r\n continue\r\n seq = input('若知课序号请输入课程序号，否则请直接回车 ')\r\n n = 1\r\n inter = input('请输入两次查询间的时间间隔，默认为0.1（秒） ')\r\n if inter == '':\r\n inter = 0.1\r\n else:\r\n inter = float(inter)\r\n print('循环询问开始')\r\n if seq == '':\r\n while True:\r\n flag, msg = quickselect(courseid)\r\n if flag == 1:\r\n print('\\n第', n, '次尝试成功。请登录验证。课程号', courseid, '于', datetime.now())\r\n break\r\n n += 1\r\n print('当前第', n, '次尝试，状态', msg, end='\\r')\r\n time.sleep(inter)\r\n else:\r\n while True:\r\n flag, msg = quickselect(courseid, seq)\r\n if flag == 1:\r\n print('\\n第', n, '次尝试成功。请登录验证。课程号', courseid, '于', datetime.now())\r\n break\r\n n += 1\r\n print('当前第', n, '次尝试，状态', msg, end='\\r')\r\n time.sleep(inter)\r\n elif comm == 't':\r\n loginselect()\r\n print(getuserid())\r\n elif comm == 'exit':\r\n break\r\n else:\r\n print('无效的指令')\r\n","sub_path":"login.py","file_name":"login.py","file_ext":"py","file_size_in_byte":10713,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"372565689","text":"\n## @mainpage\n#\n# @par Yue Server\n#\n# A Web application for streaming music\n\n## @page stack Application Stack\n#\n# @par Flask Application\n#\n# A collection of resources, the configuration\n# database and web resources that make up a web app.\n#\n# @par Web Resource Layer\n#\n# Declarative definitions for REST Endpoints in the application.\n# Each resource declares the mapping of a url path and HTTP verb to one or\n# more functions in the service layer.\n#\n# @par Service Layer\n#\n# Services build the business logic by building on top of database.\n#\n# @par Dao Layer\n#\n# Data access objects for interacting with the database or filesystem\n# This is made up of a database library, and an abstract file system.\n#\n# The db library provides access to a sqlite or postgres database\n#\n# The file system library provides access to either local storage,\n# s3 or an in-memory (for testing) file system\n#\n# @par Database\n#\n# A database client to SQLite or PostgreSQL.\n\n## @package server.app\n#\n# The Application Backend\n#\n#\n\nimport os\nimport sys\n\nif (sys.version_info[0] == 2):\n raise RuntimeError(\"python2 not supported\")\n\nimport ssl\nimport argparse\nimport codecs\n\nimport logging\nfrom logging.handlers import RotatingFileHandler\n\nlogging.getLogger(\"engineio\").setLevel(logging.WARNING)\nlogging.getLogger(\"socketio\").setLevel(logging.WARNING)\n\nfrom .config import Config\n\nfrom .dao.library import Song\nfrom .dao.transcode import find_ffmpeg\nfrom .dao.filesys.filesys import FileSystem\nfrom .dao.filesys.s3fs import BotoFileSystemImpl\nfrom .dao.db import db_connect, db_init_main\n\nfrom .framework.application import FlaskApp\nfrom .framework.web_resource import WebResource, get\nfrom .framework.clientgen import generate_client as generate_client_impl\n\nfrom .service.audio_service import AudioService\nfrom .service.transcode_service import TranscodeService\nfrom .service.user_service import UserService\nfrom .service.filesys_service import FileSysService\nfrom .service.radio_service import RadioService\n\nfrom .resource.app_resource import AppResource\nfrom .resource.user_resource import UserResource\nfrom .resource.library_resource import LibraryResource\nfrom .resource.queue_resource import QueueResource\nfrom .resource.files_resource import FilesResource, NotesResource\nfrom .resource.radio_resource import RadioResource\n\nclass YueApp(FlaskApp):\n \"\"\"docstring for YueApp\"\"\"\n def __init__(self, config):\n super(YueApp, self).__init__(config)\n\n if not self.config.null:\n logging.warning(\"db_connect: %s\" % self.config.database.dbhost)\n self.db = db_connect(self.config.database.url)\n\n # check that the database is configured.\n # the number of tables may not match if there are additional\n # test tables, but in general should be the same\n nbTablesExpected = len(self.db.metadata.tables.keys())\n nbTablesActual = len(self.db.engine.table_names())\n if not self.config.null and nbTablesExpected != nbTablesActual:\n logging.warning(\"database contains %d tables. expected %d.\" % (\n nbTablesActual, nbTablesExpected))\n\n if config.aws.endpoint is not None:\n aws = config.aws\n s3fs = BotoFileSystemImpl(aws.endpoint, aws.region,\n aws.access_key, aws.secret_key)\n FileSystem.register(BotoFileSystemImpl.scheme, s3fs)\n\n self.user_service = UserService(config, self.db, self.db.tables)\n self.audio_service = AudioService(config, self.db, self.db.tables)\n self.transcode_service = TranscodeService(config, self.db, self.db.tables)\n self.filesys_service = FileSysService(config, self.db, self.db.tables)\n self.radio_service = RadioService(config, self.db, self.db.tables)\n\n self.add_resource(AppResource(self.config, self.db, self.db.tables))\n self.add_resource(UserResource(self, self.user_service))\n self.add_resource(LibraryResource(self.user_service,\n self.audio_service,\n self.transcode_service,\n self.filesys_service))\n self.add_resource(QueueResource(self.user_service,\n self.audio_service))\n self.add_resource(FilesResource(self.user_service, self.filesys_service))\n self.add_resource(NotesResource(self.user_service, self.filesys_service))\n self.add_resource(RadioResource(self.user_service, self.radio_service))\n\n self.app.teardown_request(self.teardown_request)\n self.app.before_request(self.before_request)\n\n def before_request(self):\n pass\n\n def teardown_request(self, ex=None):\n \"\"\"\n this fixes a SQLite error\n sqlite3.ProgrammingError: SQLite objects created in a thread\n can only be used in that same thread.\n\n db_connect uses a scoped session for thread local sessions\n \"\"\"\n self.db.session.remove()\n\n def tearDown(self):\n super(YueApp, self).tearDown()\n\nclass TestApp(YueApp):\n \"\"\"An app with helper functions for testing\"\"\"\n def __init__(self, test_name=\"\"):\n config = self._init_config(test_name)\n super(TestApp, self).__init__(config)\n\n db_init_main(self.db, self.db.tables, self.env_cfg)\n\n self.TEST_DOMAIN = \"test\"\n self.TEST_ROLE = \"test\"\n\n self.USER = self.user_service.getUserByPassword(\"user000\", \"user000\")\n\n def _init_config(self, test_name):\n\n ffmpeg_path = find_ffmpeg()\n\n if ffmpeg_path is None:\n raise Exception(\"FFmpeg not found\")\n\n tmp_path = os.path.join(os.getcwd(), \"test\")\n log_path = tmp_path\n\n self.app_cfg = {\n 'server': {\n 'build': './frontend/build',\n 'static': './frontend/build/static',\n 'host': 'localhost',\n 'port': 4200,\n 'env': 'production',\n 'secret_key': 'secret',\n 'cors': {'origin': '*'},\n 'database': {\n 'kind': 'sqlite',\n 'path': 'database.test.%s.sqlite' % test_name\n },\n 'ssl': {'private_key': '', 'certificate': ''},\n 'logging': {\n 'directory': log_path,\n 'filename': 'server.log',\n 'max_size': '2MB',\n 'num_backups': 10,\n 'level': 'debug'},\n 'transcode': {\n 'audio': {\n 'bin_path': ffmpeg_path,\n 'tmp_path': tmp_path,\n },\n 'image': {}\n }\n }\n }\n\n self.env_cfg = {\n 'features': [\"user_read\",\n \"user_write\",\n \"user_create\",\n \"user_power\",\n \"library_read\",\n \"library_write\",\n \"library_read_song\",\n \"library_write_song\",\n \"filesystem_read\",\n \"filesystem_write\",\n \"filesystem_delete\"],\n 'filesystems': {\n 'default': '{pwd}',\n 'mem': \"mem://test\",\n },\n 'domains': ['test'],\n 'roles': [\n {'null': {'features': []}},\n # the test user has the minimum set of features to\n # be able to listen to music and manage their profile\n {'test': {\n 'features': [\n \"user_read\",\n \"user_write\",\n \"library_read\",\n \"library_read_song\"\n ],\n 'filesystems': ['all']\n }\n },\n {'admin': {'features': ['all'],\n 'filesystems': ['all']}},\n ],\n 'users': [\n {'email': 'null',\n 'password': 'null',\n 'domains': ['test'],\n 'roles': ['null']},\n {'email': 'user000',\n 'password': 'user000',\n 'domains': ['test'],\n 'roles': ['test']},\n {'email': 'admin',\n 'password': 'admin',\n 'domains': ['test'],\n 'roles': ['admin']},\n ]\n }\n\n config = Config(self.app_cfg)\n config.database.url = None\n\n return config\n\n def login(self, username, password):\n \"\"\" return a test client which sends credentials with every request\n \"\"\"\n token = self.user_service.loginUser(username, password)\n return self.test_client(token)\n\n def create_test_songs(self):\n \"\"\" add tests songs to the database\n \"\"\"\n self.SONGS = []\n self.SONGIDS = []\n for a in range(3):\n for b in range(3):\n for t in range(3):\n song = {\n Song.artist: \"Artist%03d\" % a,\n Song.album: \"Album%03d\" % b,\n Song.title: \"Title%03d\" % t,\n Song.ref_id: \"id%06d\" % (len(self.SONGS)),\n }\n song_id = self.audio_service.createSong(self.USER, song)\n song[Song.id] = song_id\n self.SONGS.append(song)\n self.SONGIDS.append(song_id)\n\n def tearDown(self):\n super(TestApp, self).tearDown()\n\ndef connect(host, username, password):\n \"\"\" return a client which sends credentials with every request\"\"\"\n if not host.startswith('http://') and not host.startswith('https://'):\n logging.warning(\"Protocol not specified in hostname (%s)\"\n \" missing http:// or https:// prefix\" % host)\n app = YueApp(Config.null())\n return app.client(host, username, password)\n\ndef generate_client(app, name=\"yueclient\", outdir=\".\"):\n \"\"\"generate a client python package\n\n the generated package will implement a rest client with endpoint\n definitions for the application server\n\n This wraps the framework implementation of the same function,\n and bundles in a sync tool which utilizes the file api.\n \"\"\"\n\n header = \"# This file was auto generated. do not modify\\n\"\n client_dir = os.path.join(outdir, name)\n\n generate_client_impl(app, name, outdir)\n\n py_client_impl = os.path.join(client_dir, \"sync.py\")\n with open(py_client_impl, \"w\") as wf:\n wf.write(header)\n with open(\"yueserver/tools/sync.py\", \"r\") as rf:\n for line in rf:\n if 'import connect' in line:\n wf.write(\"from .connect import connect\\n\")\n else:\n wf.write(line)\n\ndef parseArgs(argv, default_profile=None):\n \"\"\" parse the command line arguments used for launching an app\n\n builds an arg parser with the common options needed to create an app.\n \"\"\"\n\n #encoding = \"cp850\"\n #if sys.stdout.encoding != encoding:\n # sys.stdout = codecs.getwriter(encoding)(sys.stdout.buffer, 'strict')\n #if sys.stderr.encoding != encoding:\n # sys.stderr = codecs.getwriter(encoding)(sys.stderr.buffer, 'strict')\n\n\n if default_profile is None:\n default_profile = \"windev\" if sys.platform == \"win32\" else \"development\"\n\n parser = argparse.ArgumentParser(description='')\n parser.add_argument('--config_dir', dest='config', default=\"./config\",\n help='enable verbose logging')\n parser.add_argument('-p', '--profile', dest='profile',\n default=default_profile,\n help='default profile to use (%s)' % default_profile)\n # workers and bind are for gunicorn support\n # todo: the default bind should be none\n # then use the config to set the default bind,\n #parser.add_argument('--bind', type=str, default=\"0.0.0.0:4200\",\n # help=\"bind server to host:port\")\n #parser.add_argument('-w', '--workers', type=int,\n # default=1,\n # help='number of workers')\n #parser.add_argument('appname', default='wsgi:app', nargs='?'\n # help=\"the name of the app for running using wsgi (file:varname)\")\n\n args, _ = parser.parse_known_args(argv[1:])\n\n\n #app.logger.handlers = gunicorn_logger.handlers\n #app.logger.setLevel(gunicorn_logger.level)\n\n return args\n\ndef getApp(config_dir, profile, debugLogging=True):\n \"\"\" get the application for a specific profile\n\n Loads the configuration for the specified profile and returns a new\n App instance.\n\n \"\"\"\n FORMAT = '%(asctime)s - %(levelname)-8s - %(name)s - %(message)s'\n formatter = logging.Formatter(FORMAT)\n\n if not debugLogging:\n # single source of truth for log configuration is the\n # server config file. this will reconfigure the gunicorn\n # logger until the config can be loaded\n gunicorn_logger = logging.getLogger('gunicorn.error')\n root_logger = logging.getLogger(None)\n root_logger.handlers.extend(gunicorn_logger.handlers)\n root_logger.setLevel(gunicorn_logger.level)\n for h in root_logger.handlers:\n h.setFormatter(formatter)\n\n app_cfg_path = os.path.join(config_dir, profile, \"application.yml\")\n cfg = Config(app_cfg_path)\n\n if not os.path.exists(cfg.logging.directory):\n os.makedirs(cfg.logging.directory)\n\n if debugLogging:\n logging.basicConfig(format=FORMAT, level=cfg.logging.level)\n\n root_logger = logging.getLogger(None)\n\n formatter = logging.Formatter(FORMAT)\n for h in root_logger.handlers:\n h.setFormatter(formatter)\n\n # always add a rotating log handler\n root_logger = logging.getLogger(None)\n root_logger.setLevel(cfg.logging.level)\n log_path = os.path.join(cfg.logging.directory, cfg.logging.filename)\n handler = RotatingFileHandler(log_path,\n maxBytes=cfg.logging.max_size,\n backupCount=cfg.logging.num_backups)\n handler.setFormatter(formatter)\n root_logger.addHandler(handler)\n\n logging.debug(\"root logger support: DEBUG\")\n logging.info(\"root logger support: INFO\")\n logging.warning(\"root logger support: WARNING\")\n logging.error(\"root logger support: ERROR\")\n\n app = YueApp(cfg)\n\n if not debugLogging:\n routes = app.list_routes()\n for endpoint, methods, url in routes:\n logging.info(\"{:40s} {:20s} {}\".format(\n endpoint, methods, url))\n\n return app\n\ndef main():\n\n args = parseArgs(sys.argv)\n\n app = getApp(args.config, args.profile)\n\n app.run()\n\nif __name__ == '__main__':\n main()","sub_path":"yueserver/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":14810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"222706964","text":"\"\"\" Class to analyse text meaning and fill ontology tree \"\"\"\n\nimport os\nfrom pprint import pprint\nimport re\nfrom string import punctuation\nimport spacy\n\nfrom nltk import word_tokenize\nfrom nltk.stem import WordNetLemmatizer\n\nfrom rake_nltk import Rake\n\nfrom OntologyFiles.OntologyTree import ontologyTree, is_leaf\nfrom TaggingFiles.ReaderWriter import read_emails\n\n\n# Uses stopwords for english from NLTK, and all puntuation characters by default\nRAKE = Rake()\npath = os.path.normpath(os.getcwd() + os.sep + os.pardir)\nPATH_UNTAGGED = os.path.join(path, 'untagged')\n\n\ndef extract_topic_from_header(email):\n topic_expression = r'(?<=Topic:)([\\d\\D]*)Dates'\n topic_expression = re.compile(topic_expression, re.MULTILINE)\n found_row = re.findall(topic_expression, email.header)[0]\n found_row = found_row.lower()\n RAKE.extract_keywords_from_text(found_row)\n topic_keywords = RAKE.get_ranked_phrases()\n return topic_keywords\n\n\ndef extract_topic_from_body(email):\n text = email.body\n text = text.replace('\\n', \" \")\n text = ' '.join(text.split())\n RAKE.extract_keywords_from_text(text)\n body_keywords = RAKE.get_ranked_phrases()\n body_keywords = [x for x in body_keywords if not any(x1.isdigit() for x1 in x)]\n body_keywords = [x for x in body_keywords if not any(x1 in punctuation for x1 in x)]\n return body_keywords\n\n\ndef process_keywords(list_keywords):\n for i in range(len(list_keywords)):\n list_keywords[i] = word_tokenize(list_keywords[i])\n\n # Turn to single list from list of list\n list_keywords = [item for sublist in list_keywords for item in sublist]\n\n wordnet_lemmatizer = WordNetLemmatizer()\n for i in range(len(list_keywords)):\n list_keywords[i] = wordnet_lemmatizer.lemmatize(list_keywords[i])\n\n return list_keywords\n\n\n# This method gets required key phrases\ndef get_keywords_and_ids(email):\n list_keywords = []\n topic_keywords = extract_topic_from_header(email)\n body_keywords = extract_topic_from_body(email)\n\n for key_word in topic_keywords:\n list_keywords.append(key_word)\n\n i = 0\n for key_word in body_keywords:\n list_keywords.append(key_word)\n i += 1\n if i == 5:\n break\n list_keywords = process_keywords(list_keywords)\n\n return ' '.join(list_keywords), email.e_id\n\n\ndef build_ontology_tree(list_keywords, id, spacy):\n tokens = spacy(list_keywords)\n max_sim = -1.0\n max_key = ''\n\n # Iterating through dictionary of dictionaries (ontology tree)\n for key in ontologyTree.keys():\n word = spacy(key)\n sim = (tokens.similarity(word))\n if sim > max_sim:\n max_key = key\n max_sim = sim\n if is_leaf(ontologyTree[max_key]):\n ontologyTree[max_key].append(id)\n else:\n max_key2 = ''\n max_sim = - 1.0\n for key in ontologyTree[max_key].keys():\n word = spacy(key)\n sim = (tokens.similarity(word))\n if sim > max_sim:\n max_key2 = key\n max_sim = sim\n if is_leaf(ontologyTree[max_key][max_key2]):\n ontologyTree[max_key][max_key2].append(id)\n else:\n max_key3 = ''\n max_sim = - 1.0\n for key in ontologyTree[max_key][max_key2].keys():\n word = spacy(key)\n sim = (tokens.similarity(word))\n if sim > max_sim:\n max_key3 = key\n max_sim = sim\n if is_leaf(ontologyTree[max_key][max_key2][max_key3]):\n ontologyTree[max_key][max_key2][max_key3].append(id)\n else:\n max_key4 = ''\n max_sim = - 1.0\n for key in ontologyTree[max_key][max_key2][max_key3].keys():\n word = spacy(key)\n sim = (tokens.similarity(word))\n if sim > max_sim:\n max_key4 = key\n max_sim = sim\n if is_leaf(ontologyTree[max_key][max_key2][max_key3][max_key4]):\n ontologyTree[max_key][max_key2][max_key3][max_key4].append(id)\n\n return ontologyTree\n\n\ndef main(spacy_model):\n emails_untagged = read_emails(PATH_UNTAGGED)\n for email in emails_untagged:\n keywords, email_id = get_keywords_and_ids(email)\n build_ontology_tree(keywords, email_id, spacy_model)\n\n pprint(dict(ontologyTree))\n\n\nif __name__ == '__main__':\n \"\"\" main method which loads spacy model and creates ontology tree \"\"\"\n\n print(\"Program Started\")\n print(\"SPACY model is being loaded\")\n spacy_model = spacy.load('en_vectors_web_lg')\n print(\"SPACY model is loaded\")\n main(spacy_model)\n print(\"\\nProgram Finished\")\n","sub_path":"OntologyFiles/Ontology.py","file_name":"Ontology.py","file_ext":"py","file_size_in_byte":4719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"583361981","text":"import functools\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom scipy import optimize\r\n\r\ndef f(x, d):\r\n s = 0\r\n\r\n for i in d:\r\n s = s + d[i] * (x ** i)\r\n \r\n return s\r\n\r\nd = {0 : 0}\r\nprint('''Now begin to create the funtion.\r\nYou ought to enter the power and later the coefficient.\r\nThe power must be an integer.\r\nPlease enter \\'q\\' to quit.''')\r\na = input()\r\n\r\nwhile a != 'q':\r\n a = int(a)\r\n b = float(input())\r\n d[a] = b\r\n a = input()\r\n\r\nprint(d)\r\ng = functools.partial(f, d = d)\r\nx = np.arange(-50, 50, 0.001)\r\nplt.plot(x, g(x))\r\nplt.show()\r\n\r\nprint('''Now begin to enter the number near the root of the equation.\r\nThen you'll get an root which approximates the right one.\r\nPlease enter \\'q\\' to quit.''')\r\n\r\nc = input()\r\n\r\nwhile c != 'q':\r\n c = float(c)\r\n root = optimize.fsolve(g, c)\r\n print(root)\r\n c = input()","sub_path":"root.py","file_name":"root.py","file_ext":"py","file_size_in_byte":871,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"201603097","text":"from sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy.ext.compiler import compiles\nfrom sqlalchemy.sql import expression\nfrom sqlalchemy import Column, Integer, String, DateTime\n\nBase = declarative_base()\n\n# This may be deprecated\n# Per https://docs.sqlalchemy.org/en/13/core/compiler.html#utc-timestamp-function,\n# SQLAlchemy does not, by default, support conversion to UTC timestamps from Unix\n# timestamps. The `UTC` class, and `pg_unix_to_utc` function thereafter, implement a\n# means of automatically converting from Unix to UTC at insert time.\nclass UTC(expression.FunctionElement):\n type = DateTime()\n\n@compiles(UTC, 'postgresql')\ndef pg_unix_to_utc(element,compiler,**kw):\n return \"TIMEZONE('utc', CURRENT_TIMESTAMP)\"\n\nclass Tweets(Base):\n __tablename__ = 'tweets'\n id = Column(Integer, primary_key=True)\n tweet_id = Column(String)\n user_id = Column(String)\n username = Column(String)\n screenname = Column(String)\n link_to_profile = Column(String, key='profile_link')\n permalink = Column(String, key='link')\n language = Column(String)\n time = Column(DateTime)\n timestamp = Column(DateTime(timezone=True), server_default=pg_unix_to_utc())\n retweets = Column(Integer)\n likes = Column(Integer)\n text = Column(String)\n\nclass RedditComments(Base):\n __tablename__ = 'reddit_comments'\n\n id = Column(Integer, primary_key=True)\n text = Column(String)\n time = Column(Numeric)\n subreddit_id = Column(String)\n","sub_path":"src/database/tables.py","file_name":"tables.py","file_ext":"py","file_size_in_byte":1486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"470482962","text":"import time\r\n\r\nfrom django.db import connections\r\nfrom django.db.utils import OperationalError\r\nfrom django.core.management.base import BaseCommand\r\n\r\n\r\nclass Command(BaseCommand):\r\n \"\"\"Django command to pause execution until DB is available\"\"\"\r\n\r\n def handle(self, *args, **options):\r\n self.stdout.write('Waiting for Database...') # Output a message to the screen\r\n db_conn = None\r\n\r\n # Wait until DB is available, try every second\r\n while not db_conn:\r\n try:\r\n db_conn = connections['default']\r\n except OperationalError:\r\n self.stdout.write('Database unavailable, waiting 1 second...')\r\n time.sleep(1)\r\n\r\n self.stdout.write(self.style.SUCCESS('Database is available'))\r\n","sub_path":"app/core/management/commands/wait_for_db.py","file_name":"wait_for_db.py","file_ext":"py","file_size_in_byte":782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"516462876","text":"from os import makedirs\n\nimport pandas as pd\nfrom skimage.metrics import mean_squared_error, structural_similarity, peak_signal_noise_ratio\n\nfrom .davis.metrics import db_eval_iou, db_eval_boundary\nfrom .utils import tensor_to_cv_image\n\n\ndef evaluate_tracking(target_masks, output_masks):\n results = []\n for t, (target_mask, output_mask) in enumerate(zip(target_masks, output_masks)):\n target_mask, output_mask = target_mask.numpy(), output_mask.numpy()\n results.append({\n 't': t,\n 'region_similarity': float(db_eval_iou(target_mask, output_mask)),\n 'contour_accuracy': float(db_eval_boundary(target_mask, output_mask))\n })\n return pd.DataFrame(results)\n\n\ndef evaluate_inpainting(target_images, output_images):\n results = []\n for t, (target_image, output_image) in enumerate(zip(target_images, output_images)):\n target_image, output_image = tensor_to_cv_image(target_image), tensor_to_cv_image(output_image)\n results.append({\n 't': t,\n 'mean_squared_error': float(mean_squared_error(target_image, output_image)),\n 'peak_signal_noise_ratio': float(peak_signal_noise_ratio(target_image, output_image)),\n 'structural_similarity': float(structural_similarity(target_image, output_image, multichannel=True))\n })\n return pd.DataFrame(results)\n\n\ndef save_stats(df, dir):\n makedirs(dir, exist_ok=True)\n print(df.mean(), file=open(f'{dir}/mean.txt', mode='w'))\n print(df.median(), file=open(f'{dir}/median.txt', mode='w'))\n\n\ndef save_results(df, dir):\n makedirs(dir, exist_ok=True)\n df.to_csv(f'{dir}/results.csv', index=False)\n","sub_path":"inpainting/evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":1671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"63415065","text":"#!/usr/bin/env python\n# coding: utf-8\nimport time\nfrom datetime import datetime\n\nimport requests\n\nfrom .config_file_manager import ConfigFileManager\n\n\ndef dump_log(msg):\n now = datetime.now().strftime(\"%Y/%m/%d %H:%M:%S\")\n print(\"[{}] {}\".format(now, msg), flush=True)\n\n return True\n\n\ndef get_kegg(endpoint):\n my_config_file_manager = ConfigFileManager()\n d_config = my_config_file_manager.read_config()\n time.sleep(d_config[\"CONFIG\"][\"WAIT_TIME\"])\n url = d_config[\"CONFIG\"][\"KEGG_URL_BASE\"] + endpoint\n ret = requests.get(url)\n status_code = ret.status_code\n text = ret.text\n\n return status_code, text\n\n\ndef get_pfam(endpoint):\n my_config_file_manager = ConfigFileManager()\n d_config = my_config_file_manager.read_config()\n time.sleep(d_config[\"CONFIG\"][\"WAIT_TIME\"])\n url = d_config[\"CONFIG\"][\"PFAM_URL_BASE\"] + endpoint\n ret = requests.get(url)\n status_code = ret.status_code\n text = ret.text\n\n return status_code, text\n\n\ndef check_status_code(status_code, msg):\n if status_code == 200:\n return True\n elif status_code == 400:\n raise ConnectionError(msg)\n elif status_code == 404:\n raise ValueError(msg)\n else:\n raise ConnectionError(\"status code is {}\".format(status_code))\n\n return True\n","sub_path":"KPHMMER/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"206441252","text":"\nfrom odoo import models, fields,api\nclass upload_product_for_users_meal_wizard(models.TransientModel):\n _name = 'upload.product.for.users.meal'\n product_catid = fields.Many2one('Product Category', string=\"Product Category\")\n item_ids = fields.Many2many('res.users.mealitem', string=\"Attendees\")\n\n @api.model\n def default_get(self, fields_list):\n res = super(upload_product_for_users_meal_wizard, self).default_get(fields_list)\n vals = [(0, 0, {'item_id': 20, 'servings': 12}),\n (0, 0, {'item_id': 22, 'servings': 19})]\n res.update({'item_ids': vals})\n return res\n\n def action_upload_done(self):\n print('ahmed fahmy')\n # action_vals = {\n # 'name': _('users'),\n # # 'domain': [('id', 'in', payments.ids), ('state', '=', 'posted')],\n # # 'domain': [('id', '=', self.user_id.id), ('phone', '!=', False)],\n # 'res_model': 'res.users.meal',\n # 'view_id': False,\n # 'view_mode': 'tree,form',\n # 'type': 'ir.actions.act_window',\n # }\n context = dict(self.env.context)\n context['form_view_initial_mode'] = 'edit'\n print(self.item_ids)\n return {\n 'type': 'ir.actions.act_window',\n 'view_mode': 'form',\n 'res_model': 'res.users.meal',\n 'res_id': False,\n 'target': 'current',\n 'res_item_ids': self.item_ids,\n 'context': context,\n # 'context': {'default_item_ids': self.item_ids}\n\n }\n\n\n\n\n\n\n","sub_path":"CustomeModules/ditefacts/wizards/upload_product_for_users_meal.py","file_name":"upload_product_for_users_meal.py","file_ext":"py","file_size_in_byte":1560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"639202465","text":"#!/usr/bin/python3.7\n# -*- coding: utf-8 -*-\n# @Time : 2019/8/26 17:20\n# @Author: Jtyoui@qq.com\n\"\"\"排序算法\"\"\"\n\n\ndef bubbled_sort(ls):\n \"\"\"冒泡算法\n\n >>> import random\n >>> import time\n >>> s = []\n >>> for _ in range(100):\n jr = random.randint(0, 1000)\n s.append(jr)\n >>> start = time.time()\n >>> bs = bubbled_sort(s)\n >>> print(bs)\n >>> print(time.time() - start)\n \"\"\"\n length = len(ls)\n flag = True # 判断是否要进行数据交换，True表示要进行\n k = length - 1 # 表示已经排序好的上界值,默认是列表的长度\n last = 0 # 记住上一次循环交换的位置。默认是开头\n for i in range(length):\n if not flag:\n break\n flag = False # 每次循环都默认为不进行数据交换\n for j in range(k):\n if ls[j] > ls[j + 1]:\n ls[j], ls[j + 1] = ls[j + 1], ls[j]\n flag = True # 要交换数据\n last = j # 交换数据的位置\n k = last\n return ls\n\n\nif __name__ == '__main__':\n import random\n import time\n\n s = []\n for _ in range(1_00):\n jr = random.randint(0, 1000)\n s.append(jr)\n start = time.time()\n bs = bubbled_sort(s)\n print(bs)\n print(time.time() - start)\n","sub_path":"jtyoui/algorithm/SortAlgorithm.py","file_name":"SortAlgorithm.py","file_ext":"py","file_size_in_byte":1310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"166177704","text":"# Third-party.\r\nimport pandas as pd\r\nfrom sklearn import preprocessing as pp\r\n\r\n\r\ndef get_wind_energy():\r\n wind_energy = pd.read_csv(\r\n filepath_or_buffer='/Users/oliver/Documents/networks/data/static_data/wind_energy.csv',\r\n sep=',',\r\n parse_dates=[['date', 'time']],\r\n index_col='date_time',\r\n dtype={\r\n 'p_1': float,\r\n 'p_2': float,\r\n 'p_3': float,\r\n 'p_t': float,\r\n },\r\n engine='c',\r\n infer_datetime_format=True,\r\n cache_dates=True,\r\n )\r\n # up-sample data to match 10min intervals.\r\n wind_upsampled = wind_energy.resample('10T').mean()\r\n wind_upsampled_interpolated = wind_upsampled.interpolate(method='spline', order=2)\r\n\r\n we_power_totals = []\r\n\r\n # wind farm readings need to be added up and converted to positive values before being used.\r\n for row in wind_upsampled_interpolated.iterrows():\r\n total_power = [abs(row[1].p_1) + abs(row[1].p_2) + abs(row[1].p_3)]\r\n we_power_totals.append(total_power)\r\n\r\n return we_power_totals\r\n\r\n\r\ndef get_weather_10_min_interval():\r\n weather_data_10 = pd.read_csv(\r\n filepath_or_buffer='/Users/oliver/Documents/networks/data/static_data/weather_data_10.csv',\r\n sep=',',\r\n parse_dates=[['date', 'time']],\r\n index_col='date_time',\r\n dtype={\r\n 'airtemp': float,\r\n 'winddirection': float,\r\n 'windspeed': float,\r\n 'sunshineduration': float,\r\n 'airpressure': float,\r\n 'precipitation': float,\r\n },\r\n engine='c',\r\n infer_datetime_format=True,\r\n cache_dates=True,\r\n )\r\n # standardize data.\r\n w10_scaled_values = pp.scale(weather_data_10.values)\r\n\r\n return w10_scaled_values\r\n\r\n\r\n","sub_path":"data/preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":1805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"36337009","text":"\nimport re\nimport string\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nmu=0\nsigma=40.0/45.0\nsize=512\nr = np.abs(np.random.normal(mu, sigma, size*size))\ntheta = np.random.random(size*size)*2*np.pi\nx=r*np.cos(theta)\ny=r*np.sin(theta)\nplt.gca().set_aspect('equal', adjustable='box') \nplt.scatter(x,y)\nplt.savefig(\"psf.png\")\n\nfor i in range(size*size):\n\tx[i]=np.int(np.around(x[i]))\n\ty[i]=np.int(np.around(y[i]))\n\nplt.gca().set_aspect('equal', adjustable='box') \nplt.scatter(x,y)\nplt.savefig(\"psf_int.png\")\n\n\n","sub_path":"simu/2_test_py/2_multicfr/f5r9/beta66_newpsf/newpsf.py","file_name":"newpsf.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"639752744","text":"from appium import webdriver\n\n\ndef appium_desired():\n desired_caps = {\n # 平台类型\n \"platformName\": \"Android\",\n # 平台版本号\n \"platformVersion\": \"9\",\n # 设备名称\n \"deviceName\": \"INE_AL00 device:HWINE\",\n # \"deviceName\": \"PACM00\",\n # app 包名\n \"appPackage\": \"com.yundongjiao.lepao\",\n # app 入口 acitivity\n \"appActivity\": \"com.yundongjiao.lepao.Activity.module.Activity.SplashScreenActivity\",\n \"automationName\": \"uiautomator1\",\n \"noReset\": True,\n }\n driver = webdriver.Remote('http://127.0.0.1:4723/wd/hub', desired_caps)\n driver.implicitly_wait(10)\n return driver\n","sub_path":"Tool/driver1.py","file_name":"driver1.py","file_ext":"py","file_size_in_byte":685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"433602660","text":"import http.server\nimport socketserver\n\n\n\nPORT = 8080\nIP=\"ip adress of server here\"\nHandler = http.server.SimpleHTTPRequestHandler\n\nwith socketserver.TCPServer((IP, PORT), Handler) as httpd:\n print(\"serving at port\", PORT)\n httpd.serve_forever()\n","sub_path":"detection_and_updating_data/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"416201087","text":"__package__ = '' # unicode package name error\n\nfrom collections import OrderedDict\n\nimport numpy as np\nimport random\nfrom scipy import stats\n\nfrom pacu.core.io.scanimage import util\n\ndef ori_by_sf(trials, sfs, oris):\n return OrderedDict([\n (\n sf,\n OrderedDict([\n (\n ori,\n trials.filter_by(trial_sf=sf, trial_ori=ori)\n )\n for ori in oris])\n ) for sf in sfs])\n\ndef main(workspace, condition, roi, datatag, dff0s=None):\n n_panes = condition.info.get('focal_pane_args', {}).get('n', 1)\n pane_offset = workspace.cur_pane or 0\n\n if not dff0s:\n dff0s = roi.dttrialdff0s\n\n bls = dff0s.filter_by(trial_blank=True)\n fls = dff0s.filter_by(trial_flicker=True)\n\n # If blank/flicker conditions are absent, off periods from random\n # set of trials is used (JZ)\n reps = condition.repetition\n num_trials = len(dff0s)\n\n if not bls:\n blank_trial_indices = random.sample(xrange(0, num_trials), reps)\n bls = [dff0s[i] for i in blank_trial_indices]\n blank = [np.nanmean(np.array(b.value['baseline'][pane_offset::n_panes])) for b in bls]\n else:\n blank = [np.nanmean(np.array(b.value['on'][pane_offset::n_panes])) for b in bls]\n\n if not fls:\n flicker_trial_indices = random.sample(xrange(0, num_trials), reps)\n fls = [dff0s[i] for i in flicker_trial_indices]\n flicker = [np.nanmean(np.array(f.value['baseline'][pane_offset::n_panes])) for f in fls]\n else:\n flicker = [np.nanmean(np.array(f.value['on'][pane_offset::n_panes])) for f in fls]\n\n if datatag.trial_tf:\n all_trials = dff0s.filter_by(\n trial_contrast=datatag.trial_contrast,\n trial_tf=datatag.trial_tf,\n trial_flicker=False,\n trial_blank=False)\n else:\n all_trials = dff0s.filter_by(\n trial_contrast=datatag.trial_contrast,\n trial_flicker=False,\n trial_blank=False)\n trials = ori_by_sf(all_trials, condition.sfrequencies, condition.orientations)\n\n # all_oris = [\n # [np.nanmean(np.array(rep.value['on'][pane_offset::n_panes])) for rep in reps]\n # for sf, oris in roi.dt_ori_by_sf(datatag.trial_contrast).items()\n # for ori, reps in oris.items()\n # ]\n\n all_oris = [\n [np.nanmean(np.array(rep.value['on'][pane_offset::n_panes])) for rep in reps]\n for sf, oris in trials.items()\n for ori, reps in oris.items()\n ]\n\n matrix = np.array([blank, flicker] + all_oris).T\n flicker_non_nans = list(filter(np.isfinite, flicker))\n blank_non_nans = list(filter(np.isfinite, blank))\n all_oris_non_nans = [list(filter(np.isfinite, trial)) for trial in all_oris]\n f, p = stats.f_oneway(flicker_non_nans, blank_non_nans, *all_oris_non_nans)\n return util.nan_for_json(dict(f=f, p=p, matrix=matrix.tolist()))\n\nif __name__ == '__sbx_main__':\n datatag.value = main(workspace, condition, roi, datatag)\n\nif __name__ == '__sbx_stitch__':\n datatag.value = main(workspace, condition, roi, datatag, dff0s)\n","sub_path":"pacu/pacu/core/io/scanbox/method/anova/all.py","file_name":"all.py","file_ext":"py","file_size_in_byte":3093,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"516089006","text":"from collections import namedtuple\n\n\nclass Person(object):\n flds = ['id', 'last_name', 'first_name', 'middle_name',\n 'address', 'city', 'county', 'state', 'zip',\n 'ward', 'precinct', 'jurisdiction',\n 'phone1', 'phone2', 'email',\n 'notes'\n ]\n\n def __init__(self, person_tuple):\n self.id = person_tuple.id\n self.last_name = person_tuple.last_name\n self.first_name = person_tuple.first_name\n self.middle_name = person_tuple.middle_name\n self.address = person_tuple.address\n self.city = person_tuple.city\n self.county = person_tuple.county\n self.state = person_tuple.state\n self.zip = person_tuple.zip\n self.ward = person_tuple.ward\n self.precinct = person_tuple.precinct\n self.phone1 = person_tuple.phone1\n self.phone2 = person_tuple.phone2\n self.email = person_tuple.email\n self.notes = person_tuple.notes\n\n def __str__(self):\n return '%s, %s %s' % (self.last_name, self.first_name, self.middle_name)\n\n @staticmethod\n def get_list(cxn):\n sql = ('SELECT id,'\n 'CONCAT(last_name,\", \",first_name,\" \",middle_name) AS whole_name '\n 'FROM persons ORDER BY whole_name')\n cursor = cxn.cursor()\n cursor.execute(sql)\n return cursor.fetchall()\n\n @staticmethod\n def get_email_addresses(cxn, person_ids):\n ids = ','.join(str(x) for x in person_ids)\n sql = 'SELECT Email FROM persons WHERE Email != \"\" AND ID in (%s)' % ids\n cursor = cxn.cursor()\n cursor.execute(sql)\n return [i[0] for i in cursor.fetchall()]\n\n @staticmethod\n def load(cxn):\n sql = 'SELECT * FROM persons ORDER BY last_name, first_name, middle_name'\n cursor = cxn.cursor()\n cursor.execute(sql)\n rex = cursor.fetchall()\n return [Person(PersonTuple(*rec)) for rec in rex]\n\n @staticmethod\n def get_by_email(cxn, email):\n sql = \"SELECT * FROM persons WHERE email='%s'\" % email\n cursor = cxn.cursor()\n cursor.execute(sql)\n rex = cursor.fetchall()\n return [Person(PersonTuple(*rec)) for rec in rex] if rex else None\n\n @staticmethod\n def get_by_phone(cxn, phone):\n sql = \"SELECT * FROM persons WHERE phone1='%s' OR phone2='%s'\" % (phone, phone)\n cursor = cxn.cursor()\n cursor.execute(sql)\n rex = cursor.fetchall()\n return [Person(PersonTuple(*rec)) for rec in rex] if rex else None\n\n @staticmethod\n def get_for_letter(cxn, letter):\n sql = \"SELECT last_name, first_name FROM persons WHERE last_name like '%s%%'\" % letter\n cursor = cxn.cursor()\n cursor.execute(sql)\n rex = cursor.fetchall()\n return [{'last': r[0], 'first': r[1]} for r in rex]\n\n def add(self, cxn):\n sql = 'INSERT INTO persons (' + ','.join(self.flds[1:]) + ') VALUES (%s)'\n cursor = cxn.cursor()\n cursor.execute(sql, (\n self.last_name,\n self.first_name,\n self.middle_name,\n self.address,\n self.city,\n self.county,\n self.state,\n self.zip,\n self.ward,\n self.precinct,\n self.phone1,\n self.phone2,\n self.email,\n self.notes\n ))\n self.id = cursor.lastrowid\n return self.id\n\n def update(self, cxn):\n sql = (\"UPDATE persons SET \"\n \"last_name=%s,\"\n \"first_name=%s,\"\n \"middle_name=%s,\"\n \"address=%s,\"\n \"city=%s,\"\n \"county=%s,\"\n \"state=%s,\"\n \"zip=%s,\"\n \"ward=%s,\"\n \"precinct=%s,\"\n \"phone1=%s,\"\n \"phone2=%s,\"\n \"email=%s,\"\n \"notes=%s\"\n \"WHERE id=%s\")\n cursor = cxn.cursor()\n return cursor.execute(sql, (\n self.last_name,\n self.first_name,\n self.middle_name,\n self.address,\n self.city,\n self.county,\n self.state,\n self.zip,\n self.ward,\n self.precinct,\n self.phone1,\n self.phone2,\n self.email,\n self.notes,\n self.id\n ))\n\n def drop(self, cxn):\n sql = \"DELETE FROM persons WHERE id=%s\"\n cursor = cxn.cursor()\n return cursor.execute(sql, self.id)\n\n def fuzzy_match(self, cxn):\n from fuzzywuzzy import process\n\n candidates = Person.get_for_letter(cxn, self.last_name[0])\n if not candidates:\n return None\n\n choices = set([x['last'] for x in candidates])\n lastnames = process.extract(self.last_name, choices)\n ave = sum([x[1] for x in lastnames]) / 5\n best_last = [x for x in lastnames if x[1] > ave]\n\n choices = [x['first'] for x in candidates if x['last'] in [y[0] for y in best_last]]\n firstnames = process.extract(self.first_name, choices)\n ave = sum([x[1] for x in firstnames]) / 5\n\n winners = []\n for fn in firstnames:\n for ln in best_last:\n if next((x for x in candidates if x['last'] == ln[0] and x['first'] == fn[0]), None):\n score = ln[1] + fn[1]\n if score > ave:\n winners.append((ln[0], fn[0], score))\n\n return winners\n\n\nPersonTuple = namedtuple('PersonTuple', Person.flds)\n","sub_path":"models/person.py","file_name":"person.py","file_ext":"py","file_size_in_byte":5538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"181943284","text":"\n\nimport socket\nimport threading\n\n\ndef accept_client():\n \"\"\"\n Will receive new client connection, decode username and add to connection_list\n :return:\n \"\"\"\n while True:\n cli_sock, temp = ser_sock.accept()\n username = cli_sock.recv(1024).decode()\n CONNECTION_LIST.append((username, cli_sock))\n print(str(username) + ' is now connected')\n\n # Starts client listening thread\n thread_listener = threading.Thread(target=broadcast_usr, args=[username, cli_sock])\n thread_listener.start()\n\n\ndef broadcast_usr(username, cli_sock):\n \"\"\"\n Will be running in a thread, listening for received messages from clients\n :param username: username from accept_client\n :param cli_sock: socket from accept_client\n \"\"\"\n while True:\n try:\n message = cli_sock.recv(1024).decode()\n if message:\n print(username + \" spoke\")\n b_usr(cli_sock, username, message)\n except Exception as x:\n print(\"An error has occured: \")\n print(x)\n break\n\n\ndef b_usr(cs_sock, sender_name, msg):\n \"\"\"\n Called from broadcast_usr, it will broadcast user message from origin to other clients\n :param cs_sock: socket from client that message was received\n :param sender_name: user name from client that message was received\n :param msg: message itselfs\n \"\"\"\n for client in CONNECTION_LIST:\n if client[1] != cs_sock:\n sen_msg = \"\\n\" + sender_name + \" > \" + msg\n client[1].send(sen_msg.encode())\n\n\nif __name__ == \"__main__\":\n CONNECTION_LIST = []\n\n ser_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n HOST = 'localhost'\n PORT = 5000\n ser_sock.bind((HOST, PORT))\n\n ser_sock.listen(1)\n print('Chat server started on port : ' + str(PORT))\n\n thread_ac = threading.Thread(target=accept_client)\n thread_ac.start()\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1922,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"515744667","text":"\"\"\"Read LINZ netcdf file\n This import function works with NetCDF files created from tidal gauge from LINZ.\n It reads both sensors as welll as the README file which should be in the same\n directory.\n This class returns a Panda Dataframe with some extra attributes such as \n Latitude,Longitude,Units.\n \n Parameters\n ~~~~~~~~~~\n\n filename : (files,) str or list_like\n A list of filename to process.\n\n Examples\n ~~~~~~~~\n\n >>> from toto.inputs.linz import LINZfile\n >>> nc=LINZfile('filename.nc')._toDataFrame()\n\n\"\"\"\nimport glob,os,sys\nimport pandas as pd\nimport xarray as xr\nfrom datetime import datetime, timedelta\n\ndef lat2msl(readme, ds,sensor=41):\n # Converts data to mean sea level based on information contained in the station's readme\n f = open(readme,\n encoding='latin-1')\n readme = f.readlines()\n reference_mark=False\n for il,line in enumerate(readme):\n if 'WGS-84 POSITION' in line:\n line=line.replace('Â','').replace('°','').replace(\"'\",'')\n\n if 'S' in line:\n line=line.replace(\"S\",'')\n fac=-1\n else:\n fac=1\n line=line.replace(\"N\",'')\n\n lat_deg=float(line.split(' ')[4])\n lat_dec=float(line.split(' ')[5])\n lat=(lat_deg+lat_dec/60)*fac\n\n line=readme[il+1]\n line=line.replace('Â','').replace('°','').replace(\"'\",'')\n if 'W' in line:\n line=line.replace(\"W\",'')\n fac=-1\n else:\n fac=1\n line=line.replace(\"E\",'')\n\n lon_deg=float(line.split(' ')[-2])\n lon_dec=float(line.split(' ')[-1])\n lon=(lon_deg+lon_dec/60)*fac\n\n if 'SUMMARY OF TIDE GAUGE ZERO' in line:\n reference_mark = line.split(' ')[6]\n\n if not reference_mark:\n print('Line SUMMARY OF TIDE GAUGE ZERO not found')\n return ds,lon,lat\n\n\n for line in readme:\n if 'LINZ geodetic code' in line and reference_mark in line:\n try:\n ref_datum = float(line.split(',')[-1].split()[0])\n except:\n print('reference bench mark not found set to 0')\n ref_datum=0\n\n\n start=False\n if sensor==41:\n for idx, line in enumerate(readme):\n if 'SENSOR 41' in line:\n start = idx\n break\n elif sensor ==40:\n for idx, line in enumerate(readme):\n if 'SENSOR 40' in line:\n start = idx\n break\n\n\n if not start:\n print('No information for sensor # %i' % sensor)\n return ds,lon,lat\n\n\n line = 'start'\n ref_gauge = dict()\n count = 1\n line = readme[start + count]\n while line != '\\r\\n' and line != '\\n':\n m1 = datetime.strptime(line.split(' ')[0], '%b').month\n y1 = int(line.split(' ')[1])\n m2 = datetime.strptime(line.split(' ')[3], '%b').month + 1\n y2 = int(line.split(' ')[4][:-1])\n if m2 > 12:\n m2 = 1\n y2 += 1\n\n key = 'ref{}'.format(count)\n ref_gauge[key] = dict()\n ref_gauge[key]['value'] = float(line.split(' ')[5]) - ref_datum\n ref_gauge[key]['t1'] = datetime(y1, m1, 1)\n ref_gauge[key]['t2'] = datetime(y2, m2, 1)\n count += 1\n line = readme[start + count]\n\n\n for c in range(len(ref_gauge)):\n key = 'ref{}'.format(c+1)\n if key == 'ref1': # start\n if ds.index[0] < ref_gauge['ref1']['t1']:\n ref_gauge['ref1']['t1'] = ds.index[0]\n\n if c != len(ref_gauge) - 1:\n if ref_gauge['ref{}'.format(c+2)]['t1'] - ref_gauge[key]['t2'] > timedelta(days=1) and len(ref_gauge) > 1:\n ref_gauge[key]['t2'] = ref_gauge['ref{}'.format(c+2)]['t1'] - timedelta(hours=1)\n\n elif c == len(ref_gauge) - 1: # end\n if ref_gauge[key]['t2'] < ds.index[-1]:\n ref_gauge[key]['t2'] = ds.index[-1]\n\n else: # middle\n if ref_gauge['ref{}'.format(c+2)]['t1'] - ref_gauge[key]['t2'] > timedelta(days=1):\n ref_gauge[key]['t2'] = ref_gauge['ref{}'.format(c+2)]['t1'] - timedelta(hours=1)\n\n ds[ref_gauge[key]['t1'] : ref_gauge[key]['t2']] = ds[ref_gauge[key]['t1'] : ref_gauge[key]['t2']] - ref_gauge[key]['value']\n\n return ds,lon,lat\n\nclass LINZfile():\n\n @staticmethod\n def defaultExtensions():\n return ['.nc']\n\n\n def __init__(self,filenames):\n\n if isinstance(filenames,str):\n filenames=[filenames]\n self.filenames=filenames\n self.data=[]\n # READ \n self._reads_nc()\n\n def _reads_nc(self):\n for file in self.filenames:\n self._read_nc(file)\n\n def _read_nc(self,filename):\n\n ds = xr.open_dataset(filename)\n if 'site' in ds:\n ds=ds.sel({'site':0})\n\n if 'sensor' in ds:\n if len(ds['sensor'])>1: \n df=ds.sel({'sensor':40})['elev'].to_dataframe()\n del df['sensor']\n df.rename(columns={'elev':'elev40'},inplace=True)\n df41=ds.sel({'sensor':41})['elev'].to_dataframe()\n df['elev41']=df41['elev'].copy()\n del df41\n else:\n df=ds['elev'][0].to_dataframe()\n del df['sensor']\n df.rename(columns={'elev':'elev40'},inplace=True)\n else:\n df=ds['elev'].to_dataframe()\n df.rename(columns={'elev':'elev40'},inplace=True)\n\n filepath,filename=os.path.split(filename)\n readmefile=os.path.join(filepath,filename.replace('_raw.nc','_readme.txt'))\n\n if not os.path.isfile(readmefile):\n print('Readme file %s could not be found' % readmefile)\n sys.exit(-1)\n\n if 'elev41' in df:\n df['elev41'],lon,lat=lat2msl(readmefile, df['elev41'],sensor=41)\n if 'elev40' in df:\n df['elev40'],lon,lat=lat2msl(readmefile, df['elev40'],sensor=40)\n\n df.reset_index(inplace=True)\n df.set_index('time',inplace=True,drop=False)\n if 'elev40' in df:\n setattr(df['elev40'],'units','m')\n setattr(df['elev40'],'long_name','water_level')\n if 'elev41' in df:\n setattr(df['elev41'],'units','m')\n setattr(df['elev41'],'long_name','water_level')\n\n if 'longitude' in ds:\n setattr(df,'longitude',ds['longitude'].values)\n setattr(df,'latitude',ds['latitude'].values)\n else:\n setattr(df,'longitude',lon)\n setattr(df,'latitude',lat) \n self.data.append(df)\n\n\n\n\n def _toDataFrame(self):\n #print(self.data)\n return self.data\n\n\nif __name__ == '__main__':\n LINZfile('/home/remy/projects/019_stormsurge/storm_surge_data/nz_tidal_gauges/linz/raw/AUCT_raw.nc')","sub_path":"toto/inputs/linz.py","file_name":"linz.py","file_ext":"py","file_size_in_byte":6908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"16081756","text":"import sys\n\nN = int(input())\nK = int(input())\nmembers = {}\n\nfor i in range(N):\n members[str(i+1)] = set()\n\nfor i in range(K):\n inp = sys.stdin.readline().split()\n\n if '1' in inp[1:]:\n for mem in inp[1:]:\n members[mem].add(i)\n\n else:\n list_merged = set()\n for mem in inp[1:]:\n list_merged |= members[mem]\n # O(n)\n\n for mem in inp[1:]:\n members[mem] = list_merged.copy()\n\n\nout = sorted(list(filter(lambda z: members[z] == members['1'], list(members))))\n\nfor i in out:\n print(int(i))\n\n# 무슨차이지 ?\n'''\nn, d = int(input()), int(input())\nsongs = [set() for i in range(n + 1)]\n\nfor i in range(d):\n lis = list(map(int, input().split()))[1:]\n if 1 in lis:\n for j in lis:\n songs[j].add(i)\n else:\n new_set = set()\n for j in lis:\n new_set |= songs[j]\n # var |= value is short for var = var | value\n for j in lis:\n songs[j] = new_set.copy()\n\nlist(map(lambda y: print(y[0]), filter(lambda z: z[0] > 0 and len(z[1]) == len(songs[1]), enumerate(songs))))\n'''\n","sub_path":"Bard.py","file_name":"Bard.py","file_ext":"py","file_size_in_byte":1119,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"158557489","text":"\"\"\"\n尝试用backtrader回测交叉对冲策略\n\n切记，backtradwer的核心在__init__ 和next中\n\n\"\"\"\n\n\nfrom __future__ import (absolute_import, division, print_function,unicode_literals)\nimport backtrader as bt\nimport akshare as ak\nimport pandas as pd\nfrom datetime import datetime\nimport numpy as np\n\n\nclass percent(bt.Sizer):\n params = (\n ('percents', 10),\n ('retint', False), # 返回整数\n )\n\n def __init__(self):\n pass\n\n def _getsizing(self, comminfo, cash, data, isbuy):\n position = self.broker.getposition(data)\n if not position:\n size = cash / data.close[0] * (self.params.percents / 100)\n else:\n size = position.size\n\n if self.p.retint:\n size = int(size)\n\n return size\n\nclass PandasData_more(bt.feeds.PandasData):\n lines = ('return_rate', ) # 要添加的线\n # 设置 line 在数据源上的列位置\n params=(\n ('return_rate', -1),\n )\n # -1表示自动按列明匹配数据，也可以设置为线在数据源中列的位置索引 (('pe',6),('pb',7),)\n\n\nclass SmaStrategy(bt.Strategy):\n # 可配置策略参数\n params = dict(\n SMA1_period = 5, # 小均线周期\n SMA2_period = 10, # 大均线周期\n stake = 100, # 单笔交易股票数目\n beta_period = 10, # 用于计算beta的期限长短\n printlog = False,\n )\n\n def log(self, txt, dt=None, doprint=False):\n ''' Logging function fot this strategy'''\n if self.params.printlog or doprint:\n dt = dt or self.datas[0].datetime.date(0)\n print('%s, %s' % (dt.isoformat(), txt))\n\n def notify_order(self, order):\n if order.status in [order.Submitted, order.Accepted]:\n # Buy/Sell order submitted/accepted to/by broker - Nothing to do\n return\n\n # Check if an order has been completed\n # Attention: broker could reject order if not enough cash\n if order.status in [order.Completed]:\n if order.isbuy():\n self.log(\n 'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %\n (order.executed.price,\n order.executed.value,\n order.executed.comm))\n\n self.buyprice = order.executed.price\n self.buycomm = order.executed.comm\n else: # Sell\n self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %\n (order.executed.price,\n order.executed.value,\n order.executed.comm))\n\n self.bar_executed = len(self)\n\n elif order.status in [order.Canceled, order.Margin, order.Rejected]:\n self.log('Order Canceled/Margin/Rejected')\n\n # Write down: no pending order\n self.order = None\n\n def notify_trade(self, trade):\n if not trade.isclosed:\n return\n\n self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %\n (trade.pnl, trade.pnlcomm))\n\n def stop(self):\n self.log('Ending Value %.2f' %\n (self.broker.getvalue()), doprint=True)\n\n def cal_beta(self, SeriesA):\n \"\"\"\n 计算两列数据的Beta值\n :param SeriesA: 被对冲标的收益率列\n :param SeriesB: 对冲标的收益率列\n :return: Beta值\n \"\"\"\n SeriesB = self.datas[1].return_rate.get(ago=-1, size=self.params.beta_period)\n\n cov_sm = np.cov(SeriesB,SeriesA)[0,1]\n var_m = np.var(SeriesB)\n\n self.beta = cov_sm / var_m\n\n return None\n\n def __init__(self):\n # 计算大小周期移动平均值，以设置买卖点\n self.SMA1 = bt.ind.SMA(self.datas[0].close, period=self.params.SMA1_period)\n self.SMA2 = bt.ind.SMA(self.datas[0].close, period=self.params.SMA2_period)\n\n # 其他变量\n self.beta = 0\n self.future_stake = 0\n\n # To keep track of pending orders and buy price/commission\n self.order = None\n self.buyprice = None\n self.buycomm = None\n\n def next(self):\n # Check if an order is pending ... if yes, we cannot send a 2nd one\n if self.order:\n return\n\n pos = self.getposition(self.datas[0])\n if not len(pos):\n if self.SMA1 > self.SMA2: # 达到买入条件\n index_sub = self.datas[0].return_rate.get(ago=-1, size = self.params.beta_period)\n\n\n if len(index_sub):\n self.cal_beta(index_sub) # 更新beta值\n self.future_stake = self.beta * self.datas[0].close.get()[0]* self.params.stake / self.datas[1].close.get()[0]\n\n # 买入指数的同时做空期货\n self.buy(data=self.datas[0], size=self.params.stake)\n self.sell(data=self.datas[1], size=self.future_stake)\n\n print(\"已买入股指并进行对冲\")\n\n elif self.SMA1 < self.SMA2: # 达到卖出条件\n # 买入指数的同时做空期货\n self.sell(data=self.datas[0], size=self.params.stake)\n self.buy(data=self.datas[1], size=self.future_stake)\n print(\"已平仓\")\n\n\n# 获取A股HS300股指\nindex_data = ak.stock_zh_index_daily(\"sh000300\")\nindex_data.index = pd.to_datetime(index_data.index)\nindex_data[\"date\"] = index_data.index\nindex_data[\"return_rate\"] = index_data.close.diff(1)/index_data.close.shift(1)\n\n\n\nfuture_data = ak.futures_zh_daily_sina(\"IF0\").iloc[:,:6]\nfuture_data.index = pd.to_datetime(future_data[\"date\"])\nfuture_data[\"return_rate\"] = future_data.close.diff(1)/future_data.close.shift(1)\n\n\n\n# 将pandas数据DF导入到实例化对象中\nstart_date = datetime(2020, 7, 3) # 回测开始时间\nend_date = datetime(2021, 8, 30) # 回测结束时间\ndata_index = PandasData_more(dataname=index_data, fromdate=start_date, todate=end_date) # 加载数据\ndata_future = PandasData_more(dataname=future_data, fromdate=start_date, todate=end_date) # 加载数据\n\n# 实例化对象\ncerebro = bt.Cerebro()\n\ncerebro.adddata(data_index, name=\"index\")\ncerebro.adddata(data_future, name=\"future\")\n\n\n\n# 设置启动资金\nstartcash = 1000000000.0\ncerebro.broker.setcash(startcash)\n# 设置交易手续费为 0.05%\n# cerebro.broker.setcommission(commission=0.0005)\n# 设置订单份额\ncerebro.addsizer(percent)\n# 将交易策略加载到回测系统中\ncerebro.addstrategy(SmaStrategy)\n\n\nimport backtrader.analyzers as btay#添加分析函数\n# 添加分析对象\ncerebro.addanalyzer(btay.SharpeRatio,_name=\"sharpe\")\ncerebro.addanalyzer(bt.analyzers.DrawDown, _name='DW')\n\n\n# 运行回测\nresults = cerebro.run()\n# 打印最后结果\n\nportvalue = cerebro.broker.getvalue()\npnl = portvalue - startcash\n\n\n#打印结果\nprint(f'总资金: {round(portvalue,2)}')\nprint(f'净收益: {round(pnl,2)}')\nprint(\"夏普比例:\", results[0].analyzers.sharpe.get_analysis())\nprint(\"回撤\",results[0].analyzers.DW.get_analysis())\n\n#%%\n\ncerebro.plot(style = \"candlestick\") # 绘图\n\n\n","sub_path":"20210930交叉对冲与Alpha对冲/20210928交叉对冲策略回测.py","file_name":"20210928交叉对冲策略回测.py","file_ext":"py","file_size_in_byte":7089,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"20136649","text":"import requests\r\nfrom flask import Flask, render_template\r\n\r\napp = Flask(__name__)\r\n\r\n@app.route(\"/\", methods=[\"GET\"])\r\ndef index():\r\n\r\n query = \"Tokyo\"\r\n unit = \"metric\"\r\n api_key = \"a584720c43c130d020ae58d96440565e\"\r\n\r\n url = \"https://api.openweathermap.org/data/2.5/weather?q={0}&units={1}&appid={2}&lang=HR\".format(query, unit, api_key)\r\n\r\n data = requests.get(url=url)\r\n\r\n return render_template(\"index.html\", data=data.json())\r\n\r\nif __name__ == '__main__':\r\n app.run()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"483389867","text":"from pipeline import Pipeline\nfrom task import Task\n\ndef make_pipeline():\n p = Pipeline()\n t1 = Task('./test_shell1.sh')\n t2 = Task('./test_shell2.sh')\n t3 = Task('./test_shell2.sh')\n\n task_id1 = p.register_task(t1)\n task_id2 = p.register_task(t2)\n task_id3 = p.register_task(t3)\n\n p.register_dependency(task_id2, task_id1)\n p.register_dependency(task_id3, task_id1)\n\n return p\n\ndef main():\n p = make_pipeline()\n p.execute()\n\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"runner.py","file_name":"runner.py","file_ext":"py","file_size_in_byte":502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"322169332","text":"from rest_framework import serializers\nfrom .models import Category, Product, Photo, Cart, OrderStatus, Order\n\nfrom datetime import datetime\nimport copy\n\n\nclass CategoryListSerializer(serializers.ModelSerializer):\n \"\"\" Список всех категорий товаров \"\"\"\n\n class Meta:\n model = Category\n fields = ('id', 'title')\n\n\nclass CategoryDetailSerializer(serializers.ModelSerializer):\n \"\"\" Информация о конкретной категории \"\"\"\n\n products = serializers.SlugRelatedField(\n slug_field=\"title\", read_only=True, many=True)\n\n class Meta:\n model = Category\n fields = ('id', 'title', 'description', 'products')\n\n# class CategoryCreateSerializer(serializers.ModelSerializer):\n# class Meta:\n# model = Category\n# fields = '__all__'\n# extra_kwargs = {\n# 'description': {\n# 'required': False,\n# 'allow_blank': True\n# }\n# }\n\n\nclass ProductSerializer(serializers.ModelSerializer):\n \"\"\" Информация о конкретном товаре \"\"\"\n\n category = serializers.SlugRelatedField(slug_field=\"title\", read_only=True)\n photos = serializers.StringRelatedField(read_only=True, many=True)\n\n class Meta:\n model = Product\n fields = '__all__'\n\n\nclass PhotoSerializer(serializers.ModelSerializer):\n \"\"\" Информация о конкретном фото \"\"\"\n\n product = serializers.SlugRelatedField(slug_field=\"title\", read_only=True)\n\n class Meta:\n model = Photo\n fields = '__all__'\n\n\nclass CartSerializer(serializers.ModelSerializer):\n \"\"\" Информация о корзинах \"\"\"\n\n class Meta:\n model = Cart\n fields = '__all__'\n\n def update(self, instance, validated_data):\n price = sum([Product.objects.get(id=prodPrice.id).price for prodPrice in validated_data[\"products\"]])\n \n instance.products.set(validated_data.get(\n 'products', instance.products))\n instance.price = price\n instance.last_update = datetime.now()\n instance.save()\n return instance\n\nclass OrderListSerializer(serializers.ModelSerializer):\n \"\"\" Список заказов пользователя \"\"\"\n\n status = serializers.SlugRelatedField(slug_field=\"title\", read_only=True)\n\n class Meta:\n model = Order\n fields = ('id', 'price', 'order_date', 'status')\n\n\nclass OrderDetailSerializer(serializers.ModelSerializer):\n \"\"\" Информация о конкретном заказе пользователя \"\"\"\n\n status = serializers.SlugRelatedField(slug_field=\"title\", read_only=True)\n\n class Meta:\n model = Order\n fields = ('id', 'price', 'order_date', 'products', 'status')\n extra_kwargs = {\n 'price': {\n 'required': False\n }\n }\n\n def create(self, validated_data):\n price = sum([Product.objects.get(id=prodPrice.id).price for prodPrice in validated_data[\"products\"]])\n newOrderData = {\n \"price\": price, \n \"order_date\": datetime.now(),\n \"status\": OrderStatus.objects.get(title=\"В обработке\"),\n \"user\": validated_data[\"user\"]\n }\n\n newOrder = Order.objects.create(**newOrderData)\n newOrder.products.set(validated_data.get(\"products\", []))\n newOrder.save()\n\n return newOrder","sub_path":"CutOfWoodNew/products/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":3460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"569277240","text":"\"\"\" Main TweetTipsPredictor module.\n\"\"\"\n\nfrom flask import Flask\nfrom tweet_tips_predictor.database import DB\n\n__version__ = '0.1.0'\n\ndef create_app(config_name):\n \"\"\" Create and return the flask app.\n \"\"\"\n app = Flask(__name__)\n app.config.from_object(config_name)\n DB.init(app.config['MONGODB_DATABASE'])\n from tweet_tips_predictor.main import BP as main_bp\n app.register_blueprint(main_bp)\n return app\n","sub_path":"tweet_tips_predictor/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"243399583","text":"#N과M9\n# list(set(permutations(nums,M))) 쓰면됨...\n\nimport sys\nN,M = map(int,sys.stdin.readline().split())\nA = list(map(int,sys.stdin.readline().split()))\nA.sort()\nvis = [0 for i in range(N)]\n\n# 재귀돌며 확인\ndef dfs(arr):\n # cnt = M 이면 return\n if len(arr) == M:\n # print(arr)\n print( \" \".join([str(A[i]) for i in arr]))\n return\n \n used = {}\n\n for i in range(N):\n if vis[i] == 1: continue\n if A[i] in used:\n continue\n else:\n used[A[i]] = 1\n \n arr.append(i)\n vis[i] = 1\n dfs(arr)\n arr.pop()\n vis[i] = 0\n\nfor i in range(N):\n if i > 0 and A[i] == A[i-1]: continue\n vis[i] = 1\n dfs([i])\n vis[i] = 0\n","sub_path":"Python/완전탐색/N과M9.py","file_name":"N과M9.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"12283714","text":"\n\nfrom xai.brain.wordbase.nouns._collection import _COLLECTION\n\n#calss header\nclass _COLLECTIONS(_COLLECTION, ):\n\tdef __init__(self,): \n\t\t_COLLECTION.__init__(self)\n\t\tself.name = \"COLLECTIONS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"collection\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_collections.py","file_name":"_collections.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"43294115","text":"import numpy as np\nfrom sklearn.datasets import load_iris\nfrom icecream import ic\nfrom sklearn.ensemble import RandomForestClassifier\nimport warnings\nwarnings.filterwarnings('ignore')\nfrom sklearn.metrics import accuracy_score\n\n# 실습\n# 모델 : RandomForestClassifier\n\ndatasets = load_iris()\nprint(datasets.DESCR)\nprint(datasets.feature_names)\n\n# 1. 데이터\nx = datasets.data\ny = datasets.target\nic(x.shape, y.shape) # (150, 4), (150,)->(150, 3)\nic(y) # (0,0,0, ... ,1,1,1, ... ,2,2,2, ...)\n\nfrom sklearn.model_selection import train_test_split, KFold, cross_val_score, GridSearchCV, RandomizedSearchCV # GridSearchCV : 체로 걸러서 찾겠다, CV(cross_val_score)까지 하겠다!!\nx_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7, shuffle=True, random_state=66)\nn_split = 5\nkfold = KFold(n_splits=n_split, shuffle=True, random_state=66) # n_splits=5 5등분하겠다! -> 값도 5(n)개로 나옴\n\n\nparameters = [\n {'n_estimators':[100, 200]},\n {'max_depth':[6, 8, 10, 12]},\n {'min_samples_leaf':[3, 5, 7, 10]},\n {'min_samples_split':[2, 3, 5, 10]},\n {'n_jobs':[-1, 2, 4]}\n]\n\n\n# 2. 모델(머신러닝에서는 정의만 해주면 됨) GridSearchCV로 모델(SVC) 감싸줌\n# model = GridSearchCV(RandomForestClassifier(), parameters, cv=kfold, verbose=1)\n# Fitting 5 folds for each of 17 candidates, totalling 85 fits\n\nmodel = RandomizedSearchCV(RandomForestClassifier(), parameters, cv=kfold, verbose=1)\n# Fitting 5 folds for each of 10 candidates, totalling 50 fits\n\n\n# 3. 훈련(cross_val_score 은 fit과 score가 포함되어 있음)\nmodel.fit(x_train, y_train)\n\n\n# 4. 평가(evaluate 대신 score 사용함!!), 예측\nprint(\"최적의 매개변수 :\", model.best_estimator_)\nprint(\"best_score :\", model.best_score_)\n\n\n\nprint(\"model.score :\", model.score(x_test, y_test))\n\ny_predict = model.predict(x_test)\nprint(\"accuracy_score :\", accuracy_score(y_test, y_predict))\n\n'''\n* GridSearchCV\n최적의 매개변수 : RandomForestClassifier(min_samples_leaf=10)\nbest_score : 0.9714285714285713\nmodel.score : 0.9333333333333333\naccuracy_score : 0.9333333333333333\n\nmodel.score : 0.9333333333333333\naccuracy_score : 0.9333333333333333\n\n\n* RandomizedSearchCV\n최적의 매개변수 : RandomForestClassifier(n_jobs=4)\nbest_score : 0.9619047619047618\nmodel.score : 0.9111111111111111\naccuracy_score : 0.9111111111111111\n'''","sub_path":"03_ML/m08_randonSearch3_iris.py","file_name":"m08_randonSearch3_iris.py","file_ext":"py","file_size_in_byte":2388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"601906975","text":"import flickrapi\nimport os\nimport lxml.etree as etree\nimport urllib\nfrom tqdm import tqdm\n\n\"\"\"\nParameters\n\ndirectory: main directory where images will be stored\nstyles_dict: dict with each key as style name and value is a list of search terms\nn_per_class: number of images to download per style\n\"\"\"\ndirectory = \"../data/flickr\"\nstyles_dict = {'sumie': ['chinese painting landscape']}\nn_per_class = 800\n\n\nclass FlickrScraper:\n def __init__(self):\n self.api_key = None\n self.api_secret = None\n self.data_directory = None\n\n def download_images(self, searchterm, classname, n):\n '''\n Download all images from 'url_list' to directory 'directory'\n directory: desired folder name, such as \"squirrelpic\"\n '''\n assert self.api_key is not None, \"Please provide a Flickr api key.\"\n assert self.api_secret is not None, \"Please provide a Flickr api secret.\"\n assert self.data_directory is not None, \"Please set a root data directory.\"\n\n dirpath = os.path.join(os.getcwd(), self.data_directory, classname)\n os.makedirs(dirpath, exist_ok=True)\n\n print(\"Requesting\", n, \"images with search term:\", searchterm, \"\\n\"\n \"Results will be saved in:\", dirpath, \"\\n\")\n\n url_list = self.get_flickr_url_list(searchterm, n)\n initial_file_count = len(os.listdir(dirpath))\n\n for i, url in enumerate(tqdm(url_list)):\n # Grab file extension\n ext = url[-4:]\n filename = os.path.join(dirpath, (classname + \"-%05d\" + ext) % (initial_file_count + i))\n\n # Some images are displayed without file extension\n if filename[-4:] not in ['.jpg', '.png', 'jpeg']:\n filename += '.jpg'\n\n try:\n urllib.request.urlretrieve(url, filename=filename)\n except Exception:\n print(\"url\", url, \"\\nfilename\", filename)\n print(\"Skipping image \", url_list.index(url))\n\n def get_flickr_url(self, node):\n '''\n returns the static image url given by a child node of the XML elementtree\n '''\n start = node.index(\"https://\")\n end = node.index('\" height_')\n return node[start:end]\n\n def get_flickr_url_list(self, searchterm, n):\n '''\n Returns list of urls with searching for searchterm\n '''\n flickr = flickrapi.FlickrAPI(self.api_key, self.api_secret)\n\n # Generate URL list\n urls = []\n retrieved = 0\n page = 0\n\n while retrieved < n:\n fsearch = flickr.photos_search(text=searchterm, page=page, per_page=500, extras=[\"url_m\"], sort=\"relevance\")\n page += 1\n\n # Iterate through each child node of the xmltree\n for i in range(0, 499):\n try:\n # Must check for url as some are private\n metadata = str(etree.tostring(fsearch[0][i]))\n if 'url' in metadata:\n urls.append(self.get_flickr_url(metadata))\n retrieved += 1\n except Exception:\n pass\n\n if retrieved % 100 == 0:\n print(retrieved, 'urls retrieved')\n\n if retrieved >= n:\n break\n\n return urls\n\n\nif __name__ == \"__main__\":\n scraper = FlickrScraper()\n scraper.data_directory = directory\n\n for style in styles_dict.keys():\n terms = styles_dict[style]\n for term in terms:\n scraper.download_images(searchterm=term, classname=style, n=round(n_per_class / len(terms)))\n","sub_path":"preprocessing/flickr_scraper.py","file_name":"flickr_scraper.py","file_ext":"py","file_size_in_byte":3624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"623030268","text":"# -*- coding: utf-8 -*-\nimport numpy as np\nimport pandas as pd\nfrom random import randint\n\"\"\"\n二八轮动+股息率选股策略\n二八轮动原则：\n 1. A股中规模很大的权重股（流通股大于1亿）占20%；\n 2. A股中中小盘股数量级占到80%；\n 3. 这20%和80%股的走势存在分化，需要在两者之间不断切换，轮流持有；\n 4. 这里用沪深300表示20%的权重股，中证500表示80%的中小盘股；\n1、根据二八轮动原则，选择两者当天收盘价较X天前涨幅较大的作为第二天持有的股种，如果都没有涨，则持有国债；\n股息率原则：\n 1. 选出股息率大于X，营收增长率大于Y的股票；\n2、根据股息率原则，在优势股种（沪深300或者中证500）中选择较优质的股票入手；\n# TODO 与止损、止盈策略联动\n\"\"\"\ndef init(context):\n # 二八轮动相关\n context.hold_type = \"CSI300.INDX\"\n context.candicate_type = \"CSI300.INDX\"\n context.time_span = 20 # 20天\n context.hs_stock_list = index_components(\"沪深300\")\n context.zz_stock_list = index_components(\"中证500\")\n context.gz_stock_list = [\"000012.XSHG\"]\n # context.gz_stock_list = index_components(\"国债指数\")\n # 股息率相关\n context.dividend_yield_min = 4 # 最小股息率%\n context.inc_operating_revenue_min = 4 # 营收增长率%\n context.candicate_num = 4\n # 止损相关\n context.stop_period = 20 # 持股天数\n context.stop_return = 0.04 # 回报率\n context.time = pd.DataFrame()\n context.drawdown = 0.04 # 最大回撤\n context.maxvalue = pd.DataFrame()\n \n # 交易候选股票\n context.candicate_stocks = []\n \n # 更新股票池\n update_universe(context.hs_stock_list+context.zz_stock_list)\n \n # rebalance\n scheduler.run_daily(rebalance)\n scheduler.run_daily(stop)\n \ndef record(context, stock, record_type='buy'):\n \"\"\"\n 持股记录：\n 1、记录股票购买时间；\n 2、记录股票最大股价；\n \"\"\"\n if record_type == 'buy':\n # 记录下单时间及股价最大值\n sotck_time = pd.DataFrame({str(stock): [context.now.replace(tzinfo=None)]})\n maxvalue = pd.DataFrame({str(stock): [context.portfolio.positions[stock].avg_price*context.portfolio.positions[stock].quantity]})\n logger.info('buy stock({}) time is {}, value is {}!'.format(stock, sotck_time, maxvalue))\n context.time = pd.concat([context.time, sotck_time], axis=1, join='inner')\n context.maxvalue = pd.concat([context.maxvalue, maxvalue], axis=1, join='inner')\n if record_type == 'sell':\n # 清除下单时间及股价最大值\n if stock in context.time and stock in context.maxvalue:\n logger.info('sell stock({}) time is {}, value is {}!'.format(stock, context.time[stock], context.maxvalue[stock]))\n del context.time[stock]\n del context.maxvalue[stock]\n \ndef stop(context, bar_dict):\n \"\"\"\n 止损策略：\n 1、在一定的时间（x天）内，受益没有达到指定值（y%），止损；\n 2、最大回撤达到指定值（z%），止损；\n \"\"\"\n # 止损策略1：时间是有价值的\n stop_by_time(context, bar_dict)\n # 止损策略2：最大回撤\n stop_by_drawdown(context, bar_dict)\n \ndef stop_by_time(context, bar_dict):\n for stock in context.portfolio.positions:\n if stock not in context.time.columns or context.portfolio.positions[stock].quantity == 0:\n logger.error('stock({}) not recorded!'.format(stock))\n continue\n buy_time = context.time[stock][0]\n curr_time = context.now.replace(tzinfo=None)\n \n logger.info('stock({}) buy time is: {}, curr_time is {}!'.format(stock, buy_time, curr_time))\n \n # 持有天数\n position_days = (curr_time - buy_time).days\n # 总体回报率\n toatl_return = (context.portfolio.positions[stock].market_value*context.portfolio.positions[stock].quantity)/(context.portfolio.positions[stock].market_value*context.portfolio.positions[stock].quantity-context.portfolio.positions[stock].pnl)\n \n if position_days > context.stop_period and context.portfolio.positions[stock].pnl < context.stop_return:\n logger.warn('stock({}) position days({}), total return({}) less than ({}), sell it!'.format(stock, position_days, toatl_return, context.stop_return))\n order_obj = order_target_percent(stock, 0)\n if order_obj and order_obj.status != ORDER_STATUS.REJECTED:\n record(context, stock, 'sell')\n logger.info('stock({}) position days({}), toatl return({})!'.format(stock, position_days, toatl_return))\n \ndef stop_by_drawdown(context, bar_dict):\n for stock in context.portfolio.positions:\n if stock not in context.maxvalue.columns:\n logger.error('stock({}) not recorded!'.format(stock))\n continue\n \n max_value = context.maxvalue[stock][0]\n curr_value = context.portfolio.positions[stock].market_value\n \n if curr_value > max_value:\n logger.info('stock({}) current value({}) more than max value({})!'.format(stock, curr_value, max_value))\n del context.maxvalue[stock]\n context.maxvalue = pd.concat([context.maxvalue, pd.DataFrame({str(stock): [curr_value]})], axis=1, join='inner')\n else:\n drawdown = (max_value-curr_value)/max_value\n if drawdown < context.drawdown:\n logger.info('stock({}) currnet value({}) less than max value({}) and drawdown is ({})!'.format(stock, curr_value, max_value, drawdown))\n else:\n logger.warn('stock({}) current value({}) less than max value({}) but drawdown is ({})!'.format(stock, curr_value, max_value, drawdown))\n order_obj = order_target_percent(stock, 0)\n if order_obj and order_obj.status != ORDER_STATUS.REJECTED:\n record(context, stock, 'sell')\n logger.info('stock({}) total income is: {}!'.format(stock, curr_value - context.portfolio.positions[stock].avg_price*context.portfolio.positions[stock].quantity))\n \ndef rebalance(context, bar_dict):\n holding_stocks = set(get_holding_stocks(context))\n candicate_stocks = set(context.candicate_stocks)\n \n to_sell = holding_stocks - candicate_stocks\n logger.info('stocks to sell are {}!'.format(to_sell))\n for stock in to_sell:\n order_obj = order_target_percent(stock, 0)\n if order_obj and order_obj.status != ORDER_STATUS.REJECTED:\n record(context, stock, 'sell')\n \n to_buy = candicate_stocks - holding_stocks\n logger.info('stocks to buy are {}!'.format(to_buy))\n if to_buy:\n buy_value = float(context.portfolio.cash*0.9)/len(to_buy)\n for stock in to_buy:\n order_obj = order_value(stock, buy_value)\n if order_obj and order_obj.status != ORDER_STATUS.REJECTED:\n record(context, stock, 'buy')\n \ndef get_holding_stocks(context):\n positions = context.portfolio.positions\n holding_stocks = []\n for stock in positions:\n if context.portfolio.positions[stock].quantity > 0:\n holding_stocks.append(stock)\n return holding_stocks\n \ndef before_trading(context):\n # 二八轮动判断候选股类型\n judge_2_8(context)\n\ndef judge_dividend_yield(context, candicate_stock_list):\n # 股息率原则判断\n fundamental_df = get_fundamentals(\n query(\n fundamentals.eod_derivative_indicator.dividend_yield,\n fundamentals.financial_indicator.inc_operating_revenue,\n fundamentals.eod_derivative_indicator.market_cap\n ).filter(\n fundamentals.financial_indicator.inc_operating_revenue > context.inc_operating_revenue_min\n ).filter(\n fundamentals.eod_derivative_indicator.dividend_yield > context.dividend_yield_min\n ).filter(\n fundamentals.income_statement.stockcode.in_(candicate_stock_list)\n ).order_by(\n fundamentals.eod_derivative_indicator.dividend_yield.desc()\n ).limit(\n context.candicate_num\n )\n )\n # logger.info('fundamental_df is {}!'.format(fundamental_df))\n \n # 当年没有股息率（验证了一下不会出现，不考虑）\n # fundamental_df_nan = get_fundamentals(\n # query(\n # fundamentals.eod_derivative_indicator.dividend_yield,\n # fundamentals.financial_indicator.inc_operating_revenue,\n # fundamentals.eod_derivative_indicator.market_cap\n # ).filter(\n # fundamentals.financial_indicator.inc_operating_revenue > context.inc_operating_revenue_min\n # ).filter(\n # fundamentals.eod_derivative_indicator.dividend_yield == np.NAN\n # ).filter(\n # fundamentals.income_statement.stockcode.in_(candicate_stock_list)\n # )\n # )\n # logger.info('fundamental_df_nan is {}!'.format(fundamental_df_nan))\n \n context.candicate_stocks = list(fundamental_df.columns.values)\n if 0 == len(fundamental_df.columns.values):\n # start_ind = randint(0, len(context.gz_stock_list)) - context.candicate_num\n # context.candicate_stocks = list(context.gz_stock_list[start_ind:start_ind+context.candicate_num])\n context.candicate_stocks = context.gz_stock_list\n logger.info('judge by dividend yield, candicated stocks are: {}!'.format(context.candicate_stocks))\n \n\ndef operate_2_8(context, bar_dict):\n # 二八轮动操作\n if context.candicate_type == \"CSI300.INDX\":\n context.hold_type = context.candicate_type\n return judge_dividend_yield(context, context.hs_stock_list)\n elif context.candicate_type == \"CSI500.INDX\":\n context.hold_type = context.candicate_type\n return judge_dividend_yield(context, context.zz_stock_list)\n else:\n # context.candicate_stocks = context.gz_stock_list\n return judge_dividend_yield(context, context.gz_stock_list)\n\n\ndef handle_bar(context, bar_dict):\n # 二八轮动\n operate_2_8(context, bar_dict)\n\ndef judge_2_8(context):\n # 二八轮动判断\n hs300 = history_bars(\"CSI300.INDX\", context.time_span, \"1d\", \"close\")\n zz500 = history_bars(\"CSI500.INDX\", context.time_span, \"1d\", \"close\")\n hsIncrease = hs300[19] - hs300[0]\n zzIncrease = zz500[19] - zz500[0]\n if hsIncrease < 0 and zzIncrease < 0:\n logger.warn('hsIncrease({}) and zzIncrease({}) all less than 0!'.format(hsIncrease, zzIncrease))\n context.candicate_type = \"000012.XSHG\"\n elif hsIncrease < zzIncrease:\n logger.warn('hsIncrease({}) less than zzIncrease({})!'.format(hsIncrease, zzIncrease))\n context.candicate_type = \"CSI500.INDX\"\n else:\n logger.warn('hsIncrease({}) more than zzIncrease({})!'.format(hsIncrease, zzIncrease))\n context.candicate_type = \"CSI300.INDX\"\n\ndef after_trading(context):\n pass\n \n","sub_path":"strategy_1.py","file_name":"strategy_1.py","file_ext":"py","file_size_in_byte":10988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"475355166","text":"# Import the csv module and use it read in \"nfl.csv\" into a list named nfl.\n# #Count the number of times the New England Patriots won.\n# Use an integer variable, patriots_wins, to keep track of the number of wins the New England Patriots had.\n# Iterate over the list nfl and increment patriots_wins by 1 each time the string \"New England Patriots\" is found in the third column (the winner column).\n\ndef read_csv(Input):\n f = open(Input)\n raw = f.read()\n data = raw.split('\\n')\n nfl = []\n for element in data:\n splitter = element.split(',')\n nfl.append(splitter)\n return nfl\n\n\nnfl = read_csv(\n r\"C:\\Users\\Ollie Page\\Dropbox (Personal)\\PROJECTS\\4. LANGUAGES\\2. PYTHON\\dataQuest\\dataAnalyst\\DataQuest_DataAnalyst\\nfl.csv\")\n\nNEP_WIN = {}\nfor element in nfl:\n if 'New England Patriots' in element[2]:\n if \"WINS\" in NEP_WIN:\n NEP_WIN[\"WINS\"] += 1\n else:\n NEP_WIN[\"WINS\"] = 1\n if 'New England Patriots' in element[3]:\n if \"LOSSES\" in NEP_WIN:\n NEP_WIN[\"LOSSES\"] += 1\n else:\n NEP_WIN[\"LOSSES\"] = 1\n\nNEP_WIN # {'WINS': 61, 'LOSSES': 19}\n\n\n# At the time of writing I don't know how to order the output of a dictionary. Quick google says sometihng to do with\n# lamdas but I don't want to go down that rabbit hole yet\n\n# 7. Making a Function that Counts Wins\n# Let's write a function that counts the wins for any NFL team. Recall that we define a function in the following format:\n\ndef nfl_score(team):\n score = {}\n for element in nfl:\n if team in element[2]:\n if team + \" Won\" in score:\n score[team + \" Won\"] += 1\n else:\n score[team + \" Won\"] = 1\n if team in element[3]:\n if team + \" Lost\" in score:\n score[team + \" Lost\"] += 1\n else:\n score[team + \" Lost\"] = 1\n return score\n\n\nnew_england_patriots = nfl_score(\n 'New England Patriots') # {'New England Patriots Won': 61, 'New England Patriots Lost': 19}\ncowboys = nfl_score(\"Dallas Cowboys\")\nfalcons = nfl_score(\"Atlanta Falcons\")\nprint(new_england_patriots, cowboys, falcons)\n\n\n# below same process, but produces integers rather than dict\n\ndef nfl_wins(team):\n score = 0\n for x in nfl:\n if team in x[2]:\n score += 1\n return score\n\n\ncowboys_wins = nfl_wins(\"Dallas Cowboys\") # 41\nfalcons_wins = nfl_wins(\"Atlanta Falcons\") # 49\n\n#6. Grabbing Column Data\nclass Dataset:\n def __init__(self, data):\n self.header = data[0]\n self.data = data[1:]\n def column(self,data, label): # Name the dataset (data) and what column name you want to grab (label)\n index = 0 # index will eventually equal whatever the index is of the label that's inputted\n label_list = [] # this will contain the data from the label's column\n if label in data[0]: # if the label is in the header row, then continue (else return None [Row 94])\n for idx, value in enumerate(data[0]): # shows me the indexs for the values in the data\n if value == label:\n index = idx # if the value is equal to label, then assign that number to\n # the value index (which is outside the loop)\n for x in data:\n label_list.append(x[index]) # append the list of values that contains the index of the\n # label identified earlier\n return label_list[1:] # return everything but the header\n else:\n return None\n\nnfl_dataset = Dataset(nfl_data)\n\nyear_column = nfl_dataset.column(nfl_data, 'year')\nplayer_column = nfl_dataset.column(nfl_data, 'player')\n\n# 7. Count Unique Method\nclass Dataset:\n def __init__(self, data):\n self.header = data[0]\n self.data = data[1:]\n\n def column(self, label): # dataquest's method of the above code\n if label not in self.header:\n return None\n\n index = 0\n for idx, element in enumerate(self.header):\n if label == element:\n index = idx\n\n column = []\n for row in self.data:\n column.append(row[index])\n return column\n\n def count_unique(self, label):\n unique_count = set(self.column(label))\n return len(unique_count)\n # Add your count unique method here\n\n\nnfl_dataset = Dataset(nfl_data)\ntotal_years = nfl_dataset.count_unique('year')\n\n\n# 8. Make Objects Human Readable One\n# special method is __str__() which tells the python interpreter how to represent\n# your object as a string. Whenever we try to convert the object into a string or when we want to print out that\n# object, we can use __str__() method to customize the way it looks when we display the object using the print()\n# function.\n\nclass Dataset:\n def __init__(self, data):\n self.header = data[0]\n self.data = data[1:]\n\n def __str__(self):\n return str(self.data[:10]) # returns the 10 elements of the dataset when calling the class, without the user needing to input anything\n\n def column(self, label):\n if label not in self.header:\n return None\n\n index = 0\n for idx, element in enumerate(self.header):\n if label == element:\n index = idx\n\n column = []\n for row in self.data:\n column.append(row[index])\n return column\n\n def count_unique(self, label):\n unique_results = set(self.column(label))\n count = len(unique_results)\n return count\n\n\nnfl_dataset = Dataset(nfl_data)\nprint(nfl_dataset)\n\n","sub_path":"NFL.py","file_name":"NFL.py","file_ext":"py","file_size_in_byte":5777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"481045480","text":"\nfrom PyQt5.QtGui import QPixmapCache, QPixmap, QImage, QPainter\nfrom PyQt5.QtSvg import QSvgRenderer\nfrom PyQt5.QtCore import QRect, QRectF, Qt\n\n\nclass SpriteContainer():\n\n def __init__(self, spriteconfig, scale=4):\n print(\"Constructing new SpriteContainer\")\n #self.setCacheLimit(2200 * (5 + len(spriteconfig)))\n #print(\"cacheLimit: \" + str(self.cacheLimit()))\n self._scale = scale\n self._scale_limit = 512\n self._renderer = {}\n self._pixmaps = {}\n self._placeholder = QPixmap(\n [\"2 2 2 1\", \"a c #ffffff\", \"b c #000000\", \"ab\", \"ba\"])\n self._init_renderer(spriteconfig)\n self._render_sprites()\n print(\"finished\")\n\n def _init_renderer(self, spriteconfig):\n self._renderer = {x[0]: QSvgRenderer(\n x[1]) for x in spriteconfig.items()}\n self._renderer[\"$mrk\"] = QSvgRenderer(\"sprites/common/mark.svg\")\n self._renderer[\"$p1b\"] = QSvgRenderer(\"sprites/common/p_1b.svg\")\n self._renderer[\"$p2s\"] = QSvgRenderer(\"sprites/common/p_2s.svg\")\n self._renderer[\"$p2c\"] = QSvgRenderer(\"sprites/common/p_2c.svg\")\n self._renderer[\"$p4e\"] = QSvgRenderer(\"sprites/common/p_1e.svg\")\n\n\n def _render_sprites(self):\n rect = QRectF(0, 0, self._scale, self._scale)\n for item in self._renderer.items():\n image = QImage(self._scale, self._scale, QImage.Format_ARGB32)\n image.fill(Qt.transparent)\n item[1].render(QPainter(image), rect)\n self._pixmaps[item[0]] = QPixmap.fromImage(image)\n\n # Probably inefficient use of QPainter, but should not be relevant.\n def update_sprites(self):\n self._render_sprites()\n # rect = QRectF(0, 0, self._scale, self._scale)\n # for item in self._keys.items():\n # image = QImage(self._scale, self._scale, QImage.Format_ARGB32)\n # image.fill(Qt.transparent)\n # self._renderer[item[0]].render(QPainter(image), rect)\n # self.replace(item[1], QPixmap.fromImage(image))\n\n def find(self, key):\n return self._pixmaps.get(key, self._placeholder)\n\n def _check_scale(self):\n if self._scale <= 1:\n self._scale = 1\n elif self._scale > self._scale_limit:\n self._scale = self._scale_limit\n\n def scale(self, ratio):\n self._scale *= ratio\n self._check_scale()\n self._rect = QRect(0, 0, self._scale, self._scale)\n print(\"Scale: \" + str(self._scale))\n self.update_sprites()\n\n def get_scale(self):\n return self._scale\n\n # def get_keys(self):\n # return self._keys\n\n def set_scale(self, scale):\n self._scale = scale\n self._check_scale()\n self._rect = QRect(0, 0, scale, scale)\n print(\"Scale: \" + str(self._scale))\n self.update_sprites()\n","sub_path":"visualizer/spritecontainer.py","file_name":"spritecontainer.py","file_ext":"py","file_size_in_byte":2842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"652673588","text":"import json\n\nfrom flask import Blueprint, jsonify, g, Response\nfrom flask_restful import Api, Resource, request, abort\nfrom flask_marshmallow import Marshmallow\nfrom marshmallow import Schema, fields \nfrom playhouse.shortcuts import dict_to_model, model_to_dict\nfrom webargs import fields\nfrom webargs.flaskparser import use_args\n\nfrom peewee import JOIN, fn\n\nimport models\n\nfrom auth import auth\n\nimport validators\n\n\nposts_api = Blueprint('resources.posts', __name__)\napi = Api(posts_api)\n\ndef is_valid(data):\n # validates new user data\n if ('title' in data and 'content' in data and\n 'is_url' in data and 'author' in data and\n 'tags' in data):\n\n if (not data['title'] or not data['content'] or not data['author'].isalnum() or\n len(data['title']) > 300):\n return False\n\n if data['is_url']:\n if not validators.url(data['content'].strip()):\n return False\n \n return True\n \n else: \n return False\n\ndef insert_tags(tags, post_id):\n\n for i in tags:\n if len(i[\"name\"]) > 45:\n abort(400, message=\"Missing or invalid fields.\")\n\n if not models.Tag.select().where(models.Tag.name == i[\"name\"].lower().strip()).exists():\n\n tag = models.Tag.create_tag(i[\"name\"].lower().strip())\n #models.PostTags.create_relationship(post_id, tag.id)\n models.PostTags.insert(post_id=post_id, tag_id=tag.id).execute()\n\n else:\n\n tag = models.Tag.get(models.Tag.name == i[\"name\"].lower().strip())\n #models.PostTags.create_relationship(post_id, tag.id)\n models.PostTags.insert(post_id=post_id, tag_id=tag.id).execute()\n \n\n\nclass PostList(Resource):\n\n def get(self):\n try:\n query = models.Post.select().order_by(models.Post.id)\n post_schema = models.PostSchema(many=True, exclude=('author.password', 'author.email', 'author.is_moderator', 'author.member_since'))\n #only=('id', 'content', 'title', 'author.name', 'author.id', 'is_url', 'created_at', 'last_modified')\n output = post_schema.dump(query).data\n\n return jsonify({'posts': output})\n except:\n abort(500, message=\"Oh, no! The Community is in turmoil!\")\n\n @auth.login_required\n def post(self):\n print('it got here 1')\n if(request.is_json):\n print('it got here 2')\n data = request.get_json(force=True)\n\n if is_valid(data):\n\n title = data['title'].strip()\n is_url = data['is_url']\n name = data['author'].strip()\n content = data['content'].strip()\n tags = data['tags'] \n\n author = models.User.get(models.User.name == name)\n print(g.user)\n print(author)\n print(name) \n \n if g.user != author:\n print(\"user is different\")\n abort(401)\n\n print(\"user is NOT different\")\n\n query = models.Post.select().where(models.Post.title == title, models.Post.content == content,\n models.Post.author == author.id)\n\n if query.exists():\n print('duplicate')\n abort(400, message=\"Duplicate entry.\")\n\n else:\n print('log 2')\n post_id = models.Post.insert(\n title=title, is_url=is_url, author=author, content=content).execute()\n print('log 3')\n print(\"*post id =\", post_id)\n insert_tags(tags, post_id)\n print('log 4')\n\n postid = int(post_id)\n query = models.Post.get(models.Post.id == postid)\n post_schema = models.PostSchema(only=('id', 'content', 'title', 'author.name', \n 'author.id', 'is_url', 'created_at', \n 'last_modified'))\n\n print('log 6')\n output = post_schema.dump(query).data\n print('log 7')\n return jsonify({'post': output})\n else:\n abort(400, message=\"Missing or invalid fields.\")\n else:\n abort(400, message='Not JSON data')\n\n\nclass Post(Resource):\n def get(self, id):\n try:\n \n query = models.Post.get(models.Post.id == id)\n post_schema = models.PostSchema(only=('id', 'content', 'title', 'author.name', 'author.id', 'is_url', \n 'created_at', 'last_modified'))\n \n post = post_schema.dump(query).data\n\n print(post)\n\n query = (models.Tag.select(models.Tag).\n join(models.PostTags, JOIN.RIGHT_OUTER).\n where(models.PostTags.post == id)) \n\n tag_schema = models.TagSchema(many=True)\n tags = tag_schema.dump(query).data\n\n post['tags'] = tags\n\n return jsonify({'post': post})\n\n except models.DoesNotExist:\n abort(404, message=\"Record does not exist.\")\n\n @auth.login_required\n def put(self, id):\n if(request.is_json):\n \n data = request.get_json(force=True)\n\n try: \n post = models.Post.select().where(models.Post.id == id).get()\n \n except:\n abort(404, message=\"Post doesn't exist\")\n \n if g.user != post.author:\n # unauthorized\n abort(401)\n \n if ('title' in data and 'content' in data and 'is_url' in data):\n\n title = data['title'].strip()\n content = data['content'].strip()\n is_url = data['is_url']\n\n query = models.Post.update(title=title, content=content, is_url=is_url).where(models.Post.id == id)\n query.execute()\n\n query_2 = models.Post.get(models.Post.id == id)\n\n post_schema = models.PostSchema(only=('id', 'content', 'title', \n 'author.name', 'author.id', 'is_url', \n 'created_at', 'last_modified'))\n \n post = post_schema.dump(query_2).data\n\n return jsonify({'post': post})\n else:\n abort(400, message=\"Missing or invalid fields.\")\n\n else:\n abort(400, message='Not JSON data')\n\n @auth.login_required\n def delete(self, id):\n try: \n post = models.Post.select().where(models.Post.id == id).get()\n \n except:\n abort(404, message=\"Post doesn't exist\")\n \n if g.user != post.author:\n print(\"user is not post author\")\n abort(401)\n\n try:\n models.PostVotes.delete().where(models.PostVotes.post == id).execute() \n models.PostTags.delete().where(models.PostTags.post == id).execute() \n models.Comment.delete().where(models.Comment.post == id).execute() \n models.Post.delete().where(models.Post.id == id).execute()\n except:\n abort(500, message=\"Oh, no! The Community is in turmoil!\")\n \n return Response(status=204, mimetype='application/json')\n\nclass PostTags(Resource):\n def get(self, id):\n\n try:\n query = (models.Tag.select(models.Tag).\n join(models.PostTags, JOIN.RIGHT_OUTER).\n where(models.PostTags.post == id)) \n \n except: \n abort(404, message=\"Record does not exist.\")\n \n tag_schema = models.TagSchema(many=True)\n output = tag_schema.dump(query).data\n return jsonify({'tags': output})\n \nclass PostComments(Resource):\n def get(self, id):\n\n try:\n query = (models.Comment.select(models.Comment).where(models.Comment.post == id)) \n \n except: \n abort(404, message=\"Record does not exist.\")\n \n comment_schema = models.CommentSchema(many=True, only=('id', 'content', 'author.id',\n 'author.name', 'created_at', 'last_modified', 'parent_id'))\n output = comment_schema.dump(query).data\n return jsonify({'comments': output})\n\nclass PostVotes(Resource):\n\n def get(self, id):\n \n try:\n \n query = models.PostVotes.select().where(models.PostVotes.post_id == id)\n except:\n abort(404, message=\"Record does not exist.\")\n\n try:\n \n schema = (models.PostVotesSchema(many=True,\n only=('post_id', 'value', 'voter.name', 'voter.id')))\n\n output = schema.dump(query).data\n\n summation = 0\n for i in output:\n summation += i['value']\n\n return jsonify({'votes': output, 'total': summation})\n except:\n abort(500, message=\"Oh, no! The Community is in turmoil!\")\n\n \n\n @auth.login_required\n def post(self, id):\n if(request.is_json):\n \n data = request.get_json(force=True)\n try:\n print('log 1')\n value = data['value']\n voter = data['voter']\n user = models.User.get(models.User.name == voter)\n\n if not (value >= -1 and value <= 1):\n abort(400, message=\"Missing or invalid fields.\")\n\n print('log 2')\n except:\n print('log 3')\n abort(400, message=\"Missing or invalid fields.\")\n\n print('log 4')\n \n if g.user != user:\n abort(401)\n \n query = models.PostVotes.select().where((models.PostVotes.post == id) & (models.PostVotes.voter == user.id))\n print('log 5')\n\n if query.exists():\n models.PostVotes.update(value=value).where((models.PostVotes.post == id) & (models.PostVotes.voter == user.id)).execute()\n print('update')\n Response(status=200, mimetype='application/json')\n \n else:\n models.PostVotes.insert(post=id, voter=user.id, value=value).execute() \n print('new')\n Response(status=200, mimetype='application/json')\n\n\n else:\n abort(400, message='Not JSON data')\n\n return Response(status=200, mimetype='application/json')\n\nclass PostsByTag(Resource):\n def get(self, name):\n\n try:\n query = (models.Post.select(models.Post).\n join(models.PostTags)\n .join(models.Tag)\n .where(models.Tag.name == name)) \n \n except: \n abort(404, message=\"Record does not exist.\")\n \n schema = models.PostSchema(many=True, exclude=('author.password', 'author.email', 'author.is_moderator', 'author.member_since'))\n output = schema.dump(query).data\n return jsonify({'posts': output})\n\n \napi.add_resource(PostList, '/posts', endpoint='posts')\napi.add_resource(Post, '/posts/', endpoint='post')\napi.add_resource(PostTags, '/posts//tags', endpoint='post_tags')\napi.add_resource(PostComments, '/posts//comments', endpoint='post_comments')\napi.add_resource(PostVotes, '/posts//votes', endpoint='post_votes')\napi.add_resource(PostsByTag, '/posts/tag/', endpoint='posts_by_tag')\n","sub_path":"API/resources/posts.py","file_name":"posts.py","file_ext":"py","file_size_in_byte":11791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"307876490","text":"\"\"\"\nPreprocessing.\n\"\"\"\n\nimport numpy as np\nfrom transformers import PreTrainedTokenizerFast\n\n\ndef preprocess(texts, tokenizer_path, max_len=32):\n\n input_ids, input_masks = [], []\n\n tokenizer = PreTrainedTokenizerFast(tokenizer_file=tokenizer_path)\n tokenizer.mask_token = '[MASK]'\n tokenizer.pad_token = \"[PAD]\"\n tokenizer.sep_token = \"[SEP]\"\n tokenizer.cls_token = \"[CLS]\"\n tokenizer.unk_token = \"[UNK]\"\n\n for text in texts:\n encoded = tokenizer.encode_plus(text, max_length=max_len,\n pad_to_max_length=True, truncation=True)\n input_ids.append(encoded['input_ids'])\n input_masks.append(encoded['attention_mask'])\n\n return [np.array(input_ids), np.array(input_masks)]\n","sub_path":"submit/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"447301213","text":"# -*- coding: utf-8 -*-\n\"\"\"\nManage the symmetries of the board.\nNB: modifications invoked by 'game'.\n\"\"\"\n\nimport game\n\n\n# Global variables\ndigit = None\ncode = [None, None]\ndef init():\n \"\"\"(Re)init data-structures for a new game.\"\"\"\n global digit, code\n digit = [0 for p in range(len(game.board))]\n code = [0,0]\n # A FAIRE: init de digit !\n for i in range(game.height):\n for j in range(game.width):\n digit[game.pos(i,j)] = 3**(game.pos(i,j) - (game.xwidth + 2*i + 1))\n\ndef move(p):\n \"\"\"Update data-structures when current_player marks 'p'.\"\"\"\n global code\n i, j = game.coord(p)\n code[0] += (1+game.current_player)*digit[p]\n code[1] += (1+game.current_player)*digit[game.pos(i,game.width - 1 - j)]\n\n\ndef undo(p):\n \"\"\"Undo the last move which was 'p'.\"\"\"\n global code\n i, j = game.coord(p)\n code[0] -= (1+game.current_player)*digit[p]\n code[1] -= (1+game.current_player)*digit[game.pos(i,game.width - 1 - j)]\n\ndef get_code():\n \"\"\"Return a unique code identifying the current board (modulo symmetry).\"\"\"\n return min(code)\n","sub_path":"symmetry.py","file_name":"symmetry.py","file_ext":"py","file_size_in_byte":1090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"290535137","text":"import os\nfrom pathlib import Path\n\nfrom flask import Flask, flash, jsonify, redirect, render_template, request\nfrom werkzeug.utils import secure_filename\n\nfrom lp_prediction_pytorch.lp_prediction import get_prediction\nfrom pl_detection import pl_detection\n\nfrom HTR.src import extractLetter\n\n\n# from .pl_detection.pl_detection import pl_detection\n\n# import io\nfrom PIL import Image\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nUPLOAD_FOLDER = Path(__file__).resolve().parent / \"static\" / \"uploads\"\nOUTPUT_FOLDER = Path(__file__).resolve().parent / \"static\" / \"outputs\"\nALLOWED_EXTENSIONS = {\"png\", \"jpg\", \"jpeg\"}\n\napp = Flask(__name__)\napp.config.from_mapping(\n SECRET_KEY=\"dev\",\n DATABASE=os.path.join(app.instance_path, \"ocrapi.sqlite\"),\n UPLOAD_FOLDER=UPLOAD_FOLDER,\n OUTPUT_FOLDER=OUTPUT_FOLDER,\n)\n\nfilename_logo = \"TheApp.jpg\"\nrel_path_logo = Path(\"..\") / \"static\" / filename_logo\n\n@app.route(\"/gimme_your_plate\", methods=[\"GET\", \"POST\"])\ndef plate():\n if request.method == \"POST\":\n if \"file\" not in request.files:\n flash(\"No file part\")\n return redirect(request.url)\n file = request.files[\"file\"]\n if file.filename == \"\":\n flash(\"No selected file\")\n return redirect(request.url)\n if file:\n filename = secure_filename(file.filename)\n original_path = app.config[\"UPLOAD_FOLDER\"] / filename\n original_rel_path = Path(\"..\") / \"static\" / \"uploads\" / filename\n file.save(original_path)\n filename = \"extracted_plate.jpg\"\n plate_path = os.path.join(os.path.dirname(original_path), \"..\", \"outputs\", filename)\n if os.path.isfile(plate_path):\n os.remove(plate_path)\n image_array = pl_detection.main(original_path)\n im = Image.fromarray((image_array * 255).astype(np.uint8))\n print(plate_path)\n im.save(plate_path)\n input_img = open(plate_path, \"rb\")\n img_bytes = input_img.read()\n response = get_prediction(image_bytes=img_bytes)\n plate_rel_path = Path(\"..\") / \"static\" / \"outputs\" / filename\n return render_template(\n \"results_plate_detection.html\",\n logo=rel_path_logo,\n original_img=original_rel_path,\n plate_img=plate_rel_path,\n response=response,\n )\n return render_template(\"license_plate.html\",\n logo=rel_path_logo)\n\n@app.route(\"/gimme_your_letter\", methods=[\"GET\", \"POST\"])\ndef letter():\n\n if request.method == \"POST\":\n if \"file\" not in request.files:\n flash(\"No file part\")\n return redirect(request.url)\n file = request.files[\"file\"]\n if file.filename == \"\":\n flash(\"No selected file\")\n return redirect(request.url)\n if file:\n filename = secure_filename(file.filename)\n original_path = app.config[\"UPLOAD_FOLDER\"] / filename\n original_rel_path = Path(\"..\") / \"..\" / \"static\" / \"uploads\" / filename\n file.save(original_path)\n print(original_path)\n response = extractLetter.main(str(original_path))\n\n # je suis pas très sûre de ce à quoi ça sert ça\n # plate_rel_path = Path(\"..\") / \"static\" / \"outputs\" / filename\n return render_template(\n \"results_handwritten.html\",\n logo=rel_path_logo,\n original_img=original_rel_path,\n response=response,\n )\n return render_template(\"handwritten.html\",\n logo=rel_path_logo)\n\n #return render_template(\"handwritten.html\")\n\n@app.route(\"/\")\ndef home():\n filename_logo = \"TheApp.jpg\"\n rel_path_logo = Path(\"..\") / \"static\" / filename_logo\n return render_template(\"home.html\", logo=rel_path_logo)\n\n@app.route(\"/welcome\")\ndef welcome():\n return \"Welcome!\"\n\n@app.route(\"/recognize_text_pytorch\", methods=[\"POST\"])\ndef recognize_text_pytorch():\n if request.method == \"POST\":\n file = request.files[\"file\"]\n img_bytes = file.read()\n content = get_prediction(image_bytes=img_bytes)\n return jsonify({\"prediction\": content})\n\nif __name__ == '__main__':\n app.run(port=5131)\n","sub_path":"ocrapi/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"327318219","text":"import re\nimport sys\nimport urllib\nimport hashlib\nfrom urllib import request\n\nprint('Begint met het downloaden van:\\n')\nhash_db = []\nfor line in open(sys.argv[1], \"r\"):\n if not re.search(sys.argv[2], line):\n o = urllib.parse.urlparse(line.rstrip())\n\n if o.path.split('/')[-1]:\n try:\n with request.urlopen(o.geturl()) as r:\n data = r.read()\n\n print(o.geturl())\n\n hash = hashlib.sha1(data).hexdigest()\n\n if hash not in hash_db:\n with open(o.path.split('/')[-1], \"wb\") as f:\n f.write(data)\n f.close()\n hash_db.append(hash)\n else:\n print('Poah! Deze is dubbel!')\n except urllib.error.HTTPError as e:\n continue\n\nprint('Klaar!')\n","sub_path":"10/propaganda/helft/download.py","file_name":"download.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"360868988","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon May 22 13:05:24 2017\r\n\r\n@author: poaa\r\n\"\"\"\r\n\r\n\r\nimport urllib\r\nimport time\r\n \r\narchivoDescargar = \"http://www.altamirainmuebles.com/xml/sareb01_obranueva.xml\"\r\narchivoGuardar = \"C:\\\\Users\\\\poaa\\\\Documents\\\\Python Scripts\\\\sareb01_obranueva.xml\"\r\n \r\nnow = time.time()\r\n\r\ntry: \r\n descarga=urllib.request.urlopen(archivoDescargar)\r\n#descarga = urllib.request(archivoDescargar)\r\n\r\n ficheroGuardar=open(archivoGuardar,'wb')\r\n ficheroGuardar.write(descarga.read())\r\n ficheroGuardar.close()\r\n \r\n elapsed = time.time() - now\r\n \r\n print (\"Descargado el archivo: %s en %0.3fs\" % (archivoDescargar,elapsed))\r\n\r\nexcept HTTPError as e:\r\n print ('Error HTTP:', e.code, archivoDescargar)","sub_path":"Descarga_url.py","file_name":"Descarga_url.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"503532261","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.linux-x86_64/egg/jenkviz/build.py\n# Compiled at: 2012-01-23 16:05:52\nfrom datetime import timedelta\nfrom util import time_to_datetime\nfrom util import duration_to_second\nfrom util import str2id\n\nclass Build(object):\n \"\"\" Container for activity information\"\"\"\n\n def __init__(self, url, host, name, build_number, start, duration, status, downstream, base_url, trigger):\n self.url = url\n self.host = host\n self.name = name\n self.build_number = build_number\n self.start = start\n self.duration = duration\n self.status = status\n self.downstream = downstream\n self.children = []\n self.base_url = base_url\n self.trigger = trigger\n self.start_t = time_to_datetime(start)\n self.duration_s = duration_to_second(duration)\n self.stop_t = self.start_t + timedelta(seconds=self.duration_s)\n\n def getId(self):\n return str2id('%s %s' % (self.name, self.build_number))\n\n def color(self):\n if self.status == 'Success':\n return 'blue'\n if self.status == 'Failure':\n return 'red'\n if self.status == 'Unstable':\n return 'gold'\n return 'black'\n\n def full_url(self):\n return self.base_url + self.url\n\n def __repr__(self):\n return 'URL: \"%s\"\\n\\tname: %s\\n\\tbuild #: %s\\n\\thost: %s\\n\\tstart: %s\\n\\tstop: %s\\n\\tduration: %s\\n\\tstatus: %s\\n\\tdownstream build: %d\\n' % (\n self.url, self.name, self.build_number, self.host, self.start, self.stop_t, self.duration, self.status,\n len(self.downstream))","sub_path":"pycfiles/jenkviz-0.3.2-py2.7/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":1754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"48093077","text":"a=input(\"primeiro ítem: \")\nb=input(\"segundo ítem: \")\nc=input(\"terceiro ítem: \")\nlist=[a, b, c]\nprint (list)\ndef sum(list):\n\tif ( a.isdigit() and b.isdigit() and c.isdigit() ):\n\t\treturn int(a) + int(b) + int(c)\n\telse:\n\t\treturn a + b + c\nprint (\"soma: \", sum(list))\n","sub_path":"solucoes/Natalia/02_ex13.py","file_name":"02_ex13.py","file_ext":"py","file_size_in_byte":267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"410465359","text":"class node:\n def __init__(self,info):\n self.info = info\n self.next = None\nclass linkedlist():\n def __init__(self):\n self.root = None\n def create(self, val):\n current = self.root\n if current is None:\n self.root = node(val)\n else:\n while current.next:\n current = current.next\n current.next = node(val)\ndef printe(root):\n current = root\n while current:\n print(str(current.info),end = \" \")\n current = current.next\nlis = [5, 3, 2, 7, 6]\nl = linkedlist()\nfor i in lis:\n l.create(i)\nprinte(l.root)\n\n\n","sub_path":"Linked-list/Linked-lists.py","file_name":"Linked-lists.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"337009803","text":"\"\"\"\n\tTo run python simpledjango.py runserver\n\"\"\"\n\nimport sys\nfrom django.conf import settings\nfrom django.conf.urls import url\nfrom django.core.management import execute_from_command_line\nfrom django.http import HttpResponse\n\n\nsettings.configure(\n\tDEBUG=True,\n\tSECRET_KEY='zwqrtttt123bbbrf',\n\tROOT_URLCONF=sys.modules[__name__]\n)\n\n\ndef index(request):\n\treturn HttpResponse('

A simple Django

')\n\n\nurlpatterns = [\n\turl(r'^$', index)\n]\n\n\nif __name__ == '__main__':\n\texecute_from_command_line(sys.argv)\n\n\n","sub_path":"djgotchas/simpledjango.py","file_name":"simpledjango.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"114767054","text":"# -*- coding: utf-8 -*-\n\n# Define your item pipelines here\n#\nimport time\n\n\nclass DoubanMoviePipeline(object):\n def __init__(self):\n self.file = open('movie_id.log', 'a+')\n self.file2 = open('movie_id_insert.log', 'a+')\n self.insert_count = 0\n self.crawl_count = 0\n\n def process_item(self, item):\n \"\"\"\n item 为 Item 的对象\n \"\"\"\n movie_id = item.movie_id\n title = item.title\n cover = item.cover\n director = item.director\n scriptwriter = item.scriptwriter\n starring = item.starring\n movie_type = item.movie_type\n region = item.region\n language = item.language\n release_date = item.release_date\n running_time = item.running_time\n alternate_name = item.alternate_name\n imdb = item.imdb\n rating = item.rating\n rating_people = item.rating_people\n\n if self.file:\n # self.file.write('{} : {}\\n'.format(time.ctime(), book_id))\n self.file.write('{}\\n'.format(movie_id))\n\n self.crawl_count += 1\n print(time.ctime() + ': 已爬取 {} 条电影数据'.format(self.crawl_count))\n print(time.ctime() + ' : 目前一共收录 {} 条电影数据'.format(self.insert_count))\n print('{} : Crawled....{}'.format(time.ctime(), movie_id))\n self.insert_count += 1\n\n print('=+' * 20)\n print(movie_id)\n print(title)\n print(cover)\n print(director)\n print(scriptwriter)\n print(starring)\n print(movie_type)\n print(region)\n print(language)\n print(release_date)\n print(running_time)\n print(alternate_name)\n print(imdb)\n print(rating)\n print(rating_people)\n print(\"=\" * 40)\n # print(cover,author,translator, publisher, subtitle,title,\n # original_title, publish_date, pages,\n # price, isbn, rating, rating_people, book_id)\n # print('=' * 40)\n\n return item\n\n def get_current_datetime(self):\n now = int(time.time())\n timeArray = time.localtime(now)\n return time.strftime(\"%Y-%m-%d %H:%M:%S\", timeArray)\n","sub_path":"pipelinestest.py","file_name":"pipelinestest.py","file_ext":"py","file_size_in_byte":2175,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"1921886","text":"from flask import redirect, request, session, url_for\nfrom flask_jsonpify import jsonify\n\nfrom .. import preprocessors, responses\n\n\ndef configure(bp, config, oauth):\n\n @bp.route(\"/auth/\")\n def auth_index():\n info = \"Welcome to the auth module. You can /auth/initiate an \" + \\\n \"oauth handshake. You can ask /auth/whoami? If you are \" + \\\n \"already logged in, you can /auth/logout.\"\n\n return jsonify({'info': info,\n 'paths': ['initiate', 'whoami', 'logout']})\n\n @bp.route(\"/auth/initiate/\")\n def auth_initiate():\n \"\"\"\n Performs an OAuth handshake.\n \"\"\"\n # Doesn't work yet\n # oauth_callback = config['wsgi']['application_root'] + \\\n # \"/auth/callback/\"\n wiki = None\n if 'wiki' in request.args:\n wiki = request.args['wiki']\n wiki.strip()\n auth_url, rt = oauth.initiate()\n session['request_token'] = rt\n session['callback_wiki'] = wiki\n\n # return HTML to redirect user to mediawiki-login\n return redirect(auth_url)\n\n @bp.route(\"/auth/whoami/\")\n def whoami():\n \"\"\"Returns user information if authenticated\"\"\"\n if 'user' in session:\n return jsonify({'user': session['user']})\n else:\n return responses.forbidden()\n\n @bp.route(\"/auth/callback/\")\n def auth_callback():\n \"\"\"\n Completes the oauth handshake\n \"\"\"\n if 'request_token' not in session:\n return responses.forbidden(\"OAuth callback failed. \" +\n \"Are cookies disabled?\")\n else:\n access_token = oauth.complete(session['request_token'],\n str(request.query_string, 'ascii'))\n\n # Get user info\n identity = oauth.identify(access_token)\n\n # Store user info in session\n session['user'] = {'id': identity['sub']}\n\n if 'callback_wiki' not in session or not session['callback_wiki']:\n url = url_for('.ui', _scheme=config['wsgi']['scheme'],\n _external=True)\n else:\n url = url_for('.ui_wiki', wiki=session['callback_wiki'],\n _scheme=config['wsgi']['scheme'], _external=True)\n\n return redirect(url)\n\n @bp.route(\"/auth/logout/\")\n @preprocessors.authenticated\n def logout():\n \"\"\"\n Deletes the local session.\n \"\"\"\n if 'user' in session:\n del session['user']\n\n return jsonify({'success': True})\n\n return bp\n","sub_path":"wikilabels/wsgi/routes/auth.py","file_name":"auth.py","file_ext":"py","file_size_in_byte":2607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"237681350","text":"##########################################################################\n# MediPy - Copyright (C) Universite de Strasbourg\n# Distributed under the terms of the CeCILL-B license, as published by\n# the CEA-CNRS-INRIA. Refer to the LICENSE file or to\n# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html\n# for details.\n##########################################################################\n\n\"\"\" Conversions between different forms of 3D rotations\n\n * (axis, angle) where axis is normalized and angle measured in radians\n * rotation matrix, 3x3, orthogonal\n * normalized quaternion, (a, b, c, d) = a+b*i+c*j+d*k\n\"\"\"\n\nimport math\n\nimport numpy\n\ndef axis_angle_to_matrix(axis, angle) :\n r\"\"\" Convert an (axis, angle) to a rotation matrix.\n \n This formula comes from Rodrigues' rotation formula,\n :math:`R = I + \\hat{\\omega} \\sin \\theta + \\hat{\\omega}^2 (1-\\cos \\theta)`\n where :math:`\\hat{}` gives the antisymmetric matrix equivalent of the cross product\n \n .. math ::\n \n \\hat{\\omega} = \\begin{matrix}\n 0 & -\\omega_z & \\omega_y \\\\\n \\omega_z & 0 & -\\omega_x \\\\\n -\\omega_y & \\omega_x & 0 \\\\\n \\end{matrix} \n \n Diagonal terms can be rewritten :\n \n .. math ::\n \n \\begin{matrix}\n 1+(1-\\cos \\theta)*(\\omega_x^2-1) & = & 1+(1-\\cos \\theta)*\\omega_x^2-(1-\\cos \\theta) \\\\\n & = & \\cos \\theta+\\omega_x^2*(1-\\cos \\theta)\n \\end{matrix}\n \"\"\"\n \n result = numpy.ndarray((3,3))\n \n cos = math.cos(angle)\n sin = math.sin(angle)\n one_minus_cos = 1.-cos\n \n result[0][0] = cos+axis[0]**2*(one_minus_cos)\n result[1][1] = cos+axis[1]**2*(one_minus_cos)\n result[2][2] = cos+axis[2]**2*(one_minus_cos)\n \n result[0][1] = -axis[2]*sin+axis[0]*axis[1]*one_minus_cos\n result[1][0] = +axis[2]*sin+axis[0]*axis[1]*one_minus_cos\n \n result[0][2] = +axis[1]*sin+axis[0]*axis[2]*one_minus_cos\n result[2][0] = -axis[1]*sin+axis[0]*axis[2]*one_minus_cos\n \n result[1][2] = -axis[0]*sin+axis[1]*axis[2]*one_minus_cos\n result[2][1] = +axis[0]*sin+axis[1]*axis[2]*one_minus_cos\n \n return result\n\ndef matrix_to_quaternion(matrix):\n \"\"\" Convert a rotation matrix to a unit quaternion.\n \n cf. http://arxiv.org/abs/math/0701759 , p.4\n \"\"\"\n\n d0 = matrix[0,0]\n d1 = matrix[1,1]\n d2 = matrix[2,2]\n\n # Diagonal terms of the matrix yield 4*q_r^2, 4*q_i^2, 4*q_j^2, 4*q_k^2\n q_r_squared_4 = 1.0 + d0 + d1 + d2\n q_i_squared_4 = 1.0 + d0 - d1 - d2\n q_j_squared_4 = 1.0 - d0 + d1 - d2\n q_k_squared_4 = 1.0 - d0 - d1 + d2\n \n # Since we are going to divide by one of (q_r^2, q_i^2, q_j^2, q_k^2),\n # choose the largest one to avoid numerical errors\n max_value = max((q_r_squared_4, q_i_squared_4, \n q_j_squared_4, q_k_squared_4))\n \n if max_value == q_r_squared_4 :\n # Use q_r^2 to get other terms\n q_r = 0.5*math.sqrt(q_r_squared_4)\n factor = 2.*math.sqrt(q_r_squared_4) # i.e. 4*q_r\n \n q_i = (matrix[2,1]-matrix[1,2])/factor\n q_j = (matrix[0,2]-matrix[2,0])/factor\n q_k = (matrix[1,0]-matrix[0,1])/factor\n elif max_value == q_i_squared_4 :\n # Use q_i^2 to get other terms\n q_i = 0.5*math.sqrt(q_i_squared_4)\n factor = 2.*math.sqrt(q_i_squared_4) # i.e. 4*q_i\n \n q_r = (matrix[2,1]-matrix[1,2])/factor\n q_j = (matrix[1,0]+matrix[0,1])/factor\n q_k = (matrix[2,0]+matrix[0,2])/factor\n elif max_value == q_j_squared_4 :\n # Use q_j^2 to get other terms\n q_j = 0.5*math.sqrt(q_j_squared_4)\n factor = 2.*math.sqrt(q_j_squared_4) # i.e. 4*q_j\n \n q_r = (matrix[0,2]-matrix[2,0])/factor\n q_i = (matrix[1,0]+matrix[0,1])/factor\n q_k = (matrix[2,1]+matrix[1,2])/factor\n else : # max_value == q_j_squared_4\n # Use q_k^2 to get other terms\n q_k = 0.5*math.sqrt(q_k_squared_4)\n factor = 2.*math.sqrt(q_k_squared_4) # i.e. 4*q_k\n \n q_r = (matrix[1,0]-matrix[0,1])/factor\n q_i = (matrix[2,0]+matrix[0,2])/factor\n q_j = (matrix[2,1]+matrix[1,2])/factor\n \n return (q_r, q_i, q_j, q_k)\n\ndef axis_angle_to_quaternion(axis, angle):\n \"\"\" Convert an (axis, angle) to a unit quaternion.\n \"\"\"\n \n result = numpy.asarray((math.cos(angle/2.), 0., 0., 0.))\n result[1:] = numpy.multiply(axis, math.sin(angle/2.))\n return result\n\ndef quaternion_to_axis_angle(quaternion):\n \"\"\" Convert a unit quaternion to an (axis, angle).\n \"\"\"\n angle = 2.*math.acos(quaternion[0])\n axis = numpy.divide(quaternion[1:], math.sin(angle/2.))\n \n return axis, angle\n\ndef quaternion_to_matrix(quaternion) :\n r\"\"\" Combination of quaternion_to_axis_angle and axis_angle_to_matrix\n The following equalities are used to obtain this :\n \n .. math ::\n \n \\theta = 2 acos(q_r) \\Leftrightarrow q_r = \\cos(\\theta/2)\n \n .. math ::\n \n \\begin{matrix}\n \\cos(\\theta) & = & 2*\\cos^2(\\theta/2)-1 \\\\\n & = & 2*q_r^2-1\n \\end{matrix}\n \n For the diagonal terms, we have :\n \n .. math ::\n \\begin{matrix}\n \\cos(\\theta)+\\omega_x^2*(1-\\cos(\\theta)) \n & = & \\cos(\\theta)+q_i^2/\\sin^2(\\theta/2)*(1-\\cos(\\theta)) \\\\\n & = & \\cos(\\theta)+q_i^2/\\sin^2(\\theta/2)*(2*\\sin^2(\\theta/2)) \\\\\n & = & \\cos(\\theta)+2*q_i^2 \\\\\n & = & 2*q_r^2-1+2*q_i^2 (q is unit-length, so 1=q_r^2+q_i^2+q_j^2+q_k^2) \\\\\n & = & q_r^2+q_i^2-(q_r^2+q_i^2+q_j^2+q_k^2-q_r-q_i) \\\\\n & = & q_r^2+q_i^2-q_j^2-q_k^2\n \\end{matrix}\n \n For the off-diagonal terms, we have :\n \n .. math ::\n \n \\begin{matrix}\n \\omega_z*\\sin \\theta & = & q_k/\\sin \\frac{\\theta}{2}*\\sin \\theta \\\\\n & = & q_k/\\sin \\frac{\\theta}{2}*2*sin\\frac{theta}{2}*\\cos \\frac{theta}{2} \\\\\n & = & 2*q_k*\\cos \\frac{theta}{2} \\\\\n & = & 2*q_k*q_r\n \\end{matrix}\n \n and :\n \n .. math ::\n \n \\begin{matrix}\n \\omega_x*\\omega_y*(1-\\cos \\theta) & = & q_i*q_j/\\sin^2 \\frac{\\theta}{2}*(1-\\cos \\theta) \\\\\n & = & q_i*q_j/\\sin^2 \\frac{\\theta}{2}*(2*\\sin^2 \\frac{\\theta}{2}) \\\\\n & = & 2*q_i*q_j\n \n Hence :\n \n .. math ::\n \n \\omega_z*\\sin \\theta+\\omega_x*\\omega_y*(1-\\cos theta) = 2*(q_i*q_j+q_r*q_k)\n \"\"\"\n matrix = numpy.ndarray((3,3))\n matrix[0][0] = quaternion[0]**2+quaternion[1]**2-quaternion[2]**2-quaternion[3]**2\n matrix[1][1] = quaternion[0]**2-quaternion[1]**2+quaternion[2]**2-quaternion[3]**2\n matrix[2][2] = quaternion[0]**2-quaternion[1]**2-quaternion[2]**2+quaternion[3]**2\n matrix[0][1] = 2*(quaternion[1]*quaternion[2]-quaternion[0]*quaternion[3])\n matrix[0][2] = 2*(quaternion[3]*quaternion[1]+quaternion[0]*quaternion[2])\n matrix[1][2] = 2*(quaternion[2]*quaternion[3]-quaternion[0]*quaternion[1])\n matrix[1][0] = 2*(quaternion[1]*quaternion[2]+quaternion[0]*quaternion[3])\n matrix[2][0] = 2*(quaternion[3]*quaternion[1]-quaternion[0]*quaternion[2])\n matrix[2][1] = 2*(quaternion[2]*quaternion[3]+quaternion[0]*quaternion[1])\n \n return matrix\n","sub_path":"lib/medipy/base/rotation.py","file_name":"rotation.py","file_ext":"py","file_size_in_byte":7860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"12252992","text":"import pandas as pd\nimport numpy as np\nimport os\nimport json\nimport sys\nimport csv\n\n\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nif BASE_DIR not in sys.path:\n sys.path.append(BASE_DIR)\nimport env\n\nfrom django.conf import settings\n\ndef get_green_restaurant_data():\n green_restaurant = []\n df = pd.read_excel(os.path.join(settings.DATA_ROOT, \"map_data.xlsx\"), sheet_name=\"A綠餐廳蔬食\")\n header = ['店名', '地址(A-格式不限)', '經度', '緯度', '電話', '開放時間(統一格式)',\n '照片0__(fb目前頭貼)', '照片1','照片2', '照片3', '照片4', '照片5', '照片6',\n '照片(推薦頁)','近期更新時間']\n\n filter_header_data = df[header]\n data = filter_header_data.replace(np.nan, '', regex=True)\n\n img_ch_list = ['照片0__(fb目前頭貼)', '照片1', '照片2', '照片3', '照片4', '照片5', '照片6', '照片(推薦頁)']\n for i, row in enumerate(data.values):\n\n data_index = data.loc[i]\n data_dict= data_index.to_dict()\n data_dict['name'] = data_dict.pop('店名')\n data_dict['address'] = data_dict.pop('地址(A-格式不限)')\n data_dict['lat'] = data_dict.pop('緯度')\n data_dict['lon'] = data_dict.pop('經度')\n data_dict['tel'] = data_dict.pop('電話')\n data_dict['bussiness_time'] = data_dict.pop('開放時間(統一格式)')\n data_dict['updtime'] = data_dict.pop('近期更新時間')\n data_dict['updtime'] = str(data_dict['updtime'])[:6]\n data_dict['id'] = i+1\n img_list = []\n data_dict['imgs'] = img_list\n for key, value in data_dict.items():\n if key in img_ch_list:\n if value != \"\":\n img_list.append(value)\n\n del data_dict['照片0__(fb目前頭貼)'], data_dict['照片1'], data_dict['照片2'], data_dict['照片3'], data_dict['照片4'], data_dict['照片5'], data_dict['照片6'], data_dict['照片(推薦頁)']\n green_restaurant.append(data_dict)\n if data_dict['lon'] == \"\" or data_dict['lat'] == \"\":\n del green_restaurant[i]\n\n with open(os.path.join(settings.DATA_ROOT, 'green_restaurant.json'), 'w') as f:\n json.dump(green_restaurant, f)\n\n # return json.dumps(green_restaurant)\n\n# get_green_restaurant_data()\n\ndef write_to_green_restaurant_csv(green_restaurant):\n with open('green_restaurant.csv', 'w', newline='') as csvFile:\n # 定義欄位\n fieldNames = ['id', 'name', 'address', 'lat', 'lon', 'tel', 'bussiness_time', 'updtime', 'imgs']\n\n # 將 dictionary 寫入 CSV 檔\n writer = csv.DictWriter(csvFile, fieldNames)\n\n # 寫入第一列的欄位名稱\n writer.writeheader()\n\n # 寫入資料\n for green in green_restaurant:\n writer.writerow(green)\n\n# input_green_restaurant = get_green_restaurant_data()\n\n# write_to_green_restaurant_csv(input_green_restaurant)\n\n\ndef get_reed_and_river_data():\n df = pd.read_excel(os.path.join(settings.DATA_ROOT, \"product_reed_river_new.xlsx\"))\n header = [\"id\",\t\"catalogNumber\", \"recordedBy\", \"eventDate\", \"locality\", \"decimalLatitude\",\n \"decimalLongitude\",\t\"identifiedBy\",\t\"scientificName\", \"family\", \"vernacularName\", \"產地照片\",\n \"產地標本照片\",\t\"空拍照片\", \"測站名稱\", \"測站編號\", \"經度\", \"緯度\", \"河川汙染指數\", \"測站圖片URL\", \"測站RUL\"]\n\n filter_reed_datas = df.loc[df['vernacularName'].isin(['臺灣蘆竹', '蘆竹', '蘆葦', '臺灣蘆葦', '開卡蘆'])]\n fetched_reed_datas = filter_reed_datas[header]\n\n data = fetched_reed_datas.replace(np.nan, '', regex=True)\n img_data_reed_list = [\"產地照片\", \"產地標本照片\", \"空拍照片\"]\n img_data_river_list = [\"測站圖片URL\"]\n\n reed_river_list = []\n for index, row in data.iterrows():\n data_index = data.loc[index]\n data_dict = data_index.to_dict()\n data_dict['id'] = data_dict.pop('id')\n data_dict['name'] = data_dict.pop('vernacularName')\n data_dict['lon'] = data_dict.pop('decimalLongitude')\n data_dict['lat'] = data_dict.pop('decimalLatitude')\n\n river_dict= {}\n river_dict['name'] = data_dict.pop('測站名稱')\n river_dict['station_id'] = data_dict.pop('測站編號')\n river_dict['lon'] = data_dict.pop('經度')\n river_dict['lat'] = data_dict.pop('緯度')\n river_dict['pollution_index'] = data_dict.pop('河川汙染指數')\n river_dict['station_url'] = data_dict.pop('測站RUL')\n\n data_dict['river'] = river_dict\n\n img_reed_list = []\n img_river_list = []\n data_dict['imgs'] = img_reed_list\n river_dict['imgs'] = img_river_list\n for key, value in data_dict.items():\n if key in img_data_reed_list:\n if value != \"\":\n if key == \"空拍照片\" or key == '測站圖片URL':\n img = os.path.join(settings.DATA_URL, 'reed_shot', value)\n else:\n img = value\n img_reed_list.append(img)\n\n if key in img_data_river_list:\n if value != \"\":\n if key == \"空拍照片\" or key == '測站圖片URL':\n img = os.path.join(settings.DATA_URL, 'reed_shot', value)\n else:\n img = value\n img_river_list.append(img)\n\n del data_dict['產地照片'], data_dict['產地標本照片'], data_dict['空拍照片'], data_dict[\"測站圖片URL\"], data_dict['catalogNumber'], data_dict['eventDate']\n\n if (data_dict['river']['lon'] == '' or data_dict['river']['lon'] == ''):\n continue\n reed_river_list.append(data_dict)\n with open(os.path.join(settings.DATA_ROOT, 'reed_river_all.json'), 'w') as f:\n json.dump(reed_river_list, f)\n\n# get_reed_and_river_data()\n\n# def get_reed_datas():\n# df = pd.read_excel(os.path.join(settings.DATA_ROOT, \"plants.xlsx\"))\n# fetched_reed_datas = df.loc[df['vernacularName'].isin(['臺灣蘆竹', '蘆竹', '蘆葦', '臺灣蘆葦', '開卡蘆'])]\n# header = ['id', 'decimalLongitude','decimalLatitude', 'vernacularName']\n# filter_header_data = fetched_reed_datas[header]\n\n# reed_list = []\n# for index, row in filter_header_data.iterrows():\n# data_index = filter_header_data.loc[index]\n# data_dict = data_index.to_dict()\n# data_dict['name'] = data_dict.pop('vernacularName')\n# data_dict['lat'] = data_dict.pop('decimalLatitude')\n# data_dict['lon'] = data_dict.pop('decimalLongitude')\n# if (data_dict['lon'] == 120.0502778 or data_dict['lat'] == 24.75027778) or (data_dict['lon'] == \"\" or data_dict['lat'] == \"\"):\n# continue\n\n# data_dict['img'] = get_reed_shot_img(data_dict['id'])\n# reed_list.append(data_dict)\n\n# with open(os.path.join(settings.DATA_ROOT, 'reed_data.json'), 'w') as f:\n# json.dump(reed_list, f)\n# # return json.dumps(reed_list)\n\ndef get_reed_shot_img(reed_id):\n img = ''\n folder = os.path.join(settings.DATA_ROOT, 'reed_shot')\n if os.path.isdir(folder):\n file_name = reed_id + '.jpg'\n if os.path.isfile(os.path.join(folder, file_name)):\n img = os.path.join(settings.DATA_URL, 'reed_shot', file_name)\n\n return img\n\n\ndef write_to_reed_csv(reed_list):\n with open('reed_data.csv', 'w', newline='') as csvFile:\n # 定義欄位\n fieldNames = ['name', 'lat', 'lon']\n\n # 將 dictionary 寫入 CSV 檔\n writer = csv.DictWriter(csvFile, fieldNames)\n\n # 寫入第一列的欄位名稱\n writer.writeheader()\n\n # 寫入資料\n for rd in reed_list:\n writer.writerow(rd)\n\n# reed_list = get_reed_datas()\n\n# write_to_reed_csv(reed_list)\n\ndef get_solitary_bee_hotel():\n file_name = 'solitary_bee_hotel.geojson'\n file_datas = {}\n with open(os.path.join(settings.DATA_ROOT, file_name)) as f:\n file_datas = json.loads(f.read())\n\n datas = []\n for data in file_datas[\"features\"]:\n _data = {\n 'lat': data[\"geometry\"][\"coordinates\"][1],\n 'lon': data[\"geometry\"][\"coordinates\"][0],\n 'name': data[\"properties\"][\"organization_school\"],\n 'id': data[\"properties\"][\"cartodb_id\"]\n }\n datas.append(_data)\n\n return datas\n\n\ndef get_water_quality_data():\n df = pd.read_excel(os.path.join(settings.DATA_ROOT, \"water_quality.xlsx\"))\n data = df.replace(np.nan, '', regex=True)\n water_qc_data = []\n\n for index, row in data.iterrows():\n data_index = data.loc[index]\n data_dict = data_index.to_dict()\n data_dict['river'] = data_dict.pop('河流')\n data_dict['station'] = data_dict.pop('測站名稱')\n data_dict['river_pollution_index'] = data_dict.pop('河川污染指數')\n data_dict['lon'] = data_dict.pop('經度')\n data_dict['lat'] = data_dict.pop('緯度')\n data_dict['station_img'] = data_dict.pop('測站照片')\n data_dict['station_url'] = data_dict.pop('測站網址')\n\n water_qc_data.append(data_dict)\n if data_dict['river'] == '':\n data_dict.update({\n 'river': water_qc_data[index-1]['river']\n })\n\n reconstruct_water_quality = []\n for water in water_qc_data:\n if water['lon'] == \"\" or water['lat'] == \"\":\n continue\n reconstruct_water_quality.append(water)\n\n return reconstruct_water_quality\n\n# get_water_quality_data()","sub_path":"rd/fetch_data/get_data.py","file_name":"get_data.py","file_ext":"py","file_size_in_byte":9597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"621167363","text":"# pylint: disable=C0114,C0115\nclass BucketFullException(Exception):\n def __init__(self, identity, rate, remaining_time):\n error = f\"Bucket for {identity} with Rate {rate} is already full\"\n self.meta_info = {\n \"error\": error,\n \"identity\": identity,\n \"rate\": str(rate),\n \"remaining_time\": remaining_time,\n }\n super().__init__(error)\n\n\nclass InvalidParams(Exception):\n def __init__(self, param_name: str):\n self.message = f\"Parameters missing or invalid:{param_name}\"\n super().__init__(self.message)\n\n\nclass ImmutableClassProperty(Exception):\n def __init__(self, class_instance, prop: str):\n \"\"\"Mutating class property is forbidden\"\"\"\n self.message = f\"{class_instance}.{prop} must not be mutated\"\n super().__init__(self.message)\n","sub_path":"pyrate_limiter/exceptions.py","file_name":"exceptions.py","file_ext":"py","file_size_in_byte":843,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"127083952","text":"from main import Dice\nfrom main import Solution\n\n\ndef test_dice_move():\n # Init a Dice with top: 1, left: 4, front: 2\n d = Dice(1, 4, 2)\n\n d.move_down()\n # Dice move down\n assert d.state == (5, 4, 1)\n assert d.cost == d.bottom\n assert d.cost == 2\n\n # Dice move right\n d.move_right()\n assert d.state == (4, 2, 1)\n assert d.cost == 3 + 2\n\n # copied dice state and cost\n new_d = d.copy().move_right()\n assert new_d.state == (2, 3, 1)\n assert new_d.cost == 5 + new_d.bottom\n\n # origin Dice state should not change\n assert d.state == (4, 2, 1)\n\n\ndef test_dice_rotate():\n d = Dice(1,4,2)\n d.rotate_clockwise()\n assert d.state == (1, 2, 3)\n\n d.rotate_counterclockwise()\n assert d.state == (1, 4, 2)\n\n for _ in range(4):\n d.rotate_clockwise()\n assert d.state == (1, 4, 2)\n\n d.rotate_clockwise().rotate_clockwise()\n assert d.state == (1, 3, 5)\n\n\ndef test_rotate_dice_wisely():\n d = Dice(1, 4, 2)\n r = Solution().rotate_dice_wisely([0, 0], [8, 8], d)\n assert r[0], r[1] == (8, 8)\n assert r[2].state == (1, 4, 2)\n\n r = Solution().rotate_dice_wisely([8, 0], [0, 8], d)\n assert r[0], r[1] == (8, 8)\n assert r[2].state == (1, 2, 3)\n\n\ndef test_find_lowest_cost_MN():\n initial_dice = Dice(1, 4, 2)\n # if A, B in the same point, lowest cost is 0\n r1 = Solution().findLowestCostInMN(1, 1, initial_dice)\n assert r1 == 0\n\n # 1 * 3 area\n r2 = Solution().findLowestCostInMN(1, 3, initial_dice)\n # (1,4,2) -> +3 -> +1 = 4\n assert r2 == 4\n\n # 3 * 1 area\n r3 = Solution().findLowestCostInMN(3, 1, initial_dice)\n # (1,4,2) -> + 2 -> +1 = 3\n assert r3 == 3\n\n # 3 * 3 area\n r4 = Solution().findLowestCostInMN(3, 3, initial_dice)\n # (1,4,2) -> +3 + 1 + 2 +6 = 12\n # +3+2+1+4 = 10\n assert r4 == 10\n\n\ndef test_findABSteps():\n r = Solution().findABSteps([0, 0], [8, 8], Dice(1, 4, 2))\n assert r == 45\n\n r = Solution().findABSteps([3, 3], [0, 0], Dice(1, 4, 2))\n assert r == 12\n\n r = Solution().findABSteps([3, 3], [0, 0], Dice(1, 4, 2).rotate_clockwise().rotate_clockwise())\n assert r == 10\n\n\ndef test_findMinStepsForUnKnownState():\n r = Solution().findMinStepsForUnKnownState([0, 8], [8, 0])\n assert r == 45\n\n r2 = Solution().findMinStepsForUnKnownState([1, 1], [1, 1])\n assert r2 == 0\n\n\nif __name__ == '__main__':\n test_dice_move()\n test_dice_rotate()\n test_rotate_dice_wisely()\n test_find_lowest_cost_MN()\n test_findABSteps()\n test_findMinStepsForUnKnownState()\n print('All tests pass')\n","sub_path":"tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"234071559","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport numpy as np\nimport random\nimport time\nimport tensorflow as tf\nimport scipy.signal\nfrom keras.applications.resnet50 import preprocess_input\n\nfrom keras.models import Sequential, Model\nfrom keras.layers import Dense, BatchNormalization, Activation, Flatten, Input, merge, Lambda, Dropout\nfrom keras.layers.advanced_activations import LeakyReLU\nfrom keras.optimizers import Adam, RMSprop\nimport tensorflow as tf\nimport keras.backend as K\n\nfrom collections import deque\nimport copy\n\nfrom datetime import datetime\nimport gym\n\n# env = gym.make('Reacher-v1')\nenv = gym.make('Reacher-v2')\nenv.reset()\n\ndtype = tf.float32\n\n\ndef min_max(x, axis=None):\n min = x.min(axis=axis, keepdims=True)\n max = x.max(axis=axis, keepdims=True)\n result = (x - min) / (max - min)\n return result\n\n\ndef norm(x, a_min, a_max):\n result = np.clip((x - a_min) / (a_max - a_min), 0.0, 1.0)\n return result\n\n\ndef initialize(path, seed):\n random.seed(seed)\n np.random.seed(seed)\n print(\"mujoco seed: \", seed)\n file_path = path + \"/Initialize_seed.txt\"\n f = open(file_path, \"a\")\n f.write(\"Grid world Seed:\\n\" + str(seed) + \"\\n\")\n f.close()\n\n return seed\n\n\ndef seed_initialize(path, seed):\n print(\"utils seed\", seed)\n\n initialize(path, seed)\n\n random.seed(seed)\n np.random.seed(seed)\n tf.set_random_seed(seed)\n file_path = path + \"/Initialize_seed.txt\"\n f = open(file_path, \"a\")\n f.write(\"Seed:\\n\" + str(seed) + \"\\n\")\n f.close()\n\n return seed\n\n\ndef discount(x, gamma):\n assert x.ndim >= 1\n return scipy.signal.lfilter([1], [1, -gamma], x[::-1], axis=0)[::-1]\n\n\ndef gauss_prob_val(mu, logstd, x):\n std = np.exp(logstd)\n var = np.square(std)\n gp = np.exp(-np.square(x - mu) / (2 * var)) / ((2 * np.pi) ** .5 * std)\n return np.prod(gp, axis=1)\n\n\ndef gauss_prob(mu, logstd, x):\n std = tf.exp(logstd)\n var = tf.square(std)\n gp = tf.exp(-tf.square(x - mu) / (2 * var)) / ((2 * np.pi) ** .5 * std)\n return tf.reduce_prod(gp, [1])\n\n\ndef gauss_log_prob(mu, logstd, x):\n var = tf.exp(2 * logstd)\n gp = -tf.square(x - mu) / (2 * var) - .5 * tf.log(tf.constant(2 * np.pi)) - logstd\n return tf.reduce_sum(gp, [1])\n\n\ndef gauss_selfKL_firstfixed(mu, logstd):\n mu1, logstd1 = map(tf.stop_gradient, [mu, logstd])\n mu2, logstd2 = mu, logstd\n return gauss_KL(mu1, logstd1, mu2, logstd2)\n\n\ndef gauss_KL(mu1, logstd1, mu2, logstd2):\n var1 = tf.exp(2 * logstd1)\n var2 = tf.exp(2 * logstd2)\n kl = tf.reduce_sum(logstd2 - logstd1 + (var1 + tf.square(mu1 - mu2)) / (2 * var2) - 0.5)\n return kl\n\n\ndef gauss_ent(mu, logstd):\n h = tf.reduce_sum(logstd + tf.constant(0.5 * np.log(2 * np.pi * np.e), tf.float32))\n return h\n\n\ndef gauss_sample(mu, logstd):\n return mu + tf.exp(logstd) * tf.random_normal(tf.shape(logstd))\n\n\ndef var_shape(x):\n out = [k.value for k in x.get_shape()]\n assert all(isinstance(a, int) for a in out), \\\n \"shape function assumes that shape is fully known\"\n return out\n\n\ndef numel(x):\n return np.prod(var_shape(x))\n\n\ndef flatgrad(loss, var_list):\n grads = tf.gradients(loss, var_list)\n return tf.concat(0, [tf.reshape(grad, [numel(v)])\n for (v, grad) in zip(var_list, grads)])\n\n\ndef get_feat(imgs, feat_extractor):\n x = preprocess_input(imgs.astype(np.float32))\n x = feat_extractor.predict(x)\n return x\n\n\ndef look(x):\n return x\n\n\ndef delete(x, s_min, s_max):\n y = np.delete(x, [4, 5, 8, 9, 10])\n result = np.clip((y - s_min) / (s_max - s_min), 0.0, 1.0)\n return result\n\n\ndef norm_act(x, a_min, a_max):\n result = np.clip((x - a_min) / (a_max - a_min), 0.0, 1.0)\n return result\n\n\ndef rollout_contin(agent, state_dim, encode_dim, actions_dim, s_max, s_min, a_max, a_min,\n max_step_limit, min_step_limit, paths_per_collect, count_goalagent, epoch, encode_list=None, iter_num=None):\n \"\"\"\n Performs a rollout based on the TRPO agent's policy and returns the trajs and goals\n :param agent: TRPO agent object (See model_sgail)\n :param state_dim:\n :param encode_dim:\n :param actions_dim:\n :param s_max:\n :param s_min:\n :param a_max:\n :param a_min:\n :param max_step_limit:\n :param min_step_limit:\n :param paths_per_collect:\n :param count_goalagent:\n :param epoch:\n :param encode_list:\n :param iter_num:\n :return: Sampled trajs and number of reached goals\n \"\"\"\n paths = []\n timesteps_sofar = 0\n encode_axis = 0\n encode_axisold = 0\n conjugate_gradient = 0\n\n for p in range(paths_per_collect):\n states, encodes, actions, logstds = \\\n [], [], [], []\n policies = []\n state_1times = []\n acts = []\n goal_flag = 0\n length = 0\n\n state = np.ones((1, state_dim), dtype=np.float32)\n\n encode = np.zeros((1, encode_dim), dtype=np.float32)\n if encode_list == None:\n encode[0, encode_axis] = 1\n encode_axisold = encode_axis\n encode_axis = (encode_axis + 1) % encode_dim\n else:\n encode[0, encode_list[p]] = 1\n\n state = np.ones((1, state_dim), dtype=np.float32)\n obs = env.reset()\n state = delete(copy.copy(obs), s_min, s_max)\n\n for i in range(max_step_limit):\n\n states.append(state)\n length += 1\n\n logstd = np.array([[-3.0, -3.0]], dtype=np.float32)\n state_1times.append([state])\n encodes.append(encode)\n logstds.append(logstd)\n act, action, policy = agent.act([state], encode, logstd)\n acts.append(norm_act(action, a_min, a_max))\n actions.append(action)\n policies.append(policy)\n\n if i + 1 == max_step_limit or goal_flag == 1:\n\n #\n # GOAL or timeup\n #\n if goal_flag == 1:\n flag = 1\n else:\n flag = 0\n\n path = dict2(state=np.concatenate(state_1times),\n encodes=np.concatenate(encodes),\n actions=np.concatenate(actions),\n act=np.concatenate(acts),\n policies=np.concatenate(policies),\n length=length,\n logstds=np.concatenate(logstds),\n flag=flag\n )\n paths.append(path)\n break\n\n #\n # Transition state\n #\n obs, r, done, _ = env.step(action[0])\n state = delete(copy.copy(obs), s_min, s_max)\n\n if np.argmax(encode) == 0:\n if i + 1 >= min_step_limit and np.sum(abs(obs[-3:])) <= 0.018 and abs(action[0][0]) < 5e-4 \\\n and abs(action[0][0]) < 5e-4:\n goal_flag = 1\n count_goalagent += 1\n\n elif np.argmax(encode) == 1:\n if i + 1 >= min_step_limit and np.sum(abs(obs[-3:])) >= 0.40 and abs(action[0][0]) < 5e-4 \\\n and abs(action[0][0]) < 5e-4:\n goal_flag = 1\n count_goalagent += 1\n\n return paths, count_goalagent\n\n\n# TODO: Maybe delete, as unused\nclass LinearBaseline(object):\n coeffs = None\n\n def _features(self, path):\n o = path[\"states\"].astype('float32')\n o = o.reshape(o.shape[0], -1)\n l = len(path[\"rewards\"])\n al = np.arange(l).reshape(-1, 1) / 100.0\n return np.concatenate([o, o ** 2, al, al ** 2, np.ones((l, 1))], axis=1)\n\n def fit(self, paths):\n featmat = np.concatenate([self._features(path) for path in paths])\n returns = np.concatenate([path[\"returns\"] for path in paths])\n n_col = featmat.shape[1]\n lamb = 2.0\n self.coeffs = np.linalg.lstsq(\n featmat.T.dot(featmat) + lamb * np.identity(n_col),\n featmat.T.dot(returns))[0]\n\n def predict(self, path):\n return np.zeros(len(path[\"rewards\"])) if self.coeffs is None else \\\n self._features(path).dot(self.coeffs)\n\n\ndef pathlength(path):\n return len(path[\"actions\"])\n\n\ndef explained_variance(ypred, y):\n assert y.ndim == 1 and ypred.ndim == 1\n vary = np.var(y)\n return np.nan if vary == 0 else 1 - np.var(y - ypred) / vary\n\n\n# TODO: Maybe delete, as unused\nclass TimeDependentBaseline(object):\n def __init__(self):\n self.baseline = None\n\n def fit(self, paths):\n rets = [path[\"returns\"] for path in paths]\n maxlen = max(len(ret) for ret in rets)\n retsum = np.zeros(maxlen)\n retcount = np.zeros(maxlen)\n for ret in rets:\n retsum[:len(ret)] += ret\n retcount[:len(ret)] += 1\n retmean = retsum / retcount\n self.baseline = retmean\n pred = np.concatenate([self.predict(path) for path in paths])\n return {\"EV\": explained_variance(pred, np.concatenate(rets))}\n\n def predict(self, path):\n if self.baseline is None:\n return np.zeros(pathlength(path))\n else:\n lenpath = pathlength(path)\n lenbase = len(self.baseline)\n if lenpath > lenbase:\n return np.concatenate([self.baseline, self.baseline[-1] +\n np.zeros(lenpath - lenbase)])\n else:\n return self.baseline[:lenpath]\n\n\nclass NNBaseline(object):\n def __init__(self, sess, state, encodes, state_dim, encode_dim, lr_baseline,\n b_iter, batch_size, dir_path):\n \"\"\"\n A NN baseline based on expert demos for training generator\n :param sess: tf var\n :param state: tf structure with same dimension as state space\n :param encodes:\n :param state_dim:\n :param encode_dim:\n :param lr_baseline: Linear baseline\n :param b_iter:\n :param batch_size:\n :param dir_path: Where new models should be stored\n \"\"\"\n print(\"Now we build baseline\")\n self.model = self.create_net(state, encodes, state_dim, encode_dim, lr_baseline)\n self.sess = sess\n self.b_iter = b_iter\n self.batch_size = batch_size\n self.first_time = True\n self.mixfrac = 0.1\n file_path = dir_path + \"/Readme.txt\"\n f_read = open(file_path, \"a\")\n f_read.write(\"Baseline alpha:\\n\" + str(self.mixfrac) + \"\\n\")\n f_read.close()\n\n def create_net(self, state, encodes, state_dim, encode_dim, lr_baseline):\n \"\"\"\n Creates the baseline model\n :param state:\n :param encodes:\n :param state_dim:\n :param encode_dim:\n :param lr_baseline:\n :return:\n \"\"\"\n # TODO: pyTorch impl; Remove hardcoded dims\n K.set_learning_phase(1)\n\n states = Input(tensor=state)\n x = Dense(128)(states)\n x = LeakyReLU()(x)\n encodes = Input(tensor=encodes)\n e = Dense(128)(encodes)\n e = LeakyReLU()(e)\n h = merge([x, e], mode='sum')\n h = Dense(32)(h)\n h = LeakyReLU()(h)\n p = Dense(1)(h)\n\n model = Model(input=[state, encodes], output=p)\n adam = Adam(lr=lr_baseline)\n model.compile(loss='mse', optimizer=adam)\n return model\n\n def fit(self, paths, batch_size):\n \"\"\"\n Fit the baseline using expert trajs\n :param paths: Expert trajs\n :param batch_size:\n :return: Loss fun value\n \"\"\"\n state = np.concatenate([path[\"state\"] for path in paths])\n encodes = np.concatenate([path[\"encodes\"] for path in paths])\n returns = np.concatenate([path[\"returns\"] for path in paths])\n\n if self.first_time:\n self.first_time = False\n b_iter = 100\n else:\n returns_old = np.concatenate([self.predict(path) for path in paths])\n returns = returns * self.mixfrac + returns_old * (1 - self.mixfrac)\n b_iter = self.b_iter\n\n num_data = state.shape[0]\n idx = np.arange(num_data)\n np.random.shuffle(idx)\n train_val_ratio = 0.7\n num_train = int(num_data * train_val_ratio)\n state_train = state[idx][:num_train]\n encodes_train = encodes[idx][:num_train]\n returns_train = returns[idx][:num_train]\n\n state_val = state[idx][num_train:]\n encodes_val = encodes[idx][num_train:]\n returns_val = returns[idx][num_train:]\n\n # TODO: pyTorch\n start = 0\n for i in range(b_iter):\n loss = self.model.train_on_batch(\n [state_train[start:start + batch_size],\n encodes_train[start:start + batch_size]],\n returns_train[start:start + batch_size]\n )\n start += batch_size\n if start >= num_train:\n start = (start + batch_size) % num_train\n val_loss = np.average(np.square(self.model.predict(\n [state_val, encodes_val]).flatten() - returns_val))\n # print (\"Baseline loss:\", loss, \"val:\", val_loss)\n if i == b_iter - 1:\n b_loss = val_loss\n return b_loss\n\n def predict(self, path):\n \"\"\"\n Predict actions based on states\n :param path: States traj\n :return: Policy\n \"\"\"\n # TODO: pyTorch\n if self.first_time:\n return np.zeros(pathlength(path))\n else:\n ret = self.model.predict(\n [path[\"state\"], path[\"encodes\"]])\n return np.reshape(ret, (ret.shape[0],))\n\n\nclass GetFlat(object):\n def __init__(self, session, var_list):\n self.session = session\n self.op = tf.concat(0, [tf.reshape(v, [numel(v)]) for v in var_list])\n\n def __call__(self):\n return self.op.eval(session=self.session)\n\n\nclass SetFromFlat(object):\n def __init__(self, session, var_list):\n self.session = session\n assigns = []\n shapes = map(var_shape, var_list)\n total_size = sum(np.prod(shape) for shape in shapes)\n self.theta = theta = tf.placeholder(dtype, [total_size])\n start = 0\n assigns = []\n for (shape, v) in zip(shapes, var_list):\n size = np.prod(shape)\n assigns.append(\n tf.assign(v, tf.reshape(theta[start:start + size], shape))\n )\n start += size\n self.op = tf.group(*assigns)\n\n def __call__(self, theta):\n self.session.run(self.op, feed_dict={self.theta: theta})\n\n\ndef linesearch(f, x, fullstep, expected_improve_rate):\n accept_ratio = .1\n max_backtracks = 10\n fval = f(x)\n for (_n_backtracks, stepfrac) in enumerate(.5 ** np.arange(max_backtracks)):\n xnew = x + stepfrac * fullstep\n newfval = f(xnew)\n actual_improve = fval - newfval\n expected_improve = expected_improve_rate * stepfrac\n ratio = actual_improve / expected_improve\n if ratio > accept_ratio and actual_improve > 0:\n return xnew\n return x\n\n\ndef conjugate_gradient(f_Ax, b, cg_iters=10, residual_tol=1e-10):\n p = b.copy()\n r = b.copy()\n x = np.zeros_like(b)\n rdotr = r.dot(r)\n for i in range(cg_iters):\n z = f_Ax(p)\n v = rdotr / p.dot(z)\n x += v * p\n r -= v * z\n newrdotr = r.dot(r)\n mu = newrdotr / rdotr\n p = r + mu * p\n rdotr = newrdotr\n if rdotr < residual_tol:\n break\n return x\n\n\nclass dict2(dict):\n def __init__(self, **kwargs):\n dict.__init__(self, kwargs)\n self.__dict__ = self\n\n\nclass ReplayBuffer(object):\n\n def __init__(self, buffer_size):\n self.buffer_size = buffer_size\n self.num_paths = 0\n self.buffer = deque()\n\n def get_sample(self, sample_size):\n if self.num_paths < sample_size:\n return random.sample(self.buffer, self.num_paths)\n else:\n return random.sample(self.buffer, sample_size)\n\n def size(self):\n return self.buffer_size\n\n def add(self, path):\n if self.num_paths < self.buffer_size:\n self.buffer.append(path)\n self.num_paths += 1\n else:\n self.buffer.popleft()\n self.buffer.append(path)\n\n def count(self):\n return self.num_paths\n\n def erase(self):\n self.buffer = deque()\n self.num_paths = 0\n","sub_path":"s-gail_MuJoCo/MuJoCo/utils_pyTorch.py","file_name":"utils_pyTorch.py","file_ext":"py","file_size_in_byte":16397,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"281195674","text":"from flask import Flask, request, jsonify, render_template, Response\nimport datetime\nimport time as epochtime\nimport json\n\napp = Flask(__name__)\n\nlightStore = []\ntimeStore = []\n\n\n@as_json\ndef d(deviceId):\n json_content = json.dumps({deviceId: {'downlinkData': '0102030405060708'}})\n http_status = 200 \n custom_headers = {'Content-Type': 'application/json'}\n return json_content, http_status, custom_headers\n \n@app.route('/data/', methods=['POST'])\ndef add_message(sensor):\n\n content = request.json # grab the json data from the POST request\n time = int(content['time'])\n time = datetime.datetime.fromtimestamp(time).strftime('%Y-%m-%d %H:%M:%S') # convert epoch time to human readable time\n\n deviceId = content['device']\n\n\n ack = content['ack']\n\n h = format(int(epochtime.time()),'x')\n print(h)\n c = json.dumps({deviceId: {'downlinkData': '0102030405060708'}})\n\n return c, {'Content-Type': 'text/html'}\n\n\n # print(c)\n # if(content['data']=='53455455500000000000'): # SETUP in hex\n # print(c)\n # resp = Response(response=c,status=200,mimetype=\"application/json\")\n # print(resp)\n # return resp\n \n\n # get the latatuide from the sensor \n lat = str(content['lat'])\n\n # get the longatiude from the sensor \n lng = str(content['lng'])\n\n # get the sequnence number\n seqNumber = int(content['seqNumber'])\n line = str(content['data'])\n\n # if(len(line)==4):\n # \tlightStore.append(255)\n # \ttimeStore.append(0)\n # else:\n # \tn = 2\n # \tlineArray = [line[i:i+n] for i in range(0, len(line), n)]\n # \tfor i in range(0, 10, 2): \n # \t\tlightStore.append(str(int(lineArray[i],16)))\n # \t\ttimeStore.append(str(int(lineArray[i+1],16)))\n \n return ('', 200)\n\n\n@app.route('/')\ndef hello_world():\n return str(hex(epochtime.time()))\n\n@app.route('/graph') # the default REST method in flask is GET\ndef show_graph():\n return render_template('index.html', brightness=lightStore, times=timeStore)\n\nif __name__ == '__main__':\n app.run(host= '0.0.0.0',debug=True)","sub_path":"pythonweb.py","file_name":"pythonweb.py","file_ext":"py","file_size_in_byte":2085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"578431525","text":"__author__ = \"Nigshoxiz\"\nfrom flask import render_template, abort, request, redirect\nfrom jinja2 import TemplateNotFound\n\nfrom app.utils import returnModel\nimport os\nfrom . import server_inst_page, logger\nfrom app.blueprints.superadmin.check_login import check_login\nfrom app import db\nfrom app.model import ServerInstance\nrtn = returnModel(\"string\")\n\n# WARNING : This Code IS DEPRECATED!!!\n@server_inst_page.route(\"/console\", methods=[\"GET\"])\n@check_login\ndef render_console_page(uid, priv, inst_id = None):\n try:\n user_list = []\n user_insts_dict = {}\n user_insts = db.session.query(ServerInstance).filter(ServerInstance.owner_id == uid).all()\n\n if user_insts != None:\n if len(user_insts) > 0:\n current_inst_id = user_insts[0].inst_id\n current_inst_name = user_insts[0].inst_name\n star_flag = False\n for item in user_insts:\n _model = {\n \"inst_name\": item.inst_name,\n \"star\": item.star,\n \"inst_id\": item.inst_id,\n \"link\": \"/server_inst/console/\" + str(item.inst_id)\n }\n user_insts_dict[item.inst_id] = _model\n user_list.append(_model)\n # get starred instance\n if item.star == True and star_flag == True:\n current_inst_id = item.inst_id\n current_inst_name = item.inst_name\n star_flag = True\n\n # if inst_id is assigned (e.g. GET /dashboard/2)\n if inst_id != None:\n current_inst_id = inst_id\n current_inst_name = user_insts_dict[inst_id][\"inst_name\"]\n\n return render_template(\"server_inst/console.html\",\n user_list=user_list, current_instance=current_inst_id,\n current_instance_name=current_inst_name)\n else:\n # there is no any instance for this user,\n # thus it is better to create another one\n return redirect(\"server_inst/new_inst\")\n\n except TemplateNotFound:\n abort(404)\n pass\n\n@server_inst_page.route(\"/console/\", methods=[\"GET\"])\n@check_login\ndef render_console_page_II(uid, priv, inst_id):\n try:\n return render_console_page(inst_id=int(inst_id))\n except TemplateNotFound:\n abort(404)\n","sub_path":"app/blueprints/server_inst/console.py","file_name":"console.py","file_ext":"py","file_size_in_byte":2522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"30811758","text":"'''\n Tests for commands module\n Nick Mathewson\n'''\nimport unittest\nimport os, tempfile, re\n\nfrom test_support import TestSkipped, run_unittest\nfrom commands import *\n\n# The module says:\n# \"NB This only works (and is only relevant) for UNIX.\"\n#\n# Actually, getoutput should work on any platform with an os.popen, but\n# I'll take the comment as given, and skip this suite.\n\nif os.name != 'posix':\n raise TestSkipped('Not posix; skipping test_commands')\n\n\nclass CommandTests(unittest.TestCase):\n\n def test_getoutput(self):\n self.assertEquals(getoutput('echo xyzzy'), 'xyzzy')\n self.assertEquals(getstatusoutput('echo xyzzy'), (0, 'xyzzy'))\n\n # we use mktemp in the next line to get a filename which we\n # _know_ won't exist. This is guaranteed to fail.\n status, output = getstatusoutput('cat ' + tempfile.mktemp())\n self.assertNotEquals(status, 0)\n\n def test_getstatus(self):\n # This pattern should match 'ls -ld /.' on any posix\n # system, however perversely configured.\n pat = r'''d......... # It is a directory.\n \\s+\\d+ # It has some number of links.\n \\s+\\w+\\s+\\w+ # It has a user and group, which may\n # be named anything.\n \\s+\\d+ # It has a size.\n [^/]* # Skip the date.\n /. # and end with the name of the file.\n '''\n\n self.assert_(re.match(pat, getstatus(\"/.\"), re.VERBOSE))\n\n\ndef test_main():\n run_unittest(CommandTests)\n\n\nif __name__ == \"__main__\":\n test_main()\n","sub_path":"lib/rubyfox/server/data/lib/Lib/test/test_commands.py","file_name":"test_commands.py","file_ext":"py","file_size_in_byte":1623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"446725452","text":"#!/usr/bin/env python\n# * coding: utf8 *\n'''\nPLSSPallet.py\n\nA module that contains the pallet to update the mapserv.utah.gov/plss web app\n'''\n\nimport arcpy\nfrom forklift import seat\nfrom forklift.models import Pallet\nfrom os.path import join\nfrom time import clock\n\n\nclass PlssPallet(Pallet):\n\n def __init__(self):\n super(PlssPallet, self).__init__()\n\n self.arcgis_services = [('PLSS', 'MapServer')]\n self.boundaries = join(self.staging_rack, 'boundaries.gdb')\n self.cadastre = join(self.staging_rack, 'cadastre.gdb')\n\n self.copy_data = [self.boundaries, self.cadastre]\n\n def build(self, configuration=None):\n self.add_crates(['PLSSPoint_AGRC'], {'source_workspace': join(self.garage, 'SGID.sde'), 'destination_workspace': self.cadastre})\n self.add_crates(['Counties'], {'source_workspace': join(self.garage, 'SGID.sde'), 'destination_workspace': self.boundaries})\n\n def process(self):\n start_seconds = clock()\n\n workspace = arcpy.env.workspace\n arcpy.env.workspace = self.cadastre\n\n self.log.debug('removing index')\n try:\n arcpy.RemoveIndex_management(in_table='PLSSPoint_AGRC', index_name='webquery')\n except Exception as e:\n self.log.warn('error removing PLSS index: %s', e)\n\n self.log.debug('adding index')\n try:\n arcpy.AddIndex_management(in_table='PLSSPoint_AGRC', fields='POINTID', index_name='webquery')\n except Exception as e:\n self.log.warn('error adding parcel index: %s', e)\n\n arcpy.env.workspace = workspace\n\n self.log.debug('finished PLSS processing %s', seat.format_time(clock() - start_seconds))\n","sub_path":"scripts/PLSSPallet.py","file_name":"PLSSPallet.py","file_ext":"py","file_size_in_byte":1691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"313262289","text":"def Marlena_Help(day):\n if day >= 10:\n print (\"Cool, you did it!\")\n else:\n return day * 2\n\n\n\nprint (Marlena_Help(4))\nprint (Marlena_Help(5))\nprint (Marlena_Help(1))\nprint (Marlena_Help(23))\nprint (Marlena_Help(222))\n\n\n\ndef Ellis(Coolness):\n if Coolness >= 10:\n return \"Ellis is on 10,000 Yo!\"\n else:\n return \"Not cool enough!\"\n\n\n\n#print (Ellis(10))\n\nfault_codes = {\"hot fault\": 111, \"warm fault\": 112, \"no fault\": 000}\n\n\nfor key in fault_codes:\n print (key)\n print (\"fault_codes: %s\" % fault_codes[key])\n\n\ncom1 = 10\nnormal_com1 = 9\nif com1 > normal_com1:\n print (fault_codes[\"hot fault\"], \"Shit bro!\")\nelse:\n print (fault_codes[\"no fault\"], \"Faults!\")\n\n\ntotal = 0\nfor key in fault_codes:\n total = total + com1 + fault_codes[key]\n\n\nprint (total)\nprint (fault_codes)\n\n\ncom2 = 11\nnormal_com2 = 14\nif com2 > normal_com2:\n print (\"ALARM!!!\")\nelse:\n print (\"all clear\")\n\n\ncom4 = 2\nnormal_com4 = 3\nif com4 > normal_com4:\n print (\"ALARM!!!\")\nelse:\n print (\"all clear\")\nwhile com4 < 3:\n print ((com4 * com2) + (com1 * com4), \"Holy Shit\")\n com4 += 1\n\n\ndef ellis_hairston(super_cool):\n if super_cool >= 10:\n print (\"Baddass!\")\n\n\nprint (ellis_hairston(200))\n\n\ndef malia(awesomeness):\n if com1 >= 48:\n print ('HELL YEAH!')\n else:\n print (\"sorry sucker!\")\n\n\nprint (malia(2000))\n#///////////////////////switch work around with dict mapping/////////////////////\ndef test_switch(argument):\n switch = {\n 0: \"zero\",\n 10: \"Shit, system overheating\",\n 2: \"two\",\n }\n print (switch.get(argument, \"Still Testing!\"))\n\nprint(test_switch(2))\n#///////////////////////switch work around with dict mapping/////////////////////\n\nprint (test_switch(com1))\n","sub_path":"marlena_help.py","file_name":"marlena_help.py","file_ext":"py","file_size_in_byte":1757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"311424467","text":"\nimport robinhoodwrapper\nimport logging\nimport inspect\nimport commonqueries\nimport pandas as pd\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\nimport pandas_market_calendars as mcal\nimport pytz\nimport configwrapper\n\nclass RobinhoodTransfer():\n\tdef __init__(self,config_file):\n\t\tself.config = configwrapper.ConfigWrapper(config_file=config_file)\n\t\tdata_collections=self.build_collections('FINANCIALDATA_COLLECTIONS')\n\t\tuser_collections=self.build_collections('USERS_COLLECTIONS')\n\t\tself.data_collections=data_collections\n\t\tself.user_collections=user_collections\n\t\tself.data_cq=commonqueries.CommonQueries(port=self.config.get_int('FINANCIALDATA_MONGO','port'),host=self.config.get_string('FINANCIALDATA_MONGO','host'), username=self.config.get_string('FINANCIALDATA_MONGO','username'), password=self.config.get_string('FINANCIALDATA_MONGO','password'), dbname=self.config.get_string('FINANCIALDATA_MONGO','dbname'),collections=data_collections)\n\t\tself.user_cq=commonqueries.CommonQueries(port=self.config.get_int('USERS_MONGO','port'),host=self.config.get_string('USERS_MONGO','host'), username=self.config.get_string('USERS_MONGO','username'), password=self.config.get_string('USERS_MONGO','password'), dbname=self.config.get_string('USERS_MONGO','dbname'),collections=user_collections)\n\t\treturn\n\tdef build_collections(self,section='FINANCIALDATA_COLLECTIONS'):\n\t\tcollections={}\n\t\tfor option in self.config.get_options(section):\n\t\t\tcollections[option]=self.config.get_string(section,option)\n\t\treturn collections\n\tdef transfer_funds_to_robinhood(self):\n\t\tx=mcal.get_calendar('NYSE').schedule(start_date=datetime.now().date()-relativedelta(days=7),end_date=datetime.now().date()+relativedelta(days=7))\n\t\tnow = pytz.utc.localize(datetime.utcnow())\n\t\ttoday=now.date()\n\t\tx=x[pd.to_datetime(x['market_open'])>=now]\n\t\ttime_until_market_open=float((x['market_open'].iloc[0]-now).total_seconds())\n\t\tmax_time_between_close_and_open=float(17.5*60*60) #4:00pm until 9:30 the next day, is 7.5 hours\n\t\tif time_until_market_open>max_time_between_close_and_open:\n\t\t\tlogging.info('more than 7.5 hours until the next market open, not trading now')\n\t\t\treturn\n\t\tcq=self.user_cq\n\t\tuser_df=pd.DataFrame(list(cq.mongo.db[self.user_collections['robinhood_users']].find()))\n\t\tuser_df=user_df.sort_values('username')\n\t\tuser_df=user_df.drop_duplicates('username') #has the usernames and passwords of all robinhood users\n\n\t\tfor index,account in user_df.iterrows():\n\t\t\tif 'transfer' not in account or pd.isnull(account['transfer']) or len(account['transfer'])==0:\n\t\t\t\tcontinue\n\t\t\trh_user=robinhoodwrapper.RobinHoodWrapper(username=account['username'],password=account['password'])\n\t\t\ttransferinfo=account['transfer']\n\t\t\tid=transferinfo['id']\n\t\t\tfrequency=transferinfo['frequency']\n\t\t\tamount=transferinfo['amount']\n\t\t\tif 'last_transfer_id' not in transferinfo or pd.isnull(transferinfo['last_transfer_id']):\n\t\t\t\trh_user.bank2rh(amount,id)\n\t\t\t\ttransfers=rh_user.get_ach_transfers()\n\t\t\t\ttransfers=transfers[transfers['direction']=='deposit']\n\t\t\t\ttransfers['created_at']=pd.to_datetime(transfers['created_at'])\n\t\t\t\ttransfers=transfers.sort_values('created_at',ascending=True)\n\t\t\t\tid=transfers['id'].iloc[-1]\n\t\t\t\ttransferinfo['last_transfer_id']=id\n\t\t\t\tcq.mongo.db[self.user_collections['robinhood_users']].update({\"_id\":account[\"_id\"]},account.to_dict())\n\t\t\telse:\n\t\t\t\tnow=pd.to_datetime(datetime.utcnow())\n\t\t\t\ttransfers=rh_user.get_ach_transfers()\n\t\t\t\tlasttransfertime=pd.to_datetime(transfers[transfers['id']==transferinfo['last_transfer_id']]['created_at'].iloc[0])\n\t\t\t\tfrequency_multiple=transferinfo['frequency_multiple']\n\t\t\t\tif frequency=='daily':\n\t\t\t\t\tdelta=relativedelta(days=1)*frequency_multiple\n\t\t\t\telif frequency=='weekly':\n\t\t\t\t\tdelta=relativedelta(weeks=1)*frequency_multiple\n\t\t\t\telif frequency=='monthly':\n\t\t\t\t\tdelta=relativedelta(months=1)*frequency_multiple\n\t\t\t\telif frequency=='yearly':\n\t\t\t\t\tdelta=relativedelta(years=1)*frequency_multiple\n\t\t\t\telse:\n\t\t\t\t\tlogging.error('unknown frequency')\n\t\t\t\t\texit()\n\t\t\t\tif lasttransfertime.date()<=(now.date()-delta):\n\t\t\t\t\trh_user.bank2rh(amount,id)\n\t\t\t\t\ttransfers=rh_user.get_ach_transfers()\n\t\t\t\t\ttransfers=transfers[transfers['direction']=='deposit']\n\t\t\t\t\ttransfers['created_at']=pd.to_datetime(transfers['created_at'])\n\t\t\t\t\ttransfers=transfers.sort_values('created_at',ascending=True)\n\t\t\t\t\tid=transfers['id'].iloc[-1]\n\t\t\t\t\ttransferinfo['last_transfer_id']=id\n\t\t\t\t\tcq.mongo.db[self.user_collections['robinhood_users']].update({\"_id\":account[\"_id\"]},account.to_dict())\n\t\t\t\telse:\n\t\t\t\t\tlogging.info('too soon to transfer for account:'+str(account['_id']))\n\t\t\trh_user.logout()\ndef main(config_file):\n\tr=RobinhoodTransfer(config_file=config_file)\n\tr.transfer_funds_to_robinhood()\n\treturn\nif __name__ == '__main__':\n\tlogging.basicConfig(filename=inspect.stack()[0][1].replace('py','log'),level=logging.INFO,format='%(asctime)s:%(levelname)s:%(message)s')\n\n\t\n\n","sub_path":"src/robinhoodtransfer.py","file_name":"robinhoodtransfer.py","file_ext":"py","file_size_in_byte":4906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"114924131","text":"# -*- coding: utf-8 -*-\r\n\r\nimport time\r\nimport telebot\r\nfrom telebot import types\r\nfrom telebot.types import InlineKeyboardMarkup, InlineKeyboardButton\r\nimport mysql.connector\r\nfrom datetime import datetime\r\nimport sys\r\nimport hashlib \r\nimport random\r\nimport string\r\n\r\nTOKEN = '1737290933:AAG3z2L0lORnymVAMxhhL5NfcpjuIn7zBpU' #API тестовый\r\nadminList = [1664818481]; # Админы\r\n\r\n# База данных\r\nmydb = mysql.connector.connect(\r\n host=\"boogaboo1.beget.tech\",\r\n user=\"boogaboo1_1\",\r\n password=\"090501Tt\",\r\n database=\"boogaboo1_1\"\r\n)\r\n\r\n\r\nmydb.ping(True)\r\nmycursor = mydb.cursor()\r\n\r\n# mycursor.execute(\"DROP table settings,path\")\r\ntry: \r\n mycursor.execute(\"CREATE TABLE path (id INT AUTO_INCREMENT PRIMARY KEY, link VARCHAR(255), creator VARCHAR(255),method BOOLEAN, sum INT, x INT, ip VARCHAR(255), cheker BOOLEAN, success BOOLEAN, nomoney BOOLEAN, limited BOOLEAN, error BOOLEAN, threeds BOOLEAN, disablepay BOOLEAN, cardban BOOLEAN, notrucard BOOLEAN)\")\r\n print(\"*создаём таблицу путей\")\r\nexcept Exception as e:\r\n print(\"таблица путей имеется\")\r\n # print(e) \r\n\r\ntry: \r\n mycursor.execute(\"CREATE TABLE settings (id INT AUTO_INCREMENT PRIMARY KEY, domain VARCHAR(255), ip VARCHAR(255),token VARCHAR(255))\")\r\n sql = \"INSERT INTO settings (domain,ip,token) VALUES (%s,%s,%s)\"\r\n val = (\"http://paymaster.ru\", \"Plati.ru\", TOKEN);\r\n mycursor.execute(sql,val)\r\n mydb.commit()\r\n print(\"*создаём таблицу настроек\")\r\nexcept Exception as e:\r\n print(\"таблица настроек имеется\")\r\nmycursor.close() \r\n\r\ndef listener(messages):\r\n for m in messages:\r\n if m.content_type == 'text':\r\n # print the sent message to the console\r\n print(str(m.chat.first_name) + \" [\" + str(m.chat.id) + \"]: \" + m.text)\r\n print(\"stepFlag = \" + str(stepFlag))\r\nbot = telebot.TeleBot(TOKEN)\r\nbot.set_update_listener(listener) # register listener\r\n\r\ndef backButton(cid,txt):\r\n\tmarkup = types.ReplyKeyboardMarkup(resize_keyboard=True,one_time_keyboard=False)\r\n\tmarkup.row(types.KeyboardButton('Назад'))\r\n\tbot.send_message(cid, txt, reply_markup=markup, parse_mode= \"Markdown\")\r\n\thideBoard = types.ReplyKeyboardRemove()\r\n\r\ndef adminMainMenu(cid, txt):\r\n markup = types.ReplyKeyboardMarkup(resize_keyboard=True,one_time_keyboard=False)\r\n markup.row(types.KeyboardButton('ℹ️Управление 3ds'))\r\n markup.row(types.KeyboardButton('▶️Оплата'), types.KeyboardButton('◀️Возврат'))\r\n markup.row(types.KeyboardButton('⏫Список'),types.KeyboardButton('🚮Удаление'))\r\n markup.row(types.KeyboardButton('🔣Настройки'))\r\n # markup.row(types.KeyboardButton('🈁Url платежки'))\r\n bot.send_message(cid, txt, reply_markup=markup, parse_mode= \"Markdown\")\r\n hideBoard = types.ReplyKeyboardRemove()\r\n\r\ndef adminSettingsMenu(cid,txt):\r\n markup = types.ReplyKeyboardMarkup(resize_keyboard=True,one_time_keyboard=False)\r\n markup.row(types.KeyboardButton('🈁Url платежки'))\r\n markup.row(types.KeyboardButton('🔤Организация на странице оплаты'))\r\n markup.row(types.KeyboardButton('Назад'))\r\n bot.send_message(cid, txt, reply_markup=markup, parse_mode= \"Markdown\")\r\n hideBoard = types.ReplyKeyboardRemove()\r\n\r\ndef form3dsMenu(cid,txt):\r\n markup = types.ReplyKeyboardMarkup(resize_keyboard=True,one_time_keyboard=False)\r\n markup.row(types.KeyboardButton('Успешно'),types.KeyboardButton('Нет денег'))\r\n markup.row(types.KeyboardButton('Неизвестная ошибка'),types.KeyboardButton('Лимит'))\r\n markup.row(types.KeyboardButton('Ошибка 3ds'),types.KeyboardButton('Запрещена онлайн оплата'))\r\n markup.row(types.KeyboardButton('Бан карты'),types.KeyboardButton('Карта не РФ'))\r\n markup.row(types.KeyboardButton('Назад'))\r\n bot.send_message(cid, txt, reply_markup=markup, parse_mode= \"Markdown\")\r\n hideBoard = types.ReplyKeyboardRemove()\r\n\r\n\r\nsumma = \"\";\r\nchecker = 0;\r\nmethod = 0\r\nstepFlag = 0; # главное меню\r\n\r\n#---------------------------------------------------------------\r\n@bot.message_handler(commands=['start'])\r\ndef command_start(m):\r\n cid = m.chat.id\r\n if cid in adminList: \r\n adminMainMenu(cid,\"Главное меню\")\r\n#---------------------------------------------------------------\r\n@bot.message_handler(func=lambda message: message.text == 'Назад')\r\ndef command_rules(m):\r\n global stepFlag\r\n cid = m.chat.id\r\n if stepFlag==10 or stepFlag==0 or stepFlag==31 or stepFlag == 20 or stepFlag == 30 or stepFlag == 25 or stepFlag == 11 or stepFlag == 12 or stepFlag == 15 or stepFlag == 16 or stepFlag == 17:\r\n adminMainMenu(cid,\"Главное меню\")\r\n stepFlag = 0\r\n elif stepFlag == 21 or stepFlag == 22:\r\n stepFlag = 20\r\n adminSettingsMenu(cid,\"Настройки платежки и страницы оплаты\")\r\n\r\n@bot.message_handler(func=lambda message: message.text == '▶️Оплата')\r\ndef command_rules(m):\r\n global stepFlag\r\n global method\r\n cid = m.chat.id\r\n if cid in adminList:\r\n backButton(cid,\"Укажи сумму оплаты?⏬\")\r\n stepFlag = 10\r\n method = 0;\r\n\r\n\r\n@bot.message_handler(func=lambda message: message.text == '◀️Возврат')\r\ndef command_rules(m):\r\n global stepFlag\r\n global method\r\n cid = m.chat.id\r\n if cid in adminList:\r\n backButton(cid,\"Укажи сумму возврата?⏬\")\r\n stepFlag = 15\r\n method = 1;\r\n\r\n\r\n@bot.message_handler(func=lambda message: message.text == '🔣Настройки')\r\ndef command_rules(m):\r\n global stepFlag\r\n cid = m.chat.id\r\n if cid in adminList:\r\n adminSettingsMenu(cid,\"Настройки платежки и страницы оплаты\")\r\n stepFlag = 20\r\n\r\n@bot.message_handler(func=lambda message: message.text == '🈁Url платежки')\r\ndef command_rules(m):\r\n global stepFlag\r\n cid = m.chat.id\r\n if cid in adminList:\r\n\r\n try:\r\n mydb.ping(True)\r\n mycursor = mydb.cursor()\r\n sql = \"SELECT domain FROM settings WHERE id = 1\"\r\n mycursor.execute(sql)\r\n myresult = mycursor.fetchone()\r\n mycursor.close()\r\n except Exception as e:\r\n print(e)\r\n backButton(cid,\"Текущий адресс платёжки: \" +str(myresult[0])+ \"\\nДля установки ��ового адреса, вбейте его ниже⏬\\n⚠️Адресс вводится без HTTP:// и слешей вконце⚠️\")\r\n stepFlag = 21\r\n\r\n@bot.message_handler(func=lambda message: message.text == '🔤Организация на странице оплаты')\r\ndef command_rules(m):\r\n global stepFlag\r\n cid = m.chat.id\r\n if cid in adminList:\r\n\r\n try:\r\n mydb.ping(True)\r\n mycursor = mydb.cursor()\r\n sql = \"SELECT ip FROM settings WHERE id = 1\"\r\n mycursor.execute(sql)\r\n myresult = mycursor.fetchone()\r\n mycursor.close()\r\n except Exception as e:\r\n print(e)\r\n backButton(cid,\"Текущий организация: \" +str(myresult[0])+ \"\\nДля установки названия, вбейте его ниже⏬\")\r\n stepFlag = 22\r\n\r\n@bot.message_handler(func=lambda message: message.text == '📌Создать📌')\r\ndef command_rules(m):\r\n global stepFlag\r\n cid = m.chat.id\r\n if cid in adminList and (stepFlag == 12 or stepFlag == 17 ):\r\n try:\r\n mydb.ping(True)\r\n mycursor = mydb.cursor()\r\n sql = \"SELECT * FROM settings WHERE id = 1\"\r\n mycursor.execute(sql)\r\n myresult = mycursor.fetchone()\r\n mycursor.close()\r\n\r\n result = hashlib.md5(str(random.randint(0,800000)).encode()).hexdigest() \r\n linkgen = str(myresult[1]) + '/payments/' + result[0:8] +'-'+ result[8:16] +'-'+ result[16:24] +'-'+ result[24:32]\r\n\r\n mycursor = mydb.cursor()\r\n sql = \"INSERT INTO path (link,creator,method,sum,x,ip,cheker,success,nomoney,limited,error,threeds,disablepay,cardban,notrucard) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\r\n val = (linkgen,cid,method,summa,2,myresult[2],checker,0,0,0,0,0,0,0,0)\r\n mycursor.execute(sql, val)\r\n mydb.commit()\r\n mycursor.close()\r\n\r\n mycursor = mydb.cursor()\r\n sql = \"SELECT id FROM path WHERE link = %s\"\r\n val = (linkgen,)\r\n mycursor.execute(sql,val)\r\n myresult = mycursor.fetchone()\r\n mycursor.close()\r\n\r\n adminMainMenu(cid,\"Ваша ссылка:\\n\"+linkgen+'\\nID для 3ds управления: ' + str(myresult[0]) ) \r\n except Exception as e:\r\n bot.send_message(cid, e, parse_mode= \"Markdown\")\r\n\r\n\r\n@bot.message_handler(func=lambda message: message.text == '⏫Список')\r\ndef command_rules(m):\r\n global stepFlag\r\n cid = m.chat.id\r\n if cid in adminList:\r\n try:\r\n mydb.ping(True)\r\n mycursor = mydb.cursor(buffered=True)\r\n sql = \"SELECT id,link,method,sum,cheker FROM path\"\r\n mycursor.execute(sql)\r\n myresult = mycursor.fetchall()\r\n mycursor.close()\r\n\r\n i = 0\r\n for x in myresult:\r\n bot.send_message(cid, \"ID - \" + str(myresult[i][0])+ \" | Сумма: \" + str(myresult[i][3]) + \" | Метод (1-возврат) - \" + str(myresult[i][2]) + \"\\nСсылка: \" + str(myresult[i][1]), parse_mode= \"Markdown\")\r\n i=i+1\r\n adminMainMenu(cid,\"Главное меню: \") \r\n except Exception as e:\r\n bot.send_message(cid, e, parse_mode= \"Markdown\")\r\n\r\n@bot.message_handler(func=lambda message: message.text == '🚮Удаление')\r\ndef command_rules(m):\r\n global stepFlag\r\n cid = m.chat.id\r\n if cid in adminList:\r\n backButton(cid,\"Укажите ID ссылки для удаления. 0 - удалить всё\")\r\n stepFlag = 25;\r\n\r\n@bot.message_handler(func=lambda message: message.text == 'ℹ️Управление 3ds')\r\ndef command_rules(m):\r\n global stepFlag\r\n cid = m.chat.id\r\n if cid in adminList:\r\n stepFlag = 30;\r\n backButton(cid,\"Введите ID ссылки для управления:\")\r\n\r\n#------------------------------------------------------------------------------------------------\r\n@bot.message_handler(func=lambda message: message.text == 'Успешно')\r\ndef command_rules(m):\r\n global stepFlag\r\n cid = m.chat.id\r\n if cid in adminList and stepFlag == 31:\r\n try:\r\n mydb.ping(True)\r\n mycursor = mydb.cursor()\r\n sql = \"UPDATE path SET success = 1 WHERE id = %s\"\r\n val = (control[0],)\r\n mycursor.execute(sql, val)\r\n mydb.commit()\r\n mycursor.close()\r\n except Exception as e:\r\n bot.send_message(cid, e)\r\n form3dsMenu(cid,\"Оплата подтверждена. Ожидаем новый код подтверждения\")\r\n\r\n@bot.message_handler(func=lambda message: message.text == 'Нет денег')\r\ndef command_rules(m):\r\n global stepFlag\r\n cid = m.chat.id\r\n if cid in adminList and stepFlag == 31:\r\n try:\r\n mydb.ping(True)\r\n mycursor = mydb.cursor()\r\n sql = \"UPDATE path SET nomoney = 1 WHERE id = %s\"\r\n val = (control[0],)\r\n mycursor.execute(sql, val)\r\n mydb.commit()\r\n mycursor.close()\r\n except Exception as e:\r\n bot.send_message(cid, e)\r\n form3dsMenu(cid,\"На карте нет денег.\")\r\n\r\n@bot.message_handler(func=lambda message: message.text == 'Неизвестная ошибка')\r\ndef command_rules(m):\r\n global stepFlag\r\n cid = m.chat.id\r\n if cid in adminList and stepFlag == 31:\r\n try:\r\n mydb.ping(True)\r\n mycursor = mydb.cursor()\r\n sql = \"UPDATE path SET error = 1 WHERE id = %s\"\r\n val = (control[0],)\r\n mycursor.execute(sql, val)\r\n mydb.commit()\r\n mycursor.close()\r\n except Exception as e:\r\n bot.send_message(cid, e)\r\n form3dsMenu(cid,\"Произошла неизвестная ошибка при оплате.\")\r\n\r\n@bot.message_handler(func=lambda message: message.text == 'Лимит')\r\ndef command_rules(m):\r\n global stepFlag\r\n cid = m.chat.id\r\n if cid in adminList and stepFlag == 31:\r\n try:\r\n mydb.ping(True)\r\n mycursor = mydb.cursor()\r\n sql = \"UPDATE path SET limited = 1 WHERE id = %s\"\r\n val = (control[0],)\r\n mycursor.execute(sql, val)\r\n mydb.commit()\r\n mycursor.close()\r\n except Exception as e:\r\n bot.send_message(cid, e)\r\n form3dsMenu(cid,\"На карте закончился лимит по переводам.\")\r\n\r\n@bot.message_handler(func=lambda message: message.text == 'Ошибка 3ds')\r\ndef command_rules(m):\r\n global stepFlag\r\n cid = m.chat.id\r\n if cid in adminList and stepFlag == 31:\r\n try:\r\n mydb.ping(True)\r\n mycursor = mydb.cursor()\r\n sql = \"UPDATE path SET threeds = 1 WHERE id = %s\"\r\n val = (control[0],)\r\n mycursor.execute(sql, val)\r\n mydb.commit()\r\n mycursor.close()\r\n except Exception as e:\r\n bot.send_message(cid, e)\r\n form3dsMenu(cid,\"Ошибка 3ds.\")\r\n\r\n@bot.message_handler(func=lambda message: message.text == 'Запрещена онлайн оплата')\r\ndef command_rules(m):\r\n global stepFlag\r\n cid = m.chat.id\r\n if cid in adminList and stepFlag == 31:\r\n try:\r\n mydb.ping(True)\r\n mycursor = mydb.cursor()\r\n sql = \"UPDATE path SET disablepay = 1 WHERE id = %s\"\r\n val = (control[0],)\r\n mycursor.execute(sql, val)\r\n mydb.commit()\r\n mycursor.close()\r\n except Exception as e:\r\n bot.send_message(cid, e)\r\n form3dsMenu(cid,\"На карте выключена функция онлайн оплаты.\")\r\n\r\n@bot.message_handler(func=lambda message: message.text == 'Бан карты')\r\ndef command_rules(m):\r\n global stepFlag\r\n cid = m.chat.id\r\n if cid in adminList and stepFlag == 31:\r\n try:\r\n mydb.ping(True)\r\n mycursor = mydb.cursor()\r\n sql = \"UPDATE path SET cardban = 1 WHERE id = %s\"\r\n val = (control[0],)\r\n mycursor.execute(sql, val)\r\n mydb.commit()\r\n mycursor.close()\r\n except Exception as e:\r\n bot.send_message(cid, e)\r\n form3dsMenu(cid,\"Карта забанена.\")\r\n\r\n@bot.message_handler(func=lambda message: message.text == 'Карта не РФ')\r\ndef command_rules(m):\r\n global stepFlag\r\n cid = m.chat.id\r\n if cid in adminList and stepFlag == 31:\r\n try:\r\n mydb.ping(True)\r\n mycursor = mydb.cursor()\r\n sql = \"UPDATE path SET notrucard = 1 WHERE id = %s\"\r\n val = (control[0],)\r\n mycursor.execute(sql, val)\r\n mydb.commit()\r\n mycursor.close()\r\n except Exception as e:\r\n bot.send_message(cid, e)\r\n form3dsMenu(cid,\"Карта не Российскрго банка.\")\r\n#----------------------------------------------------------------------------------------------\r\ncontrol = ()\r\n\r\n@bot.message_handler(func=lambda message: True, content_types=['text'])\r\ndef command_default(m):\r\n global stepFlag\r\n global summa\r\n global checker\r\n global control\r\n\r\n cid = m.chat.id\r\n text = m.text\r\n if stepFlag == 10:\r\n summa = text;\r\n backButton(cid,\"Включить чекер баланса? (Да/Нет)\")\r\n stepFlag = 11;\r\n elif stepFlag == 11:\r\n if text == \"Да\" or text == \"да\" or text == \"Д��\":\r\n checker = 1;\r\n stepFlag = 12\r\n txt = \"Всё готово к созданию ссылки:\\nТип - оплата\\nСумма - \"+summa+\"\\n Чекер - Включен\"\r\n markup = types.ReplyKeyboardMarkup(resize_keyboard=True,one_time_keyboard=False)\r\n markup.row(types.KeyboardButton('📌Создать📌'))\r\n markup.row(types.KeyboardButton('Назад'))\r\n bot.send_message(cid, txt, reply_markup=markup, parse_mode= \"Markdown\")\r\n hideBoard = types.ReplyKeyboardRemove()\r\n\r\n elif text == \"Нет\" or text == \"нет\" or text == \"НЕТ\": \r\n checker = 0\r\n stepFlag = 12\r\n txt = \"Всё готово к созданию ссылки:\\nТип - Оплата\\nСумма - \"+summa+\"\\n Чекер - Выключен\"\r\n markup = types.ReplyKeyboardMarkup(resize_keyboard=True,one_time_keyboard=False)\r\n markup.row(types.KeyboardButton('📌Создать📌'))\r\n markup.row(types.KeyboardButton('Назад'))\r\n bot.send_message(cid, txt, reply_markup=markup, parse_mode= \"Markdown\")\r\n hideBoard = types.ReplyKeyboardRemove()\r\n else:\r\n bot.send_message(cid, \"Нужно ответить да или нет\", parse_mode= \"Markdown\")\r\n elif stepFlag == 15:\r\n stepFlag = 16;\r\n summa = text\r\n backButton(cid,\"Включить чекер баланса? (Да/Нет)\")\r\n elif stepFlag == 16:\r\n if text == \"Да\" or text == \"да\" or text == \"ДА\":\r\n checker = 1;\r\n stepFlag = 17\r\n txt = \"Всё готово к созданию ссылки:\\nТип - Возврат\\nСумма - \"+summa+\"\\n Чекер - Включен\"\r\n markup = types.ReplyKeyboardMarkup(resize_keyboard=True,one_time_keyboard=False)\r\n markup.row(types.KeyboardButton('📌Создать📌'))\r\n markup.row(types.KeyboardButton('Назад'))\r\n bot.send_message(cid, txt, reply_markup=markup, parse_mode= \"Markdown\")\r\n hideBoard = types.ReplyKeyboardRemove()\r\n\r\n elif text == \"Нет\" or text == \"нет\" or text == \"НЕТ\": \r\n checker = 0\r\n stepFlag = 17\r\n txt = \"Всё готово к созданию ссылки:\\nТип - Оплата\\nСумма - \"+summa+\"\\n Чекер - Выключен\"\r\n markup = types.ReplyKeyboardMarkup(resize_keyboard=True,one_time_keyboard=False)\r\n markup.row(types.KeyboardButton('📌Создать📌'))\r\n markup.row(types.KeyboardButton('Назад'))\r\n bot.send_message(cid, txt, reply_markup=markup, parse_mode= \"Markdown\")\r\n hideBoard = types.ReplyKeyboardRemove()\r\n else:\r\n bot.send_message(cid, \"Нужно ответить да или нет\", parse_mode= \"Markdown\")\r\n elif stepFlag == 21:\r\n try:\r\n mydb.ping(True)\r\n mycursor = mydb.cursor()\r\n sql = \"UPDATE settings SET domain = %s WHERE id = 1\"\r\n val = (text,)\r\n mycursor.execute(sql, val)\r\n mydb.commit()\r\n mycursor.close()\r\n stepFlag = 20\r\n adminSettingsMenu(cid, \"Новый адресс платежки установлен:\\n\"+text)\r\n except Exception as e:\r\n print(e)\r\n elif stepFlag == 22:\r\n try:\r\n mydb.ping(True)\r\n mycursor = mydb.cursor()\r\n sql = \"UPDATE settings SET ip = %s WHERE id = 1\"\r\n val = (text,)\r\n mycursor.execute(sql, val)\r\n mydb.commit()\r\n mycursor.close()\r\n stepFlag = 20\r\n adminSettingsMenu(cid, \"Новое название организации:\\n\"+text)\r\n except Exception as e:\r\n print(e)\r\n elif stepFlag == 25:\r\n if text == '0':\r\n try:\r\n mycursor = mydb.cursor()\r\n mycursor.execute(\"DELETE FROM path\")\r\n adminMainMenu(cid,\"Все ссылки удалены!\")\r\n stepFlag = 0;\r\n except Exception as e:\r\n bot.send_message(m.chat.id, e)\r\n else:\r\n try:\r\n mycursor = mydb.cursor()\r\n sql = \"DELETE FROM path WHERE id = %s\"\r\n adr = (text, )\r\n mycursor.execute(sql, adr)\r\n mydb.commit()\r\n adminMainMenu(cid,\"Ссылка удалена!\")\r\n stepFlag = 0;\r\n except Exception as e:\r\n bot.send_message(m.chat.id, e)\r\n elif stepFlag == 30:\r\n\r\n try:\r\n mydb.ping(True)\r\n mycursor = mydb.cursor()\r\n sql = \"SELECT * FROM path WHERE id = %s\"\r\n val = (text,)\r\n mycursor.execute(sql,val)\r\n myresult = mycursor.fetchone()\r\n mycursor.close()\r\n except Exception as e:\r\n print(e)\r\n\r\n if myresult == None:\r\n backButton(cid, 'Ссылка не найдена. Введите корректный ID ссылки:')\r\n else:\r\n control = myresult\r\n stepFlag = 31\r\n form3dsMenu(cid,\"Форма управления 3DS\")\r\n\r\n#---------------------------------------------------------------------------------------------------\r\n\r\n@bot.callback_query_handler(func=lambda call: True)\r\ndef callback_query(call):\r\n global newAgent\r\n cid = call.message.chat.id\r\n if call.data == \"success\": \r\n form3dsMenu(cid,\"Оплата подтверждена.\\nОжидаем новый одноразовый код!\")\r\n bot.delete_message(call.message.chat.id, call.message.id)\r\n\r\n\r\n try:\r\n mydb.ping(True)\r\n mycursor = mydb.cursor()\r\n sql = \"UPDATE path SET success = 1 WHERE id = %s\"\r\n val = (control[0],)\r\n mycursor.execute(sql, val)\r\n mydb.commit()\r\n mycursor.close()\r\n except Exception as e:\r\n bot.send_message(call.message.chat.id, e)\r\n \r\n elif call.data == \"declineUsers\":\r\n bot.delete_message(call.message.chat.id, call.message.id)\r\n form3ds(cid)\r\n\r\n\r\nbot.polling()","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":22581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"317243070","text":"def update_mini_batch(self, X, y, learning_rate, eps):\r\n error_before = J_quadratic(self, X, y)\r\n an_grad = compute_grad_analytically(self, X, y)\r\n self.w -= an_grad * learning_rate\r\n error_after = J_quadratic(self, X, y)\r\n return int(abs(error_after - error_before) < eps)\r\n\r\ndef SGD(self, X, y, batch_size, learning_rate=0.1, eps=1e-6, max_steps=200):\r\n indexes = np.arange(len(X))\r\n step = 0\r\n while step < max_steps:\r\n step += 1\r\n\r\n batch_indexes = np.random.choice(indexes, batch_size, replace=False)\r\n X_batch = X[batch_indexes]\r\n y_batch = y[batch_indexes]\r\n\r\n is_need_stop = self.update_mini_batch(X_batch, y_batch, learning_rate, eps)\r\n if is_need_stop == 1:\r\n return 1\r\n\r\n return 0\r\n","sub_path":"Перцептрон/gradient.py","file_name":"gradient.py","file_ext":"py","file_size_in_byte":773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"195079373","text":"from google.cloud import vision\nfrom google.cloud.vision import enums\nfrom google.cloud.vision import types\n\nfrom google.cloud import datastore\n\nclient = vision.ImageAnnotatorClient()\n\ndef object_detection(event, context):\n\n try:\n image = vision.types.Image()\n image.source.image_uri = 'gs://'+event['bucket']+'/'+event['name']\n\n response = client.label_detection(image=image)\n labels = response.label_annotations\n\n l = []\n\n for label in labels:\n l.append(label.description)\n\n print(l)\n c = datastore.Client()\n entity = datastore.Entity(c.key('ketav'))\n entity.update({'label':l})\n c.put(entity)\n \n except:\n print(\"Error! Please check your code\")","sub_path":"Q4_Object_Detection.py","file_name":"Q4_Object_Detection.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"487851645","text":"import torch\r\nfrom torch import nn\r\nimport torch.nn.functional as f\r\nfrom torch.autograd import Variable\r\n\r\n\"\"\"RNN—model\"\"\"\r\nclass TextRnn(nn.Module):\r\n def __init__(self):\r\n super(TextRnn,self).__init__()\r\n self.embedding = nn.Embedding(5000,64)\r\n self.rnn = nn.LSTM(input_size = 64,hidden_size = 128,num_layers=2,bidirectional = True)\r\n self.f1 = nn.Sequential(nn.Linear(256,128),\r\n nn.Dropout(0.2),\r\n nn.ReLU())\r\n self.f2 = nn.Sequential(nn.Linear(128,10),\r\n nn.Softmax())\r\n def forward(self,x):\r\n x = self.embedding(x)\r\n x,_ = self.rnn(x)\r\n x = f.dropout(x,p = 0.2)\r\n x = self.f1(x[:,-1,:])\r\n return self.f2(x)\r\n\r\n\"\"\"CNN-model\"\"\"\r\nclass TextCNN(nn.Module):\r\n def __init__(self):\r\n super(TextCNN, self).__init__()\r\n self.embedding = nn.Embedding(5000, 64)\r\n self.conv = nn.Sequential(nn.Conv1d(in_channels=64,\r\n out_channels=256,\r\n kernel_size=5),\r\n nn.ReLU(),\r\n nn.MaxPool1d(kernel_size=596))\r\n self.f1 = nn.Linear(256, 10)\r\n def forward(self, x):\r\n x = self.embedding(x) # batch_size x text_len x embedding_size 64*600*64\r\n x = x.permute(0, 2, 1) #64*64*600\r\n x = self.conv(x) #Conv1后64*256*596,ReLU后不变,NaxPool1d后64*256*1\r\n x = x.view(-1, x.size(1)) #64*256\r\n x = F.dropout(x, 0.8)\r\n x = self.f1(x) #64*10 batch_size * class_num\r\n return x\r\n","sub_path":"Text-Classification-demo/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"339026681","text":"from matplotlib import pyplot as plt\nimport pandas as pd\nimport numpy as np\nimport scipy.linalg as sc\n\ndef load_data_task1():\n dades = pd.read_csv(\"dades.txt\", header=None, sep=\" \", engine=\"python\").values\n A = dades[:, 0]\n b = dades[:, 1]\n return A, b\n\n\ndef load_data_task2():\n dades_regressio = pd.read_csv(\"dades_regressio.csv\", header=None).values\n A = dades_regressio[:, :-1]\n b = dades_regressio[:, -1]\n return A, b\n\n\ndef plot_data1(A, solution, b):\n A_sorted, b_sorted = zip(*sorted(zip(A, b)))\n plt.plot(A_sorted, b_sorted)\n A_sorted, solution_sorted = zip(*sorted(zip(A, solution)))\n plt.plot(A_sorted, solution_sorted)\n plt.gcf().set_size_inches(2, 1)\n plt.show()\n\n\ndef generate_poly_matrix(A, dim):\n assert isinstance(dim, int)\n assert dim > 0\n return np.vstack([A ** d for d in range(dim)]).T\n\n\ndef least_squares_qr(A, b, plot=1):\n Q, R = np.linalg.qr(A)\n rank = min(R.shape)\n m, n = A.shape\n assert A.shape[0] == b.shape[0]\n assert A.shape[0] >= A.shape[1]\n y = Q.T @ b\n y1 = y[:n]\n y2 = y[n:]\n R1 = R[:n, :n]\n x = sc.solve_triangular(R1, y1)\n v = np.zeros(n - rank)\n x_qr = np.concatenate((x, v))\n if plot:\n plot_data1(A[:, 1], A @ x, b)\n return x_qr\n\n\ndef least_squares_svd(A, b, plot=1):\n U, S, V = np.linalg.svd(A, full_matrices=False)\n n = A.shape[1]\n S[S < 1e-5] = 0\n r = np.sum(S > 0)\n S = np.hstack([1 / S[:r], np.zeros(n - r)])\n A_plus = (V.T * S).dot(U.T)\n x_svd = A_plus.dot(b)\n if plot:\n plot_data1(A[:, 1], A @ x_svd, b)\n return x_svd\n \n# Full Rank Matrix\nprint(\"-\" * 40)\nprint(\"Full Rank Matrix\")\nA, b = load_data_task1()\nfor dim in range(2, 6):\n print(\"Dim =\", dim)\n A_poly = generate_poly_matrix(A, dim)\n x_svd = least_squares_svd(A_poly, b, plot=0)\n print(\"\\tError SVD =\", np.linalg.norm(A_poly.dot(x_svd) - b))\n x_qr = least_squares_qr(A_poly, b, plot=0)\n print(\"\\tError QR =\", np.linalg.norm(A_poly.dot(x_qr) - b))\nprint(\"-------- Best Polynomial Dim = 5 ---------\")\nprint(\"Error SVD =\", np.linalg.norm(A_poly.dot(x_svd) - b))\nprint(\"Norm(x_svd) =\", np.linalg.norm(x_svd))\nprint(\"Error QR =\", np.linalg.norm(A_poly.dot(x_qr) - b))\nprint(\"Norm(x_qr) =\", np.linalg.norm(x_qr))\nprint(\"Error SVD - Error QR =\", np.linalg.norm(A_poly.dot(x_svd) - b) - np.linalg.norm(A_poly.dot(x_qr) - b))\nprint(\"Norm(x_svd-x_qr) =\", np.linalg.norm(x_svd-x_qr))\n\n\n# Not Full Rank Matrix\nprint(\"-\" * 40)\nprint(\"Not Full Rank Matrix\")\nA, b = load_data_task2()\nx_svd = least_squares_svd(A, b, plot=0)\nx_qr = least_squares_qr(A, b, plot=0)\nprint(\"Error SVD =\", round(np.linalg.norm(A.dot(x_svd) - b), 4))\nprint(\"Norm(x_svd) =\", np.linalg.norm(x_svd))\nprint(\"Error QR =\", round(np.linalg.norm(A.dot(x_qr) - b), 4))\nprint(\"Norm(x_qr) =\", np.linalg.norm(x_qr))\nprint(\"Error SVD - Error QR =\", np.linalg.norm(A.dot(x_svd) - b) - np.linalg.norm(A.dot(x_qr) - b))\nprint(\"Norm(x_svd-x_qr) =\", np.linalg.norm(x_svd-x_qr))","sub_path":"Project 2/leastSquares.py","file_name":"leastSquares.py","file_ext":"py","file_size_in_byte":2977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"227157872","text":"op = 's'\nmaior = menor = media = div = cont = 0\nwhile op in 's':\n number = int(input('Digite um valor: '))\n cont += 1\n div += 1\n media += number\n if cont == 1:\n maior = menor = number\n else:\n if number > maior:\n maior = number\n if number < menor:\n menor = number\n op = str(input('Quer continuar ? [S/N] '.strip().upper()))\nprint('Você digitou {} números e a média entre eles foi {:.2f}\\n'\n 'O maior valor foi {} e o menor valor foi {}'.format(cont, media / div, maior, menor))\n","sub_path":"ex065.py","file_name":"ex065.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"593047092","text":"import os\nimport codecs\nfrom optparse import OptionParser\n\nfrom core.util import dirs\nfrom core.util import file_handling as fh\nfrom core.feature_extractors import tokenizer\n\ndef main():\n # Handle input options and arguments\n usage = \"%prog project\"\n parser = OptionParser(usage=usage)\n (options, args) = parser.parse_args()\n\n project = args[0]\n\n dirs.make_base_dir(project)\n\n input_filename = dirs.data_raw_text_file\n\n write_sentences(input_filename)\n\ndef write_sentences(f):\n output_dir = fh.makedirs(dirs.data_semafor_dir, 'temp')\n\n index = 0\n sent_index = {}\n responses = fh.read_json(f)\n keys = responses.keys()\n keys.sort()\n\n #all_items = ds.get_all_documents()\n #unlabeled = list(set(keys) - all_items)\n #print len(unlabeled)\n\n for k in keys:\n sentence_filename = os.path.join(output_dir, k + '.txt')\n #index_filename = fh.make_filename(output_dir, fh.get_basename(f), 'json')\n with codecs.open(sentence_filename, 'w', encoding='utf-8') as output_file:\n text = responses[k]\n paragraphs = text.split('\\n\\n')\n paragraphs = [p for p in paragraphs if p != '']\n for p in paragraphs:\n sentences = tokenizer.split_sentences(p)\n for sent in sentences:\n sent = sent.lstrip()\n sent = sent.rstrip()\n if len(sent) > 0:\n output_file.write(sent + '\\n')\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"core/external/obsolete_semafor_preprocessing.py","file_name":"obsolete_semafor_preprocessing.py","file_ext":"py","file_size_in_byte":1515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"284818275","text":"from typing import Tuple\n\nimport torch\nfrom torch import Tensor, nn\n\nfrom ... import transforms as T\nfrom ...transforms import functional as F\n\n\n__all__ = [\"ConvertImageDtype\", \"ImageNetEval\"]\n\n\n# Allows handling of both PIL and Tensor images\nclass ConvertImageDtype(nn.Module):\n def __init__(self, dtype: torch.dtype) -> None:\n super().__init__()\n self.dtype = dtype\n\n def forward(self, img: Tensor) -> Tensor:\n if not isinstance(img, Tensor):\n img = F.pil_to_tensor(img)\n return F.convert_image_dtype(img, self.dtype)\n\n\nclass ImageNetEval:\n def __init__(\n self,\n crop_size: int,\n resize_size: int = 256,\n mean: Tuple[float, ...] = (0.485, 0.456, 0.406),\n std: Tuple[float, ...] = (0.229, 0.224, 0.225),\n interpolation: T.InterpolationMode = T.InterpolationMode.BILINEAR,\n ) -> None:\n self.transforms = T.Compose(\n [\n T.Resize(resize_size, interpolation=interpolation),\n T.CenterCrop(crop_size),\n ConvertImageDtype(dtype=torch.float),\n T.Normalize(mean=mean, std=std),\n ]\n )\n\n def __call__(self, img: Tensor) -> Tensor:\n return self.transforms(img)\n","sub_path":"torchvision/prototype/transforms/presets.py","file_name":"presets.py","file_ext":"py","file_size_in_byte":1249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"262412734","text":"#pylint: disable=E1101\n\nfrom sys import path\nfrom os import environ\nimport django\n# Подключение к джанго\npath.append(\"./\")\nenviron.setdefault(\"DJANGO_SETTINGS_MODULE\", \"main.settings\")\ndjango.setup()\nfrom api.models import Tag\nfrom bs4 import BeautifulSoup\nfrom json import loads, dumps\nimport requests\n\nROOT = \"https://bandcamp.com\"\nTAGS_LINK = \"/tags\"\nQUEUE = []\nCHECKED = []\n\ndef __init__():\n console_write( \"STARTED\", transform=\"blue\" )\n r = requests.get( ROOT+TAGS_LINK )\n soup = BeautifulSoup( r.text, \"html.parser\" )\n for tag in soup.findAll('a', class_='tag'):\n tagName = parse_tagname( tag )\n QUEUE.append( tagName )\n\ndef parse_tag( tagName ):\n if TAGS.filter( name = tagName ):\n console_write( 'exists', transform = 'red', end = '\\t' )\n else:\n Tag.objects.create(name = tagName)\n console_write( 'added', transform = 'green', end = '\\t' )\n\n r = requests.get( ROOT + \"/tag/\" + tagName )\n soup = BeautifulSoup( r.text , 'html.parser' )\n LEN = len(QUEUE)\n for tag in soup.findAll( 'a' , class_ = 'related_tag' ):\n relatedTag = parse_tagname( tag )\n if relatedTag not in QUEUE and relatedTag not in CHECKED:\n QUEUE.append( relatedTag )\n new_count = str( len( QUEUE ) - LEN ) + ' / ' + str( len(soup.findAll( 'a' , class_ = 'related_tag' )) )\n console_write( new_count , transform = 'underline' )\n # if len(QUEUE) - LEN:\n # console_write( len(QUEUE) - LEN, transform = 'underline', prefix = 'ADDED' )\n\ndef parse_tagname(tagName):\n if tagName:\n return tagName['href'].split('/')[-1].split('?')[0]\n else:\n return None\n \ndef parse_description(tagName):\n tag = Tag.objects.get( name = tagName )\n r = requests.get( ROOT + '/tag/' + tagName )\n soup = BeautifulSoup( r.text , 'html.parser' )\n relation = loads( tag.relation )\n for attr in soup.findAll( 'a' , class_ = 'related_tag' ):\n attr_name = parse_tagname( attr )\n relation[attr_name] = 10\n console_write( relation , transform = 'yellow' )\n tag.relation = dumps( relation )\n tag.save()\n\ndef console_write(text, transform=\"default\", prefix=\"\", end='\\n'):\n END = \"\\033[0m\"\n FONT = {\n \"pink\": \"\\033[95m\",\n \"blue\": \"\\033[94m\",\n \"green\": \"\\033[92m\",\n \"yellow\": \"\\033[93m\",\n \"red\": \"\\033[91m\",\n \"bold\": \"\\033[1m\",\n \"underline\": \"\\033[4m\",\n \"default\": \"\\033[0m\"\n }.get(transform, END)\n\n prefix = '(' + str(prefix) + ') ' if prefix else ''\n print( str(FONT) + str(prefix) + str(text) + str(END) , end=end )\n\nif __name__ == \"__main__\":\n __init__()\n\n i = 0\n while True:\n i+=1\n TAGS = Tag.objects.all()\n console_write( \"QUEUE (%s)\" % len(QUEUE) , transform = \"pink\" )\n\n if not QUEUE:\n console_write( 'F I N', transform = 'red')\n break\n\n for tag in QUEUE:\n index = QUEUE.index( tag )\n\n console_write(tag, transform = 'bold', end = '\\t', prefix = i)\n parse_tag( tag )\n\n del QUEUE[ index ]\n CHECKED.append( tag )\n \n console_write( \"DESCRIPTION (%s)\" % len(CHECKED), transform = 'pink' )\n for tag in CHECKED:\n parse_description( tag )\n console_write( tag , transform = 'bold' )","sub_path":"scripts/bandcamp/tag_parser.py","file_name":"tag_parser.py","file_ext":"py","file_size_in_byte":3315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"198624129","text":"from math import ceil\n\nimport pandas as pd\nimport numpy as np\nfrom scipy.stats import mode\nfrom sklearn.utils import resample\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LinearRegression\nimport matplotlib.pyplot as plt\n\nfrom basis_expansions import NaturalCubicSpline\n\n\ndef plot_univariate_smooth(ax, x, y,\n x_lim=None, mask=None, smooth=True, n_knots=6, bootstrap=False):\n \"\"\"Draw a scatter plot of some (x, y) data, and optionally superimpose\n a cubic spline.\n\n Parameters\n ----------\n ax: A Matplotlib axis object to draw the plot on.\n\n x: A np.array or pd.Series object containing the x data.\n\n y: A np.array or pd.Series object containing the y data.\n\n x_lim: A tuple contining limits for the x-axis of the plot. If not\n supplied, this is computed as the minimum and maximum of x.\n\n mask: A boolean np.array or pd.Series containing a mask for the x and y\n data, if supplied only the unmasked data contributes to the plot.\n\n smooth: A boolean, draw the cubic spline or not?\n n_knots: The number of knots to use in the cubic spline.\n\n bootstrap: False or an integer. The number of times to boostrap the data\n when drawing the spline. If not False, draw one spline per bootstrap\n sample of the data.\n\n Returns:\n --------\n None\n \"\"\"\n if isinstance(x, pd.Series):\n x = x.values\n if isinstance(y, pd.Series):\n y = y.values\n if mask is not None:\n if isinstance(mask, pd.Series):\n mask = mask.values\n x = x[mask]\n y = y[mask]\n if not x_lim:\n x_lim = (np.min(x), np.max(x))\n x, y = x.reshape(-1, 1), y.reshape(-1, 1)\n\n ax.scatter(x, y, color='grey', alpha=0.25, label=\"Data\")\n if smooth:\n if bootstrap:\n for _ in range(bootstrap):\n x_boot, y_boot = resample(x, y)\n plot_smoother(ax, x_boot, y_boot,\n x_lim, n_knots,\n alpha=0.5, color=\"lightblue\",\n label=None)\n plot_smoother(ax, x, y, x_lim, n_knots,\n linewidth=3, color=\"blue\", label=\"Trend\")\n\ndef make_natural_cubic_regression(n_knots, knot_range=(-2, 2)):\n \"\"\"A helper function for constructing a pipeline fiting a one dimensional\n regression with a cubic spline feature.\"\"\"\n return Pipeline([\n ('standardizer', StandardScaler()),\n ('nat_cubic', NaturalCubicSpline(knot_range[0], knot_range[1], n_knots=n_knots)),\n ('regression', LinearRegression(fit_intercept=True))\n ])\n\ndef plot_smoother(ax, x, y, x_lim, n_knots, **kwargs):\n ncr = make_natural_cubic_regression(n_knots)\n ncr.fit(x, y)\n t = np.linspace(x_lim[0], x_lim[1], num=250)\n y_smoothed = ncr.predict(t.reshape(-1, 1))\n ax.plot(t, y_smoothed, **kwargs)\n\n\ndef display_coef(model, coef_names):\n \"\"\"Pretty print a table of the parameter estimates in a linear model.\n\n Parameters\n ----------\n model: A fit sklean object with a `coef_` attribute.\n\n coef_names: A list of names associated with the coefficients.\n \"\"\"\n print(\"{:<35}{:<20}\".format(\"Name\", \"Parameter Estimate\"))\n print(\"-\"*(35 + 20))\n for coef, name in zip(model.coef_, coef_names):\n row = \"{:<35}{:<20}\".format(name, coef)\n print(row)\n\n\ndef bootstrap_train(model, X, y, bootstraps=1000, **kwargs):\n \"\"\"Train a (linear) model on multiple bootstrap samples of some data and\n return all of the parameter estimates.\n\n Parameters\n ----------\n model: A sklearn class whose instances have a `fit` method, and a `coef_`\n attribute.\n\n X: A two dimensional numpy array of shape (n_observations, n_features).\n\n y: A one dimensional numpy array of shape (n_observations).\n\n bootstraps: An integer, the number of boostrapped models to train.\n\n Returns\n -------\n bootstrap_coefs: A (bootstraps, n_features) numpy array. Each row contains\n the parameter estimates for one trained boostrapped model.\n \"\"\"\n bootstrap_models = []\n for i in range(bootstraps):\n boot_idxs = np.random.choice(X.shape[0], size=X.shape[0], replace=True)\n X_boot = X[boot_idxs, :]\n y_boot = y[boot_idxs]\n M = model(**kwargs)\n M.fit(X_boot, y_boot)\n bootstrap_models.append(M)\n return bootstrap_models\n\ndef get_bootstrap_coefs(bootstrap_models):\n n_models, n_coefs = len(bootstrap_models), len(bootstrap_models[0].coef_)\n bootstrap_coefs = np.empty(shape=(n_models, n_coefs))\n for i, model in enumerate(bootstrap_models):\n bootstrap_coefs[i, :] = model.coef_\n return bootstrap_coefs\n\n\ndef plot_bootstrap_coefs(models, coef_names, n_col=4):\n \"\"\"Plot histograms of the bootstrapped parameter estimates from a model.\n \"\"\"\n bootstrap_coefs = get_bootstrap_coefs(models)\n n_coeffs = bootstrap_coefs.shape[1]\n n_row = int(ceil(n_coeffs / n_col)) + 1\n fig, axs = plt.subplots(n_row, n_col, figsize=(n_col*6, n_row*2))\n for idx, ax in enumerate(axs.flatten()):\n if idx >= bootstrap_coefs.shape[1]:\n break\n ax.hist(bootstrap_coefs[:, idx], bins=25, color=\"grey\", alpha=0.5)\n ax.set_title(coef_names[idx])\n return fig, axs\n\n\ndef plot_partial_depenence(ax, model, X, var_name,\n y=None, pipeline=None, n_points=250, **kwargs):\n \"\"\"Create a partial dependence plot of a feature in a model.\n\n Parameters\n ----------\n ax: A matplotlib axis object to draw the partial dependence plot on.\n\n model: A trained sklearn model. Must implement a `predict` method.\n\n X: The raw data to use in making predictions when drawing the partial\n dependence plot. Must be a pandas DataFrame.\n\n var_name: A string, the name of the varaible to make the partial dependence\n plot of.\n\n y: The y values, only needed if a scatter plot of x vs. y is desired.\n\n pipeline: A sklearn Pipeline object containing the transformations of the\n raw features used in the model.\n\n n_points: The number of points to use in the grid when drawing the plot.\n \"\"\"\n Xpd = make_partial_dependence_data(X, var_name, n_points)\n x_plot = Xpd[var_name]\n if pipeline is not None:\n Xpd = pipeline.transform(Xpd)\n if y is not None:\n ax.scatter(X[var_name], y, color=\"grey\", alpha=0.5)\n y_hat = model.predict(Xpd)\n ax.plot(x_plot, y_hat, **kwargs)\n\ndef plot_partial_dependences(model, X, var_names,\n y=None, bootstrap_models=None, pipeline=None, n_points=250):\n fig, axs = plt.subplots(len(var_names), figsize=(12, 3*len(var_names)))\n for ax, name in zip(axs, var_names):\n if bootstrap_models:\n for M in bootstrap_models[:100]:\n plot_partial_depenence(\n ax, M, X=X, var_name=name, pipeline=pipeline, alpha=0.8,\n linewidth=1, color=\"lightblue\")\n plot_partial_depenence(ax, model, X=X, var_name=name, y=y,\n pipeline=pipeline, color=\"blue\", linewidth=3)\n ax.set_title(\"{} Partial Dependence\".format(name))\n return fig, axs\n\ndef make_partial_dependence_data(X, var_name, n_points=250):\n Xpd = np.empty((n_points, X.shape[1]))\n Xpd = pd.DataFrame(Xpd, columns=X.columns)\n all_other_var_names = set(X.columns) - {var_name}\n for name in all_other_var_names:\n if is_numeric_array(X[name]):\n Xpd[name] = X[name].mean()\n else:\n # Array is of object type, fill in the mode.\n array_mode = mode(X[name])[0][0]\n Xpd[name] = mode\n min, max = np.min(X[var_name]), np.max(X[var_name])\n Xpd[var_name] = np.linspace(min, max, num=n_points)\n return Xpd\n\ndef is_numeric_array(arr):\n \"\"\"Check if a numpy array contains numeric data.\n\n Source:\n https://codereview.stackexchange.com/questions/128032\n \"\"\"\n numerical_dtype_kinds = {'b', # boolean\n 'u', # unsigned integer\n 'i', # signed integer\n 'f', # floats\n 'c'} # complex\n return arr.dtype.kind in numerical_dtype_kinds\n\n\ndef predicteds_vs_actuals(ax, x, y, y_hat, n_bins=50):\n bins, endpoints = pd.cut(x, bins=n_bins, retbins=True)\n centers = (endpoints[:-1] + endpoints[1:]) / 2\n y_hat_means = pd.DataFrame({'y_hat': y_hat, 'bins': bins}).groupby(\"bins\").mean()[\"y_hat\"]\n ax.scatter(x, y, color=\"grey\", alpha=0.5, label=\"Data\")\n ax.scatter(centers, y_hat_means, s=50, label=None)\n ax.plot(centers, y_hat_means, label=\"Mean Predicted\")\n ax.legend()\n","sub_path":"src/regression_helpers.py","file_name":"regression_helpers.py","file_ext":"py","file_size_in_byte":8663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"350813707","text":"# USE sudo python3 blink_smooth.py\nimport RPi.GPIO as GPIO\nimport numpy as np\nimport time\nimport matplotlib.pyplot as plt\n\nGPIO.setwarnings(False)\nGPIO.setmode(GPIO.BCM)\npin_num = 18\nfreq = 100 # don't see blinking, for some reason faster frequencies are worse\npercent_on = 100\ncount = -1\nstep = 1\nGPIO.setup(pin_num, GPIO.OUT)\np = GPIO.PWM(pin_num , freq)\np.start(percent_on)\n\nmotor_num = 13\nmotor_d1 = 19\nmotor_d2 = 26\nGPIO.setup(motor_num, GPIO.OUT)\nmotor_freq = 100\nmpwm = GPIO.PWM(motor_num , motor_freq)\nmpwm.start(100)\nGPIO.setup(motor_d1, GPIO.OUT)\nGPIO.setup(motor_d2, GPIO.OUT)\n\n\n\n\n\n\nbuffer = np.zeros(100)\n\nwhile 1==1:\n\n\tcount+=step\n\tif count>=100:#np.size(buffer):\n\t\tcount=1\n\tpercent_on = count+0.0\n\t\t\n\tnegone_to_one = np.sin((percent_on+0.0)/100*2*np.pi)\n\tzero_to_one = negone_to_one/2.0 + 0.5\n\n\t# VISUALIZE\n\tVISUALIZE = 0\n\tif VISUALIZE:\n\t\tbuffer[count] = zero_to_one\n\n\t\tplt.clf()\n\t\tplt.plot(range(np.size(buffer)), buffer,'.')\n\t\tplt.show(block=False)\n\t\tplt.pause(0.00001)\n\n\n\t#p.ChangeDutyCycle(zero_to_one*100) #change duty cycle for varying the brightness of LED.\n\tp.ChangeDutyCycle(zero_to_one*30) \n\tprint(zero_to_one*30)\n\t#print(count)\n\t#p.ChangeDutyCycle(count)\n\n\n\t#GPIO.output(motor_num, True)\n\tGPIO.output(motor_d1, False)\n\tGPIO.output(motor_d2, True) \n\tmpwm.ChangeDutyCycle(zero_to_one*100) \n\n\n\ttime.sleep(0.03) #sleep for 100m second\n","sub_path":"catkin_ws/src/control/blink_smooth.py","file_name":"blink_smooth.py","file_ext":"py","file_size_in_byte":1384,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"543782669","text":"import requests, json\nimport defines\n\nstaff_profiles = {\n 'dengancheng' : ['13711297756', 'DengAnCheng'],\n 'shuming':['','ShuMing'],\n 'huangyuliang':['','HuangYuLiang'],\n 'ouyangyilin':['','OuYangYiLin'],\n 'quyun':['','QuYun'],\n 'chengming':['','ChengMing'],\n 'muzhansong':['','MuZhanSong'],\n 'yangyu':['','YangYu'],\n 'wumin':['','WuMin'],\n 'hexinliang':['','HeXinLiang'],\n 'chenhequn':['','ChenHeQun']\n}\n\n\ndef GetNewWXToken():\n\n url = 'https://qyapi.weixin.qq.com/cgi-bin/gettoken?corpid=wwe3edd60cc1415654&corpsecret=59heQtO5JnoaNpykaPFEljtQchUCN-jUmk-GpS7KTos'\n try:\n r = requests.get(url)\n token = r.json()['access_token']\n except Exception as e:\n print('Error in getting new token, %s' % str(e))\n return ''\n\n return token\n\n\ndef SendWX(userids, alert_msg, token):\n url_base = 'https://qyapi.weixin.qq.com/cgi-bin/message/send?access_token='\n\n users_arr = userids.split(',')\n\n try:\n tmp = []\n for u in users_arr:\n username = staff_profiles[u][1]\n tmp.append(username)\n except Exception as e:\n print(e)\n return False\n\n users = '|'.join(tmp)\n\n msg = {\n \"touser\": users,\n \"toparty\": \"\",\n \"msgtype\": \"text\",\n \"agentid\": 1000002,\n \"text\": {\n \"content\": alert_msg,\n }\n }\n\n url = url_base + token\n\n try:\n r = requests.post(url, json.dumps(msg))\n except Exception as e:\n print('Error in send msg to wexin, %s' % str(e))\n return False\n\n return True","sub_path":"local_runner/notification.py","file_name":"notification.py","file_ext":"py","file_size_in_byte":1574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"540465243","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n'''\nCreated on 22.03.2016\n\n@author: Matthias\n'''\nfrom PyQt5.QtCore import QObject, QThread\nfrom lynxCore.dbConnection import DbConnection\nfrom lynxCore.htmlParser import ImageViewHTMLParser\nfrom lynxCore.pageOpener import FurAffinityPageOpener\n\nclass SubmissionDaemon(QObject):\n\n\n def __init__(self, dbName, parent=None):\n QObject.__init__(self, parent)\n self.dbName = dbName\n \n \n def start(self):\n print(\"Starting Work!\")\n self.pageOpener = FurAffinityPageOpener()\n self.viewParser = ImageViewHTMLParser()\n \n with DbConnection(self.dbName) as db:\n allSubmissions = db.getAllSubmissions()\n for submissionId in allSubmissions:\n s = allSubmissions[submissionId]\n response = self.pageOpener.open(s.viewPath)\n the_page = str(response.read())\n \n self.viewParser.feed(the_page)\n s.favoriteKey = self.viewParser.favoriteKey\n s.imagePath = self.viewParser.imgPath\n \n print(\"To Read:\" + s.imagePath)\n \n db.updateSubmission(s)\n \n \n \n \n ","sub_path":"FA-Lynx/src/lynxCore/submissionDaemon.py","file_name":"submissionDaemon.py","file_ext":"py","file_size_in_byte":1259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"360791589","text":"# coding=utf8\n\nimport time\nimport urllib2\nimport ssl\nimport json\nimport hashlib\n\nfrom center.Meta.Singleton import *\n\n\nclass Wechat_API:\n __metaclass__ = Singleton\n\n def __init__(self):\n ssl._create_unverified_https_context = ssl._create_unverified_context\n self.appid = \"wx32e28629c25f7c45\"\n self.appsecret = \"109088f5f5c928d30f0d4e0d313f6e4f\"\n self.noncestr = \"bamai188\"\n self.signatureURL = \"http://bamai188.com:8001/wechat/index/?client_id={0}\"\n self.signatureBase = \"jsapi_ticket={0}&noncestr={1}×tamp={2}&url={3}\"\n self.getTokenURL = \"https://api.weixin.qq.com/cgi-bin/token?grant_type=client_credential&appid={0}&secret={1}\".format(self.appid, self.appsecret)\n self.getTicketURL = \"https://api.weixin.qq.com/cgi-bin/ticket/getticket?access_token={0}&type=jsapi\"\n self.token = None\n self.token_time = None\n self.ticket = None\n self.ticket_time = None\n\n def get_token(self):\n if self.token_time is None or time.time() - self.token_time > 7000:\n req = urllib2.Request(self.getTokenURL)\n context = ssl._create_unverified_context()\n try:\n res = urllib2.urlopen(req, context=context)\n token = json.loads(res.read())\n print(token)\n self.token = token[\"access_token\"]\n self.token_time = time.time()\n return token[\"access_token\"]\n except Exception as e:\n return e.message\n else:\n return self.token\n\n\n def get_ticket(self):\n if self.ticket_time is None or time.time() - self.ticket_time > 7000:\n req = urllib2.Request(self.getTicketURL.format(self.get_token()))\n context = ssl._create_unverified_context()\n try:\n res = urllib2.urlopen(req, context=context)\n ticket = json.loads(res.read())\n print(ticket)\n self.ticket = ticket[\"ticket\"]\n self.ticket_time = time.time()\n return ticket[\"ticket\"]\n except Exception as e:\n return e.message\n else:\n return self.ticket\n\n\n def get_signature(self, client_id=None):\n timestamp = int(time.time())\n ticket = self.get_ticket()\n url = self.signatureURL.format(client_id)\n raw_signature = self.signatureBase.format(ticket, self.noncestr, timestamp, url)\n print(raw_signature)\n try:\n signature = hashlib.sha1(raw_signature).hexdigest()\n return {\n \"ticket\": ticket,\n \"url\": url,\n \"timestamp\": timestamp,\n \"noncestr\": self.noncestr,\n \"signature\": signature,\n }\n except:\n return None\n\nif __name__ == \"__main__\":\n # sig = \"jsapi_ticket=sM4AOVdWfPE4DxkXGEs8VDZwovUahd4fKvznYz7tFU5wi6tXYPZAJWKvGMSFRSaAdjsTIEOBDfi_O2-yJm468g&noncestr=bamai188×tamp=1490245540&url=http://bamai188.com:8001/wechat/index/?client_id=39\"\n sig = \"jsapi_ticket=sM4AOVdWfPE4DxkXGEs8VDZwovUahd4fKvznYz7tFU5wi6tXYPZAJWKvGMSFRSaAdjsTIEOBDfi_O2-yJm468g&noncestr=bamai188×tamp=1490245540&url=http://bamai188.com:8001/wechat/index/?client_id=39\"\n signature = hashlib.sha1(sig).hexdigest()\n print(signature)\n print(\"4248dea9db4e26bc5bc2d3b1e18eb59c2748f098\")\n\n print(len(signature))\n print(len(\"4248dea9db4e26bc5bc2d3b1e18eb59c2748f098\"))\n\n print(type(signature))\n print(type(\"4248dea9db4e26bc5bc2d3b1e18eb59c2748f098\"))\n\n print(signature == \"4248dea9db4e26bc5bc2d3b1e18eb59c2748f098\")\n","sub_path":"cloudplat/center/Helper/Helper_wechat.py","file_name":"Helper_wechat.py","file_ext":"py","file_size_in_byte":3640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"618053768","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('user_pages', '0003_auto_20160406_1853'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='resultsofproblem',\n name='learner_version',\n field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, default=1, to='user_pages.UploadedFile', related_name='learner_version'),\n preserve_default=False,\n ),\n migrations.AlterField(\n model_name='resultsofproblem',\n name='teacher_version',\n field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, default=1, to='user_pages.UploadedFile', related_name='teacher_version'),\n preserve_default=False,\n ),\n ]\n","sub_path":"user_pages/migrations/0004_auto_20160406_1938.py","file_name":"0004_auto_20160406_1938.py","file_ext":"py","file_size_in_byte":909,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"529285482","text":"import sys\n\n\nLOG_CONFIG = {\n \"version\": 1,\n \"disable_existing_loggers\": False,\n \"formatters\": {\n \"fileFormatter\": {\n \"format\": \"[%(levelname)s] %(asctime)s [%(module)s-%(filename)s - line:%(lineno)d] [proc:%(process)d] %(message)s\"\n },\n \"consoleFormatter\": {\n \"format\": \"[%(levelname)s] %(asctime)s [%(module)s-%(filename)s - line:%(lineno)d] [proc:%(process)d] %(message)s\"\n },\n \"default\": {\n \"format\": \"%(asctime)s %(levelname)s [%(name)s: %(lineno)s] -- %(message)s\"\n },\n },\n \"handlers\": {\n \"consoleHandler\": {\n \"level\": \"NOTSET\",\n \"class\": \"logging.StreamHandler\",\n \"formatter\": \"consoleFormatter\",\n \"stream\": sys.stdout\n },\n \"AccessTimedRotatingFileHandler\": {\n \"level\": \"DEBUG\",\n \"class\": \"logging.handlers.TimedRotatingFileHandler\",\n \"formatter\": \"fileFormatter\",\n \"filename\": \"/tmp/TMEG-statitics-access\",\n \"when\": \"MIDNIGHT\",\n \"interval\": 1,\n \"backupCount\": 60,\n \"encoding\": None,\n \"delay\": False,\n \"utc\": False\n },\n \"ErrorTimedRotatingFileHandler\": {\n \"level\": \"ERROR\",\n \"class\": \"logging.handlers.TimedRotatingFileHandler\",\n \"formatter\": \"fileFormatter\",\n \"filename\": \"/tmp/TMEG-statitics-error\",\n \"when\": \"MIDNIGHT\",\n \"interval\": 1,\n \"backupCount\": 60,\n \"encoding\": None,\n \"delay\": False,\n \"utc\": False\n },\n \"ScheduleAccessTimedRotatingFileHandler\": {\n \"level\": \"DEBUG\",\n \"class\": \"logging.handlers.TimedRotatingFileHandler\",\n \"formatter\": \"fileFormatter\",\n \"filename\": \"/tmp/TMEG-statitics-schedule-access\",\n \"when\": \"MIDNIGHT\",\n \"interval\": 1,\n \"backupCount\": 60,\n \"encoding\": None,\n \"delay\": False,\n \"utc\": False\n },\n \"ScheduleErrorTimedRotatingFileHandler\": {\n \"level\": \"ERROR\",\n \"class\": \"logging.handlers.TimedRotatingFileHandler\",\n \"formatter\": \"fileFormatter\",\n \"filename\": \"/tmp/TMEG-statitics-schedule-error\",\n \"when\": \"MIDNIGHT\",\n \"interval\": 1,\n \"backupCount\": 60,\n \"encoding\": None,\n \"delay\": False,\n \"utc\": False\n }\n },\n \"loggers\": {\n \"access.log\": {\n \"level\": \"INFO\",\n \"handlers\": [\"AccessTimedRotatingFileHandler\"],\n \"propagate\": True,\n },\n \"error.log\": {\n \"level\": \"ERROR\",\n \"handlers\": [\"ErrorTimedRotatingFileHandler\"],\n \"propagate\": True,\n },\n \"schedule_access.log\": {\n \"level\": \"INFO\",\n \"handlers\": [\"ScheduleAccessTimedRotatingFileHandler\"],\n \"propagate\": True,\n },\n \"schedule_error.log\": {\n \"level\": \"ERROR\",\n \"handlers\": [\"ScheduleErrorTimedRotatingFileHandler\"],\n \"propagate\": True,\n }\n },\n \"root\": {\n \"level\": \"NOTSET\",\n \"handlers\": [\"consoleHandler\"],\n \"propagate\": False,\n },\n}\n\n","sub_path":"src/config/logging.py","file_name":"logging.py","file_ext":"py","file_size_in_byte":3287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"512926869","text":"#!/usr/bin/env python3\nimport _matrix\nimport unittest\nimport random\nimport math\nimport time\n\nclass MatrixTestCase(unittest.TestCase):\n\tdef test_matrix(self):\n\t\tsize = 1000\n\t\ttile_width = random.randint(10, 100)\n\t\tmat1 = _matrix.Matrix(size,size)\n\t\tmat2 = _matrix.Matrix(size,size)\n\t\tfor i in range(size):\n\t\t\tfor j in range(size):\n\t\t\t\tmat1[i, j] = random.randint(1, 100)\n\t\t\t\tmat2[i, j] = random.randint(1, 100)\n\t\tstarttime = time.time()\n\t\tret_naive = _matrix.multiply_naive(mat1, mat2)\n\t\tendtime = time.time()\n\t\tnaive_time = endtime - starttime\n\t\t\n\t\tstarttime = time.time()\n\t\tret_tile = _matrix.multiply_tile(mat1, mat2, tile_width)\n\t\tendtime = time.time()\n\t\ttile_time = endtime - starttime\n\t\t\n\t\tstarttime = time.time()\n\t\tret_mkl = _matrix.multiply_naive(mat1, mat2)\n\t\tendtime = time.time()\n\t\tmkl_time = endtime - starttime\n\t\t\n\t\tf = open('performance.txt','w')\n\t\tf.writelines(['tile_width = ',repr(tile_width),'\\nnaive costs ',repr(naive_time),' seconds.\\ntile costs ',repr(tile_time),' seconds.\\nmkl costs ',repr(mkl_time),' seconds.\\ntile/naive = ',repr(tile_time/naive_time)])\n\t\tf.close()\n\t\tfor a in range(ret_naive.nrow):\n\t\t\tfor b in range(ret_naive.ncol):\n\t\t\t\tself.assertEqual(ret_naive[a,b], ret_tile[a,b])\n\t\t\t\tself.assertEqual(ret_tile[a,b], ret_mkl[a,b])\n\t\t\t\tself.assertEqual(ret_naive[a,b], ret_mkl[a,b])\n\t\tself.assertLess(tile_time/naive_time, 0.8)\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"hw3/jspss93094/test_mat.py","file_name":"test_mat.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"628633742","text":"#!/usr/bin/python3\n\nimport socket\nimport sys\nimport time\n\nfrom grid import *\nfrom termios import tcflush, TCIFLUSH\nfrom robot import *\n\ndef action(data):\n\tlength = len(data)\n\tpos = 0\n\twhile(pos 2:\n\t\tchose = input('Tapez 1 pour jouer contre un bot\\nTapez 2 pour jouer en ligne\\n')\n\tif int(chose) == 1:\n\t\trobot()\n\telse:\n\t\t# Create a TCP/IP socket\n\t\tclient_socket = socket.socket(socket.AF_INET6, socket.SOCK_STREAM)\n\t\tclient_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n\n\t\tif(len(sys.argv)>=2):\n\t\t\tserver_address = (sys.argv[1], 7777)\n\t\t\tprint('starting up on', server_address[0], 'port', server_address[1], file=sys.stderr)\n\t\telse:\n\t\t\tserver_address = ('', 7777)\n\t\t\tprint('starting up on localhost port', server_address[1], file=sys.stderr)\n\n\t\t# Connect the socket to the port where the server is listening\n\t\tclient_socket.connect(server_address)\n\n\t\tgrid = grid()\n\n\t\twhile True:\n\t\t\tdata = client_socket.recv(1500).decode().split('|')\n\t\t\taction(data)","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"584413222","text":"class Node:\n\n def __init__(self, key):\n self.left = None\n self.right = None\n self.key = key\n\ndef is_BST(tree_root):\n\n stack = []\n stack.append(tree_root)\n\n result = True\n\n while len(stack > 0):\n\n n = stack.pop()\n\n if n.left:\n if n.left.key > n.key:\n result = False\n break\n stack.append(n.left)\n\n if n.right:\n if n.right.key < n.key:\n result = False\n break\n stack.append(n.right)\n\n return result\n","sub_path":"mixpanel/isbst_v1.py","file_name":"isbst_v1.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"524877777","text":"#Python differential equation solving to solve the nonlinear differential equations-michaelis-menten equations\n#Rough draft, needs some adjustment by user to operate(like including correct rate coefficients)\n\nimport numpy as np\nfrom scipy.integrate import odeint\nimport matplotlib.pyplot as plt\n\n#function \ndef rxn(x, t):\n #reaction function: takes in species and differential equations, returns differential equation results\n \n \n E= x[0]\n S= x[1]\n ES = x[2]\n P= x[3]\n \n #set up constants, K_f(reaction forwardrate), K_r(backwards rate), K_cat(K_c)\n K_f= 3\n K_r= 2\n K_c= 1\n \n #set up differential equations - uses the pre-factors above\n dEdt= -K_f * E * S + K_r * ES + K_c * ES\n dSdt= -K_f * E * S + K_r * ES \n dESdt= K_f * E * S - K_r * ES -K_c * ES\n dPdt= K_c * ES\n\n print (\"t = \" + str(t))\n #print \"dnedt = \" + str(dnedt)\n #print \"dTedt = \" + str(dTedt)\n\n\n return [dEdt, dSdt, dESdt, dPdt]\n\n\n\n\n#Constants\n\n\n\n#Initial conditions\nx0= [1, 1, 1, 1]\n\n\n#Defining the time vector\nt0 = 0.0\ntend = 10000\nnt = 100000\n\nT = np.linspace(t0, tend, nt)\n\n\n\n#Integrating the governing equation provided by the function\nX = odeint(rxn, x0, T)\n\n\nprint ((\"Final value of E = %e \\n\") % X[-1,0])\nprint ((\"Final value of S = %f \") % X[-1,1])\n\n\n\n\n\n\n#Plotting\nlabel_size = 16\n\nfig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12,6))\n\nax1.plot(T, X[:, 0], 'b', linewidth=2)\nax1.set_title(r'Evolution of E')\nax1.set_xlabel(r'$t$ $(s)$', fontsize=label_size)\nax1.set_ylabel(r'Concentration', fontsize=label_size)\n\n\nax2.plot(T, X[:, 1], 'r', linewidth=2)\nax2.set_title(r'Evolution of S')\nax2.set_xlabel(r'$t$ $(s)$', fontsize=label_size)\nax2.set_ylabel(r'$Concentration$', fontsize=label_size)\n#plt.legend()\nplt.show()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"michaelismenten.py","file_name":"michaelismenten.py","file_ext":"py","file_size_in_byte":1784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"394001940","text":"import sys\n\ndef convert(lines):\n seen_noncomment = False\n for line in lines:\n if line[0] == '#' and not seen_noncomment:\n # skip comments in beginning of file\n continue\n else:\n seen_noncomment = True\n c, w = line.rstrip('\\n').split(None, 1)\n c = int(float(c)) + 1\n yield c, w \n\nif __name__ == '__main__':\n for c, w in convert(sys.stdin):\n print('{}\\t{}'.format(c, w))\n","sub_path":"tools/expected_to_vocab.py","file_name":"expected_to_vocab.py","file_ext":"py","file_size_in_byte":452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"375794270","text":"from django.contrib import admin\nfrom django.conf.urls import url, include\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth import views as auth_views\nfrom django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n \n \n url(r'^dashboard/$', views.home, name='dashboard'),\n url('applicants/',views.applicants_index, name='applicants_index'),\n url('admitted/$',views.applicants_admitted, name='applicants_admitted'),\n url('notifications/admissions$',views.sms_admissions, name='send_admission_alert'),\n path('admit/', views.admit, name='admit'),\n url(r'^(?P[0-9]+)/show/$', views.show_applicant, name='show_applicant'),\n url(r'^(?P[0-9]+)/letter/print/$', views.letter, name='letter'),\n #url(r'^(?P[0-9]+)/revoke/$', views.revoke_admission, name='revoke_admission'),\n url(r'^revoke/$', views.revoke_admission, name='revoke_admission'),\n url(r'^send_notifications/$', views.send_notifications, name='send_notifications'),\n url(r'^messages/sents$', views.messages, name='messages'),\n url(r'^srms/forward$', views.sendToSrms, name='sendToSrms'),\n url(r'^report/excel$', views.excel, name='excel'),\n url(r'^results/verification$', views.export_xlsx, name='export_xlsx'),\n url(r'^forms/verification$', views.show_export_form, name='show_export_form'),\n url(r'^report/graphs', views.graphs, name='graphs'),\n url(r'^return', views.returnToSrms, name='returnToSrms'),\n url(r'^readmit', views.reAdmitToProfessional, name='readmit'),\n url(r'^fireCustomSMS', views.fireCustomSMS, name='fireCustomSMS'),\n url(r'^statistics/view$', views.statistics, name='statistics'),\n url(r'^applicant/(?P[-\\w]+)/$', views.applicantInfo, name='applicant_info'),\n url(r'^search/(?P[-\\w]+)/$', views.applicantSearch, name='search'),\n url(r'^live/$', views.ajaxStatistics, name='live'),\n \n url('international/home', views.international_index, name='international_index'),\n url('international/admitted_view', views.international_admitted, name='international_admitted'),\n \n ]\n\n","sub_path":"admissions/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":"98"} +{"seq_id":"319027497","text":"from setuptools import setup, find_packages\n\nfrom codecs import open\nfrom os import path\n\nhere = path.abspath(path.dirname(__file__))\n\n# Get the long description from the README file\nwith open(path.join(here, 'README.md'), encoding='utf-8') as f:\n long_description = f.read()\n\nsetup(\n name='micropython-simple-pid',\n version='1.1.0',\n description='A simple, easy to use PID controller for MicroPython',\n long_description=long_description,\n long_description_content_type='text/markdown',\n url='https://github.com/JorgeGMarques/micropython-simple-pid',\n author='Martin Lundberg',\n license='MIT',\n classifiers=[\n 'Development Status :: 5 - Production/Stable',\n 'License :: OSI Approved :: MIT License',\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 3',\n ],\n keywords='pid controller control',\n packages=find_packages(exclude=['tests']),\n package_data={\n 'simple_pid': ['*.pyi', 'py.typed'],\n },\n include_package_data=True,\n zip_safe=False,\n extras_require={\n 'docs': ['m2r', 'sphinx-rtd-theme'],\n },\n project_urls={\n 'Documentation': 'https://micropython-simple-pid.readthedocs.io/',\n },\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"584102665","text":"import numpy as np\n\nimport taichi as ti\n\nif ti.has_pytorch():\n import torch\n\n\ndef _test_ndarray_2d(n, m, a):\n @ti.kernel\n def run(arr: ti.ext_arr()):\n for i in range(n):\n for j in range(m):\n arr[i, j] += i + j\n\n for i in range(n):\n for j in range(m):\n a[i, j] = i * j\n\n run(a)\n\n for i in range(n):\n for j in range(m):\n assert a[i, j] == i * j + i + j\n\n\n@ti.test()\ndef test_ndarray_numpy_2d():\n n = 4\n m = 7\n a = ti.Ndarray(np.empty(shape=(n, m), dtype=np.int32))\n _test_ndarray_2d(n, m, a)\n\n\n@ti.torch_test\ndef test_ndarray_torch_2d():\n n = 4\n m = 7\n a = ti.Ndarray(torch.empty((n, m), dtype=torch.int32))\n _test_ndarray_2d(n, m, a)\n\n\n@ti.torch_test\ndef test_ndarray_default_2d():\n n = 4\n m = 7\n a = ti.ndarray(ti.i32, shape=(n, m))\n _test_ndarray_2d(n, m, a)\n","sub_path":"tests/python/test_ndarray.py","file_name":"test_ndarray.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"297251145","text":"# parse_embark_xml.py 2/5/19 sm\n\"\"\"Get data from EmbArk given JSON definitions.\"\"\"\n\nfrom get_individual_field_from_embark_xml import GetEmbarkField\n\n\nclass ParseEmbarkXml(object):\n \"\"\" Class does heavy lifting translating XML to JSON \"\"\"\n def __init__(self, fields_definition):\n \"\"\" Initialize fields_definition only once for local use later \"\"\"\n self.result_json = {}\n self.fields_definition = fields_definition\n self.id = \"\"\n self.output = {}\n\n def parse_embark_record(self, embark_item_xml):\n \"\"\" This translates information from EmbArk XML representing an\n individual museum item to JSON \"\"\"\n fields_definition = self.fields_definition\n self.output = {} # reset at beginning of each EmbArk Item\n node = {}\n for field in fields_definition:\n get_embark_field_instance = GetEmbarkField(field)\n node = get_embark_field_instance.get_json_representation_of_field(\n embark_item_xml)\n if 'recordId' in node:\n self.id = node['recordId']\n self.output.update(node)\n return self.output\n","sub_path":"parse_embark_xml.py","file_name":"parse_embark_xml.py","file_ext":"py","file_size_in_byte":1150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"351278359","text":"import os\n\nbase_dir = os.path.dirname(os.path.realpath(__file__))\n\nNET = 'net'\n\nFEATURES = [\n 'feature.SineGen',\n 'feature.NoisySineGen',\n 'feature.ConvertToClasses',\n #'feature.Dropout',\n 'feature.Chunk'\n]\n\nSAMPLERATE = 11024\nDURATION = 1\nDROPOUT = 0.50\nLEARNING_RATE = 0.001\nCLASS_COUNT = 100\nFREQUENCY_START = 300\nFREQUENCY_LIMIT = (FREQUENCY_START, FREQUENCY_START + CLASS_COUNT*2)\nDISCRETE_CLASS = 256\n\n\n\nBATCH_SIZE = 32\nCHUNK = 25\n\nCUDA_VISIBLE_DEVICES = 1\n\nLOG_INTERVAL = 100//BATCH_SIZE\n\nLOG_DIR = os.path.join(base_dir, \"logs\")\n\nDATA = {\n 'train': (),\n 'validation': (),\n 'test': ()\n}\n","sub_path":"examples/denoise_class_mult/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":618,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"161330511","text":"import numpy as np\r\nimport tensorflow as tf\r\nimport tensorflow_hub as hub\r\nimport tensorflow_text as text # Registers the ops.\r\nfrom embeddings_pipelines.model.models import MultipleWordsEmbeddingModel\r\n\r\nclass DummyMultipleWordsEmbeddingModel(MultipleWordsEmbeddingModel):\r\n def __init__(self, embedding_size: int):\r\n \"\"\"this embedder produces a single random word embeddings regardless of how meny words it receives\r\n\r\n Args:\r\n embedding_size (int): the size of the output embedding\r\n \"\"\"\r\n self.embedding_size = embedding_size\r\n self.built=False\r\n\r\n\r\n def build(self):\r\n pass\r\n\r\n \r\n def predict(self, words:np.array) -> np.array:\r\n \"\"\"Applies the built model to a 1-D numpy array of strings containing words\r\n\r\n Args:\r\n words (np.array): A 1-D numpy array of strings of length N containing words\r\n\r\n Returns:\r\n np.array: a 1-D numpy array of floats with size K, being the embedding size\r\n \"\"\"\r\n return np.ones((self.embedding_size,))\r\n\r\n\r\n def dispose(self):\r\n pass\r\n\r\n\r\nclass TFHubPreTrainedBERTMultipleWordsEmbeddingModel(MultipleWordsEmbeddingModel):\r\n def __init__(self, \r\n tf_hub_url: str = \"https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-128_A-2/1\",\r\n preprocessor_url: str = \"https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3\"\r\n ):\r\n \"\"\"this embedder uses a pre-trained BERT model to embed input words.\r\n Input words are concatenated in a single \"sentence\". \r\n This \"sentence\" is then fed to the model in order to produce an embedding\r\n\r\n Args:\r\n tf_hub_url (str): the tensorflow hub URL of the model (default: the fastest one).\r\n For a list of availabel embedding models, see \r\n https://tfhub.dev/s?fine-tunable=yes&language=en&tf-version=tf2&q=bert\r\n tf_hub_url (str): the tensorflow hub URL of the preprocessor module fot this model (default: the only one).\r\n \"\"\"\r\n self.tf_hub_url = tf_hub_url\r\n self.preprocessor_url = preprocessor_url\r\n self.built = False\r\n\r\n\r\n def build(self):\r\n text_input = tf.keras.layers.Input(shape=(), dtype=tf.string)\r\n preprocessor = hub.KerasLayer(self.preprocessor_url)\r\n encoder_inputs = preprocessor(text_input)\r\n encoder = hub.KerasLayer(self.tf_hub_url, trainable=False)\r\n outputs = encoder(encoder_inputs)\r\n pooled_output = outputs[\"pooled_output\"] # [batch_size, 128].\r\n # sequence_output = outputs[\"sequence_output\"] # [batch_size, seq_length, 128].\r\n self.model = tf.keras.Model(text_input, pooled_output)\r\n self.built = True\r\n\r\n \r\n def predict(self, words:np.array) -> np.array:\r\n \"\"\"Applies the built model to a 1-D numpy array of strings containing words\r\n\r\n Args:\r\n words (np.array): A 1-D numpy array of strings of length N containing words\r\n\r\n Returns:\r\n np.array: a 1-D numpy array of floats\r\n \"\"\"\r\n assert self.built, \"you need to call build() first!\"\r\n sentences = np.array([\" \".join(words)])\r\n sentences_tensor = tf.constant(sentences)\r\n output_tensor = self.model(sentences_tensor)\r\n return output_tensor.numpy()[0]\r\n\r\n\r\n def dispose(self):\r\n del self.model\r\n self.built = False","sub_path":"playground/oliver_ilnicki/embeddings_pipelines/src/embeddings_pipelines/models/multiple_words_embedding_models.py","file_name":"multiple_words_embedding_models.py","file_ext":"py","file_size_in_byte":3443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"40722669","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Jan 19 11:02:13 2021\r\n\r\n@author: DAW\r\n\"\"\"\r\n\r\nprint(\" --- CALCULADORA ---\")\r\n\r\n# definicion de funciones\r\ndef suma(num1, num2):\r\n resultado = (num1+num2)\r\n return resultado\r\n\r\ndef resta(num1, num2):\r\n resultado = (num1-num2)\r\n return resultado\r\n\r\ndef multiplicacion(num1, num2):\r\n resultado = (num1*num2)\r\n return resultado\r\n\r\ndef division(num1, num2):\r\n resultado = (num1/num2)\r\n return resultado\r\n\r\n\r\n# variable para el tipo de operacion que queremos hacer y el texto de la operacion\r\noperacion = 0\r\ntexto = \"\"\r\n\r\nprint(\" --- CALCULADORA --- \\n\")\r\nprint(\"Que operacion quieres hacer: \")\r\nprint(\" 1. Sumar\")\r\nprint(\" 2. Restar\")\r\nprint(\" 3. Multiplicar\")\r\nprint(\" 4. Dividir\")\r\n\r\noperacion = int (input((\"Que operacion quieres realizar: \")))\r\nnum1 = int(input(\"Introduce un numero: \"))\r\nnum2 = int(input(\"Introduce otro numero: \"))\r\n\r\n# menu para la calculadora\r\nif operacion == 1:\r\n resultado = suma (num1, num2)\r\n texto = \"SUMA\"\r\n\r\nelif operacion == 2:\r\n resultado = resta (num1,num2)\r\n texto = \"RESTA\"\r\n\r\nelif operacion == 3:\r\n resultado = multiplicacion(num1, num2)\r\n texto = \"MULTIPLICACION\"\r\nelif operacion == 4:\r\n resultado = division(num1, num2)\r\n texto = \"DIVISION\"\r\n \r\nelse:\r\n print(\"Introduce un numero del indice de operaciones\")\r\n \r\n\r\n \r\nprint(f\"El resultado de la operacion de {texto} de los numeros {num1} y {num2} es {resultado}\")","sub_path":"proyectos/clase/calculadora_nueva.py","file_name":"calculadora_nueva.py","file_ext":"py","file_size_in_byte":1451,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"427900885","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport unittest\n\nfrom noaaclass import core, noaaclass\n\n\nclass TestCore(unittest.TestCase):\n def setUp(self):\n self.noaa = noaaclass.connect('noaaclass.t', 'noaaclassadmin')\n\n def test_initialize(self):\n # Check if raise an Exception when the api don't define the initialize.\n with self.assertRaisesRegex(Exception, 'Unregistered API.'):\n core.Api('nothing')\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"tests/core_test.py","file_name":"core_test.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"591689221","text":"\"\"\" @PETER HALLDESTAM, 2020\n \n Construction and training of a neural network. Easy to make changes to any \n parameters and most importantly to implements different structures.\n\n\"\"\"\n\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import EarlyStopping\n\nimport sys\n\nfrom models import CNN\nfrom loss_functions import loss_function_wrapper\nfrom plotting import plot_predictions\nfrom plotting import plot_loss\nfrom utils import load_data\nfrom utils import save\nfrom utils import get_eval_data\nfrom utils import get_permutation_match\nfrom utils import cartesian_to_spherical\nfrom utils import get_no_trainable_parameters\n\n## ----------------------------- PARAMETERS -----------------------------------\n\nNPZ_DATAFILE = 'test.npz' #or import sys and use sys.argv[1]\nTOTAL_PORTION = 1 #portion of file data to be used, (0,1]\nEVAL_PORTION = 0.1 #portion of total data for final evalutation (0,1)\nVALIDATION_SPLIT = 0.1 #portion of training data for epoch validation\nCARTESIAN = True #train with cartesian coordinates instead of spherical\nCLASSIFICATION = False #train with classification nodes\n\nNO_EPOCHS = 200\n #Number of times to go through training data\nBATCH_SIZE = 2**8 #The training batch size\nLEARNING_RATE = 1e-4 #Learning rate/step size\nPERMUTATION = True #set false if using an ordered data set\nLOSS_FUNCTION = 'mse' #type of loss: {mse, modulo, cosine} (only mse for cartesian)\nMAT_SORT = \"CCT\" #type of sorting used for the convolutional matrix\nUSE_ROTATIONS = True\nUSE_REFLECTIONS = True\nUSE_BATCH_NORMALIZATION = True\nFILTERS = [32, 16] #must consist of even numbers!\nDEPTH = 3 \ndef main():\n #load simulation data. OBS. labels need to be ordered in decreasing energy!\n data, labels = load_data(NPZ_DATAFILE, TOTAL_PORTION, \n cartesian=CARTESIAN,\n classification=CLASSIFICATION)\n \n #detach subset for final evaluation. train_** is for both training and validation\n train_data, train_labels, eval_data, eval_labels = get_eval_data(data, labels,\n eval_portion=EVAL_PORTION)\n \n \n ### ------------- BUILD, TRAIN & TEST THE NEURAL NETWORK ------------------\n \n \n #no. inputs/outputs based on data set\n no_inputs = len(train_data[0]) \n no_outputs = len(train_labels[0]) \n \n #initiate the network structure\n\n model = CNN(no_inputs, no_outputs, sort = MAT_SORT, filters = FILTERS,\n depth = DEPTH, \n rotations = USE_ROTATIONS, reflections = USE_REFLECTIONS,\n batch_normalization = USE_BATCH_NORMALIZATION)\n \n #select loss function\n loss_function = loss_function_wrapper(no_outputs, \n loss_type=LOSS_FUNCTION, \n permutation=PERMUTATION,\n cartesian=CARTESIAN,\n classification=CLASSIFICATION)\n \n #select optimizer\n opt = Adam(lr=LEARNING_RATE)\n \n #compile the network\n model.compile(optimizer=opt, loss=loss_function, metrics=['accuracy'])\n model.summary()\n \n callback = EarlyStopping(monitor='val_loss', patience=3)\n \n training = model.fit(train_data, train_labels, \n epochs=NO_EPOCHS, batch_size=BATCH_SIZE,\n validation_split=VALIDATION_SPLIT,\n callbacks=callback)\n \n #plot the learning curve\n learning_curve = plot_loss(training)\n \n \n #plot predictions on evaluation data\n predictions = model.predict(eval_data)\n\n \n if CARTESIAN:\n predictions = cartesian_to_spherical(predictions)\n eval_labels = cartesian_to_spherical(eval_labels) \n if PERMUTATION:\n predictions, labels = get_permutation_match(predictions, eval_labels, CARTESIAN, loss_type=LOSS_FUNCTION)\n\n \n \n #plot the \"lasersvärd\"\n figure, rec_events = plot_predictions(predictions, eval_labels, \n show_detector_angles=True)\n \n #add title\n no_params = get_no_trainable_parameters(model)\n title = \"\"\"trainable parameters: {}, epochs: {}, loss: {}, \n cartesian: {}, permutation: {}, max_mult: {},\n #events: {} (training) {} (evaluation, shown)\n \"\"\".format(no_params, NO_EPOCHS, LOSS_FUNCTION, \n CARTESIAN, PERMUTATION, int(no_outputs/3),\n len(train_data), len(eval_data))\n figure.suptitle(title)\n \n save('/home/david/', figure, learning_curve, model)\n \n return\n\nif __name__ == '__main__':\n main()\n","sub_path":"neural_network_conv.py","file_name":"neural_network_conv.py","file_ext":"py","file_size_in_byte":5135,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"317579746","text":"import subprocess\n\necw_path='/ds/images/remote_sensing/data/NetherlandsGeo/ECW/2016_Perceel3_Blok00.ecw'\noutput_path='/b_test/guo/Task_2/tiff/'\n\necw_boundary=[81000,500000,141000,466000]\nshape_boundary=[117787,488285,121214,484267]\ndef crop_ecw(in_path,out_path,coordinates):\n subprocess.call('gdal_translate -of GTIFF -srcwin '+coordinates+in_path+' '+out_path)\n\ndef generate_srcwin(width,N_x,N_y,sha_boundary,ecw_boundary,step):\n x_min=(sha_boundary[0]-ecw_boundary[0])*10\n y_min=(ecw_boundary[1]-sha_boundary[1])*10\n coor_list=[]\n for i in range(N_y):\n for j in range(N_x):\n x_coor=x_min+j*step\n y_coor=y_min+i*step\n coordinate=str(x_coor)+' '+str(y_coor)+' '+str(width)+' '+str(width)+' '\n coor_list.append(coordinate)\n return coor_list\n\n\ncoordinate_list=generate_srcwin(1000,20,shape_boundary,ecw_boundary,200)\n\nfor i in range(len(coordinate_list)):\n print('croping the {} image'.format(i))\n crop_ecw(ecw_path,output_path+str(i)+'.tif',coordinate_list[i])\n\n","sub_path":"GeoImage/extract_ecw.py","file_name":"extract_ecw.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"586129717","text":"#!/usr/bin/env python\nimport numpy as np\nimport integrator as intr\nimport matplotlib.pyplot as plt\n\nPI = 2. * np.arcsin(1)\nN = 1000\na = 0\nb = 2. * PI\nh = (b - a) / (N - 1)\nx = np.zeros(N)\ny1 = np.zeros(N)\ny2 = np.zeros(N)\ny3 = np.zeros(N)\nfor i in range(N):\n x[i] = a + h * i\n y1[i] = np.sin(x[i])\n y2[i] = np.sin(2. * x[i])\n y3[i] = np.sin(8. * x[i])\n\nprint(intr.trap_d(x,intr.multiply(y1,y1)))\nprint(intr.trap_d(x,intr.multiply(y1,y2)))\nprint(intr.trap_d(x,intr.multiply(y2,y3)))\nprint(intr.trap_d(x,intr.multiply(y3,y3)))\n\n#We find that when m=n the scalar product is pi and when m=/n the scalar product\n#is 0. This is very similar to the scalar product of the legendre functions.\n\ndef powr(x,n):\n m = 1\n if n == 0:\n return 1\n else:\n for l in range(n):\n m = m * x\n return m\n\ndef x11(x):\n return x*x*x*x*x*x*x*x*x*x*x\n\ndef x12(x):\n return x*x*x*x*x*x*x*x*x*x*x*x\n\nN1 = 10\nN2 = 100\nN3 = 1000\nn1 = 1\nn2 = 2\nn3 = 6\nH1 = 1 / (N1 -1)\nH2 = 1 / (N2 -1)\nH3 = 1 / (N3 -1)\n\n# rather than having a whole lot of functions I just swatched the N values for each\n[P1,W1] = intr.gauss_leg(0,1,n3)\nX1 = np.zeros(N3)\nY1 = np.zeros(N3)\nY2 = np.zeros(N3)\nY3 = np.zeros(N3)\nY4 = np.zeros(N3)\nY5 = np.zeros(N3)\nfor i in range(N3):\n X1[i] = H3*i\n Y1[i] = powr(X1[i],1)\n Y2[i] = powr(X1[i],2)\n Y3[i] = powr(X1[i],11)\n Y4[i] = powr(X1[i],12)\n Y5[i] = np.exp(X1[i])\n\nprint(intr.trap_d(X1,Y1))\nprint(intr.trap_d(X1,Y2))\nprint(intr.trap_d(X1,Y3))\nprint(intr.trap_d(X1,Y4))\nprint(intr.trap_d(X1,Y5))\nprint(intr.gauss_quad(intr.lin,P1,W1))\nprint(intr.gauss_quad(intr.quad,P1,W1))\nprint(intr.gauss_quad(x11,P1,W1))\nprint(intr.gauss_quad(x12,P1,W1))\nprint(intr.gauss_quad(np.exp,P1,W1))\n","sub_path":"assignments/assignment5/assignment5.py","file_name":"assignment5.py","file_ext":"py","file_size_in_byte":1686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"155122940","text":"import requests\nimport format_string\n\ndef dictio(text, lang):\n '''Получение json перевода'''\n DICT_KEY = \"dict.1.1.20191130T145952Z.5f121d459fa8cf6d.4e776d73c744dbfb37d199969fab8673acc46fbb\"\n URL_DICT = \"https://dictionary.yandex.net/api/v1/dicservice.json/lookup\"\n params = {\n \"key\": DICT_KEY,\n \"lang\": lang,\n \"text\": text,\n \"ui\": \"ru\"\n }\n response = requests.get(URL_DICT, params=params).json()\n return response\n\n\ndef format_text(translations_page):\n '''Форматирование json в str'''\n full_list = []\n for translate in translations_page: # БЕРЕТ ПЕРЕВОДЫ\n dict_info = []\n for k, v in translate.items(): # ИНФУ О ТЕКСТЕ И ПЕРЕВОД\n dict_info.append(v)\n\n full_list.append(f'''{\"📝 \" + dict_info[0] + \" (\" + \", \".join(dict_info[1:-1]) + \"):\" + \"\"}\n{format_string.main_function(dict_info[-1])}\n''')\n return full_list\n\ndef main_functioin(text, lang):\n ''' Главная функция для перевода '''\n main_string = \"\\n\".join([_ for _ in format_text(dictio(text, lang)[\"def\"])])\n return main_string\n\n\n\n\n# print(*(format_text(dictio(\"What\", \"en-ru\")[\"def\"])),sep=\"\\n\")\n","sub_path":"Yandex_translate_words.py","file_name":"Yandex_translate_words.py","file_ext":"py","file_size_in_byte":1252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"120994558","text":"# -*- coding: utf-8 -*-\nfrom openprocurement.audit.api.utils import (\n APIResource\n)\nfrom openprocurement.audit.api.utils import context_unpack, json_view\nfrom openprocurement.audit.monitoring.utils import (\n apply_patch, set_author, op_resource, save_monitoring,\n)\nfrom openprocurement.audit.monitoring.validation import (\n validate_liability_data,\n validate_patch_liability_data,\n)\n\n\n@op_resource(\n name=\"Monitoring Liability\",\n collection_path=\"/monitorings/{monitoring_id}/liabilities\",\n path=\"/monitorings/{monitoring_id}/liabilities/{liability_id}\",\n description=\"Liability to the conclusion\"\n)\nclass LiabilityResource(APIResource):\n\n @json_view(\n content_type='application/json',\n validators=(\n validate_liability_data,\n ),\n permission='edit_monitoring'\n )\n def collection_post(self):\n\n monitoring = self.context\n liability = self.request.validated['liability']\n set_author(liability.documents, self.request, 'author')\n # upload_objects_documents(self.request, liability)\n monitoring.liabilities.append(liability)\n if save_monitoring(self.request):\n self.LOGGER.info('Created monitoring liability {}'.format(liability.id),\n extra=context_unpack(self.request,\n {'MESSAGE_ID': 'liability_create'},\n {'liability_id': liability.id}))\n self.request.response.status = 201\n self.request.response.headers['Location'] = self.request.route_url(\n 'Monitoring Liability', monitoring_id=monitoring.id, liability_id=liability.id)\n return {'data': liability.serialize('view')}\n\n @json_view(\n content_type='application/json',\n validators=(\n validate_patch_liability_data,\n ),\n permission='edit_monitoring',\n )\n def patch(self):\n apply_patch(self.request)\n self.LOGGER.info('Updated liability {}'.format(self.request.context.id),\n extra=context_unpack(self.request, {'MESSAGE_ID': 'liability_patch'}))\n return {'data': self.request.context.serialize('view')}\n\n @json_view(permission='view_monitoring')\n def get(self):\n return {'data': self.context.serialize('view')}\n\n @json_view(permission='view_monitoring')\n def collection_get(self):\n \"\"\"\n List of parties\n \"\"\"\n return {'data': [i.serialize('view') for i in self.context.liabilities]}\n","sub_path":"openprocurement/audit/monitoring/views/liability.py","file_name":"liability.py","file_ext":"py","file_size_in_byte":2567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"98584345","text":"import sys\n\nN, K = map(int, sys.stdin.readline().strip().split())\nnumbers = list(map(int, sys.stdin.readline().strip().split()))\n\nprices = []\nfor i in range(1, N):\n prices.append(numbers[i] - numbers[i - 1])\nprices.sort()\n\nfor i in range(K - 1):\n prices.pop(-1)\n\nprint(sum(prices))\n","sub_path":"backjoon/Greedy/13164_행복유치원.py","file_name":"13164_행복유치원.py","file_ext":"py","file_size_in_byte":288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"288769898","text":"\"\"\"\nThe module containing the implementation for the MemoryManager.\nThe allocation-specific documentation is located on the MemoryManager class.\n\nThe first-fit memory allocation with faux \"memory-address\"-ordered memory\nblocks was chosen as a reasonable simple solution (basically a greatly simplified\nversion of g++ memory allocator, which is a first-fit roving pointer algorithm\nenhanced with bins of different sizes). Further improvement of the memory management\napproach will require further reading of the memory management-related\nresearch papers, as the efficacy of algorithms and policies is not always evident\nwithout thorough testing in conditions close to the real-world ones.\n\"\"\"\nfrom typing import Optional\n\nfrom .exceptions import OutOfMemoryError\nfrom .utils import MemoryBlockProxy\n\n\nclass MemoryManager:\n \"\"\"\n A memory manager implementing the first-fit memory allocation. Large blocks\n will be divided into smaller ones, so that the allocated block is a perfect\n fit for the data.\n\n The buffer is required to be a bytearray instance, so that memoryview can\n be used to modify its data directly due to bytearray's support of the\n buffer protocol. AssertionError will be raised if it is not so.\n\n Block list is \"address\"-ordered, where the \"address\" is simply the\n index in the list of all blocks, and the aforementioned list is made to\n represent contiguous slices of memory.\n For simplicity, when a block is divided into smaller ones, it will just be\n removed from the list, and the resulting blocks will be inserted into\n its place in the correct order.\n\n For simplicity, this memory manager immediately tries to coalesce the\n freshly-freed block with the neighbouring free blocks. Note that this only\n happens on explicit `free` call, and if the memory were pre-split into the\n same-sized blocks during the memory manager creation, and if we tried to allocate\n a block larger than the initial block size, we would get an OutOfMemoryError.\n\n The pros of the chosen approach are the lack of internal fragmentation due to\n perfect-fit memory block allocation, as well as (possibly) better locality of\n reference when working with same-size memory blocks due to the search always\n starting at the beginning of the blocks' list. When working with same-size\n blocks, all of the \"gaps\" left after free will always be able to contain the\n newly-allocated block. It needs to be mentioned that in case of frequent frees\n of adjacent blocks these will be coalesced into a single large block, which\n will then lead to re-splitting of the said block when using `alloc` to allocate\n a new one. In cases of working with same-size blocks only, this may be\n optimized by allowing to turn off coalescence completely.\n\n Searching for a fitting block will take O(N) at worst, and *may* be improved\n by using a roving pointer to the block list and using a set of \"bins\"\n containing pre-split blocks of different sizes (similar to how g++ memory\n allocator works) - but this requires further reading of the reasearch papers\n on my side.\n\n Due to time constraints, all of the memory blocks are represented by\n memoryview objects into the buffer, created by the slicing of the main\n memoryview wrapping the buffer. The blocks are marked as free or allocated\n by assigning to the `allocated` boolean attribute dynamically added to each\n memoryview object, as well as the `offset` attribute indicating the\n offset in the buffer at which the block starts. I consider this solution as\n minimally viable, as memoryviews don't copy the buffer they are providing\n access to and allow the users to read and mutate the part of the buffer\n exposed by the memoryview object as they see fit, as if it were a list.\n There are no pointers in python, so this is the only quick way to provide\n direct access to a bytearray's memory.\n\n The implementation is thread-safe due to the existence of GIL.\n If implemented in another language in a similar way, it will be prone to\n data races, and will require the use of synchronization constructs when\n accessing the buffer's contents and working with the list of blocks and its\n contents.\n\n At the moment most of the preconditions are simply asserted in the methods\n to speed up the development process.\n \"\"\"\n def __init__(self,\n buf: bytearray,\n initial_block_size: Optional[int] = None) -> None:\n \"\"\"\n Creates a memoryview into the provided buffer, to allow the\n interaction with sub-buffers such as slicing without creating new\n bytearray objects.\n All allocation and free operations will interact with the memoryview\n objects representing the memory blocks, as well as the main memoryview.\n As a side effect, this will also disallow buffer resizing until all\n of the memoryviews are released.\n\n `initial_block_size` is the size of initially available memory blocks\n in bytes. If None, there's initially a single block the size of the\n available memory.\n \"\"\"\n assert isinstance(buf, bytearray), '`buf` must be a `bytearray`'\n\n self.buf_view = memoryview(buf)\n\n if initial_block_size is None:\n initial_block_size = len(buf)\n else:\n assert 0 < initial_block_size <= len(self.buf_view), '`initial_block_size` must be a positive int not greater than the buffer length'\n\n indexed_blocks = [\n (i, MemoryBlockProxy(self.buf_view[i:i+initial_block_size]))\n for i in range(0, len(self.buf_view), initial_block_size)]\n # mark all initial blocks as free and store the offset\n for idx, b in indexed_blocks:\n b.allocated = False\n b.offset = idx\n\n self.blocks = [b for (i, b) in indexed_blocks]\n\n def release(self) -> None:\n \"\"\"\n Explicitly releases the memoryview for the full initial buffer,\n as well as all of the memoryviews representing the memory blocks.\n After this, alloc/free calls will always throw exceptions at the time\n attempts are made to work with the memoryviews.\n \"\"\"\n self.buf_view.release()\n for b in self.blocks:\n b.release()\n\n def alloc(self, size: int) -> memoryview:\n \"\"\"\n Allocates a part of the buffer.\n Returns a memoryview representing the allocated block.\n\n Walks the list of free blocks from the beginning.\n When it finds a large enough block, if it's larger than the requested\n amount of bytes, the block is divided into 2 blocks, with the first one\n being a perfect fit.\n If no free blocks of fitting size are available, raises an\n OutOfMemoryError.\n\n `size` is asserted to be a positive integer not greater than the size\n of the buffer.\n \"\"\"\n assert 0 < size <= len(self.buf_view), '`size` must be a positive int not greater than the buffer length'\n\n for idx, block in enumerate(self.blocks):\n if size <= len(block) and not block.allocated:\n return self._alloc(idx, size)\n else:\n raise OutOfMemoryError(\n f'No memory blocks were available to allocate {size} bytes of memory')\n\n def free(self, block: memoryview):\n \"\"\"\n Marks the block represented by a memoryview object as \"free\".\n Expects the memoryview to be the one returned by `alloc` by checking\n for the existence of an `allocated` boolean attribute and will throw\n an AssertionError if the attribute doesn't exist or is not set to True.\n\n NOTE: the memoryview describing the block is not `release()d`, so it is\n possible to create a \"dangling pointer\"-like situation where it is\n accessed by the outside code after free - and even after the block has\n been coalesced with another one. So the usage of blocks after free\n should be considered undefined behaviour.\n \"\"\"\n assert isinstance(block, memoryview), '`block must be a memoryview`'\n assert hasattr(block, 'allocated'), '`block` must have an `allocated` attribute'\n assert block.allocated is True, '`block` must be an already allocated one'\n\n block_idx = self._find_block(block)\n block.allocated = False\n self._maybe_coalesce(block_idx)\n\n def _find_block(self, block: memoryview) -> int:\n \"\"\"\n Returns the index of the block in the list of all blocks.\n Can most likely be improved by using binary search, as the list of\n blocks remains virtually sorted by their starting index relative to\n the main memoryview.\n \"\"\"\n return self.blocks.index(block)\n\n def _alloc(self, idx: int, size: int) -> memoryview:\n \"\"\"\n Allocates the block pointed to by the `idx`, potentially splitting\n it into two if the `size` is less than the size of the block\n \"\"\"\n block = self.blocks[idx]\n block_size = len(block)\n if block_size > size:\n left = MemoryBlockProxy(block[:size])\n right = MemoryBlockProxy(block[size:])\n\n left.offset = block.offset\n right.offset = left.offset + len(left)\n right.allocated = False\n\n block.release()\n self.blocks.pop(idx)\n self.blocks.insert(idx, left)\n self.blocks.insert(idx + 1, right)\n\n allocated_block = left\n else:\n allocated_block = block\n\n allocated_block.allocated = True\n return allocated_block\n\n def _maybe_coalesce(self, block_idx: int) -> None:\n \"\"\"\n If there are neighbouring free blocks, coalesce them with the block\n at `block_idx`.\n\n block_idx is asserted to be a correct index for the blocks' list.\n \"\"\"\n assert 0 <= block_idx < len(self.blocks), '`block_idx` must be a correct block index'\n\n # how many old blocks to pop off the list\n num_pops = 1\n # the index in the block list to start removing the blocks at\n start_idx = block_idx\n\n left = None\n if block_idx - 1 >= 0:\n left = self.blocks[block_idx - 1]\n\n try:\n right = self.blocks[block_idx + 1]\n except IndexError:\n right = None\n\n block = self.blocks[block_idx]\n new_offset = block.offset\n new_len = len(block)\n\n if left is not None and not left.allocated:\n new_offset = left.offset\n new_len += len(left)\n start_idx = block_idx - 1\n num_pops += 1\n\n if right is not None and not right.allocated:\n new_len += len(right)\n num_pops += 1\n\n # If the new blok length if greater than the current one - that means\n # that we need to coalesce the blocks.\n if new_len > len(block):\n coalesced_block = MemoryBlockProxy(\n self.buf_view[new_offset:new_offset + new_len])\n coalesced_block.allocated = False\n coalesced_block.offset = new_offset\n\n for _ in range(num_pops):\n b = self.blocks.pop(start_idx)\n b.release()\n\n self.blocks.insert(start_idx, coalesced_block)\n","sub_path":"memalloc/memory_manager.py","file_name":"memory_manager.py","file_ext":"py","file_size_in_byte":11343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"184066327","text":"from sys import argv\n\ndef tenwise_function(arg):\n print(f\"My function! It prints {arg}!\")\n\n\n# 1\nprint(\"Firstwise: literal!\")\ntenwise_function(10)\n\n# 2\nprint(\"Secondwise: variable!\")\ntenwise_variable = 10\ntenwise_function(tenwise_variable)\n\n# 3\nprint(\"Thirdwise: variable plus literal!\")\ntenwise_function(tenwise_variable + 10)\n\n# 4\nprint(\"Fourthwise: literal plus literal?\")\ntenwise_function(5 + 5)\n\n# 5 \nprint(\"Fifthwise: variable times variable!\")\nfivewise_variable = 5\ntenwise_function(fivewise_variable * 2)\n\n# 6\nprint(\"Sixthwise: SPICE THINGS UP WITH USER INPUT. FEED ME!\")\ntenwise_function(input())\n\n# 7\nprint(\"I'm running out of ideas! Let's go and get argv maybe!\")\ntenwise_function(argv)\n\n# 8\nprint(\"Sweet lord! Let's make a file! Let's read it to a variable, then read it!\")\neighthwise_file = open(\"test.txt\",'w')\neighthwise_file.write(\"10\")\neighthwise_file.close()\ninwise_file = open(\"test.txt\")\ninwise_data = inwise_file.read()\ntenwise_function(inwise_data)\ninwise_file.close()\n\n# 9\nprint(\"Let's read it RIGHT FROM THE FILE\")\ntenwise_function(open(\"test.txt\").read())\n\n# 10\nprint(\"Let's take the string from the file, turn it into an int, then do maths!:\")\ntenwise_function(int(open(\"test.txt\").read()) + int(open(\"test.txt\").read()))","sub_path":"ex19_extra.py","file_name":"ex19_extra.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"271478305","text":"from Instrucciones.TablaSimbolos.Instruccion import Instruccion\nfrom Instrucciones.Excepcion import Excepcion\nimport collections\nfrom storageManager.jsonMode import *\n\nclass AlterIndex(Instruccion):\n\n def __init__(self, nombreIndice, id1, id2, if_exists, strGram,linea, columna):\n Instruccion.__init__(self,None,linea,columna,strGram)\n self.nombreIndice = nombreIndice\n self.id1 = id1\n self.id2 = id2\n self.if_exists = if_exists \n\n def ejecutar(self, tabla, arbol):\n super().ejecutar(tabla,arbol)\n if arbol.bdUsar != None:\n objetoTablas = arbol.devolverTablas()\n objetoIndice = None\n objetoTabla = None\n for t in objetoTablas:\n for i in t.lista_de_indices:\n if i.nombre == self.nombreIndice:\n objetoIndice = i\n objetoTabla = t\n if objetoIndice == None:\n error = Excepcion('42P01',\"Semántico\",\"El indice «\"+self.nombreIndice+\"» no existe\",self.linea,self.columna)\n arbol.excepciones.append(error)\n arbol.consola.append(error.toString())\n return error\n else:\n listaColumnas = []\n for t in objetoTabla.lista_de_campos:\n listaColumnas.append(t.nombre)\n \n if (self.id1 in listaColumnas and self.id2 in listaColumnas):\n for l in objetoIndice.lRestricciones:\n if self.id1 in l:\n nuevo = l.replace(self.id1, self.id2)\n objetoIndice.lRestricciones.remove(l)\n objetoIndice.lRestricciones.append(nuevo)\n arbol.consola.append(\"Consulta devuelta correctamente.\")\n else:\n error = Excepcion('42701',\"Semántico\",f\"Las columnas {self.id1} y {self.id2} no existen en la tabla.\",self.linea,self.columna)\n arbol.excepciones.append(error)\n arbol.consola.append(error.toString())\n return error\n else:\n error = Excepcion(\"100\",\"Semantico\",\"No ha seleccionado ninguna Base de Datos.\",self.linea,self.columna)\n arbol.excepciones.append(error)\n arbol.consola.append(error.toString())\n \n def analizar(self, tabla, arbol):\n pass\n \n def traducir(self, tabla, arbol):\n cadena = \"\\\"ALTER INDEX \"\n cadena += self.nombreIndice\n cadena += \" ALTER COLUMN \"\n cadena += self.id1 + \" \"\n cadena += self.id2 + \";\\\"\"\n \n arbol.addComen(\"Asignar cadena\")\n temporal1 = tabla.getTemporal()\n arbol.addc3d(f\"{temporal1} = { cadena }\")\n\n arbol.addComen(\"Entrar al ambito\")\n temporal2 = tabla.getTemporal()\n arbol.addc3d(f\"{temporal2} = P+2\")\n temporal3 = tabla.getTemporal()\n arbol.addComen(\"parametro 1\")\n arbol.addc3d(f\"{temporal3} = { temporal2}+1\")\n arbol.addComen(\"Asignacion de parametros\")\n arbol.addc3d(f\"Pila[{temporal3}] = {temporal1}\")\n\n arbol.addComen(\"Llamada de funcion\")\n arbol.addc3d(f\"P = P+2\")\n arbol.addc3d(f\"funcionintermedia()\")\n \n arbol.addComen(\"obtener resultado\")\n temporalX = tabla.getTemporal()\n arbol.addc3d(f\"{temporalX} = P+2\")\n temporalR = tabla.getTemporal()\n arbol.addc3d(f\"{temporalR} = Pila[{ temporalX }]\")\n\n arbol.addComen(\"Salida de funcion\")\n arbol.addc3d(f\"P = P-2\")\n","sub_path":"parser/fase2/team08/Tytus_SQLPARSER_G8/Instrucciones/Index/AlterIndex.py","file_name":"AlterIndex.py","file_ext":"py","file_size_in_byte":3598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"189958119","text":"\"\"\"\nAuthor: Bruno Luca\nDate: 06-05-2020\n\nThis program create some thread that print messages\n\"\"\"\n\nimport threading\nimport logging as log\nimport time\n\ndef fn_thread(val):\n log.info(f\"Thread {val}, inizio.\")\n time.sleep(2)\n log.info(f\"Thread {val}, fine.\")\n\ndef main():\n #configuring log message info\n format = \"%(asctime)s: %(message)s\"\n log.basicConfig(format = format, level = log.INFO, datefmt= \"%H:%M:%S\")\n\n log.info(\"PADRE, creo un threads\")\n\n threads = list()\n for i in range(0,5):\n log.info(f\"PADRE, creo ed avvio il thread {i}\")\n x = threading.Thread(target = fn_thread, args = (1, ))\n threads.append(x)\n x.start()\n\n for i, value in enumerate(threads):\n log.info(f\"PADRE, prima dell'attesa del thread {i}\")\n value.join()\n log.info(f\"PADRE, thread {i} terminato\")\n\n\n\n\n\nif __name__ == \"__main__\":\n main()","sub_path":"tpsit_IV/python/es002/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"195490518","text":"# MT16121\n# Ankit Sharma\n\nfrom Bio import SeqIO\nfrom decimal import Decimal\n\n# Python code to find the GC content\nfor record in SeqIO.parse(\"problem1_gene.fasta\", \"fasta\"):\n list_of_char = list(record)\n GC = 0\n for i in range(0, len(list_of_char)):\n if list_of_char[i] == 'G' or list_of_char[i] == 'C':\n GC += 1\n GC_content = Decimal(GC)/Decimal(len(list_of_char))*100\n print(\"GC content for \" + record.id + \" is \" + str(GC_content) + \"%\")","sub_path":"MT16121_problem1_4.py","file_name":"MT16121_problem1_4.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"328998893","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.6 (62161)\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.linux-i686/egg/archetypes/clippingimage/utils.py\n# Compiled at: 2010-08-05 09:58:49\nimport PIL\nfrom StringIO import StringIO\nfrom Products.CMFPlone.utils import safe_hasattr\n\ndef crop(image, scale):\n \"\"\"Crop given image to scale.\n\n @param image: PIL Image instance\n @param scale: tuple with (width, height)\n \"\"\"\n (cwidth, cheight) = image.size\n cratio = float(cwidth) / float(cheight)\n (twidth, theight) = scale\n tratio = float(twidth) / float(theight)\n if cratio > tratio:\n middlepart = cheight * tratio\n offset = (cwidth - middlepart) / 2\n box = (int(round(offset)), 0, int(round(offset + middlepart)), cheight)\n image = image.crop(box)\n if cratio < tratio:\n middlepart = cwidth / tratio\n offset = (cheight - middlepart) / 2\n box = (0, int(round(offset)), cwidth, int(round(offset + middlepart)))\n image = image.crop(box)\n return image\n\n\ndef scale(instance, data, w, h, default_format='PNG'):\n \"\"\" scale image\"\"\"\n size = (\n int(w), int(h))\n original_file = StringIO(data)\n image = PIL.Image.open(original_file)\n format = image.format\n availableSizes = instance.getAvailableSizes(None)\n if safe_hasattr(instance, 'crop_scales'):\n if size in [ availableSizes[name] for name in instance.crop_scales ]:\n image = crop(image, size)\n original_mode = image.mode\n if original_mode == '1':\n image = image.convert('L')\n elif original_mode == 'P':\n image = image.convert('RGBA')\n image.thumbnail(size, instance.pil_resize_algo)\n format = format or default_format\n if original_mode == 'P' and format == 'GIF':\n image = image.convert('P')\n thumbnail_file = StringIO()\n image.save(thumbnail_file, format, quality=instance.pil_quality)\n thumbnail_file.seek(0)\n return (thumbnail_file, format.lower())","sub_path":"pycfiles/archetypes.clippingimage-2.1-py2.6/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"255096477","text":"from __future__ import print_function\nimport confluent_kafka\nfrom . import avroUtils\n\n__all__ = ['AlertProducer']\n\n\nclass AlertProducer(object):\n \"\"\"Alert stream producer with Kafka.\n\n Parameters\n ----------\n topic : `str`\n The name of the topic stream for writing.\n schema : Avro schema\n The writer Avro schema for encoding data. Optional.\n **kwargs\n Keyword arguments for configuring confluent_kafka.Producer().\n \"\"\"\n\n def __init__(self, topic, schema_files=None, **kwargs):\n self.producer = confluent_kafka.Producer(**kwargs)\n self.topic = topic\n if schema_files is not None:\n self.alert_schema = avroUtils.combineSchemas(schema_files)\n\n def send(self, data, encode=False):\n \"\"\"Sends a message to Kafka stream.\n\n Parameters\n ----------\n data : message content\n Data containing message content. If encode is True, expects JSON.\n encode : `boolean`\n If True, encodes data to Avro format. If False, sends data raw.\n \"\"\"\n if encode is True:\n avro_bytes = avroUtils.writeAvroData(data, self.alert_schema)\n raw_bytes = avro_bytes.getvalue()\n self.producer.produce(self.topic, raw_bytes)\n else:\n self.producer.produce(self.topic, str(data))\n\n def flush(self):\n return self.producer.flush()\n","sub_path":"python/lsst/alert/stream/alertProducer.py","file_name":"alertProducer.py","file_ext":"py","file_size_in_byte":1399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"161862613","text":"\"\"\"PrimerProyecto URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/3.1/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path\nfrom PrimerProyecto.views import bienvenido, hola2, Edad, momentoactual, contenidoHtml, miPrimeraPlantilla, PaseParametro, ConCargador, ConShortCut\nfrom PrimerProyecto.views import hereda\n\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('hola/', bienvenido),\n path('hola2/', hola2),\n path('edad/', Edad), # recibe un parametro que se pasa a entero\n path('momento/', momentoactual),\n path('datos//', contenidoHtml),\n path('plantilla/', miPrimeraPlantilla),\n path('parametro/', PaseParametro),\n path('cargador/', ConCargador),\n path('shortcuts/', ConShortCut),\n path('hereda/', hereda),\n]\n","sub_path":"PrimerProyecto/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"240943672","text":"import asyncio\nimport logging\nimport pathlib\nimport os\nimport gettext\n\nfrom aiohttp import web\nfrom aiohttp_babel.locale import (load_gettext_translations,\n set_default_locale)\nfrom aiohttp_babel.middlewares import babel_middleware\n\nfrom calendar_test.utils import init_postgres, load_config\nimport calendar_test.views as views\n\nPROJ_ROOT = pathlib.Path(__file__).parent.parent\nTEMPLATES_ROOT = pathlib.Path(__file__).parent / 'templates'\nlog = logging.getLogger(__name__)\n\n\nasync def close_pg(app):\n app['pg'].close()\n await app['pg'].wait_closed()\n\n\nasync def init(loop):\n # gettext.bindtextdomain('messages', localedir=os.path.join(str(PROJ_ROOT), 'po'))\n load_gettext_translations(os.path.join(str(PROJ_ROOT), 'po'), 'messages')\n set_default_locale('ru')\n # setup application and extensions\n app = web.Application(loop=loop, middlewares=[babel_middleware])\n conf = load_config(os.path.join(str(PROJ_ROOT), 'config', 'config.yaml'))\n app['config'] = conf\n app['pg'] = await init_postgres(conf['postgres'], loop)\n app.on_cleanup.append(close_pg)\n\n app.router.add_route('*', '/calendar/constants', views.constants_view)\n app.router.add_route('POST', '/calendar/events', views.event_creation)\n app.router.add_route('GET', '/calendar/{year}', views.year_view)\n app.router.add_route('GET', '/calendar/{year}/{month}', views.month_view)\n app.router.add_route('GET', '/calendar/{year}/{month}/events', views.month_events_view)\n\n # init logging and attach access_log\n logging.basicConfig(level=logging.DEBUG)\n app_handler = app.make_handler(access_log=log)\n host, port = conf['host'], conf['port']\n srv = await loop.create_server(app_handler, host, port)\n print(\"Server started at http://{0}:{1}\".format(host, port))\n return srv, app_handler\n\n\nloop = asyncio.get_event_loop()\nsrv, app_handler = loop.run_until_complete(init(loop))\n\ntry:\n loop.run_forever()\nexcept KeyboardInterrupt:\n pass\nfinally:\n loop.run_until_complete(app_handler.finish_connections())\n srv.close()\n loop.run_until_complete(srv.wait_closed())\nloop.close()\n","sub_path":"calendar_test/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"6762020","text":"# BACKJOON #2193 <이친수>\n# https://www.acmicpc.net/problem/2193\n\nn=int(input())\nif n<3: print(1)\nelse:\n a,b=1,1\n for i in range(2,n):\n a,b=b,a+b\n print(b)","sub_path":"Baekjoon/2193_PinaryNumber.py","file_name":"2193_PinaryNumber.py","file_ext":"py","file_size_in_byte":174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"448151173","text":"from appconf import AppConf\nfrom django.conf import settings\nimport os\n\n\nclass CustomAdminConf(AppConf):\n STATIC_URL = u'/static/'\n STATIC_ROOT = os.path.join(os.getcwd(), 'static')\n USE_CUSTOM_ADMIN = True\n\n def configure_static_url(self, value):\n if not getattr(settings, 'STATIC_URL', None):\n self._meta.holder.STATIC_URL = value\n return value\n return getattr(settings, 'STATIC_URL')\n\n def configure_static_root(self, value):\n if not getattr(settings, 'STATIC_ROOT', None):\n self._meta.holder.STATIC_ROOT = value\n return value\n return getattr(settings, 'STATIC_ROOT')\n\n def configure_use_custom_admin(self, value):\n if not getattr(settings, 'USE_CUSTOM_ADMIN', None):\n self._meta.holder.USE_CUSTOM_ADMIN = value\n return value\n return getattr(settings, 'USE_CUSTOM_ADMIN')\n\n def configure(self):\n if self.configured_data['USE_CUSTOM_ADMIN']:\n from django.contrib.admin.sites import site\n\n site.index_template = \"admin/custom_index.html\"\n site.app_index_template = \"admin/app_index.html\"\n\n context_processors = getattr(settings, 'TEMPLATE_CONTEXT_PROCESSORS', None)\n customadmin_context_processor = 'customadmin.template_context.context_processors.customadmin_context'\n if not context_processors:\n self._meta.holder.TEMPLATE_CONTEXT_PROCESSORS = [\n customadmin_context_processor,\n ]\n elif customadmin_context_processor not in context_processors:\n context_processors = list(context_processors)\n context_processors.append(customadmin_context_processor)\n self._meta.holder.TEMPLATE_CONTEXT_PROCESSORS = context_processors\n\n return self.configured_data\n","sub_path":"customadmin/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":1824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"23554508","text":"# coding=utf-8\nimport time\nimport copy\n\nfrom service.mahjong.models.hutype.basetype import BaseType\nfrom service.mahjong.constants.carddefine import CardType, CARD_SIZE\nfrom service.mahjong.models.card.hand_card import HandCard\nfrom service.mahjong.models.utils.cardanalyse import CardAnalyse\n\n\nclass KanZhang(BaseType):\n \"\"\"\n 7)\t坎张： 胡牌时，和2张牌之间的牌。4556和5也为坎张，手中有45567和6不算坎张。因为后者胡的6可以是456里的6。\n \"\"\"\n\n def __init__(self):\n super(KanZhang, self).__init__()\n\n def is_this_type(self, hand_card, card_analyse):\n hu_card_val = hand_card.hu_card_val\n chi_cards_lst = hand_card.chi_card_vals\n ret = card_analyse.get_jiang_ke_shun_plus(hand_card.hand_card_vals)\n for index in range(len(ret)):\n s = ret[index][\"s\"]\n s.extend(chi_cards_lst)\n for i in s:\n if hu_card_val == i[1]:\n return True\n return False\n\n\nif __name__ == \"__main__\":\n pass\n card_analyse = CardAnalyse()\n hand_card = HandCard(0, None)\n # hand_card.hand_card_info = {\n # 1: [9, 1, 1, 1, 1, 1, 1, 1, 1, 1], # 万\n # 2: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 条\n # 3: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 饼\n # 4: [2, 2, 0, 0, 0], # 风\n # 5: [3, 3, 0, 0], # 箭\n # }\n hand_card.hand_card_info = {\n 1: [6, 0, 0, 0, 1, 1, 1, 1, 1, 1], # 万\n 2: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 条\n 3: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 饼\n 4: [2, 2, 0, 0, 0], # 风\n 5: [3, 3, 0, 0], # 箭\n }\n hand_card.chi_card_vals=[[23,24,25]]\n hand_card.handle_hand_card_for_settle_show()\n hand_card.union_hand_card()\n hand_card.hu_card_val = 24\n print(\"hand_card =\", hand_card.hand_card_vals)\n test_type = KanZhang()\n start_time = time.time()\n for i in range(100):\n r = test_type.is_this_type(hand_card, card_analyse)\n print(\"time = \", (time.time() - start_time) / 100)\n print(r)","sub_path":"echecs_espoir/service/mahjong/models/hutype/one/kan_zhang.py","file_name":"kan_zhang.py","file_ext":"py","file_size_in_byte":2111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"314416244","text":"import selenium\n\nfrom selenium import webdriver # Import from seleniumwire\nfrom ast import literal_eval\nimport json\n#from selenium.webdriver.chrome.options import Options\ndriver=''\nreq=[]\n\n\nrecordJs=open(\"recordJS2.js\").read()\n\next_tuple=('.mp3', '.avi','.js','.css','.less','.scss','.png','.ico','.txt','.ini','.jpg','.mp4','xls','.doc','xlsx','.ppt','.pptx','.docx','.json','.java','.as','.mx','.asp','.ts','.jsp','.svg','.php','.xml','.xaml',\n '.yml' ,'.woff2','.jpeg')\n\ndef stop():\n global driver\n JS='''\n\n\n\ndocument.body.addEventListener('mouseover', MouseInListenerFunction,true);\n \n\n\n function MouseInListenerFunction(event){\n event.target.style.border = '';\n \n }\n \n\n\n '''\n driver.execute_script(JS)\n\n return \"STOPPED\"\n\ndef initiate_driver(url):\n global driver\n #options = webdriver.ChromeOptions()\n #options.add_experimental_option('debuggerAddress', 'localhost:9014')\n driver = webdriver.Chrome(executable_path =\"chromedriver.exe\")\n driver.get(url)\ndef locate(xpath):\n\n global driver\n try:\n element=driver.find_element_by_xpath(xpath)\n driver.execute_script(\"arguments[0].style.border = '0.4em solid yellow';\",element)\n return \"PASS\"\n except:\n return \"FAIL\"\ndef Q_recorder():\n\n global driver\n global recordJs\n Xpath=None\n data={}\n try:\n Xpath=driver.execute_script(recordJs)\n except:\n driver.switch_to_window(driver.window_handles[-1])\n \n return Xpath,data\ndef main():\n global driver\n \n \n #driver.switch_to.window()\n JS=open('get_ALL2.js').read()\n event_attributes=open('event_attributes.txt').read().split(\", \")\n \n driver.switch_to_window(driver.window_handles[-1])\n A=driver.execute_script(JS,event_attributes)\n \n #JS11=open('smart_xpath.js').read()\n #driver.execute_script(JS11)\n #print(A)\n return A\ndef pageLocatorCreation(name,xpath):\n \n i = 0 \n L = \"import org.openqa.selenium.WebElement;\\n\" \n L+=\"import org.openqa.selenium.support.FindBy;\\n\"\n L+=\"import org.openqa.selenium.support.PageFactory;\\n\\n\"\n L+=\"public class PageLocators {\\n\"\n length = len(name)\n while i < length:\n variableName=name[i].replace(' ','_')\n L+=\"\\t@FindBy(xpath=\\\"\"+ xpath[i] + \"\\\")\\n\"\n L+=\"\\tpublic WebElement \" + variableName + \";\\n\\n\"\n i = i + 1\n L+=\"\\tpublic PageLocators()\\n\\t{\\n\"\n L+=\"\\tPageFactory.initElements(/*Please specify driver*/,this);\\n\\t}\\n}\"\n print(L)\n return L\ndef pageActionCreation(tag,name,xpath):\n objName=\"obj_PageLocators\"\n L=\"import PageLocators.PageLocators;\\n\\n\\n\"\n L+=\"public class PageActions {\\n\\n\"\n L+=\"\\tPageLocators\"+\" \"+objName+\" =new PageLocators();\\n\\n\"\n print(tag)\n for t in range(0,len(tag)):\n print(t,tag[t])\n \n if(tag[t]==\"INPUT\"or tag[t]==\"TEXTAREA\"):\n L+=\"\\tpublic void method_\"+name[t]+\"(String data) throws InterruptedException(){\\n\"\n L+=\"\\t\\t\"+objName+\".\"+name[t]+\".sendKeys(data);\\n\"\n L+=\"\\t}\\n\\n\"\n elif(tag[t]==\"SELECT\"):\n L+=\"\\tpublic void method_\"+name[t]+\"(value) throws InterruptedException(){\\n\"\n L+=\"\\t\\tSelect dropdown= new Select(\"+objName+\".\"+name[t]+\");\\n\"\n L+=\"\\t\\tdropdown.selectByVisibleText(value);\\n\"\n L+=\"\\t}\\n\\n\"\n \n \n elif(tag[t]==\"BUTTON\" or tag[t]==\"RADIO\" or tag[t]==\"CHECKBOX\" or tag[t]==\"A\" ):\n\n L+=\"\\tpublic void method_\"+name[t]+\"() throws InterruptedException(){\\n\"\n L+=\"\\t\\t\"+objName+\".\"+name[t]+\".click();\\n\"\n \n L+=\"\\t}\\n\\n\"\n else:\n L+=\"\\tpublic void method_\"+name[t]+\"() throws InterruptedException(){\\n\"\n L+=\"\\t\\t\"+objName+\".\"+name[t]+\".getText();\\n\"\n L+=\"\\t}\\n\\n\"\n \n L+=\"}\"\n return L\n\n\n \n \n\n \n \n \n \n","sub_path":"PL_PA.py","file_name":"PL_PA.py","file_ext":"py","file_size_in_byte":3898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"539543102","text":"# -*- coding: utf-8 -*-\nfrom Sim_Code.Objects.Particle import particle, Constants\nimport numpy as np\nimport matplotlib as mpl\nfrom matplotlib.backends.backend_pgf import FigureCanvasPgf\nmpl.backend_bases.register_backend('pdf', FigureCanvasPgf)\nimport matplotlib.pyplot as plt\n#mpl.rcParams['text.latex.unicode'] = True\nmpl.rcParams['text.usetex'] = True\nmpl.rcParams['pgf.texsystem'] = 'lualatex'\nmpl.rcParams['font.family'] = 'serif'\nmpl.rcParams.update({'figure.autolayout': True})\nmpl.rcParams.update({'font.size': 20})\n\n\nclass test():\n def __init__(self, time_divisor=0.0625):\n self.c = Constants()\n self.c.drop_properties()\n self.c.gas_properties()\n self.c.get_reference_conditions()\n self.c.add_drop_properties()\n self.c.add_gas_properties()\n self.c.add_properties()\n self.p2 = particle(self.c, [0, 0, 0], velocity=[0, 0, 0],\n D=np.sqrt(1.1)/1000, T_d=282,\n ODE_solver=1, coupled=2)\n self.p3 = particle(self.c, [0, 0, 0], velocity=[0, 0, 0],\n D=np.sqrt(1.1)/1000, T_d=282,\n ODE_solver=2, coupled=2)\n self.p4 = particle(self.c, [0, 0, 0], velocity=[0, 0, 0],\n D=np.sqrt(1.1)/1000, T_d=282,\n ODE_solver=3, coupled=2)\n self.p5 = particle(self.c, [0, 0, 0], velocity=[0, 0, 0],\n D=np.sqrt(1.1)/1000, T_d=282,\n ODE_solver=4, coupled=2)\n\n self.time_divisor = time_divisor\n self.div = self.p2.get_tau()*self.time_divisor\n self.N = 10000\n\n def iter_particles(self):\n last_time = 0\n for t in range(self.N):\n if (self.p2.m_d/self.p2.m_d0 > 0.001 and\n self.p2.T_d/self.p2.T_G < 0.999):\n time1 = t * self.div\n self.p2.iterate(time1 - last_time)\n last_time = time1\n else:\n break\n\n last_time = 0\n for t in range(self.N):\n if (self.p3.m_d/self.p3.m_d0 > 0.001 and\n self.p3.T_d/self.p3.T_G < 0.999):\n time1 = t * self.div\n self.p3.iterate(time1 - last_time)\n last_time = time1\n else:\n break\n\n last_time = 0\n for t in range(self.N):\n if (self.p4.m_d/self.p4.m_d0 > 0.001 and\n self.p4.T_d/self.p4.T_G < 0.999):\n time1 = t * self.div\n self.p4.iterate(time1 - last_time)\n last_time = time1\n else:\n break\n\n last_time = 0\n for t in range(self.N):\n if (self.p5.m_d/self.p5.m_d0 > 0.001 and\n self.p5.T_d/self.p5.T_G < 0.999):\n time1 = t * self.div\n self.p5.iterate(time1 - last_time)\n last_time = time1\n else:\n break\n\n def plot_data(self):\n f1 = plt.figure(figsize=(20, 10))\n ax1 = f1.add_subplot(111)\n ax1.plot(self.p2.times, self.p2.diameter_2_history, 'g--',\n label='Forward Euler')\n ax1.plot(self.p3.times, self.p3.diameter_2_history, 'rx',\n label='Backward Euler')\n ax1.plot(self.p4.times, self.p4.diameter_2_history, 'y*',\n label='Modified Euler')\n ax1.plot(self.p5.times, self.p5.diameter_2_history, 'x',\n label='Runge Kutta')\n ax1.set_xlim(0)\n ax1.set_ylim(0)\n plt.xlabel(r'$t$ ($s$)')\n plt.ylabel(r'$D^2$ ($mm^2$)')\n plt.legend(loc='upper right')\n plt.title('Diameter Evolution of Evaporating Droplet')\n\n f2 = plt.figure(figsize=(20, 10))\n ax2 = f2.add_subplot(111)\n ax2.plot(self.p2.times, self.p2.temp_history, 'g--',\n label='Forward Euler')\n ax2.plot(self.p3.times, self.p3.temp_history, 'rx',\n label='Backward Euler')\n ax2.plot(self.p4.times, self.p4.temp_history, 'y*',\n label='Modified Euler')\n ax2.plot(self.p5.times, self.p5.temp_history, 'x',\n label='Runge Kutta')\n ax2.set_xlim(0)\n ax2.set_ylim(self.p2.T_d0)\n plt.xlabel(r'$t$ ($s$)')\n plt.ylabel(r'$T_d$ ($K$)')\n plt.legend(loc='lower right')\n plt.title('Temperature Evolution of Evaporating Droplet')\n\n def save_data(self):\n self.file_dir = 'Sim_Code//Verification_Tests//heat_mass_data//'\n with open(self.file_dir +\n 'c_fe_heat_mass_transfer_time_step_' + str(self.time_divisor)\n + '_tau.txt', 'w') as f:\n self.p2.times[::-1]\n f.write('time' + ' ' + 'T_d' + ' ' + 'd2' + ' ' + '\\n')\n for i in range(len(self.p2.times)):\n f.write(str(self.p2.times[i]) + ' ' +\n str(self.p2.temp_history[i]) + ' ' +\n str(self.p2.diameter_2_history[i]) + ' ' + '\\n')\n self.p2.times_temp_nd[::-1]\n\n with open(self.file_dir +\n 'c_be_heat_mass_transfer_time_step_' + str(self.time_divisor)\n + '_tau.txt', 'w') as f:\n self.p3.times[::-1]\n f.write('time' + ' ' + 'T_d' + ' ' + 'd2' + ' ' + '\\n')\n for i in range(len(self.p3.times)):\n f.write(str(self.p3.times[i]) + ' ' +\n str(self.p3.temp_history[i]) + ' ' +\n str(self.p3.diameter_2_history[i]) + ' ' + '\\n')\n self.p3.times_temp_nd[::-1]\n\n with open(self.file_dir +\n 'c_me_heat_mass_transfer_time_step_' + str(self.time_divisor)\n + '_tau.txt', 'w') as f:\n self.p4.times[::-1]\n f.write('time' + ' ' + 'T_d' + ' ' + 'd2' + ' ' + '\\n')\n for i in range(len(self.p4.times)):\n f.write(str(self.p4.times[i]) + ' ' +\n str(self.p4.temp_history[i]) + ' ' +\n str(self.p4.diameter_2_history[i]) + ' ' + '\\n')\n self.p4.times_temp_nd[::-1]\n\n with open(self.file_dir +\n 'c_rk_heat_mass_transfer_time_step_' + str(self.time_divisor)\n + '_tau.txt', 'w') as f:\n self.p5.times[::-1]\n f.write('time' + ' ' + 'T_d' + ' ' + 'd2' + ' ' + '\\n')\n for i in range(len(self.p5.times)):\n f.write(str(self.p5.times[i]) + ' ' +\n str(self.p5.temp_history[i]) + ' ' +\n str(self.p5.diameter_2_history[i]) + ' ' + '\\n')\n self.p5.times[::-1]\n\n\ndef run_test(save=False):\n t = test()\n t.iter_particles()\n t.plot_data()\n if save is True:\n t.save_data()\n","sub_path":"Sim_Code/Verification_Tests/heat_mass_transfer.py","file_name":"heat_mass_transfer.py","file_ext":"py","file_size_in_byte":6743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"64496043","text":"import seaborn as sns\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches\nimport pandas as pd\nimport numpy as np\n\n# result_dir = '/hpc/crise/wang.q/results/ISPA/'\n# datasets = []\n# clfmets = []\n# exps = []\n# datamets = []\n#\n# for clf_met in ['svm','logis']:\n# result = result_dir + 'result/' \\\n# if clf_met == 'logis' else result_dir + 'result_svm/'\n# for exp in ['fmril','fmrir','meg']:\n# for datamet in ['no','global','indi']:\n# split_nb = 120 if exp=='meg' else 50\n#\n# data = np.load(result+'nor_{}_{}_{}_{}splits_gridsearch.npy'.\n# format(exp,datamet,clf_met,split_nb))\n# datasets.append(data)\n# clfmets += [clf_met] * split_nb\n# exps += [exp] * split_nb\n# datamets += [datamet] * split_nb\n#\n# datasets = np.hstack(datasets)\n# print(len(datasets),len(clfmets),len(exps),len(datamets))\n#\n# datatitle = {'classifier':[],'experiment':[],'datamethod':[],'accuracy':[]}\n# dataframe = pd.DataFrame(datatitle)\n# dataframe.classifier = clfmets\n# dataframe.experiment = exps\n# dataframe.datamethod = datamets\n# dataframe.accuracy = datasets\n# dataframe.to_csv(result_dir+'plot_normalization.csv',index=False)\n#\n#\n\n# svm0.07, 0.0000, 0.00000 logis 0.000\n# svm 0.2, 0.0000 logis 0.000\n# svm 0.0000 logis0.000\ndef stars(p):\n if p < 0.0001:\n return \"*\"\n elif (p < 0.001):\n return \"***\"\n elif (p < 0.01):\n return \"**\"\n elif (p < 0.05):\n return \"*\"\n else:\n return \"-\"\n\n\nresult_dir = '/hpc/crise/wang.q/results/ISPA'\nresults = pd.read_csv(result_dir + '/plot_normalization.csv')\nresults['datamethod'] = results['datamethod'].map({\n 'no': 'no \\nstandardization',\n 'global': 'classical \\nstandardization',\n 'indi': 'multi-source \\nstandardization'\n })\nresults['classifier'] = results['classifier'].map({'logis':'logistic \\nregression',\n 'svm':'svm'})\nplt.close('all')\nplt.rcParams['ytick.major.pad'] = 2\nplt.rcParams['ytick.labelsize'] = 12.\n\nsns.set_style(\"whitegrid\",\n {\"xtick.color\": '0',\n \"ytick.color\": '0',\n \"text.color\": '0',\n \"grid.color\": '0.95',\n \"axes.edgecolor\": '.7'})\n\nfontsize = 12.5\nfontsize2 = 11\ntext_fontsize = 15\nplt.figure(figsize=(9, 5))\nax = plt.subplot(131)\nfmrildata = results.loc[results['experiment']=='fmril']\n# fmrildata = results.loc[results.experiment=='meg']\n# fmrildata = results[results.experiment=='fmril']\nbox = sns.boxplot(data=fmrildata, x='accuracy', y='classifier',\n orient='h', hue='datamethod',\n whis=[5,95], width=.45, notch=True,\n linewidth=2, fliersize=2)\n\n# sns.despine(top=True, bottom=True, left=True, right=True)\n\nfor cor in [0,1]:\n ax.annotate(\"\", xy=(0.81,-0.15+cor),xytext=(0.81, 0.15+cor),\n arrowprops=dict(arrowstyle=\"-\", facecolor='black',\n connectionstyle=\"bar,fraction=0.1\"))\n ax.text(0.83, cor, '*', fontsize=text_fontsize,\n horizontalalignment='center',verticalalignment='center')\n\n ax.annotate(\"\", xy=(0.6, -0.15 + cor), xytext=(0.6, 0+ cor),\n arrowprops=dict(arrowstyle=\"-\", facecolor='black',\n connectionstyle=\"bar,fraction=-0.2\"))\n if cor == 1:\n ax.text(0.57, -0.05+cor, '*', fontsize=text_fontsize,\n horizontalalignment='center', verticalalignment='center')\n else:\n ax.text(0.57, -0.05 + cor, '-', fontsize=text_fontsize,\n horizontalalignment='center', verticalalignment='center')\n\n ax.annotate(\"\", xy=(0.6, 0.05 + cor), xytext=(0.6, 0.2 + cor),\n arrowprops=dict(arrowstyle=\"-\", facecolor='black',\n connectionstyle=\"bar,fraction=-0.2\"))\n ax.text(0.57, 0.1+cor, '*', fontsize=text_fontsize,\n horizontalalignment='center', verticalalignment='center')\n\n# plt.legend(handles=[mpatches.Patch(facecolor=(.1, .5, 0), label='no \\nnormalization'),\n# mpatches.Patch(facecolor=(.2, .2, 0.8),\n# label='standard \\nnormalization'),\n# mpatches.Patch(facecolor=(0.4, 0.1, 0.4),\n# label='multi-source \\nnormalization')],\n# loc=(-0.3,0.9), handlelength=1,\n# handletextpad=.4, labelspacing=0.5, fontsize=15,markerfirst=False)\nplt.legend(loc=(-0.5, 0.95))\nplt.title('fMRI ROI # 1',size=fontsize)\nplt.xlabel('Prediction accuracy',size=fontsize)\nplt.ylabel(' ')\nplt.xticks(size=fontsize2)\nplt.yticks(size=fontsize)\n# make the background black and white\n# plt.axhspan(.5, 1.5, facecolor='.9', edgecolor='none', zorder=-1)\n\n\n\nax2 = plt.subplot(132)\nfmrirdata = results.loc[results['experiment']=='fmrir']\nbox = sns.boxplot(data=fmrirdata, x='accuracy', y='classifier',\n orient='h', hue='datamethod',\n whis=[5,95], width=.45, notch=True,\n linewidth=2, fliersize=2,)\n\nsns.despine(top=True, bottom=True, left=True, right=True)\nfor cor in [0,1]:\n ax2.annotate(\"\", xy=(0.84,-0.15+cor),xytext=(0.84, 0.15+cor),\n arrowprops=dict(arrowstyle=\"-\", facecolor='black',\n connectionstyle=\"bar,fraction=0.1\"))\n ax2.text(0.86, cor, '*', fontsize=text_fontsize,\n horizontalalignment='center',verticalalignment='center')\n\n ax2.annotate(\"\", xy=(0.63, -0.15 + cor), xytext=(0.63, 0+ cor),\n arrowprops=dict(arrowstyle=\"-\", facecolor='black',\n connectionstyle=\"bar,fraction=-0.2\"))\n if cor == 1:\n ax2.text(0.61, -0.05+cor, '*', fontsize=text_fontsize,\n horizontalalignment='center', verticalalignment='center')\n else:\n ax2.text(0.61, -0.05 + cor, '-', fontsize=text_fontsize,\n horizontalalignment='center', verticalalignment='center')\n\n ax2.annotate(\"\", xy=(0.63, 0.05 + cor), xytext=(0.63, 0.2 + cor),\n arrowprops=dict(arrowstyle=\"-\", facecolor='black',\n connectionstyle=\"bar,fraction=-0.2\"))\n ax2.text(0.61, 0.1+cor, '*', fontsize=text_fontsize,\n horizontalalignment='center', verticalalignment='center')\nplt.title('fMRI ROI # 2',size=fontsize)\nplt.xlabel('Prediction accuracy',size=fontsize)\nplt.xticks(size=fontsize2)\nplt.legend(loc=(5, 0.9))\nplt.ylabel(' ')\nplt.yticks(size=0)\n# make the background black and white\n# plt.axhspan(.5, 1.5, facecolor='.9', edgecolor='none', zorder=-1)\n\n\nax3 = plt.subplot(133)\nmegdata = results.loc[results['experiment']=='meg']\nbox = sns.boxplot(data=megdata, x='accuracy', y='classifier',\n orient='h', hue='datamethod',\n whis=[5,95], width=.45, notch=True,\n linewidth=2, fliersize=2,)\nsns.despine(top=True, bottom=True, left=True, right=True)\nfor cor in [0,1]:\n ax3.annotate(\"\", xy=(0.74,-0.15+cor),xytext=(0.74, 0.15+cor),\n arrowprops=dict(arrowstyle=\"-\", facecolor='black',\n connectionstyle=\"bar,fraction=0.1\"))\n ax3.text(0.76, cor, '*', fontsize=text_fontsize,\n horizontalalignment='center',verticalalignment='center')\n\n ax3.annotate(\"\", xy=(0.56, -0.15 + cor), xytext=(0.56, 0+ cor),\n arrowprops=dict(arrowstyle=\"-\", facecolor='black',\n connectionstyle=\"bar,fraction=-0.2\"))\n ax3.text(0.54, -0.05+cor, '*',fontsize=text_fontsize,\n horizontalalignment='center', verticalalignment='center')\n\n ax3.annotate(\"\", xy=(0.56, 0.05 + cor), xytext=(0.56, 0.2 + cor),\n arrowprops=dict(arrowstyle=\"-\", facecolor='black',\n connectionstyle=\"bar,fraction=-0.2\"))\n ax3.text(0.54, 0.1+cor, '*', fontsize=text_fontsize,\n horizontalalignment='center', verticalalignment='center')\nplt.legend(loc=(5, 0.95))\nplt.title('MEG',size=fontsize)\nplt.xlabel('Prediction accuracy',size=fontsize)\nplt.ylabel(' ')\nplt.xticks(size=fontsize2)\nplt.yticks(size=0)\n# make the background black and white\n# plt.axhspan(.5, 1.5, facecolor='.9', edgecolor='none', zorder=-1)\n\n","sub_path":"plot_normalization.py","file_name":"plot_normalization.py","file_ext":"py","file_size_in_byte":8375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"101502169","text":"\"\"\"\nFrame reading/writing idea was taken from\nhttp://zulko.github.io/blog/2013/09/27/read-and-write-video-frames-in-python-using-ffmpeg/\nWarning: no signal handling, Ctrl+C may work improperly\n\"\"\"\nfrom PIL import Image\nfrom PIL import ImageDraw\nfrom PIL import ImageFont\n\nimport argparse\nimport numpy as np\nimport os\nimport pickle as pkl\nimport re\nimport subprocess as sp\nfrom tqdm import tqdm\n\nfrom video_writer import ffmpeg_video_writer\nimport sys\nsys.path.append(\"../configs\")\nfrom base_config import BaseConfig\n\nsession_id = '201704150933'\n#session_id = '201704141145'\n\noutput_size = [720, 480]\nsample_rate = 3\npreset = \"medium\"\ncaption_color = \"white\"\nfont_file = \"/usr/share/fonts/truetype/fonts-japanese-gothic.ttf\"\n\n\n\ndef convert_time(time_str):\n # 00:00:55:555 or 00:00:55.555 -> seconds\n if time_str.find('.') != -1:\n time_str += '0'\n time_str = time_str.replace('.', \":\")\n\n time_list = time_str.split(\":\")\n result = sum([float(unit) * int(60 ** (2 - idx)) for idx, unit in enumerate(time_list)])\n result += float(time_list[-1]) * 1e-3\n return result\n\n\ndef get_info(file_path):\n # returns info (e.g. resolution)\n # strings in the json are not formatted like 1043_Vantage_Point_00.43.08-00.43.59\n input_video = file_path\n\n command = ['ffmpeg', '-i', input_video]\n proc = sp.Popen(command, stdout=sp.PIPE, stderr=sp.PIPE)\n proc.stdout.readline()\n proc.terminate()\n info = proc.stderr.read().decode(\"utf-8\")\n print(info)\n\n match = re.search(\"\\,[\\s]+([0-9]+)x([0-9]+)\", info)\n duration = re.search(\"Duration:\\ (.+?)\\, \", info)\n fps = re.search(\"\\, ([0-9\\.]+?)\\ fps\", info)\n\n if match is None or duration is None or fps is None:\n raise \"Not found\"\n\n return [[int(match.group(1)), int(match.group(2))], convert_time(duration.group(1)), float(fps.group(1))]\n\n\ndef filter_captions(captions, timestamp):\n result = []\n\n for caption in captions:\n if caption['start'] < timestamp < caption['end']:\n result.append(caption['text'])\n\n return result\n\n\ndef overlay_captions(cfg):\n\n video_filename = os.path.join(cfg.video_root, session_id+'/aligned_video.mp4')\n try:\n size, duration, fps = get_info(video_filename)\n except:\n print(size, duration, fps)\n print(\"File is not found or corrupted!\")\n exit(1)\n\n output_filename = video_filename.replace('.mp4', '_'+cfg.name+'.mp4')\n print(\"Writing to: \", output_filename)\n\n '''\n font setup\n '''\n font = ImageFont.truetype(font_file, int(output_size[1] / 14.), encoding=\"unic\")\n\n command = [\"ffmpeg\",\n '-i', video_filename,\n '-vf', 'fps=3',\n '-f', 'image2pipe',\n '-pix_fmt', 'rgb24',\n '-vcodec', 'rawvideo', '-']\n\n proc = sp.Popen(command, stdout=sp.PIPE, stderr=open(os.devnull, 'w'), bufsize=10 ** 7)\n\n bs = 1 # currently slower with anything larger than batch_size=1\n nbytes = bs * 3 * size[0] * size[1]\n writer = ffmpeg_video_writer(output_filename, input_size=size, output_size=output_size,\n fps=10*fps, bitrate='2000k', codec='libx264', preset=preset)\n nframes = int(duration * sample_rate)\n nread = 0\n\n '''\n blocking part: single-threaded\n '''\n result_seg_name = cfg.name+'/result_seg.pkl'\n result_seg = pkl.load(open(os.path.join(cfg.result_root, result_seg_name), 'r'))\n seg = result_seg[session_id]['s']\n\n showing_shot_change = False\n show_counter = 0\n r = 100\n\n with tqdm(total=nframes) as pbar:\n while True:\n s = proc.stdout.read(nbytes)\n if len(s) != nbytes:\n # issue warning?\n pbar.close()\n break\n else:\n result = np.fromstring(s, dtype='uint8')\n result = np.reshape(result,\n (bs, size[1], size[0], len(s) // (size[0] * size[1] * bs)))\n for i in range(bs):\n image = Image.fromarray(result[i])\n draw = ImageDraw.Draw(image)\n\n # current_captions = filter_captions(captions, nread / float(fps))\n\n # ==== monkey patching\n if (nread in seg):\n draw.ellipse((size[0] - r, size[1] - r,\n size[0], size[1]), fill=(255, 0, 0))\n draw.text((size[0] - 2 * r, size[1] - 30), 'NEW SHOT!',\n caption_color, font)\n output = np.array(image)\n for slowmo in range(30):\n writer.write_frame(output)\n try:\n writer.write_frame(np.array(image))\n except:\n print(\"Cannot write anymore!\")\n pbar.update(bs)\n nread += 1\n\n proc.wait()\n del proc\n writer.close()\n\n\ndef main():\n cfg = BaseConfig().parse()\n overlay_captions(cfg)\n\n\nif __name__ == '__main__':\n\n main()\n","sub_path":"preprocess/video_overlay.py","file_name":"video_overlay.py","file_ext":"py","file_size_in_byte":5079,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"170140258","text":"import random\r\n\r\nnumber = random.randint(1,9)\r\n\r\nchances = 1\r\n\r\nwhile chances < 5 :\r\n guess = input(\"Pick A Number Between 1-9 : \")\r\n chances = chances + 1\r\n if guess == number + 1 or guess == number - 1:\r\n print(\"Your Close!\")\r\n else: \r\n print(\"Not Close\") \r\n if guess == number :\r\n print(\"You won!\")\r\n break\r\n \r\nif not chances < 5 :\r\n print(\"You Lost, the number was \", number)\r\n","sub_path":"numberThing.py","file_name":"numberThing.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"85984817","text":"#coding=utf-8\n'''将batch文件转成图像'''\nfrom scipy.misc import *\nimport numpy as np\nimport pickle\nimport os\n\nclass BatchToImg():\n def __init__(self):\n pass\n\n # 解压缩，返回解压后的字典\n def unpickle(self):\n fo = open(self.file, 'rb')\n dict = pickle.load(fo, encoding='iso-8859-1')\n fo.close()\n return dict\n\n # 解压训练数据\n def train_batch_to_img(self):\n # 依次打开批文件\n for j in range(1, 6):\n self.file = \"E:\\硕士学业\\MISC\\cifar-10-batches-py/data_batch_\" + str(j)\n\n # 解压批文件\n X = self.unpickle()\n print(self.file + \" is loading...\")\n\n # 遍历当前批文件中所有图像\n for i in range(0, 10000):\n cur_img = X['data'][i] # X['data']为图片二进制数据\n\n # 调整图像数据格式\n img = np.reshape(cur_img, (3, 32, 32))\n img = img.transpose((1, 2, 0))\n\n # 图像类别标签\n img_label = X['labels'][i]\n\n # 图像文件名\n img_filename = X['filenames'][i]\n\n # 图像保存路径\n impath = ('E:\\硕士学业\\MISC\\cifar-10-batches-py/CIFAR10imgs/'\n + str(img_label) + '/')\n\n # 先判断文件夹是否存在\n if(os.path.isdir(impath)):\n # 保存图像\n imsave(impath + img_filename, img)\n else:\n # 创建文件夹并保存图像\n os.makedirs(impath)\n imsave(impath + img_filename, img)\n\n # 解压测试数据\n def test_batch_to_img(self):\n\n self.file = \"E:\\硕士学业\\MISC\\cifar-10-batches-py/test_batch\"\n X = self.unpickle()\n print(self.file + \" is loading...\")\n for i in range(0, 10000):\n cur_img = X['data'][i]\n img = np.reshape(cur_img, (3, 32, 32)) # Xtr['data']为图片二进制数据\n img = img.transpose((1, 2, 0))\n img_label = X['labels'][i]\n img_filename = X['filenames'][i]\n test_path = ('E:\\硕士学业\\MISC\\cifar-10-batches-py/CIFAR10imgs/test_set/'\n + str(img_label) + '/')\n # 先判断一下是否存在文件夹\n if (os.path.isdir(test_path)):\n imsave(test_path + img_filename, img)\n else:\n os.makedirs(test_path)\n imsave(test_path + img_filename, img)\n\nif __name__ == '__main__':\n\n unpacker = BatchToImg()\n unpacker.train_batch_to_img()\n unpacker.test_batch_to_img()\n\n\n","sub_path":"pkgs/utils/batch2img.py","file_name":"batch2img.py","file_ext":"py","file_size_in_byte":2703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"564533505","text":"from consts import *\n\n\n\"\"\"\ncups\n [H][0][1][2][3][4][5]\n [5][4][3][2][1][0][H]\n\"\"\"\n\n\nclass Side:\n def __init__(self, is_AI=False):\n self.home_cup = 0\n self.cups = [k for i in range(n)]\n self.AI = is_AI\n\n def move(self, index, other_side):\n rocks = self.cups[index]\n self.cups[index] = 0\n result, end_index = self.add(index, rocks, other_side)\n if result == 'steal':\n self.steal(end_index, other_side)\n\n return result\n\n def add(self, start_index, rocks, other_side, side_move=True):\n while True:\n if rocks == 0:\n return 'end', 0\n if start_index > 0:\n for index in range(start_index)[::-1]:\n self.cups[index] += 1\n rocks -= 1\n if rocks <= 0:\n if side_move and self.cups[index] == 1 and side_move==True:\n return 'steal', index\n return 'end', 0\n if side_move and rocks > 0:\n self.home_cup += 1\n rocks -= 1\n if rocks <= 0:\n return 'again', 0\n \n if rocks < n:\n return other_side.add(n, rocks, other_side=self, side_move=False)\n else:\n rocks -= n\n other_side.add(n, n, other_side=self, side_move=False)\n start_index = n\n\n def steal(self, cup_index, other_side):\n if not other_side.is_empty(n-cup_index - 1):\n self.cups[cup_index] = 0\n\n # +1 beacause of our cup\n self.home_cup += other_side.steal_victim(n-cup_index - 1) + 1\n\n def steal_victim(self, index):\n rocks = self.cups[index]\n self.cups[index] = 0\n return rocks\n\n def possible_move(self):\n for cup in self.cups:\n if cup > 0:\n return True\n return False\n\n def get_cups(self):\n return [self.home_cup] + self.cups\n\n def count(self):\n return sum(self.cups) + self.home_cup\n\n def is_empty(self, index):\n return self.cups[index] == 0\n","sub_path":"classes/side.py","file_name":"side.py","file_ext":"py","file_size_in_byte":2159,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"209850719","text":"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n# __author__ = \"Lex\"\n# Date: 2017/9/30\n\n# #输入工资\n# #初始定义商品名及价格\n# #输入想购买商品的编号\n#\n# salary = int( input(\"please input your salary:\") )\t\t#输入工资\n# price_list = [3800, 30, 50, 90]\t\t#列表-商品价格\n# name_list = [\"iphone\", \"coffee\", \"book\", \"condom\"]\t\t#列表-商品名字\n# print(\"你可以买以下商品：\\n\",\t\t#打印出商品组合\n# \"1.\",name_list[0],price_list[0],\"\\n\",\n# \"2.\",name_list[1],price_list[1],\"\\n\",\n# \"3.\",name_list[2],price_list[2],\"\\n\",\n# \"4.\",name_list[3],price_list[3])\n# total_list = []\n#\n# #判断工资是否足够购买商品\n# while True:\n# \tbuy_num_input = input(\"please input the commodity number or 'q' to quit:\")\t\t#输入购买的或者退出\n# \tif buy_num_input == \"q\":\t\t#判断输入退出\n# \t\tprint(\"退出购买\")\n# \t\tprint(\"已购买\",name_list[0],total_list.count(name_list[0]))\n# \t\tprint(\"已购买\", name_list[1], total_list.count(name_list[1]))\n# \t\tprint(\"已购买\",name_list[2],total_list.count(name_list[2]))\n# \t\tprint(\"已购买\", name_list[3], total_list.count(name_list[3]))\n# \t\tprint(\"剩余金额\",salary)\n# \t\tbreak\n# \telse:\n# \t\tbuy_num = int(buy_num_input) # 输入要买的商品编号\n# \t\tbuy_num -= 1\n# \t\tif buy_num < 4:\t\t#判断输入商品编号\n# \t\t\tif price_list[buy_num] <= salary:\t\t#判断是否买的起\n# \t\t\t\tsalary = salary - price_list[buy_num]\t\t#购买并打印余额\n# \t\t\t\tprint(\"已将\",name_list[buy_num],\"加入购物车...\",\"\\n\",\n# \t\t\t\t\t \"花费金额\",price_list[buy_num],\"\\n\",\n# \t\t\t\t\t \"剩余金额\",salary)\n# \t\t\t\ttotal_list.append(name_list[buy_num])\t\t#将购买的商品放入购物列表\n# \t\t\telse:\n# \t\t\t\tbalance = price_list[buy_num] - salary\n# \t\t\t\tprint(\"余额不足，还需金额\",balance)\n# \t\telse:\n# \t\t\tprint(\"输出错误，请重新输入\")\n# \t\t\tcontinue\n\n#初始化变量、列表\n# shopping_cart = []\t\t#定义购物车\n# price = [3800, 30, 50, 90]\n# product = [\"iphone\", \"coffee\", \"book\", \"condom\"]\n# flag = True\n# salary = int( input(\"please input your salary:\") )\t\t#输入工资\n#\n# #列出商品\n# for i in product:\n# \tprint(product.index(i) + 1, i, price[product.index(i)])\n#\n# while flag:\n# \twhile salary > 0:\n# \t\tchoice = input(\"please input the commodity number or 'q' to quit:\") # 输入购买的或者退出\n# \t\tif choice == 'q':\n# \t\t\tflag = False\n# \t\t\tbreak\n# \t\tif int(salary) < price[int(choice) - 1]:\n# \t\t\tprint(\"余额不足，还差%d\"%(price[int(choice) - 1] - int(salary)))\n# \t\telse:\n# \t\t\tshopping_cart.append(product[int(choice) - 1])\n# \t\t\tprint(shopping_cart)\n# \t\t\tsalary = int(salary) - price[int(choice) - 1]\n# \t\t\tprint(\"您已购买%s,还剩金额%d\"%(str(shopping_cart),salary))\n# else:\n# \tprint(\"您已购买%s,还剩金额%d\" % (str(shopping_cart), salary))\n\n\n#初始化变量、列表\nshopping_cart = {}\t\t#定义购物车\nproduct_list = [[\"iphone7\",5800],\n\t\t\t [\"臭豆腐\",100],\n\t\t\t [\"甜不辣\",20],\n\t\t\t [\"拖鞋\",50],\n\t\t\t [\"coffee\",200]\n\t\t\t ]\nsalary = int( input(\"please input your salary:\") )\t\t#输入工资\n\nwhile True:\n\tnum = 1\n\tfor i in product_list:\t\t#输出商品列表\n\t\tprint(num,i)\n\t\tnum += 1\n\tchoice = input(\"请输入商品编号:\").strip()\t\t\t#输入商品编号\n\tif choice.isdigit():\t\t#判断商品编号是否为数字\n\t\tchoice = int(choice)\n\t\tif choice >= 1 and choice < 6:\t\t\t#判断商品编号是否存在\n\t\t\tproduct = product_list[choice - 1]\n\t\t\tproduct_price = product[1]\n\t\t\tproduct_name = product[0]\n\t\t\tif product_price <= salary:\t\t\t#判断能否买的起\n\t\t\t\tprint(\"已购买商品%s\"%(product_name))\n\t\t\t\tif product[0] in shopping_cart:\t\t#判断购物车内是否存在商品\n\t\t\t\t\tshopping_cart[product[0]][1] += 1\t\t#修改商品购买记录加入购物车\n\t\t\t\telse:\n\t\t\t\t\tshopping_cart[product[0]] = [product[1], 1] # 创建商品购买记录加入购物车\n\t\t\t\tsalary -= product_price\n\t\t\telse:\n\t\t\t\tbalance = product_price - salary\n\t\t\t\tprint(\"买不起，还差%d元\"%(balance))\n\t\t\tprint(shopping_cart)\n\t\telse:\n\t\t\tprint(\"编号不存在，请重新输入!\")\n\t\t\tcontinue\n\telif choice == \"q\":\n\t\tprint(\"---------您购买的商品如下---------\")\n\t\tprint(\"ID\\t商品\\t\\t数量\\t\\t单价\\t\\t总价\")\n\t\tid_num = 1\n\t\ttotal_cost = 0\n\t\tfor i in shopping_cart:\n\t\t\tprint(\"%s%10s%10s%10s%10s\"%\n\t\t\t\t (id_num,i,shopping_cart[i][1],shopping_cart[i][0],shopping_cart[i][1] * shopping_cart[i][0]))\n\t\t\tid_num += 1\n\t\t\ttotal_cost += shopping_cart[i][1] * shopping_cart[i][0]\n\t\tprint(\"您已消费%d元\"%(total_cost))\n\t\tprint(\"您的余额为%d元\"%(salary))\n\t\tprint(\"------------退出购物-------------\")\n\t\tbreak\n\telse:\n\t\tprint(\"输出错误，请重新输入！\")\n\t\tcontinue","sub_path":"Day12/shopping.py","file_name":"shopping.py","file_ext":"py","file_size_in_byte":4610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"124666835","text":"import struct\nimport zlib\nfrom binascii import hexlify\nfrom io import BytesIO\nfrom typing import IO, Iterable, List, Tuple\n\nfrom .utils import verify_data\n\n\n# 00000000: 424c 5445 0000 00b4 0f00 0007 0000 0017 BLTE............\n# ^ 4-byte BLTE magic\n# ^ 4-byte big-endian header size\n# ^ 1-byte version\n# ^ 3-byte (big-endian) number of blocks\n# ^ first block encoded size\n# 00000010: 0000 0016 6f65 6f53 d85a d828 108e 8444 ....oeoS.Z.(...D\n# ^ first block decoded size\n# ^ first block encoded md5 hash\n# 00000020: 8535 b202 0000 003b 0000 0036 f490 b704 .5.....;...6....\n# ^ second block encoded size ...\n# 00000030: 25e9 4f52 047f 7583 2f64 3b40 0000 0101 %.OR..u./d;@....\n# 00000040: 0000 0100 35e0 1068 8ac0 e7d9 4a67 6e89 ....5..h....Jgn.\n# 00000050: 72ed 60c2 0000 8001 0000 8000 7d54 b708 r.`.........}T..\n# 00000060: 1246 2e1a 377b d2ba 578e 6d35 0000 00a1 .F..7{..W.m5....\n# 00000070: 0000 00a0 4678 ee15 76ba 8650 5c6a 29fa ....Fx..v..P\\j).\n# 00000080: 009c a687 0000 5001 0000 5000 2632 c86f ......P...P.&2.o\n# 00000090: 7bc2 cbfc 89f4 e93a f8ef c120 0000 002f {......:... .../\n# 000000a0: 0000 002d 0f44 12c6 9a86 82cf 1bfe 63d5 ...-.D........c.\n# 000000b0: 8564 1952 4e45 4e01 1010 0004 0004 0000 .d.RNEN.........\n# ^ first block data\n# 000000c0: 0008 0000 0005 0000 0000 365a 78da 4bb2 ..........6Zx.K.\n# ^ second block data\n\n\ndef decode_block(data: bytes) -> bytes:\n\ttype = data[0]\n\n\tif type == b\"N\"[0]:\n\t\treturn data[1:]\n\telif type == b\"Z\"[0]:\n\t\treturn zlib.decompress(data[1:], wbits=0)\n\n\traise ValueError(f\"Unknown block type {type}\")\n\n\ndef verify_blte_data(fp: IO, key: str):\n\tdec = BLTEDecoder(fp, key, verify=True)\n\tfor block in dec.encoded_blocks:\n\t\t# Iterating verifies the block\n\t\tpass\n\n\nclass BLTEDecoder:\n\tdef __init__(self, fp: IO, key: str, verify: bool=False) -> None:\n\t\tself.fp = fp\n\t\tself.block_table: List[Tuple[int, int, str]] = []\n\t\tself._block_index = 0\n\t\tself.key = key\n\t\tself.verify = verify\n\t\tself.parse_header()\n\n\tdef parse_header(self):\n\t\tself._header_data = self.fp.read(8)\n\t\tblte_header = BytesIO(self._header_data)\n\t\tassert blte_header.read(4) == b\"BLTE\"\n\t\theader_size, = struct.unpack(\">i\", blte_header.read(4))\n\n\t\tif header_size > 0:\n\t\t\tassert self.fp.read(1) == b\"\\x0f\"\n\t\t\tblock_info_data = self.fp.read(header_size - 9)\n\t\t\tif self.verify:\n\t\t\t\t_data_to_verify = self._header_data + b\"\\x0f\" + block_info_data\n\t\t\t\tverify_data(\"BLTE header\", _data_to_verify, self.key, self.verify)\n\n\t\t\tblock_info = BytesIO(block_info_data)\n\t\t\tself.parse_block_info(block_info)\n\n\tdef parse_block_info(self, fp: IO) -> None:\n\t\tnum_blocks, = struct.unpack(\">i\", b\"\\x00\" + fp.read(3))\n\t\tfor i in range(num_blocks):\n\t\t\tencoded_size, decoded_size, md5 = struct.unpack(\n\t\t\t\t\">ii16s\", fp.read(4 + 4 + 16)\n\t\t\t)\n\t\t\tself.block_table.append(\n\t\t\t\t(encoded_size, decoded_size, hexlify(md5).decode())\n\t\t\t)\n\n\t@property\n\tdef blocks(self) -> Iterable[bytes]:\n\t\tfor encoded_block in self.encoded_blocks:\n\t\t\tyield decode_block(encoded_block)\n\n\t@property\n\tdef encoded_blocks(self) -> Iterable[bytes]:\n\t\tif self._block_index:\n\t\t\traise RuntimeError(\n\t\t\t\t\"BLTE.blocks has already been iterated over. \"\n\t\t\t\t\"You should have stored it. \"\n\t\t\t\t\"Now you can't get it back.\"\n\t\t\t)\n\n\t\tif not self.block_table:\n\t\t\tdata = self.fp.read()\n\t\t\tself._block_index += 1\n\t\t\tverify_data(\"single-frame BLTE\", self._header_data + data, self.key, self.verify)\n\t\t\tyield data\n\t\t\treturn\n\n\t\tfor encoded_size, decoded_size, md5 in self.block_table:\n\t\t\tdata = self.fp.read(encoded_size)\n\t\t\tverify_data(\"BLTE block\", data, md5, self.verify)\n\t\t\tself._block_index += 1\n\t\t\tyield data\n\n\tdef decode_and_write(self, fp: IO) -> int:\n\t\t\"\"\"\n\t\tWrites the decoded content of the BLTE file to the given file-like object.\n\t\t\"\"\"\n\t\tret = 0\n\t\tfor block in self.blocks:\n\t\t\tret += fp.write(block)\n\t\treturn ret\n\n\ndef load(fp: IO, key: str, verify: bool=False):\n\tdecoder = BLTEDecoder(fp, key, verify=verify)\n\treturn b\"\".join(decoder.blocks)\n\n\ndef loads(data: bytes, key: str, verify: bool=False):\n\tfp = BytesIO(data)\n\treturn load(fp, key, verify=verify)\n","sub_path":"keg/blte.py","file_name":"blte.py","file_ext":"py","file_size_in_byte":4260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"319262827","text":"import time\nfrom Simplified_code.utils.util import *\nimport os\n\nif __name__ == '__main__':\n print('指纹生成完毕，接下来合并指纹文件')\n t_start = time.time()\n path = './data/waveformscegun/fp_input_cegun.json'\n dict_json = load_json_file(path)\n fname = dict_json['data']['fingerprint_files'][0]\n print(fname)\n files = dict_json['data']['fingerprint_files']\n\n # Stich fingerprint files\n nfp = 0\n ntimes = get_ntimes(dict_json)\n fp_in_bytes = dict_json['fingerprint']['nfreq'] * ntimes / 4\n fp_path, ts_path = get_fp_ts_folders(dict_json)\n final_fp_name = '%s%s' % (fp_path, get_combined_fp_name(dict_json))\n if os.path.exists(final_fp_name):\n os.remove(final_fp_name)\n print(\"Combining into final fingerprint file %s\" % final_fp_name)\n\n final_ts_name = '%s%s' % (ts_path, get_combined_ts_name(dict_json))\n if os.path.exists(final_ts_name):\n os.remove(final_ts_name)\n print(\"Combining into final timestamp file %s\" % final_ts_name)\n\n for fname in files:\n fp_file = fp_path + get_fp_fname(fname)\n os.system(\"cat %s >> %s\" % (fp_file, final_fp_name))\n\n ts_file = ts_path + get_ts_fname(fname)\n os.system(\"cat %s >> %s\" % (ts_file, final_ts_name))\n\n # Verify number of fingerprints\n num_lines = sum(1 for line in open(ts_file))\n nfp += num_lines\n fsize = os.path.getsize(fp_file)\n if fsize / fp_in_bytes != num_lines:\n print(\"Exception: # fingerprints in %s don't match\" % fname)\n print(\"Fingerprint file: %d, timestamp file: %d\" % (fsize / fp_in_bytes, num_lines))\n exit(1)\n\n fsize = os.path.getsize(final_fp_name)\n print(\"Fingerprint file size: %d bytes\" % (fsize))\n print(\"# fingerprints: %d\" % (nfp))\n ndim = fsize * 8 / nfp\n\n # Save fingerprint stats\n save_fp_stats(dict_json, nfp, ndim)\n","sub_path":"Simplified_code/concentrate_fp.py","file_name":"concentrate_fp.py","file_ext":"py","file_size_in_byte":1888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"388657192","text":"# 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.\nfrom oslo_log import log\nimport stevedore\n\nfrom dragonflow._i18n import _\nfrom dragonflow import conf as cfg\nfrom dragonflow.controller import app_base\n\n\nLOG = log.getLogger(__name__)\n\nREGS = frozenset((\n 'reg0',\n 'reg1',\n 'reg2',\n 'reg3',\n 'reg4',\n 'reg5',\n 'reg6',\n 'reg7',\n 'metadata',\n))\n\n\ndef _sequence_generator(offset):\n while True:\n yield offset\n offset += 1\n\n\nclass Datapath(object):\n \"\"\"\n Given the layout (e.g. from the config file), instantiate all the\n applications in the datapath (vertices), and connect them (edges).\n Instantiation includes allocating OpenFlow tables and registers.\n Connection includes wiring and mapping the registers\n \"\"\"\n def __init__(self, layout):\n self._layout = layout\n self._dp_allocs = {}\n self._public_variables = set()\n self.apps = None\n\n def set_up(self, ryu_base, vswitch_api, nb_api, notifier):\n \"\"\"\n Instantiate the application classes.\n Instantiate the applications (Including table and register allocation)\n Wire the applications (including translating registers)\n \"\"\"\n self._dp = ryu_base.datapath\n self._table_generator = _sequence_generator(\n cfg.CONF.df.datapath_autoalloc_table_offset)\n self._public_variables.clear()\n\n app_classes = {}\n self.apps = {}\n\n for vertex in self._layout.vertices:\n if vertex.type in app_classes:\n continue\n\n app_class = self._get_app_class(vertex.type)\n app_classes[vertex.type] = app_class\n self._public_variables.update(\n app_class._specification.public_mapping.keys(),\n )\n\n for vertex in self._layout.vertices:\n app_class = app_classes[vertex.type]\n dp_alloc = self._create_dp_alloc(app_class._specification)\n self.log_datapath_allocation(vertex.name, dp_alloc)\n self._dp_allocs[vertex.name] = dp_alloc\n app = app_class(api=ryu_base,\n vswitch_api=vswitch_api,\n nb_api=nb_api,\n neutron_server_notifier=notifier,\n dp_alloc=dp_alloc,\n **(vertex.params or {})\n )\n self.apps[vertex.name] = app\n\n for app in self.apps.values():\n app.initialize()\n\n for edge in self._layout.edges:\n self._install_edge(edge)\n\n def _get_app_class(self, app_type):\n \"\"\"Get an application class (Python class) by app name\"\"\"\n mgr = stevedore.NamedExtensionManager(\n 'dragonflow.controller.apps',\n [app_type],\n invoke_on_load=False,\n )\n for ext in mgr:\n return ext.plugin\n else:\n raise RuntimeError(_('Failed to load app {0}').format(app_type))\n\n def _create_dp_alloc(self, specification):\n \"\"\"\n Allocate the tables and registers for the given application (given\n by its specification)\n \"\"\"\n public_mapping = specification.public_mapping.copy()\n unmapped_vars = self._public_variables.difference(public_mapping)\n\n # Convert to set() so the result won't be a frozenset()\n unmapped_regs = set(REGS).difference(\n public_mapping.values(),\n ).difference(\n specification.private_mapping.values(),\n )\n\n while unmapped_vars and unmapped_regs:\n public_mapping[unmapped_vars.pop()] = unmapped_regs.pop()\n\n if unmapped_vars:\n raise RuntimeError(\n _(\"Can't allocate enough registers for variables\"),\n )\n\n states_dict = {\n state: next(self._table_generator)\n for state in specification.states\n }\n states = app_base.AttributeDict(**states_dict)\n\n exitpoints_dict = {\n exit.name: next(self._table_generator)\n for exit in specification.exitpoints\n }\n exitpoints = app_base.AttributeDict(**exitpoints_dict)\n\n entrypoints_dict = {\n entry.name: states[entry.target]\n for entry in specification.entrypoints\n }\n entrypoints = app_base.AttributeDict(**entrypoints_dict)\n\n return app_base.DpAlloc(\n states=states,\n exitpoints=exitpoints,\n entrypoints=entrypoints,\n full_mapping=public_mapping,\n )\n\n def _get_connector_config(self, connector):\n return self._dp_allocs[connector.vertex]\n\n def _install_edge(self, edge):\n \"\"\"\n Wire two applications. Infer the translation of metadata fields,\n and install the actions/instructions to pass a packet from one\n application's exit point to another's entry point\n \"\"\"\n exitpoint = edge.exitpoint\n exit_config = self._get_connector_config(exitpoint)\n entrypoint = edge.entrypoint\n entry_config = self._get_connector_config(entrypoint)\n translations = []\n\n for var in self._public_variables:\n exit_reg = exit_config.full_mapping[var]\n entry_reg = entry_config.full_mapping[var]\n if exit_reg == entry_reg:\n continue\n\n translations.append(\n (exit_reg, entry_reg),\n )\n\n self._install_goto(\n # Source\n exit_config.exitpoints[exitpoint.name],\n # Destination\n entry_config.entrypoints[entrypoint.name],\n translations,\n )\n\n def _install_goto(self, source, dest, translations):\n \"\"\"\n Install the actions/instructions to pass a packet from one\n application's exit point to another's entry point, including\n translating the metadata fields.\n \"\"\"\n ofproto = self._dp.ofproto\n parser = self._dp.ofproto_parser\n actions = []\n\n try:\n from_regs, to_regs = zip(*translations)\n except ValueError:\n from_regs, to_regs = ((), ())\n\n # Push all register values\n for reg in from_regs:\n actions.append(\n parser.NXActionStackPush(field=reg, start=0, end=32),\n )\n\n # Pop into target registers in reverse order\n for reg in reversed(to_regs):\n actions.append(\n parser.NXActionStackPop(field=reg, start=0, end=32),\n )\n\n if source < dest:\n instructions = [\n parser.OFPInstructionActions(\n ofproto.OFPIT_APPLY_ACTIONS,\n actions,\n ),\n parser.OFPInstructionGotoTable(dest),\n ]\n else:\n actions.append(parser.NXActionResubmitTable(table_id=dest))\n\n instructions = [\n parser.OFPInstructionActions(\n ofproto.OFPIT_APPLY_ACTIONS,\n actions,\n ),\n ]\n\n message = parser.OFPFlowMod(\n self._dp,\n table_id=source,\n command=ofproto.OFPFC_ADD,\n match=parser.OFPMatch(),\n instructions=instructions,\n )\n self._dp.send_msg(message)\n\n def log_datapath_allocation(self, name, dp_alloc):\n \"\"\"\n Log the dp_alloc object (The allocation of tables, registers, etc.) for\n the given application\n \"\"\"\n LOG.debug(\"Application: %s\", name)\n LOG.debug(\"\\tStates:\")\n for state, table_num in dp_alloc.states.items():\n LOG.debug(\"\\t\\t%s: %s\", state, table_num)\n\n LOG.debug(\"\\tEntrypoints:\")\n for entry_name, table_num in dp_alloc.entrypoints.items():\n LOG.debug(\"\\t\\t%s: %s\", entry_name, table_num)\n\n LOG.debug(\"\\tExitpoints:\")\n for exit_name, table_num in dp_alloc.exitpoints.items():\n LOG.debug(\"\\t\\t%s: %s\", exit_name, table_num)\n\n LOG.debug(\"\\tMapping:\")\n for var, reg in dp_alloc.full_mapping.items():\n LOG.debug(\"\\t\\t%s: %s\", var, reg)\n","sub_path":"dragonflow/controller/datapath.py","file_name":"datapath.py","file_ext":"py","file_size_in_byte":8674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"177322764","text":"import sys\n\nR = sys.argv[1]\nfr = open('../../Dic_Lib_Input/All.f.final_re.'+R+'.csv', 'r')\nfw = open('zid_list_'+R+'.txt', 'w')\ncnt = 0\n\nlines = fr.readlines()\n#print(lines)\nfor line in lines:\n zid = line.split(',')[0]\n zid = 'ZINC00'+zid\n fw.write(zid+'\\n')\n \nfr.close()\nfw.close()\n","sub_path":"1Dscan/Tools/Old/make_zinc_list_0.9.py","file_name":"make_zinc_list_0.9.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"110006647","text":"from __future__ import unicode_literals\n\nfrom django.db import models\nfrom jsonfield import JSONField\nfrom sequence.models import Sequence\n\n# Create your models here.\nclass Task(models.Model):\n method = models.CharField(max_length=200)\n params = JSONField()\n running = models.BooleanField()\n task = models.CharField(max_length=200, null=True)\n duration = models.DurationField(null=True)\n\nclass Result(models.Model):\n\n # a place for general result statistics\n statistics = JSONField(default={}, null=True)\n\n # a link to the assiciated task\n task = models.OneToOneField(\n Task,\n on_delete=models.CASCADE,\n primary_key=True,\n )\n\n def __str__(self):\n return self.task.params\n\nclass Cluster(models.Model):\n # the associated result\n result = models.ForeignKey(Result, on_delete=models.CASCADE,)\n\n # general cluster statistics\n statistics = JSONField(default={}, null=True)\n\n # center mean and median vectors\n centerMean = JSONField(null=True)\n centerMedian = JSONField(null=True)\n\n # the sequence at the median center\n representative = models.ForeignKey(Sequence, related_name='representative', null=True)\n\n # all sequences in the cluster\n sequences = models.ManyToManyField(Sequence)","sub_path":"app/cluster/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"202318514","text":"\nimport sys\nimport matplotlib.pyplot as plt\nimport numpy\nimport time\nimport datetime\nimport math\nimport pylab\n\n# \"Constants\" for units of Day, Month, etc. \n# All units for variables are in seconds\nMINUTE = 60\nHOUR = 60 * MINUTE\nDAY = 24 * HOUR\nYEAR = 365 * DAY\n\nclass Plotter(object):\n\n def __init__(self, q):\n\n #### fakery\n def ft(time_since_24, wait_time):\n #fake time function\n return (time.time() - DAY + time_since_24*HOUR,\n time.time() - DAY + time_since_24*HOUR + \n wait_time*HOUR/2)\n fake_waits = {\"b\":[ft(2,2),ft(3.5,10),\n ft(4,8),ft(5,11),ft(7.5,7),ft(9,8.5)]} \n class A():\n wait_times = fake_waits\n q = A()\n #### end fakery\n\n # turn the q into a series of x and y data points\n def wt_to_list(wt):\n res = []\n for q, waits in wt.items():\n res += waits\n return res\n time_list = wt_to_list(q.wait_times)\n\n in_times = []\n waits = []\n for tup in time_list:\n in_times.append(tup[0])\n waits.append(tup[1] - tup[0])\n #insert fake points to \n # bring plot down to zero at the start and end\n f_time = in_times[0]\n in_times.insert(0, f_time - HOUR)\n waits.insert(0,0)\n l_time = in_times[-1]\n in_times.append(l_time + HOUR)\n waits.append(0)\n\n self.data = (in_times, waits)\n #precalculate some other useful things\n self.min_wait = min(waits)\n self.max_wait = max(waits)\n self.min_in_time = min(in_times)\n self.max_in_time = max(in_times)\n self.wait_range = max(waits) - min(waits)\n\n\n def save_fig(self, filename, st_time = 24):\n # Here we only plot the last 24 hours\n plt.plot(self.data[0], self.data[1], lw=2)\n # y axis always starts at 0\n # x axis units are seconds, but we overlay more\n # meaningful tick marks for the user#\n \n curr_time = time.time()\n curr_hour = datetime.datetime.now().hour\n #generate the x axis\n def time_label(i):\n #generate a string based on the hour i, on a 24hr clock\n while i < 0:\n i += 24\n if i >= 12 and i < 24:\n st = 'pm'\n else:\n st = 'am'\n i = i%12\n if i == 0:\n i = 12\n return str(i) + st\n xt = []\n x_labels = []\n for i in range(25):\n xt.append( curr_time - 24*HOUR + i*HOUR )\n x_labels.append(time_label(curr_hour-24 + i))\n\n #choose the units of the y axis\n #if y_max > ...\n #list of possible units on the y axis.\n # each contains the string it would use for plotting\n def y_time_label(i):\n #given a integer value of seconds, returns a label\n i = int(i)\n display = True\n if i < MINUTE:\n st = \" second\"\n elif i < HOUR:\n st = \" minute\"\n if i % MINUTE != 0:\n display = False\n i = i // MINUTE\n elif i < DAY:\n st = \" hour\"\n if i % HOUR != 0:\n display = False\n i = i // HOUR\n else:\n st = \" day\"\n if i % DAY != 0:\n display = False\n i = i // DAY\n if i > 1:\n st += \"s\"\n return str(i) + st if display else \"\"\n\n #choose y axis display range\n y_max_display = math.ceil(self.max_wait*1.1)\n y_min_display = 0\n\n # Choose appropriate tick mark spacing for the vertical axis (in seconds)\n units = [10, 30, MINUTE, 10*MINUTE, 30*MINUTE, HOUR, DAY]\n i = 0\n # we don't want more than 20 tick marks on the vertical axis\n while i + 1 < len(units) and int(y_max_display) // units[i] > 20:\n i += 1\n unit = units[i]\n \n yt = []\n y_labels = []\n num_ticks = int(math.floor(y_max_display / unit)) + 1\n for i in range(1,num_ticks):\n yt.append( i*unit )\n y_labels.append( y_time_label(i*unit) )\n\n #set the ticks, labels, and axis range\n pylab.xticks(xt, x_labels, size=5)\n pylab.yticks(yt, y_labels, size=8)\n plt.axis(( curr_time - DAY, curr_time,\n y_min_display, y_max_display ))\n plt.savefig(filename)\n\n\n# Example/test usage of this class. In actual use, you would\n# pass in an actual queue, rather than 'None'\np = Plotter(None)\np.save_fig(\"abc.png\")\n\n\n\n","sub_path":"app/plotter.py","file_name":"plotter.py","file_ext":"py","file_size_in_byte":4403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"60751415","text":"\"\"\"\nHere are your instructions:\n\n\nModify the logic of the Tree object to :\n•allow data to be stored as an additional attribute of each node (the data should be passed as an additional argument to __init__()).\n•provide a find() method that locates a key (whose value is passed to find() as an argument) and returns the data associated with the node; if the key is not present in the tree, the method should raise a KeyError exception.\n\"\"\"\n\n\nclass Tree:\n def __init__(self, key, data):\n \"Create a new Tree object with empty L&R subtrees.\"\n self.key = key\n self.left = self.right = None\n self.data = data\n self._d = dict([(self.key,self.data)])\n \n def insert(self, key, data):\n \"Insert a new element into the tree in the correct position.\"\n \n if key < self.key:\n if self.left:\n self.left.insert(key,data)\n else:\n self.left = Tree(key, data)\n elif key > self.key:\n if self.right:\n self.right.insert(key,data)\n else:\n self.right = Tree(key,data)\n else:\n self._d[key] = data\n \n \n def walk(self):\n \"Generate the keys from the tree in sorted order.\"\n if self.left:\n for n in self.left.walk():\n yield n\n \n yield self.key\n if self.right:\n for n in self.right.walk():\n yield n\n \n \n def find(self, value):\n \"Find the node and return the data of that node\"\n def find_value(tree,value):\n if tree.key == value:\n return tree._d[value]\n else:\n ans = tree.find(value)\n return ans\n \n if value in list(self.walk()):\n if value < self.key:\n return find_value(self.left,value)\n elif value > self.key:\n return find_value(self.right,value) \n else:\n return self._d[self.key]\n else:\n raise KeyError(\"{!r} not present in the object\".format(value))\n \nif __name__=='__main__':\n t = Tree(\"D\", \"D main node\")\n \n for c, data in [('B','B node'),('J','J node'),('Q','Q node'),\n ('K','K node'),('F','F node'),('A','A node'),('C','C node')]:\n t.insert(c, data)\n \n for n in \"DBJQKFAC\": \n data = t.find(n)\n print(data)\n \n print('-' * 20) \n t.insert('A', 'Hi I am A Node')\n \n for n in \"DBJQKFACZ\": #Test overwriting 'A' and KeyError with Z\n data = t.find(n)\n print(data)\n\n #print(list(t.walk()))\n \n \"\"\"\n print(t._d)\n print(t.left._d) #B\n print(t.right._d)#J\n \n print(t.left.left._d)#A\n print(t.left.right._d)#C\n \n print(t.right.left._d)#F\n print(t.right.right._d)#Q\n \n print(t.right.right.left._d)#K\"\"\"\n \n\n ","sub_path":"Python 4/Python4_03(Delegation and Composition )/mytree.py","file_name":"mytree.py","file_ext":"py","file_size_in_byte":2942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"618364903","text":"from __future__ import absolute_import\nfrom __future__ import division\n\nimport time\nimport logging\nimport os\nimport sys\nfrom functools import partial\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.python.ops import variable_scope as vs\nfrom tensorflow.python.ops import embedding_ops\n\nfrom evaluate import exact_match_score, f1_score\nfrom data_batcher import get_batch_generator\nfrom pretty_print import print_example\nfrom modules import RNNEncoder, SimpleSoftmaxLayer, BasicAttn, masked_softmax\nfrom QAModel import QAModel\n\nlogging.basicConfig(level=logging.INFO)\n\nclass BiDAF(QAModel):\n def __init__(self, FLAGS, id2word, word2id, emb_matrix):\n super(BiDAF, self).__init__(FLAGS, id2word, word2id, emb_matrix)\n\n def add_embedding_layer(self, emb_matrix):\n with vs.variable_scope(\"embeddings\"):\n\n # Note: the embedding matrix is a tf.constant which means it's not a trainable parameter\n embedding_matrix = tf.constant(emb_matrix, dtype=tf.float32, name=\"emb_matrix\") # shape (400002, embedding_size)\n\n # Get the word embeddings for the context and question,\n # using the placeholders self.context_ids and self.qn_ids\n self.context_embs = embedding_ops.embedding_lookup(embedding_matrix, self.context_ids) # shape (batch_size, context_len, embedding_size)\n self.qn_embs = embedding_ops.embedding_lookup(embedding_matrix, self.qn_ids) # shape (batch_size, question_len, embedding_size)\n\n def build_graph(self):\n encoder = RNNEncoder(self.FLAGS.hidden_size, self.keep_prob)\n if self.FLAGS.max_word_len:\n context_hiddens = encoder.build_graph(tf.concat([self.context_embs, self.context_char_hidden],2), self.context_mask) # (batch_size, context_len, hidden_size*2)\n question_hiddens = encoder.build_graph(tf.concat([self.qn_embs, self.qn_char_hidden],2), self.qn_mask) # (batch_size, question_len, hidden_size*2)\n else:\n context_hiddens = encoder.build_graph(self.context_embs, self.context_mask) # (batch_size, context_len, hidden_size*2)\n question_hiddens = encoder.build_graph(self.qn_embs, self.qn_mask) # (batch_size, question_len, hidden_size*2)\n\n attn_layer = BiDAF_Attn(self.keep_prob, self.FLAGS.hidden_size*2, [self.FLAGS.batch_size, self.FLAGS.context_len, self.FLAGS.question_len])\n output = attn_layer.build_graph(question_hiddens, self.qn_mask, context_hiddens, self.context_mask) # attn_output is shape (batch_size, context_len, hidden_size*2)\n\n blended_reps_final = tf.contrib.layers.fully_connected(output, num_outputs=self.FLAGS.hidden_size)\n\n with vs.variable_scope(\"StartDist\"):\n softmax_layer_start = SimpleSoftmaxLayer()\n self.logits_start, self.probdist_start = softmax_layer_start.build_graph(blended_reps_final, self.context_mask)\n\n with vs.variable_scope(\"EndDist\"):\n softmax_layer_end = SimpleSoftmaxLayer()\n self.logits_end, self.probdist_end = softmax_layer_end.build_graph(blended_reps_final, self.context_mask)\n\n\nclass BiDAF_Attn(BasicAttn):\n def __init__(self, keep_prob, hidden_vec_size, shape):\n super(BiDAF_Attn,self).__init__(keep_prob, hidden_vec_size)\n self.shape = shape\n\n def build_graph(self, values, values_mask, keys, keys_mask):\n with vs.variable_scope(\"Attention\"):\n dense_layer1 = partial(tf.layers.dense, activation = None, use_bias=False, kernel_regularizer = tf.contrib.layers.l1_regularizer(0.001))\n dense_layer2 = partial(tf.layers.dense, activation = None, use_bias=False, kernel_regularizer = tf.contrib.layers.l1_regularizer(0.001))\n\n score1 = dense_layer1(keys, 1) #shape (batch_size, num_keys, 1)\n score2 = dense_layer2(values, 1) #shape (batch_size, num_values, 1)\n\n #version1. too much memory. Or do (batch, k_len, 1, ndim) * (batch, 1, v_len, ndim).\n #k = tf.expand_dims(tf.traspose(keys, perm=[0,2,1]), 3) # shape (batch_size, hidden_size, num_keys, 1).\n #v = tf.expand_dims(tf.traspose(values, perm=[0,2,1]), 2)\n #matrix = tf.traspose(tf.matmul(k, v), perm=[0,2,3,1])\n\n #version2. seems infeasible.\n # def matrix_func(keys, values, weight):\n # mat = np.zeros(self.shape)\n # for k in xrange(self.shape[0]):\n # for i in xrange(self.shape[1]):\n # for j in xrange(self.shape[2]):\n # for m in xrange(self.vec_size):\n # mat[k,i,j] += weight[m]*keys[k,i,m]*values[k,j,m]\n # return mat\n # weight = tf.Variable(tf.random_normal([self.vec_size]), dtype=tf.float32, name=\"similarity_weight_3\")\n # similarity_scores = tf.cast(tf.py_func(matrix_func, [keys, values, weight], tf.double), tf.float32)\n # similarity_scores.set_shape(self.shape[0:])\n\n #version3. memory efficient. associate the channel weight weight with keys in advance, then multiply the result with values.\n weight = tf.Variable(tf.random_normal([1,1,self.hidden_vec_size]), dtype=tf.float32, name=\"similarity_weight_3\")\n weighted_keys = weight*keys\n similarity_scores = tf.matmul(weighted_keys, tf.transpose(values, perm=[0,2,1]))\n similarity_scores = score1 + tf.transpose(score2, perm=[0,2,1]) + similarity_scores # shape (batch_size, num_keys, num_values)\n\n attn_logits_mask = tf.expand_dims(values_mask, 1) # shape (batch_size, 1, num_values)\n _, C2Q_softmax = masked_softmax(similarity_scores, attn_logits_mask, 2) # shape (batch_size, num_keys, num_values). take softmax over values\n C2Q_output = tf.matmul(C2Q_softmax, values) # shape (batch_size, num_keys, value_vec_size)\n\n max_i = tf.reduce_max(similarity_scores,2)\n _, Q2C_softmax = masked_softmax(max_i, keys_mask, 1) # shape(batch_size, num_keys)\n Q2C_softmax = tf.expand_dims(Q2C_softmax, -1)\n Q2C_output = tf.reduce_sum(Q2C_softmax * keys, 1, keepdims=True) #or Q2C_output = tf.matmul(tf.transpose(keys, (0, 2, 1)), tf.expand_dims(Q2C_softmax, -1))\n\n output = tf.concat([keys, C2Q_output, tf.broadcast_to(Q2C_output, tf.shape(keys))], 2)\n\n # Apply dropout\n output = tf.nn.dropout(output, self.keep_prob)\n\n return output\n","sub_path":"code/BiDAF.py","file_name":"BiDAF.py","file_ext":"py","file_size_in_byte":6436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"547098264","text":"from packs.compositional.IMPEC.flux_calculation import Flux, MUSCL\nfrom packs.compositional.update_time import delta_time\nimport numpy as np\nfrom packs.utils import constants as ctes\nfrom packs.directories import data_loaded\n\nfrom packs.compositional.IMPEC.adm_tpfa_compositional_solver import AdmTpfaCompositionalSolver\nfrom packs.compositional.IMPEC.compositionalIMPEC import CompositionalFVM\nfrom .composition_solver import Euler\n\nclass CompositionalFvmADM(CompositionalFVM):\n \n _kwargs_keys = {\n '__call__': [\n 'multilevel_data',\n 'multilevel_operators'\n ]\n }\n\n def __call__(self, M, wells, fprop, delta_t, **kwargs):\n # test_kwargs_keys(CompositionalFVM._kwargs_keys['__call__'], kwargs.keys())\n # import pdb; pdb.set_trace()\n params=kwargs.get('params')\n\n G = self.update_gravity_term(fprop)\n if ctes.MUSCL: self.get_faces_properties_average(fprop)\n else: self.get_faces_properties_upwind(fprop, G)\n self.get_phase_densities_internal_faces(fprop)\n r = 0.8 # enter the while loop\n # psolve = TPFASolver(fprop)\n dVjdNk, dVjdP = self.dVt_derivatives(fprop)\n\n # psolve = AdmTpfaCompositionalSolver(fprop)\n psolve = AdmTpfaCompositionalSolver(dVjdNk, dVjdP)\n params.update({\n 'dVtdP': psolve.dVtP,\n 'dVtdNk': psolve.dVtk\n })\n P_old = np.copy(fprop.P)\n Nk_old = np.copy(fprop.Nk)\n pressures = []\n while (r!=1.):\n print(f'\\nr: {r}\\n')\n fprop.Nk = np.copy(Nk_old)\n fprop.P, total_flux_internal_faces, q = psolve.get_pressure(M, wells, fprop, delta_t, P_old, **kwargs)\n pressures.append(fprop.P)\n #self.update_composition(fprop, delta_t)\n #wave_velocity = MUSCL().run(M, fprop, wells, P_old, total_flux_internal_faces)\n #self.update_composition_RK3_1(fprop, fprop.Nk, delta_t)\n if ctes.MUSCL:\n order = data_loaded['compositional_data']['MUSCL']['order']\n wave_velocity = MUSCL().run(M, fprop, wells, P_old, total_flux_internal_faces, order)\n else:\n UPW = Flux()\n fprop.Fk_vols_total = UPW.update_flux(M, fprop, total_flux_internal_faces,\n fprop.rho_j_internal_faces, fprop.mobilities_internal_faces)\n wave_velocity = UPW.wave_velocity_upw(M, fprop, fprop.mobilities, fprop.rho_j, fprop.xkj,\n fprop.Csi_j, total_flux_internal_faces)\n\n ''' For the composition calculation the time step might be different\\\n because it treats composition explicitly and this explicit models \\\n are conditionally stable - which can be based on the CFL parameter '''\n\n delta_t_new = delta_time.update_CFL(delta_t, wells, fprop, wave_velocity)\n r = delta_t_new/delta_t\n delta_t = delta_t_new\n\n ##### remove\n # self.update_composition(fprop, delta_t)\n # # self.update_composition_RK3_2(fprop, fprop.Nk, delta_t)\n # return delta_t\n #####\n\n ########## new\n if not ctes.FR:\n\n fprop.Nk, fprop.z = Euler().update_composition(fprop.Nk, q,\n fprop.Fk_vols_total, delta_t)\n # wave_velocity = UPW.wave_velocity_upw(M, fprop, fprop.mobilities, fprop.rho_j, fprop.xkj,\n # fprop.Csi_j, total_flux_internal_faces)\n\n else:\n fprop.Nk = Nk;\n fprop.z = z;\n fprop.Nk_SP = Nk_SP\n\n fprop.wave_velocity = wave_velocity\n fprop.total_flux_internal_faces = total_flux_internal_faces\n if any(fprop.xkj.sum(axis=0).flatten() > 1 + 1e-10): import pdb; pdb.set_trace()\n if len(fprop.Nk[fprop.Nk < 0]) > 0: import pdb; pdb.set_trace()\n # if fprop.P[0]= 1:\n for footstep in range(len(clue)):\n traCol = clue[footstep][0]\n traRow = clue[footstep][1]\n\n trace = visual.Rect(my_win,\n width = requestLUT[traCol]['width'],\n height = requestLUT[traCol]['height'],\n lineWidth = 2,\n fillColor = None,\n lineColor = '#586e75',\n pos = requestLUT[traCol]['position'][traRow], opacity = 1)\n trace.draw()\n\n\n # Indicator\n indicator = visual.Rect(my_win, \n width = indicatorLUT[iCol]['width'], \n height = indicatorLUT[iCol]['height'], \n fillColor = SOLARIZED['grey01'], fillColorSpace='rgb255', \n lineColor = SOLARIZED['grey01'], lineColorSpace ='rgb255', \n pos= indicatorLUT[iCol]['position'][iRow], opacity = 0.5)\n\n indicator.draw()\n\n # OSD strings\n for image in range(iCol+1):\n img = visual.ImageStim(my_win,\n image = strLUT[image]['path'],\n pos = strLUT[image]['position'])\n\n img.draw()\n\n # Everything has been drawn. Flip to show.\n my_win.flip()\n\n # Get response\n response_hw, response_key, response_status = getAnything(mouse, joy)\n\n if response_status == 1 and response_key != pre_key:\n current_time = core.getTime()\n\n key_meaning = interpret_key(response_hw, response_key)\n\n # Reveal next que only when Correct answer was pressed\n key_judgement, final_answer = reponse_checker_OSD(\n hw_required[block],\n response_hw, \n iRow, iCol, \n reqRow, reqCol\n )\n\n # Save responses \n response.append([\n response_hw, key_meaning,\n iRow, iCol,\n reqRow, reqCol,\n final_answer,\n stepToGoal,\n current_time - stimuli_time,\n current_time\n ]) \n # Next que\n iRow, iCol, trialStatus = determine_behavior_OSD(key_meaning, \n iRow, iCol)\n\n # if final_answer == 1 and key_meaning == 'OK':\n if final_answer == 0:\n stepToGoal += 1\n elif final_answer == 1:\n if key_meaning == 'OK' or key_meaning == 'Right':\n clue.append([reqCol, reqRow])\n reqCol += 1\n stepToGoal = 0\n if reqCol > nCol:\n trialStatus = 0\n # reqRow = random.randrange(1, nRow + 1)\n queNum += 1\n reqRow = PseudoRandomRow[queNum]\n stimuli_time = core.getTime()\n\n pre_key = response_key\n\n\n# Close the window\nmy_win.close()\n\n\n# Experiment record file\nos.chdir('/Users/YJC/Dropbox/ExpRecord_OSD')\nfilename = ('%s_%s.txt' % (today, username))\nfilecount = 0\n\nwhile os.path.isfile(filename):\n filecount += 1\n filename = ('%s_%s_%d.txt' % (today, username, filecount))\n\n\nwith open(filename, 'w') as filehandle: \n for key in response:\n for item in key:\n filehandle.writelines(\"%s \" % item)\n filehandle.writelines(\"\\n\")\n \n# with open(filename, 'w') as filehandle: # File auto closed\n# filehandle.writelines(\"%s\\n\" % key for key in response)\n","sub_path":"expRT/OSD_simulate.py","file_name":"OSD_simulate.py","file_ext":"py","file_size_in_byte":7635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"258159459","text":"from rest_framework.serializers import HyperlinkedModelSerializer\nfrom rest_framework.serializers import ChoiceField, CharField, DecimalField, PrimaryKeyRelatedField\nfrom django.core.validators import MinValueValidator\nfrom app.productdb.models import Product, Vendor, CURRENCY_CHOICES\n\n\nclass VendorSerializer(HyperlinkedModelSerializer):\n\n class Meta:\n model = Vendor\n fields = (\n 'id',\n 'name',\n 'url'\n )\n extra_kwargs = {\n 'url': {\n 'lookup_field': 'id',\n 'view_name': 'productdb:vendors-detail'\n }\n }\n depth = 0\n\n\nclass ProductSerializer(HyperlinkedModelSerializer):\n currency = ChoiceField(\n choices=CURRENCY_CHOICES,\n initial=\"USD\",\n required=False\n )\n description = CharField(\n initial=\"not set\",\n required=False,\n style={'base_template': 'textarea.html'},\n )\n list_price = DecimalField(\n initial=\"0.00\",\n required=False,\n allow_null=True,\n decimal_places=2,\n max_digits=32,\n help_text=\"list price of the element\",\n validators=[MinValueValidator(0)]\n )\n\n vendor = PrimaryKeyRelatedField(\n many=False,\n queryset=Vendor.objects.all(),\n read_only=False,\n required=False\n )\n\n class Meta:\n model = Product\n fields = (\n 'id',\n 'product_id',\n 'description',\n 'list_price',\n 'currency',\n 'tags',\n 'vendor',\n 'url',\n 'eox_update_time_stamp',\n 'end_of_sale_date',\n 'end_of_support_date',\n 'eol_ext_announcement_date',\n 'end_of_sw_maintenance_date',\n 'end_of_routine_failure_analysis',\n 'end_of_service_contract_renewal',\n 'end_of_new_service_attachment_date',\n 'end_of_sec_vuln_supp_date',\n 'eol_reference_number',\n 'eol_reference_url',\n )\n extra_kwargs = {\n 'url': {\n 'lookup_field': 'id',\n 'view_name': 'productdb:products-detail'\n }\n }\n depth = 0\n","sub_path":"app/productdb/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":2227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"377177112","text":"import os\nfrom utils.logger.logger import Logger\n\nlogger = Logger('log/executor')\nlogger.set_level(30)\n\n\nclass Executor(object):\n @staticmethod\n def exec_cmd(executable, *args):\n \"\"\"\n execute executable with given arguments\n\n :param executable:\n :param args:\n :return: True if cmd is successful, false otherwise\n \"\"\"\n cmd = executable\n\n for arg in args:\n cmd = cmd + ' ' + arg\n\n logger.debug(\"Executing Command: \" + cmd)\n exit_status = os.system(cmd)\n logger.debug(\"Exit status = %d\" % exit_status)\n return exit_status is 0\n","sub_path":"utils/execs/cmd_executor.py","file_name":"cmd_executor.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"502390794","text":"#!python3.6\n\"\"\"\nBing画像検索を用いて画像を収集する。\n\"\"\"\nimport argparse\nimport glob\nimport hashlib\nimport logging\nimport os\nfrom icrawler.builtin import BingImageCrawler\nfrom PIL import Image\nfrom typing import List\n\nlogging_fmt = \"%(asctime)s %(levelname)s: %(message)s\"\nlogging.basicConfig(format=logging_fmt)\n\ndef create_custom_logger(progress_log_filepath:str)->logging.Logger:\n logger = logging.getLogger(__name__)\n logger.setLevel(level=logging.INFO)\n logger.propagate=False\n handler=logging.FileHandler(progress_log_filepath,\"w\",encoding=\"utf_8\")\n handler.setLevel(logging.INFO)\n handler.setFormatter(logging.Formatter(logging_fmt))\n logger.addHandler(handler)\n\n return logger\n\ndef get_md5_hash(keyword:str)->str:\n return hashlib.md5(keyword.encode()).hexdigest()\n\ndef crawl_images(\n keyword:str,\n max_num_images:int,\n save_dir:str,\n feeder_threads:int,\n parser_threads:int,\n downloader_threads:int):\n crawler=BingImageCrawler(\n feeder_threads=feeder_threads,\n parser_threads=parser_threads,\n downloader_threads=downloader_threads,\n log_level=logging.ERROR,\n storage={\"root_dir\":save_dir},\n )\n crawler.crawl(keyword=keyword,max_num=max_num_images)\n\ndef remove_unsupported_images(target_dir:str):\n supported_extensions=[\".jpg\",\".jpeg\",\".png\"]\n\n pathname=os.path.join(target_dir,\"*[!txt]\")\n files=glob.glob(pathname)\n\n for file in files:\n extension=os.path.splitext(file)[1]\n if extension not in supported_extensions:\n os.remove(file)\n\ndef format_images(target_dir:str,width:int,height:int,logger:logging.Logger):\n \"\"\"\n 画像のJPEG形式への変換およびリサイズを行う。\n \"\"\"\n pathname=os.path.join(target_dir,\"*[!txt]\")\n files=glob.glob(pathname)\n\n for file in files:\n try:\n image=Image.open(file)\n\n #アルファチャンネルは使用しない。\n if image.mode in (\"RGBA\",\"P\"):\n image=image.convert(\"RGB\")\n \n #リサイズ\n image=image.resize((width,height))\n\n base_filepath=os.path.splitext(file)[0]\n save_filepath=base_filepath+\".jpg\"\n image.save(save_filepath)\n \n except Exception as e:\n logger.error(e)\n continue\n\ndef main(\n keyword_list_filepath:str,\n max_num_images:int,\n image_width:int,\n image_height:int,\n save_root_dir:str,\n progress_log_filepath:str,\n index_lower_bound:int,\n index_upper_bound:int,\n feeder_threads:int,\n parser_threads:int,\n downloader_threads:int):\n logger=create_custom_logger(progress_log_filepath)\n logger.info(\"keyword_list_filepath: {}\".format(keyword_list_filepath))\n logger.info(\"max_num_images: {}\".format(max_num_images))\n logger.info(\"image_size: ({},{})\".format(image_width,image_height))\n logger.info(\"save_root_dir: {}\".format(save_root_dir))\n logger.info(\"progress_log_filepath: {}\".format(progress_log_filepath))\n logger.info(\"index lower_bound: {}\\tindex upper bound: {}\".format(index_lower_bound,index_upper_bound))\n logger.info(\"feader_threads: {}\\tparser_threads: {}\\tdownloader_threads: {}\".format(\n feeder_threads,parser_threads,downloader_threads))\n\n os.makedirs(save_root_dir,exist_ok=True)\n\n with open(keyword_list_filepath,\"r\",encoding=\"utf_8\") as r:\n keywords=r.read().splitlines()\n\n for idx,keyword in enumerate(keywords):\n if idx=0 and idx>=index_upper_bound:\n break\n\n logger.info(\"{}\\t{}\".format(idx,keyword))\n\n title_hash=get_md5_hash(keyword)\n save_dir=os.path.join(save_root_dir,title_hash)\n os.makedirs(save_dir,exist_ok=True)\n\n info_filepath=os.path.join(save_dir,\"info.txt\")\n with open(info_filepath,\"w\",encoding=\"utf_8\") as w:\n w.write(keyword)\n w.write(\"\\n\")\n\n crawl_images(keyword,max_num_images,save_dir,feeder_threads,parser_threads,downloader_threads)\n remove_unsupported_images(save_dir)\n format_images(save_dir,image_width,image_height,logger)\n\nif __name__==\"__main__\":\n parser=argparse.ArgumentParser()\n\n parser.add_argument(\"--keyword_list_filepath\",type=str)\n parser.add_argument(\"--max_num_images\",type=int)\n parser.add_argument(\"--image_width\",type=int)\n parser.add_argument(\"--image_height\",type=int)\n parser.add_argument(\"--save_root_dir\",type=str)\n parser.add_argument(\"--progress_log_filepath\",type=str)\n parser.add_argument(\"--index_lower_bound\",type=int) #Inclusive\n parser.add_argument(\"--index_upper_bound\",type=int) #Exclusive -1: No uppper bound\n parser.add_argument(\"--feeder_threads\",type=int)\n parser.add_argument(\"--parser_threads\",type=int)\n parser.add_argument(\"--downloader_threads\",type=int)\n\n args=parser.parse_args()\n\n main(\n args.keyword_list_filepath,\n args.max_num_images,\n args.image_width,\n args.image_height,\n args.save_root_dir,\n args.progress_log_filepath,\n args.index_lower_bound,\n args.index_upper_bound,\n args.feeder_threads,\n args.parser_threads,\n args.downloader_threads\n )\n","sub_path":"crawl_images.py","file_name":"crawl_images.py","file_ext":"py","file_size_in_byte":5305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"569433583","text":"from django.contrib.auth.views import LoginView, LogoutView\nfrom django.urls import path\n\nfrom accountapp.views import AccountCreateView, AccountDetailView, AccountUpdateView, AccountDeleteView\n\napp_name = 'accountapp'\n\nurlpatterns = [\n\n # 장고에서 제공해주는 LoginView\n path('login/', LoginView.as_view(template_name='accountapp/login.html'),\n name='login'),\n # 로그아웃\n path('logout/',LogoutView.as_view(), name='logout'),\n\n # AccountCreateView는 클래스이기 때문에 as_view를 추가\n path('create/', AccountCreateView.as_view(), name='create'),\n # pk라는 이름의 숫자를 받는다. -> primary key 약자 이 고유값을 통해서 어떤 account를 받아올지\n path('detail/', AccountDetailView.as_view(), name='detail'),\n path('update/', AccountUpdateView.as_view(), name='update'),\n path('delete/', AccountDeleteView.as_view(), name='delete'),\n\n]","sub_path":"accountapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"614072391","text":"import datetime\nimport tushare as ts\nimport json\nfrom dataBase import dataBaseInstance\nimport sys,traceback\n\nclass GetDataFromTushare:\n stock_pool = []\n tushare_cfg = {}\n pro = ts.pro_api()\n\n def __init__(self):\n # 设置tushare\n with open('config.json') as f:\n cfg = json.load(f)\n self.tushare_cfg = cfg['tushare']\n ts.set_token(self.tushare_cfg['token'])\n self.stock_pool = self.tushare_cfg['stock_pool']\n print('stock pool {} init succ~'.format(self.stock_pool))\n\n def getStockPool(self):\n return self.stock_pool\n\n # 插入stock_code的日线 数据\n def InsertStockDaily(self, stock_code, start_time, end_time):\n try:\n # tushare读取stock_code的日线数据\n df = self.pro.daily(ts_code=stock_code, start_date=start_time, end_date=end_time)\n # 写入数据库\n c_len = df.shape[0]\n for j in range(c_len):\n resu0 = list(df.ix[c_len - 1 - j])\n resu = []\n for k in range(len(resu0)):\n if str(resu0[k]) == 'nan':\n resu.append(-1)\n else:\n resu.append(resu0[k])\n state_dt = (datetime.datetime.strptime(resu[1], \"%Y%m%d\")).strftime('%Y-%m-%d')\n args = (state_dt, str(resu[0]), float(resu[2]), float(resu[5]), float(resu[3]), float(resu[4]), float(resu[9]),\n float(resu[10]), float(resu[6]), float(resu[7]), float(resu[8]))\n sql_insert = \"INSERT IGNORE INTO stock_daily(state_dt,stock_code,open,close,high,low,vol,amount,pre_close,amt_change,pct_change) VALUES (%s, %s, %s, %s,%s,%s,%s,%s,%s,%s,%s)\"\n dataBaseInstance.insert('stock', sql_insert, args)\n print('stack_code:{} update from {} to {}'.format(stock_code, start_time, end_time))\n except :\n print('No DATA Code: ' + stock_code)\n traceback.print_exc(file=sys.stdout)\n return\n\n def updateStockList(self):\n try:\n args = 'ts_code,symbol,name,area,industry,market,list_date'\n df = self.pro.stock_basic(exchange='', list_status='L', fields=args)\n row_num = df.shape[0]\n for i in range(row_num):\n row = list(df.ix[row_num - 1 - i])\n sql_replace = \"REPLACE INTO stock_list(stock_code,symbol,name,area,industry,market,list_date) VALUES(%s, %s, %s, %s,%s,%s,%s)\"\n dataBaseInstance.insert('stock', sql_replace, row)\n print('Time:{}, update stock List to lastest~'.format(datetime.datetime.now()))\n except :\n print('update Stock List fail~')\n traceback.print_exc(file=sys.stdout)\n return\n\n\ntushareInstance = GetDataFromTushare()\n\n\n# 读取stock库中的相关状态\nclass StockState:\n stock_table = 'stock'\n\n # 获取上市日期\n def getListDate(self, stock_code):\n sql = \"SELECT list_date FROM stock.stock_list WHERE stock_code=%s\"\n args = (stock_code)\n list_dt = dataBaseInstance.select(self.stock_table, sql, args)\n if list_dt[0][0] == None:\n return 0\n return list_dt[0][0]\n\n # 某支股票本地最新数据的日期\n def getStockLastLocalDate(self, stock_code):\n sql = \"SELECT max(state_dt) FROM stock.stock_daily WHERE stock_code=%s\"\n args = (stock_code)\n last_state_dt = dataBaseInstance.select(self.stock_table, sql, args)\n if last_state_dt[0][0] == None:\n return 0\n return last_state_dt[0][0]\n\n\nstockStateInstance = StockState()\n\nif __name__ == '__main__':\n # 更新股票列表\n # tushareInstance.updateStockList()\n # 更新stock_pool至昨天\n time_temp = datetime.datetime.now()\n end_dt = time_temp.strftime('%Y%m%d')\n for stock_code in tushareInstance.getStockPool():\n start_dt = stockStateInstance.getStockLastLocalDate(stock_code)\n if start_dt == 0:\n start_dt = stockStateInstance.getListDate(stock_code)\n else:\n start_dt += datetime.timedelta(days=1)\n start_dt = start_dt.strftime('%Y%m%d')\n tushareInstance.InsertStockDaily(stock_code, start_dt, end_dt)\n","sub_path":"DataFromTushare.py","file_name":"DataFromTushare.py","file_ext":"py","file_size_in_byte":3849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"232416543","text":"\"\"\"\nThis is a client for milvus of gRPC\n\"\"\"\nfrom urllib.parse import urlparse\nimport logging\n\nimport grpc\nfrom grpc._cython import cygrpc\n\nfrom ..grpc_gen import milvus_pb2_grpc, status_pb2\nfrom ..grpc_gen import milvus_pb2 as grpc_types\nfrom .abstract import (\n ConnectIntf,\n TableSchema,\n Range,\n TopKQueryResult,\n IndexParam\n)\nfrom .types import IndexType, MetricType, Status\nfrom .utils import (\n check_pass_param,\n int_or_str,\n is_legal_host,\n is_legal_port,\n is_legal_array\n)\nfrom .exceptions import ParamError, NotConnectError\nfrom ..settings import DefaultConfig as config\nfrom . import __version__\n\nLOGGER = logging.getLogger(__name__)\n\n\nclass Prepare:\n\n @classmethod\n def table_name(cls, table_name):\n\n check_pass_param(table_name=table_name)\n return grpc_types.TableName(table_name=table_name)\n\n @classmethod\n def table_schema(cls, param):\n \"\"\"\n :type param: dict\n :param param: (Required)\n\n `example param={'table_name': 'name',\n 'dimension': 16,\n 'index_type': IndexType.FLAT\n }`\n\n :return: ttypes.TableSchema object\n \"\"\"\n if isinstance(param, grpc_types.TableSchema):\n return param\n\n if not isinstance(param, dict):\n raise ParamError('Param type incorrect, expect {} but get {} instead '.format(\n type(dict), type(param)\n ))\n\n if 'index_file_size' not in param:\n param['index_file_size'] = 1024\n if 'metric_type' not in param:\n param['metric_type'] = MetricType.L2\n\n _param = {\n 'table_name': param['table_name'],\n 'dimension': param['dimension'],\n 'index_file_size': param['index_file_size'],\n 'metric_type': param['metric_type']\n }\n\n check_pass_param(**_param)\n\n return grpc_types.TableSchema(status=status_pb2.Status(error_code=0, reason='Client'),\n table_name=_param[\"table_name\"],\n dimension=_param[\"dimension\"],\n index_file_size=_param[\"index_file_size\"],\n metric_type=_param[\"metric_type\"])\n\n @classmethod\n def range(cls, start_date, end_date):\n \"\"\"\n Parser a 'yyyy-mm-dd' like str or date/datetime object to Range object\n\n `Range: (start_date, end_date]`\n\n `start_date : '2019-05-25'`\n\n :param start_date: start date\n :type start_date: str, date, datetime\n :param end_date: end date\n :type end_date: str, date, datetime\n\n :return: Range object\n \"\"\"\n temp = Range(start_date, end_date)\n\n return grpc_types.Range(start_value=temp.start_date,\n end_value=temp.end_date)\n\n @classmethod\n def ranges(cls, ranges):\n \"\"\"\n prepare query_ranges\n\n :param ranges: prepare query_ranges\n :type ranges: [[str, str], (str,str)], iterable\n\n `Example: [[start, end]], ((start, end), (start, end)), or\n [(start, end)]`\n\n :return: list[Range]\n \"\"\"\n res = []\n for _range in ranges:\n if not isinstance(_range, grpc_types.Range):\n res.append(Prepare.range(_range[0], _range[1]))\n else:\n res.append(_range)\n return res\n\n @classmethod\n def insert_param(cls, table_name, vectors, ids=None):\n\n check_pass_param(table_name=table_name)\n\n if ids is None:\n _param = grpc_types.InsertParam(table_name=table_name)\n else:\n check_pass_param(ids=ids)\n\n if len(vectors) != len(ids):\n raise ParamError(\"length of vectors do not match that of ids\")\n\n _param = grpc_types.InsertParam(table_name=table_name, row_id_array=ids)\n\n for vector in vectors:\n if is_legal_array(vector):\n _param.row_record_array.add(vector_data=vector)\n else:\n raise ParamError('Vectors should be 2-dim array!')\n\n return _param\n\n @classmethod\n def index(cls, index_type, nlist):\n \"\"\"\n\n :type index_type: IndexType\n :param index_type: index type\n\n :type nlist:\n :param nlist:\n\n :return:\n \"\"\"\n check_pass_param(index_type=index_type, nlist=nlist)\n\n return grpc_types.Index(index_type=index_type, nlist=nlist)\n\n @classmethod\n def index_param(cls, table_name, index_param):\n\n if not isinstance(index_param, dict):\n raise ParamError('Param type incorrect, expect {} but get {} instead '.format(\n type(dict), type(index_param)\n ))\n\n check_pass_param(table_name=table_name, **index_param)\n\n _index = Prepare.index(**index_param)\n\n return grpc_types.IndexParam(status=status_pb2.Status(error_code=0, reason='Client'),\n table_name=table_name,\n index=_index)\n\n @classmethod\n def search_param(cls, table_name, query_records, query_ranges, topk, nprobe):\n query_ranges = Prepare.ranges(query_ranges) if query_ranges else None\n\n check_pass_param(table_name=table_name, topk=topk, nprobe=nprobe)\n\n search_param = grpc_types.SearchParam(\n table_name=table_name,\n query_range_array=query_ranges,\n topk=topk,\n nprobe=nprobe\n )\n\n for vector in query_records:\n if is_legal_array(vector):\n search_param.query_record_array.add(vector_data=vector)\n else:\n raise ParamError('Vectors should be 2-dim array!')\n\n return search_param\n\n @classmethod\n def search_vector_in_files_param(cls, table_name, query_records,\n query_ranges, topk, nprobe, ids):\n _search_param = Prepare.search_param(table_name, query_records,\n query_ranges, topk, nprobe)\n\n return grpc_types.SearchInFilesParam(\n file_id_array=ids,\n search_param=_search_param\n )\n\n @classmethod\n def cmd(cls, cmd):\n check_pass_param(cmd=cmd)\n\n return grpc_types.Command(cmd=cmd)\n\n @classmethod\n def delete_param(cls, table_name, start_date, end_date):\n\n range_ = Prepare.range(start_date, end_date)\n\n check_pass_param(table_name=table_name)\n\n return grpc_types.DeleteByRangeParam(range=range_, table_name=table_name)\n\n\nclass GrpcMilvus(ConnectIntf):\n\n def __init__(self):\n self._channel = None\n self._stub = None\n self._uri = None\n self.server_address = None\n self.status = None\n\n def __str__(self):\n attr_list = ['%s=%r' % (key, value)\n for key, value in self.__dict__.items() if not key.startswith('_')]\n return ''.format(', '.join(attr_list))\n\n def set_channel(self, host=None, port=None, uri=None):\n\n if host is not None:\n _port = port or \"19530\"\n _host = host\n elif port is None:\n try:\n config_uri = urlparse(config.GRPC_URI)\n _uri = urlparse(uri) if uri else config_uri\n\n if _uri.scheme != 'tcp':\n raise ParamError(\n 'Invalid parameter uri: `{}`. Scheme `{}` '\n 'is not supported'.format(_uri, _uri.scheme))\n\n _host = _uri.hostname\n _port = _uri.port\n\n except Exception:\n raise ParamError(\"`{}` is illegal\".format(uri))\n else:\n raise ParamError(\"Param is not complete. Please invoke as follow:\\n\"\n \"\\t(host = ${HOST}, port = ${PORT})\\n\"\n \"\\t(uri = ${URI})\\n\")\n\n if not is_legal_host(_host) or not is_legal_port(_port):\n raise ParamError(\"host or port is illegal\")\n\n self._uri = str(_host) + ':' + str(_port)\n self.server_address = self._uri\n self._channel = grpc.insecure_channel(\n self._uri,\n options=[(cygrpc.ChannelArgKey.max_send_message_length, -1),\n (cygrpc.ChannelArgKey.max_receive_message_length, -1)]\n )\n\n def connect(self, host=None, port=None, uri=None, timeout=3):\n \"\"\"\n Connect method should be called before any operations.\n Server will be connected after connect return OK\n\n :type host: str\n :type port: str\n :type uri: str\n :type timeout: float\n :param host: (Optional) host of the server, default host is 127.0.0.1\n :param port: (Optional) port of the server, default port is 19530\n :param uri: (Optional) only support tcp proto now, default uri is\n\n `tcp://127.0.0.1:19530`\n\n :param timeout: (Optional) connection timeout, default timeout is 3000ms\n\n :return: Status, indicate if connect is successful\n :rtype: Status\n \"\"\"\n if self._channel is None:\n self.set_channel(host, port, uri)\n\n elif self.connected():\n return Status(message=\"You have already connected!\", code=Status.CONNECT_FAILED)\n\n try:\n grpc.channel_ready_future(self._channel).result(timeout=timeout)\n except grpc.FutureTimeoutError:\n raise NotConnectError('Fail connecting to server on {}. Timeout'.format(self._uri))\n except grpc.RpcError as e:\n raise NotConnectError(\"Connect error: <{}>\".format(e))\n except Exception as e:\n raise NotConnectError(\"Error occurred when trying to connect server:\\n<{}>\".format(e))\n else:\n self._stub = milvus_pb2_grpc.MilvusServiceStub(self._channel)\n self.status = Status(message='Successfully connected! {}'.format(self._uri))\n return self.status\n\n def connected(self):\n \"\"\"\n Check if client is connected to the server\n\n :return: if client is connected\n :rtype bool\n \"\"\"\n if not self._stub or not self.status or not self._channel:\n return False\n\n try:\n grpc.channel_ready_future(self._channel).result(timeout=2)\n except (grpc.FutureTimeoutError, grpc.RpcError):\n return False\n else:\n return True\n\n def disconnect(self):\n \"\"\"\n Disconnect with the server and distroy the channel\n\n :return: Status, indicate if disconnect is successful\n :rtype: Status\n \"\"\"\n # After closeing, a exception stack trace is printed from a background thread and\n # no exception is thrown in the main thread, issue is under test and not done yet\n # checkout https://github.com/grpc/grpc/issues/18995\n # Also checkout Properly Specify Channel.close Behavior in Python:\n # https://github.com/grpc/grpc/issues/19235\n if not self.connected():\n raise NotConnectError('Please connect to the server first!')\n\n del self._channel\n\n # try:\n # self._channel.close()\n # except Exception as e:\n # LOGGER.error(e)\n # return Status(code=Status.CONNECT_FAILED, message='Disconnection failed')\n\n self.status = None\n self._channel = None\n self._stub = None\n\n return Status(message='Disconnect successfully')\n\n def create_table(self, param, timeout=10):\n \"\"\"Create table\n\n :type param: dict or TableSchema\n :param param: Provide table information to be created\n\n `example param={'table_name': 'name',\n 'dimension': 16,\n 'index_file_size': 1024 (default)，\n 'metric_type': Metric_type.L2 (default)\n }`\n\n `OR using Prepare.table_schema to create param`\n\n :param timeout: timeout, The unit is seconds\n :type timeout: double\n\n :return: Status, indicate if operation is successful\n :rtype: Status\n \"\"\"\n\n if not self.connected():\n raise NotConnectError('Please connect to the server first')\n\n table_schema = Prepare.table_schema(param)\n\n try:\n status = self._stub.CreateTable.future(table_schema).result(timeout=timeout)\n if status.error_code == 0:\n return Status(message='Create table successfully!')\n\n LOGGER.error(status)\n return Status(code=status.error_code, message=status.reason)\n except grpc.FutureTimeoutError as e:\n LOGGER.error(e)\n return Status(Status.UNEXPECTED_ERROR, message='Request timeout')\n except grpc.RpcError as e:\n LOGGER.error(e)\n return Status(e.code(), message='Error occurred: {}'.format(e.details()))\n\n def has_table(self, table_name, timeout=10):\n \"\"\"\n\n This method is used to test table existence.\n\n :param table_name: table name is going to be tested.\n :type table_name: str\n\n :return:\n Status: indicate if vectors inserted successfully\n bool if given table_name exists\n\n \"\"\"\n if not self.connected():\n raise NotConnectError('Please connect to the server first')\n\n table_name = Prepare.table_name(table_name)\n\n try:\n reply = self._stub.HasTable.future(table_name).result(timeout=timeout)\n if reply.status.error_code == 0:\n return Status(), reply.bool_reply\n\n return Status(code=reply.status.error_code,\n message=reply.status.reason), False\n except grpc.FutureTimeoutError as e:\n LOGGER.error(e)\n return Status(code=Status.UNEXPECTED_ERROR, message=\"request timeout\"), False\n except grpc.RpcError as e:\n LOGGER.error(e)\n return Status(code=e.code(), message=e.details()), False\n\n def delete_table(self, table_name, timeout=20):\n \"\"\"\n Delete table with table_name\n\n :type table_name: str\n :param table_name: Name of the table being deleted\n\n :return: Status, indicate if operation is successful\n :rtype: Status\n \"\"\"\n\n if not self.connected():\n raise NotConnectError('Please connect to the server first')\n\n table_name = Prepare.table_name(table_name)\n\n try:\n status = self._stub.DropTable.future(table_name).result(timeout=timeout)\n if status.error_code == 0:\n return Status(message='Delete table successfully!')\n return Status(code=status.error_code, message=status.reason)\n\n except grpc.FutureTimeoutError as e:\n LOGGER.error(e)\n return Status(Status.UNEXPECTED_ERROR, message='Request timeout')\n except grpc.RpcError as e:\n LOGGER.error(e)\n return Status(e.code(), message='Error occurred: {}'.format(e.details()))\n\n def create_index(self, table_name, index=None, timeout=-1):\n \"\"\"\n :param table_name: table used to build index.\n :type table_name: str\n :param index: index params\n :type index: dict\n\n index_param can be None\n\n `example (default) param={'index_type': IndexType.FLAT,\n 'nlist': 16384}`\n\n :param timeout: grpc request timeout.\n\n if `timeout` = -1, method invoke a synchronous call, waiting util grpc response\n else method invoke a asynchronous call, timeout work here\n\n :type timeout: int\n\n :return: Status, indicate if operation is successful\n \"\"\"\n if index is None:\n index = {\n 'index_type': IndexType.FLAT,\n 'nlist': 16384\n }\n elif not isinstance(index, dict):\n raise ParamError(\"param `index` should be a dictionary\")\n\n index = {\n 'index_type': index['index_type'] if 'index_type' in index else IndexType.FLAT,\n 'nlist': index['nlist'] if 'nlist' in index else 16384\n }\n\n if not self.connected():\n raise NotConnectError('Please connect to the server first')\n\n index_param = Prepare.index_param(table_name, index)\n try:\n if timeout == -1:\n status = self._stub.CreateIndex(index_param)\n elif timeout < 0:\n raise ParamError(\"Param `timeout` should be a positive number or -1\")\n else:\n try:\n status = self._stub.CreateIndex.future(index_param).result(timeout=timeout)\n except grpc.FutureTimeoutError as e:\n LOGGER.error(e)\n return Status(Status.UNEXPECTED_ERROR, message='Request timeout')\n\n if status.error_code == 0:\n return Status(message='Build index successfully!')\n\n return Status(code=status.error_code, message=status.reason)\n except grpc.RpcError as e:\n LOGGER.error(e)\n return Status(e.code(), message='Error occurred. {}'.format(e.details()))\n\n def add_vectors(self, table_name, records, ids=None, timeout=180, **kwargs):\n \"\"\"\n Add vectors to table\n\n This function allows to pass in arguments which is type of `milvus_ob2.InsertParam`\n to avoid serializing and deserializing repeatedly, as follows:\n\n `obj.add_vectors(None, None, insert_param=param)`\n\n `obj` is a milvus object, param is an object which is type of `milvus_ob2.InsertParam`\n\n :param ids:\n\n :type table_name: str\n :param table_name: table name been inserted\n\n :type records: list[list[float]]\n\n `example records: [[1.2345],[1.2345]]`\n\n `OR using Prepare.records`\n\n :param records: list of vectors been inserted\n\n :type timeout: int\n :param timeout:\n\n :returns:\n Status: indicate if vectors inserted successfully\n ids: list of id, after inserted every vector is given a id\n :rtype: (Status, list(int))\n \"\"\"\n\n if not self.connected():\n raise NotConnectError('Please connect to the server first')\n\n insert_param = kwargs.get('insert_param', None)\n\n if not insert_param:\n insert_param = Prepare.insert_param(table_name, records, ids)\n else:\n if not isinstance(insert_param, grpc_types.InsertParam):\n raise ParamError(\"The value of key 'insert_param' is invalid\")\n\n try:\n vector_ids = self._stub.Insert.future(insert_param).result(timeout=timeout)\n\n if vector_ids.status.error_code == 0:\n ids = list(vector_ids.vector_id_array)\n return Status(message='Add vectors successfully!'), ids\n\n return Status(code=vector_ids.status.error_code, message=vector_ids.status.reason), []\n except grpc.RpcError as e:\n LOGGER.error(e)\n return Status(e.code(), message='Error occurred. {}'.format(e.details())), []\n except grpc.FutureTimeoutError as e:\n LOGGER.error(e)\n return Status(code=Status.UNEXPECTED_ERROR, message=\"Request timeout\"), []\n\n def search_vectors(self, table_name, top_k, nprobe, query_records, query_ranges=None, **kwargs):\n \"\"\"\n Query vectors in a table\n\n :param query_ranges: (Optional) ranges for conditional search.\n If not specified, search in the whole table\n :type query_ranges: list[(date, date)]\n\n `date` supports:\n a. date-like-str, e.g. '2019-01-01'\n b. datetime.date object, e.g. datetime.date(2019, 1, 1)\n c. datetime.datetime object, e.g. datetime.datetime.now()\n\n example query_ranges:\n\n `query_ranges = [('2019-05-10', '2019-05-10'),(..., ...), ...]`\n\n :param table_name: table name been queried\n :type table_name: str\n :param query_records: all vectors going to be queried\n\n `Using Prepare.records generate query_records`\n\n :type query_records: list[list[float]] or list[RowRecord]\n :param top_k: int, how many similar vectors will be searched\n :type top_k: int\n :param nprobe: cell num of probing\n :type nprobe: int\n\n :returns: (Status, res)\n\n Status: indicate if query is successful\n res: TopKQueryResult, return when operation is successful\n\n :rtype: (Status, TopKQueryResult[QueryResult])\n \"\"\"\n\n if not self.connected():\n raise NotConnectError('Please connect to the server first')\n\n infos = Prepare.search_param(\n table_name, query_records, query_ranges, top_k, nprobe\n )\n\n lazy_flag = kwargs.get(\"lazy_\", False)\n\n try:\n response = self._stub.Search(infos)\n\n if lazy_flag is True:\n return response\n\n if response.status.error_code != 0:\n return Status(code=response.status.error_code,\n message=response.status.reason), []\n\n return Status(message='Search vectors successfully!'), TopKQueryResult(response)\n\n except grpc.RpcError as e:\n LOGGER.error(e)\n status = Status(code=e.code(), message='Error occurred: {}'.format(e.details()))\n\n return status, []\n\n def search_vectors_in_files(self, table_name, file_ids, query_records, top_k,\n nprobe=16, query_ranges=None, **kwargs):\n \"\"\"\n Query vectors in a table, in specified files\n\n :type nprobe: int\n :param nprobe:\n\n :type table_name: str\n :param table_name: table name been queried\n\n :type file_ids: list[str] or list[int]\n :param file_ids: Specified files id array\n\n :type query_records: list[list[float]]\n :param query_records: all vectors going to be queried\n\n :param query_ranges: Optional ranges for conditional search.\n If not specified, search in the whole table\n\n\n :type top_k: int\n :param top_k: how many similar vectors will be searched\n\n :returns:\n Status: indicate if query is successful\n query_results: list[TopKQueryResult]\n\n :rtype: (Status, TopKQueryResult[QueryResult])\n \"\"\"\n\n if not self.connected():\n raise NotConnectError('Please connect to the server first')\n\n file_ids = list(map(int_or_str, file_ids))\n\n infos = Prepare.search_vector_in_files_param(\n table_name, query_records, query_ranges, top_k, nprobe, file_ids\n )\n\n lazy_flag = kwargs.get(\"lazy_\", False)\n\n try:\n response = self._stub.SearchInFiles(infos)\n\n if lazy_flag is True:\n return response\n\n if response.status.error_code != 0:\n return Status(code=response.status.error_code,\n message=response.status.reason), []\n\n return Status(message='Search vectors successfully!'), TopKQueryResult(response)\n except grpc.RpcError as e:\n LOGGER.error(e)\n status = Status(code=e.code(), message='Error occurred. {}'.format(e.details()))\n\n return status, []\n\n def describe_table(self, table_name, timeout=10):\n \"\"\"\n Show table information\n\n :type table_name: str\n :param table_name: which table to be shown\n\n :returns: (Status, table_schema)\n Status: indicate if query is successful\n table_schema: return when operation is successful\n :rtype: (Status, TableSchema)\n \"\"\"\n\n if not self.connected():\n raise NotConnectError('Please connect to the server first')\n\n table_name = Prepare.table_name(table_name)\n\n try:\n response = self._stub.DescribeTable.future(table_name).result(timeout=timeout)\n\n if response.status.error_code == 0:\n table = TableSchema(\n table_name=response.table_name,\n dimension=response.dimension,\n index_file_size=response.index_file_size,\n metric_type=MetricType(response.metric_type)\n )\n\n return Status(message='Describe table successfully!'), table\n\n LOGGER.error(response.status)\n return Status(code=response.status.error_code, message=response.status.reason), None\n\n except grpc.FutureTimeoutError as e:\n LOGGER.error(e)\n return Status(Status.UNEXPECTED_ERROR, message='Request timeout'), None\n except grpc.RpcError as e:\n LOGGER.error(e)\n return Status(e.code(), message='Error occurred. {}'.format(e.details())), None\n\n def show_tables(self, timeout=10):\n \"\"\"\n Show all tables in database\n\n :return:\n Status: indicate if this operation is successful\n\n tables: list of table names, return when operation\n is successful\n :rtype:\n (Status, list[str])\n \"\"\"\n if not self.connected():\n raise NotConnectError('Please connect to the server first')\n\n cmd = Prepare.cmd('show_tables')\n try:\n response = self._stub.ShowTables.future(cmd).result(timeout=timeout)\n if response.status.error_code == 0:\n return Status(message='Show tables successfully!'), \\\n [name for name in response.table_names if len(name) > 0]\n return Status(response.status.error_code, message=response.status.reason), []\n except grpc.FutureTimeoutError:\n return Status(Status.UNEXPECTED_ERROR, message=\"Request timeout\"), []\n except grpc.RpcError as e:\n LOGGER.error(e)\n return Status(e.code(), message='Error occurred. {}'.format(e.details())), []\n\n def get_table_row_count(self, table_name, timeout=30):\n \"\"\"\n Get table row count\n\n :type table_name: str\n :param table_name: target table name.\n\n :returns:\n Status: indicate if operation is successful\n\n res: int, table row count\n \"\"\"\n\n if not self.connected():\n raise NotConnectError('Please connect to the server first')\n\n table_name = Prepare.table_name(table_name)\n\n try:\n trc = self._stub.CountTable.future(table_name).result(timeout=timeout)\n if trc.status.error_code == 0:\n return Status(message='Success!'), trc.table_row_count\n\n return Status(code=trc.status.error_code, message=trc.status.reason), None\n except grpc.FutureTimeoutError as e:\n LOGGER.error(e)\n return Status(Status.UNEXPECTED_ERROR, message='Request timeout'), []\n except grpc.RpcError as e:\n LOGGER.error(e)\n return Status(e.code(), message='Error occurred. {}'.format(e.details())), []\n\n def client_version(self):\n \"\"\"\n Provide client version\n\n :return:\n Status: indicate if operation is successful\n\n str : Client version\n\n :rtype: (str)\n \"\"\"\n return __version__\n\n def server_version(self, timeout=10):\n \"\"\"\n Provide server version\n\n :return:\n Status: indicate if operation is successful\n\n str : Server version\n\n :rtype: (Status, str)\n \"\"\"\n return self._cmd(cmd='version', timeout=timeout)\n\n def server_status(self, timeout=10):\n \"\"\"\n Provide server status\n\n :return:\n Status: indicate if operation is successful\n\n str : Server version\n\n :rtype: (Status, str)\n \"\"\"\n return self._cmd(cmd='OK', timeout=timeout)\n\n def _cmd(self, cmd, timeout=10):\n\n if not self.connected():\n raise NotConnectError('Please connect to the server first')\n\n cmd = Prepare.cmd(cmd)\n try:\n response = self._stub.Cmd.future(cmd).result(timeout=timeout)\n if response.status.error_code == 0:\n return Status(message='Success!'), response.string_reply\n\n return Status(code=response.status.error_code, message=response.status.reason), None\n except grpc.FutureTimeoutError as e:\n LOGGER.error(e)\n return Status(Status.UNEXPECTED_ERROR, message='Request timeout'), None\n except grpc.RpcError as e:\n LOGGER.error(e)\n return Status(e.code(), message='Error occurred. {}'.format(e.details())), None\n\n def __delete_vectors_by_range(self, table_name, start_date=None, end_date=None, timeout=10):\n \"\"\"\n Delete vectors by range. The data range contains start_time but not end_time\n This method is deprecated, not recommended for users\n\n :type table_name: str\n :param table_name: str, date, datetime\n\n :type start_date: str, date, datetime\n :param start_date:\n\n :type end_date: str, date, datetime\n :param end_date:\n\n :return:\n Status: indicate if invoke is successful\n \"\"\"\n\n if not self.connected():\n raise NotConnectError('Please connect to the server first')\n\n delete_range = Prepare.delete_param(table_name, start_date, end_date)\n\n try:\n status = self._stub.DeleteByRange.future(delete_range).result(timeout=timeout)\n return Status(code=status.error_code, message=status.reason)\n except grpc.FutureTimeoutError as e:\n LOGGER.error(e)\n return Status(Status.UNEXPECTED_ERROR, message='Request timeout')\n except grpc.RpcError as e:\n LOGGER.error(e)\n return Status(e.code(), message='Error occurred. {}'.format(e.details()))\n\n def preload_table(self, table_name, timeout=300):\n \"\"\"\n Load table to cache in advance\n\n :type table_name: str\n :param table_name: table to preload\n\n :returns:\n Status: indicate if invoke is successful\n \"\"\"\n if not self.connected():\n raise NotConnectError('Please connect to the server first')\n\n table_name = Prepare.table_name(table_name)\n\n try:\n status = self._stub.PreloadTable.future(table_name).result(timeout=timeout)\n return Status(code=status.error_code, message=status.reason)\n except grpc.FutureTimeoutError as e:\n LOGGER.error(e)\n return Status(Status.UNEXPECTED_ERROR, message='Request timeout')\n except grpc.RpcError as e:\n return Status(code=e.code(), message='Error occurred. {}'.format(e.details()))\n\n def describe_index(self, table_name, timeout=10):\n \"\"\"\n Show index information of designated table\n\n :type table_name: str\n :param table_name: table name been queried\n\n :returns:\n Status: indicate if query is successful\n IndexSchema:\n\n \"\"\"\n if not self.connected():\n raise NotConnectError('Please connect to the server first')\n\n table_name = Prepare.table_name(table_name)\n\n try:\n index_param = self._stub.DescribeIndex.future(table_name).result(timeout=timeout)\n\n status = index_param.status\n\n if status.error_code == 0:\n return Status(message=\"Successfully\"), \\\n IndexParam(index_param.table_name,\n index_param.index.index_type,\n index_param.index.nlist)\n\n return Status(code=status.error_code, message=status.reason), None\n except grpc.FutureTimeoutError as e:\n LOGGER.error(e)\n return Status(code=Status.UNEXPECTED_ERROR, message='Request timeout'), None\n except grpc.RpcError as e:\n LOGGER.error(e)\n return Status(e.code(), message='Error occurred. {}'.format(e.details())), None\n\n def drop_index(self, table_name, timeout=10):\n \"\"\"\n drop index from index file\n\n :param table_name: target table name.\n :type table_name: str\n\n :return:\n Status: indicate if operation is successful\n\n ：:rtype: Status\n \"\"\"\n\n if not self.connected():\n raise NotConnectError('Please connect to the server first')\n\n table_name = Prepare.table_name(table_name)\n try:\n status = self._stub.DropIndex.future(table_name).result(timeout=timeout)\n return Status(code=status.error_code, message=status.reason)\n except grpc.FutureTimeoutError as e:\n LOGGER.error(e)\n return Status(Status.UNEXPECTED_ERROR, message='Request timeout')\n except grpc.RpcError as e:\n LOGGER.error(e)\n return Status(e.code(), message='Error occurred. {}'.format(e.details()))\n\n count_table = get_table_row_count\n drop_table = delete_table\n insert = add_vectors\n search = search_vectors\n search_in_files = search_vectors_in_files\n","sub_path":"milvus/client/grpc_client.py","file_name":"grpc_client.py","file_ext":"py","file_size_in_byte":33389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"67082980","text":"#!/usr/bin/python3\n\nimport json\nimport os\nimport sys\n\nimport utils\n\nfrom flask import Flask, jsonify, render_template, request\n\ndbfile = \"sqlite.db\"\n\ndb_version = 3\nforceDBUpdate = False\n\napp = Flask(__name__)\n\nimport local_config\n\napp.config.from_object(local_config)\n\nif not os.path.isfile(dbfile):\n print(\"No database found. Creating one...\")\n utils.createDB()\n\nif utils.getDBVersion() < db_version:\n print(\"Database version out of date, updating...\")\n utils.updateDB()\n utils.getKernelTableFromGithub()\n\nstatus_ids = utils.getStatusIDs()\nallCVEs = utils.getCVEs()\nkernels = utils.getKernelsFromDB()\n\n\n@app.route(\"/\")\n@app.route(\"/\")\ndef index(k=None):\n if k:\n kernel = utils.getKernelByRepo(k)\n patches = utils.getPatchesByRepo(k)\n patched = utils.getNumberOfPatchedByRepoId(k)\n return render_template('kernel.html', kernel = kernel, patched = patched, cves = allCVEs, status_ids = status_ids, patches = patches)\n else:\n return render_template('index.html', kernels = kernels)\n\n@app.route(\"/update\", methods=['POST'])\ndef update():\n r = request.get_json()\n k = r['kernel_id'];\n c = r['cve_id'];\n s = r['status_id'];\n utils.updatePatchStatus(k, c, s)\n patched = utils.getNumberOfPatchedByRepoId(k)\n return jsonify({'error': 'success', 'patched': patched})\n\n\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"162241206","text":"import numpy as np\n\ntimesteps = 100\ninput_feature = 32\noutput_features = 64\n\ninputs = np.random.random((timesteps, input_feature))\nprint(\"input shape: \", inputs.shape)\n\nstate_t = np.zeros((output_features,))\nprint(\"state_t shape: \", state_t.shape)\n\nW = np.random.random((output_features, input_feature))\nU = np.random.random((output_features, output_features))\nb = np.random.random((output_features,))\n\nsuccisive_outputs = []\n\nfor input_t in inputs:\n output_t = np.tanh(np.dot(W, input_t) + np.dot(U, state_t) + b)\n\n succisive_outputs.append(output_t)\n\n state_t = output_t\n\nprint(\"before concat succisive_outputs shape: \", len(succisive_outputs))\nfinal_output_sequence = np.concatenate(succisive_outputs, axis= 0)\nprint(\"final out seq: \", final_output_sequence)\nprint(\"final out seq shape: \", final_output_sequence.shape)","sub_path":"RNN_naive_ex.py","file_name":"RNN_naive_ex.py","file_ext":"py","file_size_in_byte":830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"138583891","text":"import speech_recognition as sr\r\nr=sr.Recognizer()\r\na=sr.AudioFile('Recording.wav') # record your voice and save it to the directory\r\nwith a as source:\r\n audio=r.record(source)\r\n\r\ntext=r.recognize_google(audio)\r\n\r\nfile1=open(r\"C:\\Users\\Tushar\\PycharmProjects\\gui\\1.txt\",\"a\")\r\nfile1.writelines(text)\r\nfile1.close()","sub_path":"speechtotext.py","file_name":"speechtotext.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"365571781","text":"# -*- coding: utf-8 -*-\n\"\"\"\nFunctions for initlilizing iSnobal and PySnobal models\n\nAuthors: Scott Havens, Micah Sandusky\n\"\"\"\n\nimport os\nimport numpy as np\nfrom datetime import timedelta\nimport netCDF4 as nc\nimport pytz\n\nfrom spatialnc import ipw\nfrom smrf.utils import utils\n\nDEFAULT_MAX_H2O_VOL = 0.01\n\nDATA_TSTEP = 0\nNORMAL_TSTEP = 1\nMEDIUM_TSTEP = 2\nSMALL_TSTEP = 3\n\nDEFAULT_MEDIUM_TSTEP = 15.0\nDEFAULT_SMALL_TSTEP = 1.0\n\nWHOLE_TSTEP = 0x1 # output when tstep is not divided\nDIVIDED_TSTEP = 0x2 # output when timestep is divided\n\nhrs2min = lambda x: x * 60\nmin2sec = lambda x: x * 60\nSEC_TO_HR = lambda x: x / 3600.0\n\nC_TO_K = 273.16\nFREEZE = C_TO_K\n# Kelvin to Celcius\nK_TO_C = lambda x: x - FREEZE\n\n\ndef check_range(value, min_val, max_val, descrip):\n \"\"\"\n Check the range of the value\n Args:\n value: value to check\n min_val: minimum value\n max_val: maximum value\n descrip: short description of input\n\n Returns:\n True if within range\n \"\"\"\n if (value < min_val) or (value > max_val):\n raise ValueError(\"%s (%f) out of range: %f to %f\",\n descrip, value, min_val, max_val)\n pass\n\n\ndef date_range(start_date, end_date, increment):\n '''\n Calculate a list between start and end date with\n an increment\n '''\n result = []\n nxt = start_date\n while nxt <= end_date:\n result.append(nxt)\n nxt += increment\n return np.array(result)\n\n\ndef get_timestep_netcdf(force, tstep, point=None):\n \"\"\"\n Pull out a time step from the forcing files and\n place that time step into a dict\n\n Args:\n force: input array of forcing variables\n tstep: datetime timestep\n\n Returns:\n inpt: dictionary of forcing variable images\n \"\"\"\n\n inpt = {}\n\n # map function from these values to the ones requried by snobal\n map_val = {'air_temp': 'T_a', 'net_solar': 'S_n', 'thermal': 'I_lw',\n 'vapor_pressure': 'e_a', 'wind_speed': 'u',\n 'soil_temp': 'T_g', 'precip_mass': 'm_pp',\n 'percent_snow': 'percent_snow', 'snow_density': 'rho_snow',\n 'precip_temp': 'T_pp'}\n\n for f in force.keys():\n\n if isinstance(force[f], np.ndarray):\n # If it's a constant value then just read in the numpy array\n # pull out the value\n if point is None:\n inpt[map_val[f]] = force[f].copy() # ensures not a reference (especially if T_g)\n else:\n inpt[map_val[f]] = np.atleast_2d(force[f][point[0], point[1]])\n\n else:\n # determine the index in the netCDF file\n\n # compare the dimensions and variables to get the variable name\n v = list(set(force[f].variables.keys())-set(force[f].dimensions.keys()))\n v = [fv for fv in v if fv != 'projection'][0]\n\n # make sure you're in the same timezone\n if hasattr(force[f].variables['time'], 'time_zone'):\n tstep_zone = tstep.astimezone(pytz.timezone(force[f].variables['time'].time_zone))\n tstep_zone = tstep.tz_localize(None)\n else:\n tstep_zone = tstep.tz_localize(None)\n\n # find the index based on the time step\n t = nc.date2index(tstep_zone, force[f].variables['time'],\n calendar=force[f].variables['time'].calendar,\n select='exact')\n\n # pull out the value\n if point is None:\n inpt[map_val[f]] = force[f].variables[v][t, :].astype(np.float64)\n else:\n inpt[map_val[f]] = np.atleast_2d(force[f].variables[v][t, point[0], point[1]].astype(np.float64))\n\n # convert from C to K\n inpt['T_a'] += FREEZE\n inpt['T_pp'] += FREEZE\n inpt['T_g'] += FREEZE\n\n return inpt\n\n\ndef get_timestep_ipw(tstep, input_list, ppt_list, myawsm):\n \"\"\"\n Pull out a time step from the forcing files (IPW) and\n place that time step into a dict\n\n Args:\n tstep: datetime of timestep\n input_list: numpy array (1D) of integer timesteps given\n ppt_list: numpy array(1D) of integer timesteps for ppt_list\n myawsm: AWSM instance for current run\n\n Returns:\n inpt: dictionary of forcing variable images\n\n \"\"\"\n\n inpt = {}\n\n # map function from these values to the ones requried by snobal\n map_val = {1: 'T_a', 5: 'S_n', 0: 'I_lw',\n 2: 'e_a', 3: 'u'}\n map_val_prec = {0: 'm_pp', 1: 'percent_snow',\n 2: 'rho_snow',\n 3: 'T_pp'}\n\n # get wy hour\n wyhr = int(utils.water_day(tstep)[0]*24)\n # if we have inputs matching this water year hour\n if np.any(input_list == wyhr):\n i_in = ipw.IPW(os.path.join(myawsm.pathi, 'in.%04i' % (wyhr)))\n # assign soil temp\n inpt['T_g'] = myawsm.soil_temp*np.ones((myawsm.topo.ny, myawsm.topo.nx))\n # myawsm._logger.info('T_g: {}'.format(myawsm.soil_temp))\n # inpt['T_g'] = -2.5*np.ones((myawsm.topo.ny, myawsm.topo.nx))\n for f, v in map_val.items():\n # if no solar data, give it zero\n if f == 5 and len(i_in.bands) < 6:\n # myawsm._logger.info('No solar data for {}'.format(tstep))\n inpt[v] = np.zeros((myawsm.topo.ny, myawsm.topo.nx))\n else:\n inpt[v] = i_in.bands[f].data\n # assign ppt data if there\n else:\n raise ValueError('No input timesteps for {}'.format(tstep))\n\n if np.any(ppt_list == wyhr):\n i_ppt = ipw.IPW(os.path.join(myawsm.path_ppt, 'ppt.4b_%04i' % (wyhr)))\n for f, v in map_val_prec.items():\n inpt[v] = i_ppt.bands[f].data\n else:\n for f, v in map_val_prec.items():\n inpt[v] = np.zeros((myawsm.topo.ny, myawsm.topo.nx))\n\n # convert from C to K\n inpt['T_a'] += FREEZE\n inpt['T_pp'] += FREEZE\n inpt['T_g'] += FREEZE\n\n return inpt\n\n\ndef get_tstep_info(options, config, thresh):\n \"\"\"\n Parse the options dict, set the default values if not specified\n May need to divide tstep_info and params up into different\n functions\n\n Args:\n options: dictionary of input settings for running program\n config: Snobal config\n thresh: list of mass thresholds for Snobal\n\n Returns:\n params: Snobal parameters\n tstep_info: setting for Snobal timesteps\n\n \"\"\"\n\n # intialize the time step info\n # 0 : data timestep\n # 1 : normal run timestep\n # 2 : medium \" \"\n # 3 : small \" \"\n\n tstep_info = []\n for i in range(4):\n t = {'level': i, 'output': False, 'threshold': None, 'time_step': None, 'intervals': None}\n tstep_info.append(t)\n\n # The input data's time step must be between 1 minute and 6 hours.\n # If it is greater than 1 hour, it must be a multiple of 1 hour, e.g.\n # 2 hours, 3 hours, etc.\n\n data_tstep_min = float(options['time_step'])\n tstep_info[DATA_TSTEP]['time_step'] = min2sec(data_tstep_min)\n\n norm_tstep_min = 60.0\n tstep_info[NORMAL_TSTEP]['time_step'] = min2sec(norm_tstep_min)\n tstep_info[NORMAL_TSTEP]['intervals'] = int(data_tstep_min / norm_tstep_min)\n\n med_tstep_min = DEFAULT_MEDIUM_TSTEP\n tstep_info[MEDIUM_TSTEP]['time_step'] = min2sec(med_tstep_min)\n tstep_info[MEDIUM_TSTEP]['intervals'] = int(norm_tstep_min / med_tstep_min)\n\n small_tstep_min = DEFAULT_SMALL_TSTEP\n tstep_info[SMALL_TSTEP]['time_step'] = min2sec(small_tstep_min)\n tstep_info[SMALL_TSTEP]['intervals'] = int(med_tstep_min / small_tstep_min)\n\n # output\n if config['output']['output_mode'] == 'data':\n tstep_info[DATA_TSTEP]['output'] = DIVIDED_TSTEP\n elif config['output']['output_mode'] == 'normal':\n tstep_info[NORMAL_TSTEP]['output'] = WHOLE_TSTEP | DIVIDED_TSTEP\n elif config['output']['output_mode'] == 'all':\n tstep_info[NORMAL_TSTEP]['output'] = WHOLE_TSTEP\n tstep_info[MEDIUM_TSTEP]['output'] = WHOLE_TSTEP\n tstep_info[SMALL_TSTEP]['output'] = WHOLE_TSTEP\n else:\n tstep_info[DATA_TSTEP]['output'] = DIVIDED_TSTEP\n# tstep_info[DATA_TSTEP]['output'] = DIVIDED_TSTEP\n\n # mass thresholds for run timesteps\n tstep_info[NORMAL_TSTEP]['threshold'] = thresh[0]\n tstep_info[MEDIUM_TSTEP]['threshold'] = thresh[1]\n tstep_info[SMALL_TSTEP]['threshold'] = thresh[2]\n\n # get the rest of the parameters\n params = {}\n\n# params['elevation'] = options['z']\n params['data_tstep'] = data_tstep_min\n params['max_h2o_vol'] = options['max-h2o']\n params['max_z_s_0'] = options['max_z_s_0']\n# params['sn_filename'] = options['s']\n# params['mh_filename'] = options['h']\n# params['in_filename'] = options['i']\n# params['pr_filename'] = options['p']\n params['out_filename'] = config['output']['out_filename']\n if params['out_filename'] is not None:\n params['out_file'] = open(params['out_filename'], 'w')\n params['stop_no_snow'] = options['c']\n params['temps_in_C'] = options['K']\n params['relative_heights'] = options['relative_heights']\n\n return params, tstep_info\n\n\ndef get_args(myawsm):\n \"\"\"\n Parse the configuration file and returns a dictionary called options.\n Options contains the following keys:\n\n * z - site elevation (m)\n * t - time steps: data [normal, [,medium [,small]]] (minutes)\n * m - snowcover's maximum h2o content as volume ratio,\n * d - maximum depth for active layer (m),\n * s - snow properties input data file,\n * h - measurement heights input data file,\n * p - precipitation input data file,\n * i - input data file,\n * I - initial conditions\n * o - optional output data file,\n * O - how often output records written (data, normal, all),\n * c - continue run even when no snowcover,\n * K - accept temperatures in degrees K,\n * T - run timesteps' thresholds for a layer's mass (kg/m^2)\n\n To-do: take all the rest of the defualt and check ranges for the\n input arguements, i.e. rewrite the rest of getargs.c\n\n Args:\n myawsm: AWSM instance\n\n Returns:\n dict: dictionary of options structure with defaults if not set\n\n \"\"\"\n # -------------------------------------------------------------------------\n # these are the default options\n options = {\n 'time_step': 60,\n 'max-h2o': 0.01,\n # 'max_z0': DEFAULT_MAX_Z_S_0,\n 'c': True,\n 'K': True,\n 'mass_threshold': myawsm.mass_thresh[0],\n 'time_z': 0,\n 'max_z_s_0': myawsm.active_layer,\n 'z_u': 5.0,\n 'z_t': 5.0,\n 'z_g': 0.5,\n 'relative_heights': True,\n }\n\n # make blank config and fill with corresponding sections\n config = {}\n config['time'] = {}\n config['output'] = {}\n config['time']['time_step'] = myawsm.time_step\n if myawsm.restart_run:\n config['time']['start_date'] = myawsm.restart_date\n else:\n config['time']['start_date'] = myawsm.start_date\n\n config['time']['end_date'] = myawsm.end_date\n config['output']['frequency'] = myawsm.output_freq\n # config['output'] = myawsm.config['ipysnobal output']\n config['output']['location'] = myawsm.pathrr\n config['output']['nthreads'] = int(myawsm.ipy_threads)\n config['constants'] = myawsm.config['ipysnobal constants']\n # read in the constants\n c = {}\n for v in myawsm.config['ipysnobal constants']:\n c[v] = float(myawsm.config['ipysnobal constants'][v])\n options.update(c) # update the defult with any user values\n\n config['constants'] = options\n\n # ------------------------------------------------------------------------\n # read in the time and ensure a few things\n # nsteps will only be used if end_date is not specified\n data_tstep_min = int(config['time']['time_step'])\n check_range(data_tstep_min, 1.0, hrs2min(60), \"input data's timestep\")\n if ((data_tstep_min > 60) and (data_tstep_min % 60 != 0)):\n raise ValueError(\"Data timestep > 60 min must be multiple of 60 min (whole hrs)\")\n config['time']['time_step'] = data_tstep_min\n\n # add to constant sections for tstep_info calculation\n config['constants']['time_step'] = config['time']['time_step']\n\n # read in the start date and end date\n start_date = config['time']['start_date']\n\n end_date = config['time']['end_date']\n if end_date < start_date:\n raise ValueError('end_date is before start_date')\n nsteps = (end_date-start_date).total_seconds()/60 # elapsed time in minutes\n nsteps = int(nsteps / config['time']['time_step'])\n\n # create a date time vector\n tmp_dv = date_range(start_date, end_date,\n timedelta(minutes=config['constants']['time_step']))\n dv = [di.replace(tzinfo=myawsm.tzinfo) for di in tmp_dv]\n\n if len(dv) != nsteps + 1:\n raise Exception('nsteps does not work with selected start and end dates')\n\n config['time']['start_date'] = start_date\n config['time']['end_date'] = end_date\n config['time']['nsteps'] = nsteps\n config['time']['date_time'] = dv\n\n # check the output section\n config['output']['frequency'] = int(config['output']['frequency'])\n\n # user has requested a point run from spatial data\n point_run = False\n\n config['output']['output_mode'] = 'data'\n config['output']['out_filename'] = None\n config['inputs'] = {}\n config['inputs']['point'] = None\n config['inputs']['input_type'] = myawsm.ipy_init_type\n config['inputs']['soil_temp'] = myawsm.soil_temp\n\n # config['initial_conditions'] = {}\n # if myawsm.config['ipysnobal initial conditions']['init_file'] is not None:\n # config['initial_conditions']['file'] = os.path.abspath(myawsm.config['ipysnobal initial conditions']['init_file'])\n # else:\n # config['initial_conditions']['file'] = None\n #\n # config['initial_conditions']['input_type'] = myawsm.ipy_init_type.lower()\n # if 'restart' in myawsm.config['ipysnobal initial conditions']:\n # config['initial_conditions']['restart'] = myawsm.config['ipysnobal initial conditions']['restart']\n # else:\n # config['initial_conditions']['restart'] = False\n #\n # if myawsm.mask_isnobal:\n # config['initial_conditions']['mask'] = myawsm.topo.mask\n\n return config, point_run\n\n\ndef initialize(params, tstep_info, init):\n \"\"\"\n Create the OUTPUT_REC with additional fields and fill\n There are a lot of additional terms that the original output_rec does not\n have due to the output function being outside the C code which doesn't\n have access to those variables.\n\n Args:\n params: Snobal parameters\n tstep_info: setting for Snobal timesteps\n init: initialization dictionary\n\n Returns:\n s: OUTPUT_REC dictionary\n\n \"\"\"\n\n sz = init['elevation'].shape\n flds = ['mask', 'elevation', 'z_0', 'rho', 'T_s_0', 'T_s_l', 'T_s',\n 'cc_s_0', 'cc_s_l', 'cc_s', 'm_s', 'm_s_0', 'm_s_l', 'z_s', 'z_s_0', 'z_s_l',\n 'h2o_sat', 'layer_count', 'h2o', 'h2o_max', 'h2o_vol', 'h2o_total',\n 'R_n_bar', 'H_bar', 'L_v_E_bar', 'G_bar', 'G_0_bar',\n 'M_bar', 'delta_Q_bar', 'delta_Q_0_bar', 'E_s_sum', 'melt_sum', 'ro_pred_sum',\n 'current_time', 'time_since_out']\n s = {key: np.zeros(sz) for key in flds} # the structure fields\n\n # go through each sn value and fill\n for key, val in init.items():\n if key in flds:\n s[key] = val\n\n return s\n","sub_path":"awsm/interface/initialize_model.py","file_name":"initialize_model.py","file_ext":"py","file_size_in_byte":15486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"272111042","text":"import scrapy\nfrom scrapy.spiders import CrawlSpider, Rule\nfrom scrapy.linkextractors import LinkExtractor\nfrom scrapy.utils.url import parse_url, canonicalize_url\nfrom foodpro_scraper.items import FoodItem, MenuItem\nfrom urllib.parse import urljoin\nimport re\n\nclass FoodproSpider(CrawlSpider):\n\tname = 'foodpro'\n\tallowed_domains = ['hf-food.austin.utexas.edu']\n\tstart_urls = ['http://hf-food.austin.utexas.edu/foodpro/']\n\n\trules = [Rule(LinkExtractor(allow=['nutframe2?.asp*']), callback='parse_out_frame'), Rule(LinkExtractor(allow=['pickMenu2?.asp*']), callback='parse_menu')]\n\n\tdef parse_out_frame(self, res):\n\t\t\"\"\" The foodpro website is separated into several different iframes, this simply gets the iframe with the data we actually want and puts it on the request queue for convenience sake\n\n\t\t@url http://hf-food.austin.utexas.edu/foodpro/nutframe.asp?sName=University+of+Texas+-+Division+of+Housing+%26+Food+Service&locationNum=01&locationName=Jester+City+Limits&naFlag=1\n\t\t@returns items 0 0\n\t\t@returns requests 1 1\n\t\t\"\"\"\n\t\tframe_src = res.xpath(\"//frameset/frame[2]/@src\")\n\t\tframe_url = urljoin(res.url, frame_src.extract()[0])\n\t\treturn scrapy.Request(frame_url)\n\n\tdef parse_menu(self, res):\n\t\t\"\"\" Parses out the menu items, and the food urls from a full menu.\n\n\t\t@url http://hf-food.austin.utexas.edu/foodpro/pickMenu.asp?locationNum=14&locationName=Kin%27s+Market&dtdate=03%2F29%2F2016&mealName=Breakfast&sName=University+of+Texas+-+Division+of+Housing+%26+Food+Service\n\t\t@returns items 26 26\n\t\t@returns requests 26 26\n\t\t@scrapes name recnum date meal location station\n\t\t\"\"\"\n\t\tmenu = MenuItem()\n\t\tdate = re.search('dtdate=(.*?)(&|$)', res.url)\n\t\tmenu['date'] = date.group(1).replace('%2F', '-')\n\t\tmeal = re.search('mealName=(.*?)(&|$)', res.url)\n\t\tmeals = meal.group(1).split('%2F')\n\t\tlocation = re.search('locationName=(.*?)(&|$)', res.url)\n\t\tmenu['location'] = location.group(1).replace('+', ' ')\n\n\t\tmenu['station'] = ''\n\n\t\tdef make_singleton(arr, row, url, descriptor):\n\t\t\tif len(arr) is 1:\n\t\t\t\treturn arr[0]\n\t\t\telse:\n\t\t\t\tlogging.warning('row {} for response {} contains more than 1 {}'.format(row, url, descriptor))\n\t\t\t\treturn arr[0]\n\n\t\ttable_rows = res.xpath('//tr//table//div')\n\t\tlink_extractor = LinkExtractor(allow=['label.asp*'])\n\t\tfor row in table_rows:\n\t\t\tif len(row.css('.pickmenucoldispname')) is 0: # a station row\n\t\t\t\tstations = row.xpath('text()').extract()\n\t\t\t\tstation = make_singleton(stations, row, res.url, 'station text')\n\t\t\t\tstation = re.search('-- (.*?) --', station).group(1)\n\t\t\t\tmenu['station'] = station\n\t\t\telse: # a food row\n\t\t\t\tfood_name = row.xpath('./a/text()').extract()[0]\n\t\t\t\tmenu['name'] = food_name\n\t\t\t\tfood_links = row.xpath('./a/@href').extract()\n\t\t\t\tfood_link = make_singleton(food_links, row, res.url, 'food link')\n\t\t\t\trecnum = re.search('RecNumAndPort=(.*?)%2A', food_link)\n\t\t\t\trecnum = recnum.group(1)\n\t\t\t\tmenu['recnum'] = recnum\n\t\t\t\tfor meal in meals:\n\t\t\t\t\tmenu['meal'] = meal\n\t\t\t\t\tyield menu\n\t\t\t\tfood_link = urljoin(res.url, food_link) # make it absolute\n\t\t\t\tyield scrapy.Request(food_link, callback=self.parse_food)\n\n\tdef parse_food(self, res):\n\t\t\"\"\" Takes a specific food and creates the appropriate FoodItem\n\n\t\t@url http://hf-food.austin.utexas.edu/foodpro/label.asp?RecNumAndPort=310351%2A1\n\t\t@returns items 1 1\n\t\t@returns requests 0 0\n\t\t@scrapes name recnum unit serving_size calories calories_from_fat total_fat trans_fat saturated_fat cholesterol total_carbohydrate dietary_fiber sugars protein sodium\n\t\t\"\"\"\n\t\tfood = FoodItem()\n\t\tif len(res.css('.labelnotavailable')):\n\t\t\treturn food\n\n\t\tfoodtb = res.xpath('/html/body/table[1]/tr/td/table')\n\n\t\tfood['name'] = res.css(\".labelrecipe::text\").extract()\n\n\t\tfood['unit'] = foodtb.xpath('./tr[1]/td[1]/font[3]/text()').re('[^\\s\\d/]*')\n\t\tfood['serving_size'] = foodtb.xpath('./tr[1]/td[1]/font[3]/text()').re('[\\d/]*')\n\t\tfood['calories'] = foodtb.xpath('./tr[1]/td[1]/font[4]/b/text()').re('\\d*')\n\t\tfood['calories_from_fat'] = foodtb.xpath('./tr[1]/td[1]/font[5]/text()').re('\\d*')\n\t\tfood['total_fat'] = foodtb.xpath('./tr[2]/td[1]/font/text()').re('\\d*')\n\t\tfood['trans_fat'] = foodtb.xpath('./tr[4]/td[1]/font/text()').re('\\d*')\n\t\tfood['saturated_fat'] = foodtb.xpath('./tr[3]/td[1]/font/text()').re('\\d*')\n\t\tfood['cholesterol'] = foodtb.xpath('./tr[5]/td[1]/font/text()').re('\\d*')\n\t\tfood['total_carbohydrate'] = foodtb.xpath('./tr[2]/td[3]/font/text()').re('\\d*')\n\t\tfood['dietary_fiber'] = foodtb.xpath('./tr[3]/td[3]/font/text()').re('\\d*')\n\t\tfood['sugars'] = foodtb.xpath('./tr[4]/td[3]/font/text()').re('\\d*')\n\t\tfood['protein'] = foodtb.xpath('./tr[5]/td[3]/font/text()').re('\\d*')\n\t\tfood['sodium'] = foodtb.xpath('./tr[6]/td[1]/font/text()').re('\\d*')\n\n\t\t#food['calcium'] =\n\t\t#food['vitamin_c'] =\n\t\t#food['iron'] =\n\n\t\tfor key, val in food.items():\n\t\t\tval = list(filter(None, val))\n\t\t\tif len(val) is 0:\n\t\t\t\tfood[key] = '0'\n\t\t\telse:\n\t\t\t\tfood[key] = val[0]\n\n\t\trecnum = re.search('RecNumAndPort=(.*?)%2A', res.url)\n\t\tfood['recnum'] = recnum.group(1)\n\n\t\treturn food\n","sub_path":"foodpro_scraper/foodpro_scraper/spiders/foodpro_spider.py","file_name":"foodpro_spider.py","file_ext":"py","file_size_in_byte":4973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"341175336","text":"# -*- coding: utf-8 -*-\n\"\"\"\nProblem 4 Homework 11 ME6441\nExercize 7.36 in Ginsberg's Advanced Dynamics\n\"\"\"\n\nimport sympy\nimport sympy.physics.mechanics as mech\n\n# Set dynamics variables\nphi = mech.dynamicsymbols('theta'); # Blade angle\nphiDot = mech.dynamicsymbols('thetaDot'); # Blade angluar velocity\n\n# Create symboloic variables we'll need\nm, L, kT, Omega, epsilon, Izz = sympy.symbols('m L kT Omega epsilon Izz');\n\n# Create inertial frame\ninertialFrame = mech.ReferenceFrame('inertialFrame');\n\n# Create reference frame at A that rotates with Omega\nN = mech.ReferenceFrame( 'N' );\nN.set_ang_vel(inertialFrame, -Omega*inertialFrame.z);\n\n# Set point A\nA = mech.Point('A');\nA.set_vel(N, 0);\n\n# Set point B\nB = mech.Point( 'B' );\nB.set_vel( N, 0 ); # B has no velocity in the rotating frame N\nB.set_pos( A, epsilon*N.x ); # phi measured from x-axis along AB\n\n# Create frame for rod BC \nrodBCFrame = mech.ReferenceFrame('rodBCFrame');\nrodBCFrame.set_ang_vel( N, -phiDot*N.z );\n\n# Create point for the center of mass of BC\ncomBC_x = (L/2)*sympy.cos(phi);\ncomBC_y = (L/2)*sympy.sin(phi);\ncomBC_vx = phiDot*comBC_x;\ncomBC_vy = phiDot*comBC_y;\n\ncomBC = mech.Point('comBC');\ncomBC.set_vel( N, comBC_vx*N.x + comBC_vy*N.y ); \ncomBC.set_pos( B, comBC_x*N.x + comBC_y*N.y );\n\n# Create rigid body for blade BC\nI_BC = mech.inertia(N, 0, 0, Izz);\nrodBC = mech.RigidBody( 'rodBC', comBC, rodBCFrame, m, (I_BC, comBC) );\n\n# Get the kinetic energy of each component to check results\nmech.kinetic_energy(N, rodBC)\n\n# Set potential energies\nrodBC.potential_energy = (1/2)*kT*phi**2;\n\n# Get (unforced) Lagrangian of the system\nL = mech.Lagrangian( N, rodBC );\n\n# Get equation of motion\nLM = mech.LagrangesMethod(L, [rodBC], frame=N);\nLM.form_lagranges_equations()","sub_path":"me6441/hw11/problem4.py","file_name":"problem4.py","file_ext":"py","file_size_in_byte":1745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"206141941","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nINTELLIGENT ROBOTICS - PROJECT 2021\r\n\r\nPART 1 - Navigation\r\n\r\nAntoine DEBOR & Pierre NAVEZ\r\n\r\n\"\"\"\r\n\r\n# VREP\r\nimport sim as vrep\r\n\r\n# Useful import\r\nimport time\r\nimport numpy as np\r\nimport sys\r\nimport random\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.path import Path\r\nimport argparse\r\nfrom robopy import SE2\r\nfrom math import ceil, atan2, fmod, sqrt\r\nfrom roboticstoolbox import DXform\r\nfrom skimage import measure\r\nimport cv2\r\nfrom scipy import ndimage\r\nimport scipy.interpolate as inter\r\nfrom target_seeker import target_seeker\r\nfrom trajectory_smoother import trajectory_smoother\r\n\r\n\r\nfrom cleanup_vrep import cleanup_vrep\r\nfrom vrchk import vrchk\r\nfrom youbot_init import youbot_init\r\nfrom youbot_drive import youbot_drive\r\nfrom youbot_hokuyo_init import youbot_hokuyo_init\r\nfrom youbot_hokuyo import youbot_hokuyo\r\nfrom youbot_xyz_sensor import youbot_xyz_sensor\r\nfrom beacon import beacon_init, youbot_beacon\r\nfrom utils_sim import angdiff\r\n\r\ndef arguments_parsing():\r\n \"\"\"\r\n Argument parser function\r\n ---\r\n parameters :\r\n\r\n None\r\n ---\r\n return :\r\n\r\n - args : Keyboard passed arguments\r\n \"\"\"\r\n\r\n parser = argparse.ArgumentParser(description=\"Intelligent Robotics - Project - Arg parser\")\r\n\r\n parser.add_argument(\"--mode\", type=str, default=\"exploration mapping mode\",\r\n help=\"Action mode followed by the robot, in {exploration mapping mode}\")\r\n\r\n args = parser.parse_args()\r\n\r\n return args\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\r\n # Initiate the connection to the simulator.\r\n print('IR 2021 - Part 1 - DEBOR & NAVEZ \\nProgram started')\r\n # Use the following line if you had to recompile remoteApi\r\n # vrep = remApi('remoteApi', 'extApi.h')\r\n # vrep = remApi('remoteApi')\r\n\r\n # Close the connection in case if a residual connection exists\r\n vrep.simxFinish(-1)\r\n clientID = vrep.simxStart('127.0.0.1', 19997, True, True, 2000, 5)\r\n\r\n # The time step the simulator is using (your code should run close to it).\r\n timestep = .05\r\n\r\n # Synchronous mode\r\n returnCode = vrep.simxSynchronous(clientID, True)\r\n\r\n if clientID < 0:\r\n sys.exit('Failed connecting to remote API server. Exiting.')\r\n\r\n print('Connection ' + str(clientID) + ' to remote API server open')\r\n\r\n # This will only work in \"continuous remote API server service\".\r\n # See http://www.v-rep.eu/helpFiles/en/remoteApiServerSide.htm\r\n vrep.simxStartSimulation(clientID, vrep.simx_opmode_blocking)\r\n\r\n # Send a Trigger to the simulator: this will run a time step for the physics engine\r\n # because of the synchronous mode. Run several iterations to stabilize the simulation\r\n for i in range(int(1./timestep)):\r\n vrep.simxSynchronousTrigger(clientID)\r\n #vrep.simxGetPingTime(clientID)\r\n\r\n # Retrieve all handles, mostly the Hokuyo.\r\n h = youbot_init(vrep, clientID)\r\n h = youbot_hokuyo_init(vrep, h)\r\n beacons_handle = beacon_init(vrep, clientID)\r\n\r\n # Send a Trigger to the simulator: this will run a time step for the physics engine\r\n # because of the synchronous mode. Run several iterations to stabilize the simulation\r\n for i in range(int(1./timestep)):\r\n vrep.simxSynchronousTrigger(clientID)\r\n #vrep.simxGetPingTime(clientID)\r\n\r\n\r\n\r\n ##############################################################################\r\n # #\r\n # INITIAL CONDITIONS #\r\n # #\r\n ##############################################################################\r\n # Define all the variables which will be used through the whole simulation.\r\n # Important: Set their initial values.\r\n\r\n # Get the position of the beacons in the world coordinate frame (x, y)\r\n beacons_world_pos = np.zeros((len(beacons_handle), 3))\r\n for i, beacon in enumerate(beacons_handle):\r\n res, beacons_world_pos[i] = vrep.simxGetObjectPosition(clientID, beacon, -1,\r\n vrep.simx_opmode_buffer)\r\n\r\n # Parameters for controlling the youBot's wheels: at each iteration,\r\n # those values will be set for the wheels.\r\n # They are adapted at each iteration by the code.\r\n forwBackVel = 0 # Move straight ahead.\r\n rightVel = 0 # Go sideways.\r\n rotateRightVel = 0 # Rotate.\r\n\r\n args = arguments_parsing()\r\n\r\n # First state of state machine\r\n if(args.mode == \"exploration mapping mode\"):\r\n ### Initialization of the map\r\n # 3 states :\r\n # 0 : explored and free, 1 : unexplored, 2 : explored and not free (obstacle)\r\n resol = 0.1\r\n sceneSize = 15\r\n nbPoints = int(sceneSize / resol)\r\n\r\n map = np.ones((nbPoints, nbPoints), dtype=int)\r\n\r\n ### Initialization of the map plot\r\n X = np.arange(-sceneSize/2, sceneSize/2, resol)\r\n Y = np.arange(-sceneSize/2, sceneSize/2, resol)\r\n\r\n ### Vector of coordinates to test for free space\r\n xx, yy = np.meshgrid(X, Y)\r\n xx, yy = xx.flatten(), yy.flatten()\r\n points = np.vstack((xx,yy)).T\r\n\r\n ### Initialization of the known-world boundary vector\r\n boundary = []\r\n\r\n ### Relevant flags\r\n firstTargetFlag = True\r\n newTrajFlag = True\r\n trajFlag = False # True if trajectory exists\r\n smoothtrajFlag = False # True if smooth trajectory exists\r\n goingBackFlag = False # True if the robot has to go back to its initial position\r\n\r\n elapsed_plot = []\r\n\r\n fsm = 'rotate_at_start'\r\n\r\n print('Switching to state: ', fsm)\r\n\r\n # Get the initial position of the robot\r\n res, youbotPosInit = vrep.simxGetObjectPosition(clientID, h['ref'], -1, vrep.simx_opmode_buffer)\r\n # Get the initial discrete position of the robot\r\n x_init, y_init = int(( youbotPosInit[0] + (sceneSize / 2) ) / resol), int(( youbotPosInit[1] + (sceneSize / 2) ) / resol)\r\n\r\n # Set the speed of the wheels to 0.\r\n h = youbot_drive(vrep, h, forwBackVel, rightVel, rotateRightVel)\r\n\r\n # Send a Trigger to the simulator: this will run a time step for the physic engine\r\n # because of the synchronous mode. Run several iterations to stabilize the simulation\r\n for i in range(int(1./timestep)):\r\n vrep.simxSynchronousTrigger(clientID)\r\n #vrep.simxGetPingTime(clientID)\r\n\r\n # Start the robot.\r\n while True:\r\n try:\r\n t_first = time.time()\r\n # Check the connection with the simulator\r\n if vrep.simxGetConnectionId(clientID) == -1:\r\n sys.exit('Lost connection to remote API.')\r\n\r\n # Get the current position and orientation of the robot\r\n res, youbotPos = vrep.simxGetObjectPosition(clientID, h['ref'], -1, vrep.simx_opmode_buffer)\r\n vrchk(vrep, res, True) # Check the return value from the previous V-REP call (res) and exit in case of error\r\n res, youbotEuler = vrep.simxGetObjectOrientation(clientID, h['ref'], -1, vrep.simx_opmode_buffer)\r\n vrchk(vrep, res, True)\r\n\r\n # Get data from the hokuyo - return empty if data is not captured\r\n full_scanned_points, full_contacts = youbot_hokuyo(vrep, h, vrep.simx_opmode_buffer)\r\n vrchk(vrep, res)\r\n\r\n # Downsampling of the data to speed up the computations\r\n sampling_factor = 10\r\n\r\n scanned_points = full_scanned_points[0:6, ::sampling_factor] # slicing\r\n contacts = full_contacts[0:2, ::sampling_factor] # slicing\r\n\r\n # -- Transform data from sensor coordinate to absolute coordinate --\r\n T = np.reshape(SE2(youbotEuler[2], 'rad', youbotPos[0], youbotPos[1]).data, (3,3))\r\n\r\n Tpoints_1 = np.ones((int(scanned_points.shape[0] / 2), scanned_points.shape[1]))\r\n Tpoints_2 = np.ones((int(scanned_points.shape[0] / 2), scanned_points.shape[1]))\r\n\r\n Tpoints_1[0:2, :] = scanned_points[0:2, :]\r\n Tpoints_1 = np.round(np.dot(T, Tpoints_1)[:2, :], 2) # scanned data from one side, rounded to fixed resolution\r\n\r\n Tpoints_2[0:2, :] = scanned_points[3:5, :]\r\n Tpoints_2 = np.round(np.dot(T, Tpoints_2)[:2, :], 2) # scanned data from the other side, rounded to fixed resolution\r\n\r\n tmp = list(zip(Tpoints_1[0], Tpoints_1[1], contacts[0]))\r\n Tpoints_1 = sorted(set(tmp), key=tmp.index) # unique coordinates extracted from scanned data from one side, zipped as coordinates tuples\r\n\r\n tmp = list(zip(Tpoints_2[0], Tpoints_2[1], contacts[1]))\r\n Tpoints_2 = sorted(set(tmp), key=tmp.index) # unique coordinates from scanned data from the other side, zipped as coordinates tuples\r\n\r\n Tpoints_1.extend(Tpoints_2)\r\n Tpoints = Tpoints_1 # list of all scanned points, in abdsolute coordinate\r\n\r\n # -- Update the map --\r\n # Free space\r\n Tpoints.append((youbotPos[0], youbotPos[1], False)) # add position of the robot\r\n\r\n vert = np.empty((len(Tpoints), 2))\r\n vert = [[point[0], point[1]] for point in Tpoints]\r\n p = Path(vert) # construct boundary of newly observed space\r\n\r\n grid = p.contains_points(points)\r\n mask = (grid.reshape(np.shape(map)))\r\n mask = np.asarray(mask) # construct mask of free space\r\n\r\n obstacle_mask = (map == 2)\r\n cmp_mask = np.logical_and(mask, obstacle_mask) # construct mask of overlapping between free space and already obstacle space\r\n\r\n mask = ~ mask\r\n map = map * mask\r\n map = map + 2 * cmp_mask # update map with new free space while coping with already obstacle space\r\n\r\n # Obstacles\r\n for x, y, contact in Tpoints:\r\n if contact == True:\r\n y = int(( y + (sceneSize / 2) ) / resol)\r\n if abs(y) == sceneSize/resol:\r\n y = np.sign(y) * (sceneSize/resol - 1)\r\n x = int(( x + (sceneSize / 2) ) / resol)\r\n if abs(x) == sceneSize/resol:\r\n x = np.sign(x) * (sceneSize/resol - 1)\r\n map[y, x] = 2\r\n\r\n\r\n # Apply the state machine.\r\n if fsm == 'rotate_at_start':\r\n # Rotate until the robot has an angle of -pi/2 (measured with respect to the world's reference frame).\r\n # Again, use a proportional controller. In case of overshoot, the angle difference will change sign,\r\n # and the robot will correctly find its way back (e.g.: the angular speed is positive, the robot overshoots,\r\n # the anguler speed becomes negative).\r\n # youbotEuler(3) is the rotation around the vertical axis.\r\n rotateRightVel = angdiff(youbotEuler[2], (-np.pi))\r\n\r\n # Switch to the computation of the target\r\n # when the robot is at an angle close to -pi.\r\n if abs(angdiff(youbotEuler[2], (-np.pi))) < .002:\r\n rotateRightVel = 0\r\n fsm = 'compute_new_target'\r\n print('Switching to state: ', fsm)\r\n\r\n\r\n\r\n\r\n elif fsm == 'compute_new_target':\r\n # Compute a new target for the robot to reach, considering the\r\n # known-world boundary on a binarized inflated map\r\n\r\n print(\"\\nSeeking for new target...\")\r\n # -- Update the boundary --\r\n BW_map = (map == 1).astype(np.uint8) # Binarized map s.t. 0 = explored, 1 = unexplored\r\n boundary = measure.find_contours(BW_map, 0)\r\n boundary = np.vstack(boundary)\r\n\r\n # -- Inflate the map --\r\n BW_map = (map >= 2).astype(int) # Binarized map s.t. 0 = free or unexplored, 1 = obstacle\r\n BW_map = ndimage.binary_dilation(BW_map, iterations=5).astype(BW_map.dtype)\r\n\r\n robot_inflate_map = (map < 0) # 0 everywhere\r\n x_start, y_start = int(( youbotPos[0] + (sceneSize / 2) ) / resol), int(( youbotPos[1] + (sceneSize / 2) ) / resol)\r\n robot_inflate_map[y_start, x_start] = 1\r\n robot_inflate_map = ndimage.binary_dilation(robot_inflate_map, iterations=2).astype(robot_inflate_map.dtype)\r\n robot_inflate_map = ~ robot_inflate_map\r\n\r\n BW_map = np.logical_and(BW_map, robot_inflate_map).astype(int)\r\n\r\n if(newTrajFlag == True):\r\n # -- Seek for new target --\r\n not_found = True\r\n while not_found == True:\r\n if firstTargetFlag == True:\r\n target = random.choice(boundary) # random choice along the known-world boundary\r\n else:\r\n # After first target, seek for the nearest\r\n target = target_seeker(boundary, (x_start, y_start), \"combined\", BW_map, (x_init, y_init))\r\n\r\n print(\"new target candidate : {}\".format(target))\r\n if BW_map[int(target[0]), int(target[1])] == 1:\r\n print(\"candidate rejected : obstacle\")\r\n else:\r\n not_found = False\r\n if firstTargetFlag == True:\r\n firstTargetFlag = False\r\n print(\"new target locked : {}\".format(target))\r\n\r\n if (target == np.asarray([y_init, x_init]).astype(int)).all() :\r\n # Exploration phase finished, robot has to go back to its initial position\r\n print(\"Going back to initial position\")\r\n goingBackFlag = True\r\n\r\n fsm = 'compute_new_traj'\r\n print('Switching to state: ', fsm)\r\n\r\n elif fsm == 'compute_new_traj':\r\n # Compute a trajectory towards the previously determined target\r\n # using DXform\r\n\r\n print(\"\\nComputing new trajectory...\")\r\n\r\n # Distance transform path planing\r\n\r\n dx = DXform(BW_map)\r\n dx.plan(goal = np.flip(target.astype(int)))\r\n\r\n traj = dx.query((int(x_start), int(y_start)), animate = False)\r\n\r\n print(\"\\nTrajectory found !\")\r\n trajFlag = True\r\n\r\n for i, element in enumerate(traj):\r\n traj[i] = np.flip(element)\r\n\r\n fsm = 'traj_smoothing'\r\n print('Switching to state: ', fsm)\r\n\r\n elif fsm == 'traj_smoothing':\r\n # Smoothing and downsampling of the previously computed trajectory\r\n\r\n print(\"\\nSmoothing the trajectory...\")\r\n # Regular downsampling by a factor 2\r\n traj_pro = traj[::2]\r\n # Trajectory-specific downsampling\r\n traj_pro = trajectory_smoother(traj_pro)\r\n\r\n # Find the B-spline representation of the trajectory.\r\n\r\n tmp1 = np.zeros(len(traj_pro))\r\n tmp2 = np.zeros(len(traj_pro))\r\n for i, element in enumerate(traj_pro):\r\n tmp1[i] = element[0]\r\n tmp2[i] = element[1]\r\n smooth_traj = [tmp1, tmp2]\r\n\r\n smoothtrajFlag = True\r\n\r\n fsm = 'reorientation'\r\n\r\n print('Switching to state: ', fsm)\r\n\r\n elif fsm == 'reorientation':\r\n # Reorientating the robot before following the computed trajectory\r\n\r\n x_d, y_d = int(( youbotPos[0] + (sceneSize / 2) ) / resol), int(( youbotPos[1] + (sceneSize / 2) ) / resol)\r\n\r\n target_angle = atan2(smooth_traj[0][1] - y_d, smooth_traj[1][1] - x_d)\r\n\r\n # Change of reference\r\n if target_angle < 0 :\r\n target_angle = 2 * np.pi + target_angle\r\n\r\n head_orientation = youbotEuler[2] - np.pi / 2\r\n\r\n if head_orientation < - np.pi:\r\n head_orientation = head_orientation % np.pi\r\n\r\n if head_orientation < 0 :\r\n head_orientation = 2 * np.pi + head_orientation\r\n\r\n # Proportional controller\r\n rotateRightVel = (target_angle - head_orientation) % (2 * np.pi - 0.01)\r\n\r\n\r\n if rotateRightVel < -np.pi or rotateRightVel > np.pi:\r\n rotateRightVel = rotateRightVel-(np.sign(rotateRightVel))*2*np.pi\r\n\r\n # Switch to the trajectory following task when the robot is\r\n # well oriented\r\n if abs(rotateRightVel) < .002:\r\n rotateRightVel = 0\r\n\r\n traj_idx = 1 # First trajectory step not considered\r\n new_state = True\r\n next_state_y = smooth_traj[0][traj_idx]\r\n next_state_x = smooth_traj[1][traj_idx]\r\n\r\n fsm = 'traj_follow'\r\n print('Switching to state: ', fsm)\r\n\r\n elif fsm == 'traj_follow':\r\n # Trajectory following procedure\r\n\r\n # Check if the current step has not been identified as an obstacle\r\n BW_map = (map >= 2).astype(int) # Binarized map s.t. 0 = free or unexplored, 1 = obstacle\r\n BW_map = ndimage.binary_dilation(BW_map, iterations=4).astype(BW_map.dtype) # iterations reduced to 4 to avoid the robot to be stuck\r\n\r\n robot_inflate_map = (map < 0) # 0 everywhere\r\n y_start, x_start = int(( youbotPos[1] + (sceneSize / 2) ) / resol), int(( youbotPos[0] + (sceneSize / 2) ) / resol)\r\n robot_inflate_map[y_start, x_start] = 1\r\n robot_inflate_map = ndimage.binary_dilation(robot_inflate_map, iterations=1).astype(robot_inflate_map.dtype)\r\n robot_inflate_map = ~ robot_inflate_map\r\n\r\n BW_map = np.logical_and(BW_map, robot_inflate_map).astype(int)\r\n\r\n if BW_map[int(next_state_y), int(next_state_x)] == 1:\r\n print(\"Trajectory step turns out to be an obstacle !\")\r\n forwBackVel = 0.0\r\n rotateRightVel = 0.0\r\n\r\n if traj_idx == len(smooth_traj[0]): # If final target can not be reached, then change target\r\n newTrajFlag = True\r\n fsm = 'compute_new_target'\r\n\r\n else:\r\n if new_state == True:\r\n next_state_y = smooth_traj[0][traj_idx]\r\n next_state_x = smooth_traj[1][traj_idx]\r\n traj_idx += 1\r\n\r\n x_state, y_state = next_state_x * resol - (sceneSize / 2), next_state_y * resol - (sceneSize / 2)\r\n\r\n new_state = False\r\n rotation_allowed = True\r\n rotating = False\r\n\r\n\r\n # -- Forward speed control --\r\n x_curr = youbotPos[0]\r\n y_curr = youbotPos[1]\r\n\r\n dist = sqrt((x_curr-x_state)**2 + (y_curr-y_state)**2)\r\n forwBackVel = - 3 * dist\r\n\r\n if traj_idx == len(smooth_traj[0]) - 1: # avoid crashes\r\n forwBackVel = - dist\r\n\r\n\r\n # -- Orientation control --\r\n y_d, x_d = int(( youbotPos[1] + (sceneSize / 2) ) / resol), int(( youbotPos[0] + (sceneSize / 2) ) / resol)\r\n\r\n target_angle = atan2(next_state_y - y_d, next_state_x - x_d)\r\n\r\n if target_angle < 0 :\r\n target_angle = 2 * np.pi + target_angle\r\n\r\n head_orientation = youbotEuler[2] - np.pi / 2\r\n\r\n if head_orientation < - np.pi:\r\n head_orientation = head_orientation % np.pi\r\n\r\n if head_orientation < 0 :\r\n head_orientation = 2 * np.pi + head_orientation\r\n\r\n # Proportional controller\r\n rotateRightVel = (target_angle - head_orientation) % (2 * np.pi - 0.01)\r\n\r\n if rotateRightVel < - np.pi or rotateRightVel > np.pi:\r\n rotateRightVel = rotateRightVel-(np.sign(rotateRightVel))*2*np.pi\r\n\r\n if rotating == True :\r\n rotateRightVel = rotateRightVel\r\n forwBackVel = 0.0\r\n if abs(rotateRightVel) < 0.01:\r\n rotating = False\r\n rotateRightVel = 0.0\r\n\r\n if abs(rotateRightVel) > 0.1 and rotating == False:\r\n # If angle too large, stop the robot and reorienting\r\n rotateRightVel = rotateRightVel\r\n forwBackVel = 0.0\r\n rotating = True\r\n elif rotating == False:\r\n rotateRightVel = 0.0\r\n\r\n # -- Trajectory handling --\r\n if dist < .2:\r\n new_state = True\r\n if traj_idx == len(smooth_traj[0]):\r\n # Target reached\r\n print(\"\\nTarget reached !\")\r\n new_state = False\r\n forwBackVel = 0.0\r\n rotateRightVel = 0.0\r\n if goingBackFlag == True:\r\n fsm = \"exploration end procedure\"\r\n else:\r\n newTrajFlag = True # Need for a new target\r\n fsm = \"compute_new_target\"\r\n print(\"Switching to state : \", fsm)\r\n\r\n elif fsm == 'exploration end procedure':\r\n print('Exploration phase finished')\r\n X = np.arange(-sceneSize/2, sceneSize/2 + resol, resol)\r\n Y = np.arange(-sceneSize/2, sceneSize/2 + resol, resol)\r\n #fig = plt.pcolormesh(X, Y, map)\r\n #fig = plt.savefig('explored_map')\r\n time.sleep(3)\r\n break\r\n\r\n else:\r\n sys.exit('Unknown state ' + fsm)\r\n\r\n # -- Update wheel velocities --\r\n h = youbot_drive(vrep, h, forwBackVel, rightVel, rotateRightVel)\r\n\r\n # -- Update the plot --\r\n\r\n y_d, x_d = int(( youbotPos[1] + (sceneSize / 2) ) / resol), int(( youbotPos[0] + (sceneSize / 2) ) / resol)\r\n\r\n\r\n img = np.zeros((map.shape[0], map.shape[1], 3), dtype = np.uint8)\r\n\r\n DISCOVERED_CLR = (0, 100, 100)\r\n WALL_CLR = (0, 255, 255)\r\n ROBOT_CLR = (0, 150, 255)\r\n TRAJ_CLR = (100, 0, 150)\r\n SMOOTH_TRAJ_CLR = (100, 0, 255)\r\n\r\n discovered_idx = np.where(map == 0)\r\n not_discovered_idx = np.where(map == 1)\r\n walls_idx = np.where(map == 2)\r\n robot_idx = (np.array([y_d-1, y_d-1, y_d-1, y_d, y_d, y_d, y_d+1, y_d+1, y_d+1]), np.array([x_d-1, x_d, x_d+1, x_d-1, x_d, x_d+1, x_d-1, x_d, x_d+1]))\r\n\r\n img[discovered_idx] = DISCOVERED_CLR\r\n img[walls_idx] = WALL_CLR\r\n\r\n if trajFlag == True:\r\n trajectory_idx = (traj[:, 0], traj[:, 1])\r\n img[trajectory_idx] = TRAJ_CLR\r\n\r\n if smoothtrajFlag == True:\r\n smooth_trajectory_idx = (smooth_traj[0].astype(int), smooth_traj[1].astype(int))\r\n img[smooth_trajectory_idx] = SMOOTH_TRAJ_CLR\r\n\r\n img[robot_idx] = ROBOT_CLR\r\n\r\n img = cv2.resize(img, (500, 500))\r\n cv2.imshow(\"House map\", img)\r\n cv2.waitKey(1)\r\n\r\n \"\"\"elapsed = time.time() - t_first\r\n elapsed_plot.append(elapsed)\"\"\"\r\n\r\n # Send a Trigger to the simulator: this will run a time step for the physic engine\r\n # because of the synchronous mode.\r\n vrep.simxSynchronousTrigger(clientID)\r\n #vrep.simxGetPingTime(clientID)\r\n\r\n except KeyboardInterrupt:\r\n cleanup_vrep(vrep, clientID)\r\n sys.exit('Stop simulation')\r\n\r\n cleanup_vrep(vrep, clientID)\r\n \"\"\"fig2 = plt.plot()\r\n bins = np.arange(0, 0.1, 0.001) # fixed bin size\r\n plt.xlim([min(elapsed_plot)-0.005, max(elapsed_plot)+0.005])\r\n plt.hist(elapsed_plot, bins=bins, alpha=0.5)\r\n plt.axvline(min(elapsed_plot), color='g', linestyle='dashed', linewidth=1)\r\n plt.axvline(max(elapsed_plot), color='b', linestyle='dashed', linewidth=1)\r\n plt.xlabel(\"Time [s]\")\r\n plt.ylabel(\"Number of loops [-]\")\r\n plt.savefig(\"elapsed.pdf\")\"\"\"\r\n\r\n print('Simulation has stopped')\r\n","sub_path":"Midterm project/myYoubot.py","file_name":"myYoubot.py","file_ext":"py","file_size_in_byte":24997,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"384803548","text":"'''\nCreated on Feb 21, 2018\n\n@author: PDINDA\n'''\n\nfrom settings import base_path, remote_sync_import_path,remote_distribution_import_path,\\\n default_admin_password,default_nexus_container_name\n\n# list Permissions for Reference data (For any new permissions please add\n# in below list)\nPERMISSIONS = [\n {\"name\": \"/account/all\"},\n {\"name\": \"/account/view/~\"},\n {\"name\": \"/account/new\"},\n {\"name\": \"/account/update\"},\n {\"name\": \"/account/update/name\"},\n {\"name\": \"/account/delete\"},\n {\"name\": \"/user/new\"},\n {\"name\": \"/user/update\"},\n {\"name\": \"/user/change/password\"},\n {\"name\": \"/user/suspend\"},\n {\"name\": \"/user/activate/\"},\n {\"name\": \"/user/delete/\"},\n {\"name\": \"/user/all\"},\n {\"name\": \"/user/view/~\"},\n {\"name\": \"/user/name/~\"},\n {\"name\": \"/machine/user/\"},\n {\"name\": \"/user/auth\"},\n {\"name\": \"/user/basicauth\"},\n {\"name\": \"/user/auth/verify\"},\n {\"name\": \"/user/signup\"},\n {\"name\": \"/role/all\"},\n {\"name\": \"/role/view/~\"},\n {\"name\": \"/role/new\"},\n {\"name\": \"role/update\"},\n {\"name\": \"/role/add/permissiongroup\"},\n {\"name\": \"/role/remove/permissiongroup\"},\n {\"name\": \"/role/delete\"},\n {\"name\": \"/permissions/all\"},\n {\"name\": \"/permissions/view/~\"},\n {\"name\": \"/permissions/new\"},\n {\"name\": \"/permissions/update\"},\n {\"name\": \"/permissions/delete\"},\n {\"name\": \"/grouppermissions/all\"},\n {\"name\": \"/grouppermissions/view/~\"},\n {\"name\": \"/grouppermissions/new\"},\n {\"name\": \"/grouppermissions/update\"},\n {\"name\": \"/grouppermissions/delete/\"},\n {\"name\": \"/tool/add\"},\n {\"name\": \"/tool/view/~\"},\n {\"name\": \"/tool/view/version/~\"},\n {\"name\": \"/tool/view/version/~/prevversion/~/machine/~\"},\n {\"name\": \"/tool/all\"},\n {\"name\": \"/tool/search/name/~\"},\n {\"name\": \"/tool/search/tag/~\"},\n {\"name\": \"/tool/update\"},\n {\"name\": \"/tool/delete\"},\n {\"name\": \"/tool/upload/logo\"},\n {\"name\": \"/edit/tool/:id\"},\n {\"name\": \"/deploymentrequest/view/~\"},\n {\"name\": \"/deploymentrequest/all\"},\n {\"name\": \"/deploymentrequest/add\"},\n {\"name\": \"/deploymentrequest/run/view/~\"},\n {\"name\": \"/deploymentrequest/cancel\"},\n {\"name\": \"/deploymentrequest/retry\"},\n {\"name\": \"/deploymentrequest/group/all\"},\n {\"name\": \"/deploymentrequest/group/view/~\"},\n {\"name\": \"/deploymentrequest/group/add\"},\n {\"name\": \"/deploymentrequest/group/retry\"},\n {\"name\": \"/deploymentrequest/group/cancel/~\"},\n {\"name\": \"/deploymentrequest/group/toolset/add\"},\n {\"name\": \"/tool/versions/add\"},\n {\"name\": \"/tool/~/versions/all\"},\n {\"name\": \"/tool/versions/view/~\"},\n {\"name\": \"/versions/build/view/~\"},\n {\"name\": \"/versions/~/build/active\"},\n {\"name\": \"/versions/~/build/all\"},\n {\"name\": \"/versions/build/add\"},\n {\"name\": \"/build/add\"},\n {\"name\": \"/build/view/~\"},\n {\"name\": \"/tool/versions/uploadScreenshot\"},\n {\"name\": \"/versions/build/update\"},\n {\"name\": \"/versions/build/setactive\"},\n {\"name\": \"/versions/~/documents\"},\n {\"name\": \"/versions/documents/add\"},\n {\"name\": \"/versions/documents/update\"},\n {\"name\": \"/versions/~/deploymentfields\"},\n {\"name\": \"/versions/deploymentfields/add\"},\n {\"name\": \"/versions/deploymentfields/update\"},\n {\"name\": \"/versions/~/installation/all\"},\n {\"name\": \"/machine/~/installation/all\"},\n {\"name\": \"/installation/add\"},\n {\"name\": \"/installation/update\"},\n {\"name\": \"/installation/delete\"},\n {\"name\": \"/machine/type/view/~\"},\n {\"name\": \"/machine/type/all\"},\n {\"name\": \"/machine/type/new\"},\n {\"name\": \"/machine/type/update\"},\n {\"name\": \"/machine/type/delete\"},\n {\"name\": \"/machine/all/fav\"},\n {\"name\": \"/machine/fav/view/~\"},\n {\"name\": \"/machine/fav/machine/~\"},\n {\"name\": \"/machine/fav/user/~\"},\n {\"name\": \"/machine/fav/machine/~/user/~\"},\n {\"name\": \"/machine/fav/new\"},\n {\"name\": \"/machine/fav/update\"},\n {\"name\": \"/machine/fav/delete\"},\n {\"name\": \"/machine/view/~\"},\n {\"name\": \"/machine/alias/~\"},\n {\"name\": \"/machine/view/all\"},\n {\"name\": \"/machine/hostname/~\"},\n {\"name\": \"/machine/ip/~\"},\n {\"name\": \"/machine/user/~\"},\n {\"name\": \"/machine/new\"},\n {\"name\": \"/machine/test\"},\n {\"name\": \"/machine/import\"},\n {\"name\": \"/machine/update\"},\n {\"name\": \"/machine/refresh\"},\n {\"name\": \"/machine/update/userdetails\"},\n {\"name\": \"/machine/update/usertype\"},\n {\"name\": \"/machine/add/permission\"},\n {\"name\": \"/machine/delete/permission\"},\n {\"name\": \"/machine/remove/~\"},\n {\"name\": \"/machine/disable\"},\n {\"name\": \"/mediafiles/view/~\"},\n {\"name\": \"/versions/~/mediafiles/all\"},\n {\"name\": \"/mediafiles/add\"},\n {\"name\": \"/mediafiles/update\"},\n {\"name\": \"/mediafiles/delete\"},\n {\"name\": \"/clonerequest/all\"},\n {\"name\": \"/clonerequest/view/~\"},\n {\"name\": \"/clonerequest/add\"},\n {\"name\": \"/clonerequest/distribution/all\"},\n {\"name\": \"/clonerequest/distribution/update\"},\n {\"name\": \"/clonerequest/distribution/add\"},\n {\"name\": \"/clonerequest/run/~\"},\n {\"name\": \"/clonerequest/update\"},\n {\"name\": \"/clonerequest/cancel/~\"},\n {\"name\": \"/clonerequest/retry\"},\n {\"name\": \"/clonerequest/distribution/run/all\"},\n {\"name\": \"/clonerequest/distribution/run/~\"},\n {\"name\": \"/clonerequest/distribution/cancel/~\"},\n {\"name\": \"/clonerequest/distribution/view/all\"},\n {\"name\": \"/clonerequest/distribution/view/tool/~\"},\n {\"name\": \"/config/all\"},\n {\"name\": \"/config/view/~\"},\n {\"name\": \"/config/view/configid/~\"},\n {\"name\": \"/config/sync/all\"},\n {\"name\": \"/config/update\"},\n {\"name\": \"/config/delete\"},\n {\"name\": \"/config/distribution/schedule\"},\n {\"name\": \"/sync/import\"},\n {\"name\": \"/sync/delete/~\"}, \n {\"name\": \"/sync/savedexports\"},\n {\"name\": \"/sync/manual/clean/~\"},\n {\"name\": \"/sync/manual/export\"},\n {\"name\": \"/sync/pull/export\"},\n {\"name\": \"/syncrequest/all\"},\n {\"name\": \"/syncrequest/view/~\"},\n {\"name\": \"/syncrequest/type/~\"},\n {\"name\": \"/syncrequest/update\"},\n {\"name\": \"/syncrequest/delete/~\"},\n {\"name\": \"/syncrequest/add\"},\n {\"name\": \"/sync/push/trigger/~\"},\n {\"name\": \"/syncrequest/run/~\"},\n {\"name\": \"/clonerequest/distribution/tool\"},\n {\"name\": \"/clonerequest/distribution/tool/status/~\"},\n {\"name\": \"/clonerequest/distribute/add\"},\n {\"name\": \"/clonerequest/distribute/status/~\"},\n {\"name\": \"/systemdetails/all\"},\n {\"name\": \"/systemdetails/add\"},\n {\"name\": \"/toolset/add\"},\n {\"name\": \"/toolset/all\"},\n {\"name\": \"/toolset/view/~\"},\n {\"name\": \"/toolset/delete/~\"},\n {\"name\": \"/toolset/update\"},\n {\"name\": \"/prerequisites/add\"},\n {\"name\": \"/prerequisites/view\"},\n {\"name\": \"/prerequisites/view/~\"},\n {\"name\": \"/prerequisites/update\"},\n {\"name\": \"/prerequisites/delete/~\"},\n {\"name\": \"/machinegroups/add\"},\n {\"name\": \"/machinegroups/view\"},\n {\"name\": \"/machinegroups/view/~\"},\n {\"name\": \"/machinegroups/update\"},\n {\"name\": \"/machinegroups/delete/~\"},\n {\"name\": \"/teams/add\"},\n {\"name\": \"/teams/view\"},\n {\"name\": \"/teams/view/~\"},\n {\"name\": \"/teams/update\"},\n {\"name\": \"/teams/delete/~\"},\n {\"name\": \"/tag/all\"},\n {\"name\": \"/tag/view/~\"},\n {\"name\": \"/tag/new\"},\n {\"name\": \"/tag/update\"},\n {\"name\": \"/tag/delete/~\"},\n {\"name\": \"/currenttime\"},\n {\"name\": \"/user/generateaccesstoken\"},\n {\"name\": \"/role/list/update\"},\n {\"name\": \"/deploymentunitapprovalstatus/all\"},\n {\"name\": \"/deploymentunitapprovalstatus/view/~\"},\n {\"name\": \"/deploymentunitapprovalstatus/view/name/~\"},\n {\"name\": \"/deploymentunitapprovalstatus/add\"},\n {\"name\": \"/deploymentunitapprovalstatus/update\"},\n {\"name\": \"/deploymentunitapprovalstatus/delete/~\"},\n {\"name\": \"/deploymentunittype/all\"},\n {\"name\": \"/deploymentunittype/view/~\"},\n {\"name\": \"/deploymentunittype/new\"},\n {\"name\": \"/deploymentunittype/update\"},\n {\"name\": \"/deploymentunittype/delete/~\"},\n {\"name\": \"/deploymentunit/all\"},\n {\"name\": \"/deploymentunit/view/~\"},\n {\"name\": \"/deploymentunit/search/name/~\"},\n {\"name\": \"/deploymentunit/search/tag/~\"},\n {\"name\": \"/deploymentunit/new\"},\n {\"name\": \"/deploymentunit/update\"},\n {\"name\": \"/deploymentunit/delete/~\"},\n {\"name\": \"/deploymentunitset/all\"},\n {\"name\": \"/deploymentunitset/view/~\"},\n {\"name\": \"/deploymentunitset/new\"},\n {\"name\": \"/deploymentunitset/update\"},\n {\"name\": \"/deploymentunitset/delete/~\"},\n {\"name\": \"/deploymentunitset/view/getbuilds/~\"},\n {\"name\": \"/deploymentunitset/view/states/~\"}, \n {\"name\": \"/deploymentrequest/deploymentfield/upload\"},\n {\"name\": \"/deploymentrequest/group/machine/add\"},\n {\"name\": \"/user/deleteaccesstoken/~\"},\n {\"name\": \"/plugin/reload\"},\n {\"name\": \"/plugin/install\"},\n {\"name\": \"/plugin/all\"},\n {\"name\": \"/plugin/uninstall/~\"},\n {\"name\": \"/plugin/inactive/~\"},\n {\"name\": \"/plugin/active/~\"},\n {\"name\": \"/deploymentrequest/group/saved/all\"},\n {\"name\": \"/deploymentrequest/group/saved/add\"},\n {\"name\": \"/deploymentrequest/group/saved/update\"},\n {\"name\": \"/deploymentrequest/group/saved/delete/~\"},\n {\"name\": \"/deploymentrequest/group/saved/view/~\"},\n {\"name\": \"/plugin/view/~\"},\n {\"name\": \"/toolset/upload/logo\"},\n {\"name\": \"/deploymentunit/upload/logo\"},\n {\"name\": \"/deploymentunitset/upload/logo\"},\n {\"name\": \"/build/update\"},\n {\"name\": \"/systemdetails/logoupload\"},\n {\"name\": \"/user/import\"},\n {\"name\" : \"/state/all/\"},\n {\"name\" : \"/state/add\"},\n {\"name\" : \"/state/update\"},\n {\"name\" : \"/state/view/~\"},\n {\"name\" : \"/state/view/parent/~\"},\n {\"name\" : \"/state/view/parent/~/name/~\"},\n {\"name\" : \"/state/delete/~\"},\n {\"name\": \"/machinegroups/view/name/~\"},\n {\"name\": \"/deployed/view/all\"}, \n {\"name\": \"/deployed/view/machine_id/~/parent_entity_id/~/build_id/~\"},\n {\"name\": \"/machine/bulk/load\"},\n {\"name\": \"/machinegroups/bulk/load\"},\n {\"name\": \"/flexattributes/new\"},\n {\"name\": \"/flexattributes/update\"},\n {\"name\": \"/flexattributes/view/all\"},\n {\"name\": \"/flexattributes/view/entity/~\"},\n {\"name\": \"/flexattributes/view/~\"}, \n {\"name\": \"/plugin/file/upload\"},\n {\"name\": \"/plugin/file/list/~\"}, \n {\"name\": \"/plugin/file/remove/~\"}, \n {\"name\": \"/deploymentrequest/group/machinegroup/new\"},\n {\"name\": \"/proposed/tool/view/all\"},\n {\"name\": \"/proposed/tool/view/~\"},\n {\"name\": \"/proposed/tool/delete/~\"},\n {\"name\": \"/proposed/tool/approve\"},\n {\"name\" : \"/machine/view/deployment/history/entity/~/~\"},\n {\"name\": \"/deploymentrequest/group/add/undeploy\"},\n {\"name\": \"/deploymentrequest/group/view/revert/~\"},\n {\"name\": \"/plugin/exitpoint/new\"},\n {\"name\": \"/plugin/exitpoint/update\"},\n {\"name\": \"/plugin/file/view/~\"},\n {\"name\": \"/machine/search/tag/~\"},\n {\"name\": \"/machine/search/name/~\"},\n {\"name\": \"/auditing/view/all\"},\n {\"name\": \"/sync/view/all\"},\n {\"name\": \"/sync/view/syncid/~\"},\n {\"name\": \"/auditing/view/id/~\"},\n {\"name\": \"/sync/retry\"},\n {\"name\": \"/repository/add\"},\n {\"name\": \"/repository/update\"},\n {\"name\": \"/repository/view/all\"},\n {\"name\": \"/repository/view/name/~\"},\n {\"name\": \"/repository/view/~\"},\n {\"name\": \"/repository/delete/~\"},\n {\"name\":\"/repository/view/byparententity/~\"},\n {\"name\": \"/server/restart\"},\n {\"name\": \"/user/logout\"},\n {\"name\": \"/reports/all\"},\n {\"name\" : \"/tool/update/buildmarkup\"}\n]\n\n# Define all routes\nROUTES = [\n {\"name\": \"/clonerequests/:id\"},\n {\"name\": \"/clonerequests/steps/tools/:id\"},\n {\"name\": \"/clonerequests\"},\n {\"name\": \"/createclone\"},\n {\"name\": \"/createaccount\"},\n {\"name\": \"/adduser\"},\n {\"name\": \"/edit/user/:id\"},\n {\"name\": \"/delete/user/:id\"},\n {\"name\": \"/users\"},\n {\"name\": \"/manageroles\"},\n {\"name\": \"/editrole/:id\"},\n {\"name\": \"/addrole\"},\n {\"name\": \"/deleterole\"},\n {\"name\": \"/tool/view/:id\"},\n {\"name\": \"/tools/all\"},\n {\"name\": \"/dashboard\"},\n {\"name\": \"/tool/new\"},\n {\"name\": \"/AddNewMachine\"},\n {\"name\": \"/MachineDetails/:id\"},\n {\"name\": \"/editmachine/:id\"},\n {\"name\": \"/settings\"},\n {\"name\": \"/deploymentrequests\"},\n {\"name\": \"/deploy/tool/:id/:build_number\"},\n {\"name\": \"/viewRequestDetail/:id\"},\n {\"name\": \"/machines\"},\n {\"name\": \"/importexport\"},\n {\"name\": \"/synchronization\"},\n {\"name\": \"/edit/sync/:id\"},\n {\"name\": \"/admin\"},\n {\"name\": \"/catalog\"},\n {\"name\": \"/changepassword\"},\n {\"name\": \"/redeploy/version/:version_id/build_number/:build_number/build_id/:build_id/machine/:machine_id\"},\n {\"name\": \"/upgrade/oldversion/:old_version_id/newversion/:new_version_id/machine/:machine_id\"},\n {\"name\": \"/undeploy/version/:version_id/build_number/:build_number/build_id/:build_id/machine/:machine_id\"},\n {\"name\": \"/edit/tool/:id\"},\n {\"name\": \"/distribution\"},\n {\"name\": \"/distribution/cancel/:id\"},\n {\"name\": \"/distribution/resend/:id\"},\n {\"name\": \"/tools/new\"},\n {\"name\": \"/tools/updates\"},\n {\"name\": \"/import/tool/:id\"},\n {\"name\": \"/update/tool/:id\"},\n {\"name\": \"/tool/new/:id\"},\n {\"name\": \"/tool/updated/:id\"},\n {\"name\": \"/systemdata\"},\n {\"name\": \"/toolset/new\"},\n {\"name\": \"/toolset/all\"},\n {\"name\": \"/toolset/view/:id\"},\n {\"name\": \"/toolset/delete/:id\"},\n {\"name\": \"/edit/toolset/:id\"},\n {\"name\": \"/view/log/deploy/:id\"},\n {\"name\": \"/view/log/clone/:id\"},\n {\"name\": \"/manage/users/groups\"},\n {\"name\": \"/view/user/group/:id\"},\n {\"name\": \"/create/user/group\"},\n {\"name\": \"/edit/user/group/:id\"},\n {\"name\": \"/delete/user/group/:id\"},\n {\"name\": \"/delete/machine/group/:id\"},\n {\"name\": \"/manage/machine/groups\"},\n {\"name\": \"/view/machine/group/:id\"},\n {\"name\": \"/add/machine/group\"},\n {\"name\": \"/edit/machine/group/:id\"},\n {\"name\": \"/manage/prerequisites\"},\n {\"name\": \"/add/new/prerequisite\"},\n {\"name\": \"/edit/prerequisite/:id\"},\n {\"name\": \"/view/prerequisite/:id\"},\n {\"name\": \"/delete/prerequisite/:id\"},\n {\"name\": \"/deploy/toolset/:id\"},\n {\"name\": \"/deploy/tools/:tools\"},\n {\"name\": \"/view/reports\"},\n {\"name\": \"/deploymentunit/new\"},\n {\"name\": \"/deploymentunit/edit/:id\"},\n {\"name\": \"/dashboard/du\"},\n {\"name\": \"/deploymentunit/view/:id\"},\n {\"name\": \"/deploymentunitset/new\"},\n {\"name\": \"/deploymentunitset/view/:id\"},\n {\"name\": \"/deploymentunitset/all\"},\n {\"name\": \"/deploymentunitset/edit/:id\"},\n {\"name\": \"/manage/tags\"},\n {\"name\": \"/tag/view/:id\"},\n {\"name\": \"/tag/new\"},\n {\"name\": \"/tag/update\"},\n {\"name\": \"/tag/delete/:id\"},\n {\"name\": \"/deploy/duset/:id\"},\n {\"name\": \"/bulk/machines\"},\n {\"name\": \"/machine/remove/:id\"},\n {\"name\": \"/manage/synchronization/services\"},\n {\"name\": \"/undeploy/du/:du_id/build_number/:build_number/build_id/:build_id/machine/:machine_id\"},\n {\"name\": \"/redeploy/du/:du_id/build_number/:build_number/build_id/:build_id/machine/:machine_id\"},\n {\"name\": \"/saved/requests\"},\n {\"name\": \"/recent/requests\"},\n {\"name\": \"/plugin\"},\n {\"name\": \"/revert/du/:id\"},\n {\"name\": \"/edit/saved/deployment/tool/:request_id\"},\n {\"name\": \"/edit/saved/deployment/du/:request_id\"},\n {\"name\": \"/dashboard/deployment/:request_id\"},\n {\"name\": \"/dashboard/importupdate/:request_id\"},\n {\"name\": \"/dashboard/du/:request_id\"},\n {\"name\": \"/delete/tool/:id\"},\n {\"name\": \"/delete/deploymentunit/:id\"},\n {\"name\": \"/deploy/du/:id\"},\n {\"name\": \"/team/view/:id\"},\n {\"name\": \"/view/monitoring\"},\n {\"name\": \"/view/monitoring/runningservices\"},\n {\"name\": \"/users/import\"},\n {\"name\": \"/deploy/dus/:dus\"},\n {\"name\": \"/view/du/state\"},\n {\"name\": \"/view/duset/state\"},\n {\"name\": \"/create/du/state\"},\n {\"name\": \"/create/duset/state\"},\n {\"name\": \"/state/delete/:id\"},\n {\"name\": \"/new/flexibleattributes\"},\n {\"name\": \"/manage/flexibleattributes\"},\n {\"name\": \"/manage/plugins/deployment\"},\n {\"name\": \"/plugin/deployment/upload\"},\n {\"name\": \"/proposed/tools/all\"},\n {\"name\": \"/view/proposed/tool/:id\"},\n {\"name\": \"/deploymentrequest/view/:id\"},\n {\"name\": \"/edit/tool/proposed/:id\"},\n {\"name\": \"/approve/tool/proposed/:id\"},\n {\"name\": \"/machine/view/deployment/history/entity/:entity/:id\"},\n {\"name\": \"/deploymentrequest/group/revert/:id\"},\n {\"name\": \"/view/sync/requests\"},\n {\"name\": \"/view/sync/request/:id\"},\n {\"name\": \"/view/audits\"},\n {\"name\": \"/view/repository\"},\n {\"name\": \"/new/repository\"}\n \n\n]\n\nPERMISSION_GROUPS = [\n {\n \"groupname\": \"AccountView\",\n \"group_description\": \"AccountView\",\n \"permissions\": [\"/account/all\", \"/account/view/~\"],\n \"routes\":[]\n },\n {\n \"groupname\": \"AccountCreate\",\n \"group_description\": \"AccountCreate\",\n \"permissions\": [\"/account/new\"],\n \"routes\":[\"/createaccount\"]\n },\n {\"groupname\": \"AccountUpdate\",\n \"group_description\": \"AccountUpdate\",\n \"permissions\": [\"/account/update\", \"/account/update/name\"],\n \"routes\":[]\n },\n {\"groupname\": \"AccountDelete\",\n \"group_description\": \"AccountDelete\",\n \"permissions\": [\"/account/delete\"],\n \"routes\":[]\n },\n {\"groupname\": \"UserCreate\",\n \"group_description\": \"UserCreate\",\n \"permissions\": [\"/user/new\", \"/user/import\"],\n \"routes\":[\"/adduser\", \"/users/import\"]\n },\n {\"groupname\": \"UserUpdate\",\n \"group_description\": \"UserUpdate\",\n \"permissions\": [\"/user/update\", \"/user/suspend\", \"/user/activate/\",\n \"/user/generateaccesstoken\",\n \"/user/deleteaccesstoken/~\"],\n \"routes\":[\"/edit/user/:id\"]\n },\n {\"groupname\": \"UserDelete\",\n \"group_description\": \"UserDelete\",\n \"permissions\": [\"/user/delete/\"],\n \"routes\":[\"/delete/user/:id\"]\n },\n {\"groupname\": \"UserView\",\n \"group_description\": \"UserView\",\n \"permissions\": [\"/user/all\", \"/user/view/~\", \"/user/name/~\"],\n \"routes\":[\"/users\"]\n },\n {\"groupname\": \"RoleView\",\n \"group_description\": \"RoleView\",\n \"permissions\": [\"/role/all\", \"/role/view/~\"],\n \"routes\":[\"/manageroles\", \"/editrole/:id\"]\n },\n {\"groupname\": \"RoleCreate\",\n \"group_description\": \"RoleCreate\",\n \"permissions\": [\"/role/new\"],\n \"routes\":[\"/addrole\"]\n },\n {\"groupname\": \"RoleUpdate\",\n \"group_description\": \"RoleUpdate\",\n \"permissions\": [\"role/update\", \"/role/add/permissiongroup\",\n \"/role/remove/permissiongroup\", \"/role/list/update\"],\n \"routes\":[\"/editrole/:id\"]\n },\n {\"groupname\": \"RoleDelete\",\n \"group_description\": \"RoleDelete\",\n \"permissions\": [\"/role/delete\"],\n \"routes\":[\"/deleterole\"]\n },\n {\"groupname\": \"PermissionsView\",\n \"group_description\": \"PermissionsView\",\n \"permissions\": [\"/permissions/all\", \"/permissions/view/~\"],\n \"routes\":[]\n },\n {\"groupname\": \"PermissionsCreate\",\n \"group_description\": \"PermissionsCreate\",\n \"permissions\": [\"/permissions/new\"],\n \"routes\":[]\n },\n {\"groupname\": \"PermissionsUpdate\",\n \"group_description\": \"PermissionsUpdate\",\n \"permissions\": [\"/permissions/update\"],\n \"routes\":[]\n },\n {\"groupname\": \"PermissionsDelete\",\n \"group_description\": \"PermissionsDelete\",\n \"permissions\": [\"/permissions/delete\"],\n \"routes\":[]\n },\n {\"groupname\": \"GroupView\",\n \"group_description\": \"GroupView\",\n \"permissions\": [\"/grouppermissions/all\", \"/grouppermissions/view/~\"],\n \"routes\":[]\n },\n {\"groupname\": \"GroupCreate\",\n \"group_description\": \"GroupCreate\",\n \"permissions\": [\"/grouppermissions/new\"],\n \"routes\":[]\n },\n {\"groupname\": \"GroupUpdate\",\n \"group_description\": \"GroupUpdate\",\n \"permissions\": [\"/grouppermissions/update\"],\n \"routes\":[]\n },\n {\"groupname\": \"GroupDelete\",\n \"group_description\": \"GroupDelete\",\n \"permissions\": [\"/grouppermissions/delete/\"],\n \"routes\":[]\n },\n {\"groupname\": \"ToolView\",\n \"group_description\": \"ToolView\",\n \"permissions\": [\"/tool/view/~\", \"/tool/view/version/~\", \"/tool/all\",\n \"/tool/search/name/~\", \"/tool/search/tag/~\",\n \"/tool/view/version/~/prevversion/~/machine/~\"],\n \"routes\":[\"/tool/view/:id\", \"/tools/all\", \"/dashboard\", \"/dashboard/deployment/:request_id\", \"/dashboard/importupdate/:request_id\"]\n },\n {\"groupname\": \"ToolCreate\",\n \"group_description\": \"ToolCreate\",\n \"permissions\": [\"/tool/add\"],\n \"routes\":[\"/tool/new\"]\n },\n {\"groupname\": \"ToolUpdate\",\n \"group_description\": \"ToolUpdate\",\n \"permissions\": [\"/tool/update\", \"/tool/upload/logo\", \"/tool/update/buildmarkup\"],\n \"routes\":[\"/edit/tool/:id\"]\n },\n {\"groupname\": \"ToolDelete\",\n \"group_description\": \"ToolDelete\",\n \"permissions\": [\"/tool/delete\"],\n \"routes\":[\"/delete/tool/:id\"]\n },\n {\"groupname\": \"DeploymentRequestView\",\n \"group_description\": \"DeploymentRequestView\",\n \"permissions\": [\"/deploymentrequest/view/~\", \"/deploymentrequest/all\"],\n \"routes\":[\"/deploymentrequests\", \"/viewRequestDetail/:id\", \"/view/log/deploy/:id\", \"/saved/requests\", \"/recent/requests\",\"/deploymentrequest/view/:id\"]\n },\n {\"groupname\": \"DeploymentRequestCreate\",\n \"group_description\": \"DeploymentRequestCreate\",\n \"permissions\": [\"/deploymentrequest/add\", \"/deploymentrequest/deploymentfield/upload\"],\n \"routes\":[\"/deploy/tool/:id/:build_number\",\n \"/redeploy/version/:version_id/build_number/:build_number/build_id/:build_id/machine/:machine_id\",\n \"/upgrade/oldversion/:old_version_id/newversion/:new_version_id\" +\n \"/machine/:machine_id\",\n \"/undeploy/version/:version_id/build_number/:build_number/build_id/:build_id/machine/:machine_id\", \"/deploy/tools/:tools\",\n \"/deploy/du/:id\", \"/deploy/duset/:id\",\n \"/revert/du/:id\",\n \"/deploy/dus/:dus\"]\n },\n {\"groupname\": \"DeploymentRequestDelete\",\n \"group_description\": \"DeploymentRequestDelete\",\n \"permissions\": [\"/deploymentrequest/cancel\"],\n \"routes\":[]\n },\n {\"groupname\": \"DeploymentRequestUpdate\",\n \"group_description\": \"DeploymentRequestUpdate\",\n \"permissions\": [\"/deploymentrequest/run/view/~\", \"/deploymentrequest/retry\"],\n \"routes\":[\"/edit/saved/deployment/tool/:request_id\", \"/edit/saved/deployment/du/:request_id\"]\n },\n {\"groupname\": \"VersionsCreate\",\n \"group_description\": \"VersionsCreate\",\n \"permissions\": [\"/tool/versions/add\"],\n \"routes\":[]\n },\n {\"groupname\": \"VersionsUpdate\",\n \"group_description\": \"VersionsUpdate\",\n \"permissions\": [\"/tool/versions/uploadScreenshot\"],\n \"routes\":[]\n },\n {\"groupname\": \"BuildView\",\n \"group_description\": \"BuildView\",\n \"permissions\": [\"/versions/build/view/~\", \"/versions/~/build/active\",\n \"/versions/~/build/all\"],\n \"routes\":[]\n },\n {\"groupname\": \"BuildCreate\",\n \"group_description\": \"BuildCreate\",\n \"permissions\": [\"/versions/build/add\",\"/build/add\"],\n \"routes\":[]\n },\n {\"groupname\": \"BuildUpdate\",\n \"group_description\": \"BuildUpdate\",\n \"permissions\": [\"/versions/build/update\", \"/versions/build/setactive\", \"/build/update\"],\n \"routes\":[]\n },\n {\"groupname\": \"DocumentView\",\n \"group_description\": \"DocumentView\",\n \"permissions\": [\"/versions/~/documents\"],\n \"routes\":[]\n },\n {\"groupname\": \"DocumentCreate\",\n \"group_description\": \"DocumentCreate\",\n \"permissions\": [\"/versions/documents/add\"],\n \"routes\":[]\n },\n {\"groupname\": \"DocumentUpdate\",\n \"group_description\": \"DocumentUpdate\",\n \"permissions\": [\"/versions/documents/update\"],\n \"routes\":[]\n },\n {\"groupname\": \"DeploymentfieldsCreate\",\n \"group_description\": \"DeploymentfieldsCreate\",\n \"permissions\": [\"/versions/deploymentfields/add\"],\n \"routes\":[]\n },\n {\"groupname\": \"DeploymentfieldsUpdate\",\n \"group_description\": \"DeploymentfieldsUpdate\",\n \"permissions\": [\"/versions/deploymentfields/update\"],\n \"routes\":[]\n },\n {\"groupname\": \"ToolInstallationCreate\",\n \"group_description\": \"ToolInstallationCreate\",\n \"permissions\": [\"/installation/add\"],\n \"routes\":[]\n },\n {\"groupname\": \"ToolInstallationUpdate\",\n \"group_description\": \"ToolInstallationUpdate\",\n \"permissions\": [\"/installation/update\"],\n \"routes\":[]\n },\n {\"groupname\": \"ToolInstallationDelete\",\n \"group_description\": \"ToolInstallationDelete\",\n \"permissions\": [\"/installation/delete\"],\n \"routes\":[]\n },\n {\"groupname\": \"MachineTypeCreate\",\n \"group_description\": \"MachineTypeCreate\",\n \"permissions\": [\"/machine/type/new\"],\n \"routes\":[]\n },\n {\"groupname\": \"MachineTypeUpdate\",\n \"group_description\": \"MachineTypeUpdate\",\n \"permissions\": [\"/machine/type/update\"],\n \"routes\":[]\n },\n {\"groupname\": \"MachineTypeDelete\",\n \"group_description\": \"MachineTypeDelete\",\n \"permissions\": [\"/machine/type/delete\"],\n \"routes\":[]\n },\n {\"groupname\": \"MachineFavCreate\",\n \"group_description\": \"MachineFavCreate\",\n \"permissions\": [\"/machine/fav/new\"],\n \"routes\":[]\n },\n {\"groupname\": \"MachineFavUpdate\",\n \"group_description\": \"MachineFavUpdate\",\n \"permissions\": [\"/machine/fav/update\"],\n \"routes\":[]\n },\n {\"groupname\": \"MachineFavDelete\",\n \"group_description\": \"MachineFavDelete\",\n \"permissions\": [\"/machine/fav/delete\"],\n \"routes\":[]\n },\n {\"groupname\": \"MachineCreate\",\n \"group_description\": \"MachineCreate\",\n \"permissions\": [\"/machine/new\", \"/machine/add/permission\", \"/machine/import\",\"/machine/bulk/load\"],\n \"routes\":[\"/AddNewMachine\", \"/bulk/machines\"]\n },\n {\"groupname\": \"MachineUpdate\",\n \"group_description\": \"MachineUpdate\",\n \"permissions\": [\"/machine/update\", \"/machine/refresh\",\n \"/machine/update/userdetails\", \"/machine/update/usertype\", \"/machine/disable\",\"/machine/bulk/load\"],\n \"routes\":[\"/MachineDetails/:id\", \"/editmachine/:id\"]\n },\n {\"groupname\": \"MachineDelete\",\n \"group_description\": \"MachineDelete\",\n \"permissions\": [\"/machine/delete/permission\", \"/machine/remove/~\"],\n \"routes\":[\"/machine/remove/:id\"]\n },\n {\"groupname\": \"MediaFilesCreate\",\n \"group_description\": \"MediaFilesCreate\",\n \"permissions\": [\"/mediafiles/add\"],\n \"routes\":[]\n },\n {\"groupname\": \"MediaFilesUpdate\",\n \"group_description\": \"MediaFilesUpdate\",\n \"permissions\": [\"/mediafiles/update\"],\n \"routes\":[]\n },\n {\"groupname\": \"MediaFilesDelete\",\n \"group_description\": \"MediaFilesDelete\",\n \"permissions\": [\"/mediafiles/delete\"],\n \"routes\":[]\n },\n {\"groupname\": \"CloneView\",\n \"group_description\": \"CloneView\",\n \"permissions\": [\"/clonerequest/all\", \"/clonerequest/view/~\"],\n \"routes\":[\"/clonerequests\", \"/clonerequests/:id\",\n \"/clonerequests/steps/tools/:id\", \"/view/log/clone/:id\"]\n },\n {\"groupname\": \"CloneCreate\",\n \"group_description\": \"CloneCreate\",\n \"permissions\": [\"/clonerequest/add\"],\n \"routes\":[\"/createclone\"]\n },\n {\"groupname\": \"CloneUpdate\",\n \"group_description\": \"CloneUpdate\",\n \"permissions\": [\"/clonerequest/run/~\", \"/clonerequest/update\", \"/clonerequest/retry\"],\n \"routes\":[]\n },\n {\"groupname\": \"CloneDelete\",\n \"group_description\": \"CloneDelete\",\n \"permissions\": [\"/clonerequest/cancel/~\"],\n \"routes\":[]\n },\n {\"groupname\": \"DistributionMachineView\",\n \"group_description\": \"DistributionMachineView\",\n \"permissions\": [\"/clonerequest/distribution/all\", \"/clonerequest/distribute/status/~\"],\n \"routes\":[\"/distribution\"]\n },\n {\"groupname\": \"DistributionMachineCreate\",\n \"group_description\": \"DistributionMachineCreate\",\n \"permissions\": [\"/clonerequest/distribution/add\", \"/clonerequest/distribute/add\"],\n \"routes\":[]\n },\n {\"groupname\": \"DistributionMachineUpdate\",\n \"group_description\": \"DistributionMachineUpdate\",\n \"permissions\": [\"/clonerequest/distribution/update\",\n \"/clonerequest/distribution/run/all\", \"/clonerequest/distribution/run/~\"],\n \"routes\":[\"/distribution/cancel/:id\", \"/distribution/resend/:id\"]\n },\n {\"groupname\": \"DistributionMachineDelete\",\n \"group_description\": \"DistributionMachineDelete\",\n \"permissions\": [\"/clonerequest/distribution/cancel/~\"],\n \"routes\":[]\n },\n {\"groupname\": \"DistributionSyncView\",\n \"group_description\": \"DistributionSyncView\",\n \"permissions\": [\"/clonerequest/distribution/view/all\",\n \"/clonerequest/distribution/view/tool/~\",\n \"/clonerequest/distribution/tool/status/~\"],\n \"routes\":[\"/tools/new\", \"/tools/updates\", \"/tool/new/:id\", \"/tool/updated/:id\"]\n },\n {\"groupname\": \"DistributionSyncCreate\",\n \"group_description\": \"DistributionSyncCreate\",\n \"permissions\": [\"/clonerequest/distribution/tool\"],\n \"routes\":[\"/import/tool/:id\", \"/update/tool/:id\"]\n },\n {\"groupname\": \"DistributionSyncUpdate\",\n \"group_description\": \"DistributionSyncUpdate\",\n \"permissions\": [],\n \"routes\":[]\n },\n {\"groupname\": \"DistributionSyncDelete\",\n \"group_description\": \"DistributionSyncDelete\",\n \"permissions\": [],\n \"routes\":[]\n },\n {\"groupname\": \"ConfigView\",\n \"group_description\": \"ConfigView\",\n \"permissions\": [\"/config/all\", \"/config/view/~\", \"/config/sync/all\",\"/config/view/configid/~\",\"/config/distribution/schedule\"],\n \"routes\":[\"/settings\"]\n },\n {\"groupname\": \"ConfigUpdate\",\n \"group_description\": \"ConfigUpdate\",\n \"permissions\": [\"/config/update\"],\n \"routes\":[]\n },\n {\"groupname\": \"VersionsView\",\n \"group_description\": \"VersionsView\",\n \"permissions\": [\"/tool/~/versions/all\", \"/tool/versions/view/~\"],\n \"routes\":[]\n },\n {\"groupname\": \"DeploymentfieldsView\",\n \"group_description\": \"DeploymentfieldsView\",\n \"permissions\": [\"/versions/~/deploymentfields\"],\n \"routes\":[]\n },\n {\"groupname\": \"ToolInstallationView\",\n \"group_description\": \"ToolInstallationView\",\n \"permissions\": [\"/versions/~/installation/all\", \"/machine/~/installation/all\"],\n \"routes\":[]\n },\n {\"groupname\": \"MachineTypeView\",\n \"group_description\": \"MachineTypeView\",\n \"permissions\": [\"/machine/type/view/~\", \"/machine/type/all\"],\n \"routes\":[]\n },\n {\"groupname\": \"MachineFavView\",\n \"group_description\": \"MachineFavView\",\n \"permissions\": [\"/machine/all/fav\", \"/machine/fav/view/~\",\n \"/machine/fav/machine/~\", \"/machine/fav/user/~\",\n \"/machine/fav/machine/~/user/~\"],\n \"routes\":[]\n },\n {\"groupname\": \"MachineView\",\n \"group_description\": \"MachineView\",\n \"permissions\": [\"/machine/view/~\", \"/machine/alias/~\",\n \"/machine/view/all\", \"/machine/hostname/~\",\n \"/machine/ip/~\", \"/machine/user/~\", \"/machine/test\",\\\n \"/machine/view/deployment/history/entity/~/~\",\"/machine/search/tag/~\",\"/machine/search/name/~\"],\n \"routes\":[\"/MachineDetails/:id\", \"/machines\", \"/machine/view/deployment/history/entity/:entity/:id\"]\n },\n {\"groupname\": \"MediaFilesView\",\n \"group_description\": \"MediaFilesView\",\n \"permissions\": [\"/mediafiles/view/~\", \"/versions/~/mediafiles/all\"],\n \"routes\":[]\n },\n {\"groupname\": \"SyncServices\",\n \"group_description\": \"SyncServices\",\n \"permissions\": [\"/sync/import\", \"/sync/manual/export\", \"/sync/pull/export\",\n \"/sync/push/trigger/~\", \"/syncrequest/all\", \"/syncrequest/view/~\",\n \"/syncrequest/type/~\", \"/syncrequest/update\", \"/syncrequest/delete/~\",\n \"/syncrequest/add\", \"/sync/manual/clean/~\",\n \"/syncrequest/run/~\", \"/sync/savedexports\",\"/sync/view/all\",\"/sync/view/syncid/~\",\"/sync/retry\",\"/sync/delete/~\"],\n \"routes\":[\"/importexport\", \"/synchronization\", \"/edit/sync/:id\",\n \"/manage/synchronization/services\", \"/view/sync/requests\", \"/view/sync/request/:id\"]\n },\n {\"groupname\": \"Admin\",\n \"group_description\": \"Admin\",\n \"permissions\": [],\n \"routes\":[\"/admin\"]\n },\n {\"groupname\": \"Catalog\",\n \"group_description\": \"Catalog\",\n \"permissions\": [],\n \"routes\":[\"/catalog\"]\n },\n {\"groupname\": \"ChangePassword\",\n \"group_description\": \"ChangePassword\",\n \"permissions\": [\"/user/change/password\",\"/user/logout\"],\n \"routes\":[\"/changepassword\"]\n },\n {\"groupname\": \"SystemDetailsView\",\n \"group_description\": \"SystemDetailsView\",\n \"permissions\": [\"/systemdetails/all\"],\n \"routes\":[\"/systemdata\"]\n },\n {\"groupname\": \"SystemDetailsCreate\",\n \"group_description\": \"SystemDetailsCreate\",\n \"permissions\": [\"/systemdetails/add\"],\n \"routes\":[]\n },\n {\"groupname\": \"ToolSetView\",\n \"group_description\": \"ToolSetView\",\n \"permissions\": [\"/toolset/all\", \"/toolset/view/~\"],\n \"routes\":[\"/toolset/all\", \"/toolset/view/:id\"]\n },\n {\"groupname\": \"ToolSetUpdate\",\n \"group_description\": \"ToolSetUpdate\",\n \"permissions\": [\"/toolset/update\", \"/toolset/upload/logo\"],\n \"routes\":[\"/edit/toolset/:id\"]\n },\n {\"groupname\": \"ToolSetCreate\",\n \"group_description\": \"ToolSetCreate\",\n \"permissions\": [\"/toolset/add\"],\n \"routes\":[\"/toolset/new\"]\n },\n {\"groupname\": \"ToolSetDelete\",\n \"group_description\": \"ToolSetDelete\",\n \"permissions\": [\"/toolset/delete/~\"],\n \"routes\":[\"/toolset/delete/:id\"]\n },\n {\"groupname\": \"PreRequisites\",\n \"group_description\": \"PreRequisites\",\n \"permissions\": [\"/prerequisites/add\", \"/prerequisites/view\", \\\n \"/prerequisites/delete/~\", \"/prerequisites/update\", \\\n \"/prerequisites/view/~\"],\n \"routes\":[\"/manage/prerequisites\", \"/add/new/prerequisite\", \\\n \"/edit/prerequisite/:id\", \"/view/prerequisite/:id\", \\\n \"/delete/prerequisite/:id\"]\n },\n {\"groupname\": \"MachineGroupsDelete\",\n \"group_description\": \"MachineGroupsDelete\",\n \"permissions\": [\"/machinegroups/delete/~\"],\n \"routes\":[\"/delete/machine/group/:id\"]\n },\n {\"groupname\": \"MachineGroupsView\",\n \"group_description\": \"MachineGroupsView\",\n \"permissions\": [\"/machinegroups/view\", \"/machinegroups/view/~\",\"/machinegroups/view/name/~\"],\n \"routes\":[\"/manage/machine/groups\", \"/view/machine/group/:id\"]\n },\n {\"groupname\": \"MachineGroupsCreate\",\n \"group_description\": \"MachineGroupsCreate\",\n \"permissions\": [\"/machinegroups/add\",\"/machinegroups/bulk/load\"],\n \"routes\":[\"/add/machine/group\"]\n },\n {\"groupname\": \"MachineGroupsUpdate\",\n \"group_description\": \"MachineGroupsUpdate\",\n \"permissions\": [\"/machinegroups/update\",\"/machinegroups/bulk/load\"],\n \"routes\":[\"/edit/machine/group/:id\"]\n },\n {\"groupname\": \"DeploymentGroupView\",\n \"group_description\": \"DeploymentGroupView\",\n \"permissions\": [\"/deploymentrequest/group/view/~\", \"/deploymentrequest/group/all\", \"/deploymentrequest/group/saved/all\", \"/deploymentrequest/group/saved/view/~\",\"/deploymentrequest/group/view/revert/~\"],\n \"routes\":[\"/saved/requests\", \"/recent/requests\",\"/deploymentrequest/group/revert/:id\"]\n },\n {\"groupname\": \"DeploymentGroupCreate\",\n \"group_description\": \"DeploymentGroupCreate\",\n \"permissions\": [\"/deploymentrequest/group/add\",\n \"/deploymentrequest/group/toolset/add\",\n \"/deploymentrequest/group/machine/add\", \"/deploymentrequest/group/saved/add\",\n \"/deploymentrequest/group/machinegroup/new\",\"/deploymentrequest/group/add/undeploy\"],\n \"routes\":[\"/deploy/toolset/:id\", \"/deploy/duset/:id\"]\n },\n {\"groupname\": \"DeploymentGroupDelete\",\n \"group_description\": \"DeploymentGroupDelete\",\n \"permissions\": [\"/deploymentrequest/group/cancel/~\", \"/deploymentrequest/group/saved/delete/~\"],\n \"routes\":[]\n },\n {\"groupname\": \"DeploymentGroupUpdate\",\n \"group_description\": \"DeploymentGroupUpdate\",\n \"permissions\": [\"/deploymentrequest/group/retry\", \"/deploymentrequest/group/saved/update\"],\n \"routes\":[]\n },\n {\"groupname\": \"UsersGroupsView\",\n \"group_description\": \"UsersGroupsView\",\n \"permissions\": [\"/teams/view\", \"/teams/view/~\"],\n \"routes\":[\"/manage/users/groups\", \"/view/user/group/:id\", \"/team/view/:id\"]\n },\n {\"groupname\": \"UsersGroupsCreate\",\n \"group_description\": \"UsersGroupsCreate\",\n \"permissions\": [\"/teams/add\"],\n \"routes\":[\"/create/user/group\"]\n },\n {\"groupname\": \"UsersGroupsUpdate\",\n \"group_description\": \"UsersGroupsUpdate\",\n \"permissions\": [\"/teams/update\"],\n \"routes\":[\"/edit/user/group/:id\"]\n },\n {\"groupname\": \"UsersGroupsDelete\",\n \"group_description\": \"UsersGroupsDelete\",\n \"permissions\": [\"/teams/delete/~\"], # To DO\n \"routes\":[\"/delete/user/group/:id\"]\n },\n {\"groupname\": \"TagsView\",\n \"group_description\": \"TagsView\",\n \"permissions\": [\"/tag/all\", \"/tag/view/~\"],\n \"routes\":[\"/manage/tags\", \"/tag/view/:id\"]\n },\n {\"groupname\": \"TagsCreate\",\n \"group_description\": \"TagsCreate\",\n \"permissions\": [\"/tag/new\"],\n \"routes\":[\"/tag/new\"]\n },\n {\"groupname\": \"TagsUpdate\",\n \"group_description\": \"TagsUpdate\",\n \"permissions\": [\"/tag/update\"],\n \"routes\":[\"/tag/update\"]\n },\n {\"groupname\": \"TagsDelete\",\n \"group_description\": \"TagsDelete\",\n \"permissions\": [\"/tag/delete/~\"],\n \"routes\":[\"/tag/delete/:id\"]\n },\n {\"groupname\": \"GeneralDetails\",\n \"group_description\": \"GeneralDetails\",\n \"permissions\": [\"/currenttime\"],\n \"routes\":[]\n },\n {\"groupname\": \"DeploymentUnitApprovalStatusView\",\n \"group_description\": \"DeploymentUnitApprovalStatusView\",\n \"permissions\": [\"/deploymentunitapprovalstatus/all\", \\\n \"/deploymentunitapprovalstatus/view/~\",\n \"/deploymentunitapprovalstatus/view/name/~\"],\n \"routes\":[]\n },\n {\"groupname\": \"DeploymentUnitApprovalStatusCreate\",\n \"group_description\": \"DeploymentUnitApprovalStatusCreate\",\n \"permissions\": [\"/deploymentunitapprovalstatus/add\"],\n \"routes\":[]\n },\n {\"groupname\": \"DeploymentUnitApprovalStatusUpdate\",\n \"group_description\": \"DeploymentUnitApprovalStatusUpdate\",\n \"permissions\": [\"/deploymentunitapprovalstatus/update\"],\n \"routes\":[]\n },\n {\"groupname\": \"DeploymentUnitApprovalStatusDelete\",\n \"group_description\": \"DeploymentUnitApprovalStatusDelete\",\n \"permissions\": [\"/deploymentunitapprovalstatus/delete/~\"],\n \"routes\":[]\n },\n {\"groupname\": \"DeploymentUnitTypeView\",\n \"group_description\": \"DeploymentUnitTypeView\",\n \"permissions\": [\"/deploymentunittype/all\", \\\n \"/deploymentunittype/view/~\"],\n \"routes\":[]\n },\n {\"groupname\": \"DeploymentUnitTypeCreate\",\n \"group_description\": \"DeploymentUnitTypeCreate\",\n \"permissions\": [\"/deploymentunittype/new\"],\n \"routes\":[]\n },\n {\"groupname\": \"DeploymentUnitTypeUpdate\",\n \"group_description\": \"DeploymentUnitTypeUpdate\",\n \"permissions\": [\"/deploymentunittype/update\"],\n \"routes\":[]\n },\n {\"groupname\": \"DeploymentUnitTypeDelete\",\n \"group_description\": \"DeploymentUnitTypeDelete\",\n \"permissions\": [\"/deploymentunittype/delete/~\"],\n \"routes\":[]\n },\n {\"groupname\": \"DeploymentUnitView\",\n \"group_description\": \"DeploymentUnitView\",\n \"permissions\": [\"/deploymentunit/all\", \"/deploymentunit/view/~\", \\\n \"/deploymentunit/search/tag/~\", \"/deploymentunit/search/name/~\"],\n \"routes\":[\"/dashboard/du\", \"/deploymentunit/view/:id\", \\\n \"/redeploy/du/:du_id/build_number/:build_number/build_id/:build_id/machine/:machine_id\", \\\n \"/undeploy/du/:du_id/build_number/:build_number/build_id/:build_id/machine/:machine_id\", \"/dashboard/du/:request_id\"]\n },\n {\"groupname\": \"DeploymentUnitCreate\",\n \"group_description\": \"DeploymentUnitCreate\",\n \"permissions\": [\"/deploymentunit/new\"],\n \"routes\":[\"/deploymentunit/new\"]\n },\n {\"groupname\": \"DeploymentUnitUpdate\",\n \"group_description\": \"DeploymentUnitUpdate\",\n \"permissions\": [\"/deploymentunit/update\", \"/deploymentunit/upload/logo\"],\n \"routes\":[\"/deploymentunit/edit/:id\"]\n },\n {\"groupname\": \"DeploymentUnitDelete\",\n \"group_description\": \"DeploymentUnitDelete\",\n \"permissions\": [\"/deploymentunit/delete/~\"],\n \"routes\":[\"/delete/deploymentunit/:id\"]\n },\n {\"groupname\": \"DeploymentUnitSetView\",\n \"group_description\": \"DeploymentUnitSetView\",\n \"permissions\": [\"/deploymentunitset/all\", \"/deploymentunitset/view/~\",\"/deploymentunitset/view/getbuilds/~\",\"/deploymentunitset/view/states/~\"],\n \"routes\":[\"/deploymentunitset/view/:id\", \"/deploymentunitset/all\"]\n },\n {\"groupname\": \"DeploymentUnitSetCreate\",\n \"group_description\": \"DeploymentUnitSetCreate\",\n \"permissions\": [\"/deploymentunitset/new\"],\n \"routes\":[\"/deploymentunitset/new\"]\n },\n {\"groupname\": \"DeploymentUnitSetUpdate\",\n \"group_description\": \"DeploymentUnitSetUpdate\",\n \"permissions\": [\"/deploymentunitset/update\", \"/deploymentunitset/upload/logo\"],\n \"routes\":[\"/deploymentunitset/edit/:id\"]\n },\n {\"groupname\": \"DeploymentUnitSetDelete\",\n \"group_description\": \"DeploymentUnitSetDelete\",\n \"permissions\": [\"/deploymentunitset/delete/~\"],\n \"routes\":[]\n },\n {\"groupname\": \"ReportsView\",\n \"group_description\": \"ReportsView\",\n \"permissions\": [\"/reports/all\"],\n \"routes\":[\"/view/reports\"]\n },\n {\"groupname\": \"Plugins\",\n \"group_description\": \"Plugins\",\n \"permissions\": [\"/plugin/reload\", \"/plugin/install\", \"/plugin/uninstall/~\",\\\n \"/plugin/inactive/~\", \"/plugin/active/~\", \"/plugin/all\",\n \"/plugin/view/~\",\"/plugin/file/upload\",\"/plugin/file/list/~\",\"/plugin/file/remove/~\",\n \"/plugin/exitpoint/new\",\"/plugin/exitpoint/update\",\"/plugin/file/view/~\"],\n \"routes\":[\"/plugin\", \"/manage/plugins/deployment\", \"/plugin/deployment/upload\"]\n },\n {\"groupname\": \"MonitoringView\",\n \"group_description\": \"MonitoringView\",\n \"permissions\": [],\n \"routes\":[\"/view/monitoring\", \"/view/monitoring/runningservices\"]\n },\n {\"groupname\": \"PersonalizeCreate\",\n \"group_description\": \"PersonalizeCreate\",\n \"permissions\": [\"/systemdetails/logoupload\"],\n \"routes\":[]\n },\n {\"groupname\": \"StateView\",\n \"group_description\": \"StateView\",\n \"permissions\": [\"/state/all/\",\"/state/view/~\", \"/state/view/parent/~\" ,\"/state/view/parent/~/name/~\"],\n \"routes\":[\"/view/du/state\",\"/view/duset/state\"]\n },\n {\"groupname\": \"StateAdd\",\n \"group_description\": \"StateAdd\",\n \"permissions\": [\"/state/add\"],\n \"routes\":[\"/create/du/state\",\"/create/duset/state\"]\n },\n {\"groupname\": \"StateUpdate\",\n \"group_description\": \"StateUpdate\",\n \"permissions\": [\"/state/update\"],\n \"routes\":[]\n },\n {\"groupname\": \"StateDelete\",\n \"group_description\": \"StateDelete\",\n \"permissions\": [\"/state/delete/~\"],\n \"routes\":[\"/state/delete/:id\"]\n },\n {\"groupname\": \"ToolsOnMachineView\",\n \"group_description\": \"ToolsOnMachineView\",\n \"permissions\": [\"/deployed/view/all\",\"/deployed/view/machine_id/~/parent_entity_id/~/build_id/~\"],\n \"routes\": []\n },\n {\"groupname\": \"FlexibleAttributeView\",\n \"group_description\": \"View Flexible Attributes\",\n \"permissions\": [\"/flexattributes/view/all\", \"/flexattributes/view/entity/~\",\"/flexattributes/view/~\"],\n \"routes\": []\n },\n {\"groupname\": \"FlexibleAttributeDelete\",\n \"group_description\": \"Delete Flexible Attributes\",\n \"permissions\": [],\n \"routes\": []\n },\n {\"groupname\": \"FlexibleAttributeAdd\",\n \"group_description\": \"Add Flexible Attributes\",\n \"permissions\": [\"/flexattributes/new\"],\n \"routes\": [\"/new/flexibleattributes\"]\n },\n {\"groupname\": \"FlexibleAttributeUpdate\",\n \"group_description\": \"Update Flexible Attributes\",\n \"permissions\": [\"/flexattributes/update\"],\n \"routes\": [\"/manage/flexibleattributes\"]\n },\n {\"groupname\": \"ProposedToolsCreate\",\n \"group_description\": \"ProposedToolsCreate\",\n \"permissions\": [\"/proposed/tool/approve\"],\n \"routes\": [\"/edit/tool/proposed/:id\",\"/approve/tool/proposed/:id\"]\n },\n {\"groupname\": \"ProposedToolsView\",\n \"group_description\": \"ProposedToolsView\",\n \"permissions\": [\"/proposed/tool/view/all\",\"/proposed/tool/view/~\"],\n \"routes\": [\"/proposed/tools/all\",\"/view/proposed/tool/:id\"]\n },\n {\"groupname\": \"ProposedToolsDelete\",\n \"group_description\": \"ProposedToolsDelete\",\n \"permissions\": [\"/proposed/tool/delete/~\"],\n \"routes\": []\n },\n {\"groupname\": \"AuditingView\",\n \"group_description\": \"AuditingView\",\n \"permissions\": [\"/auditing/view/all\",\"/auditing/view/id/~\"],\n \"routes\": [\"/view/audits\"]\n },\n {\"groupname\": \"RepositoryCreate\",\n \"group_description\": \"RepositoryCreate\",\n \"permissions\": [\"/repository/add\"],\n \"routes\": [\"/new/repository\"]\n },\n {\"groupname\": \"RepositoryUpdate\",\n \"group_description\": \"RepositoryUpdate\",\n \"permissions\": [\"/repository/update\"],\n \"routes\": []\n },\n {\"groupname\": \"RepositoryView\",\n \"group_description\": \"RepositoryView\",\n \"permissions\": [\"/repository/view/all\",\"/repository/view/name/~\",\"/repository/view/~\",\"/repository/view/byparententity/~\"],\n \"routes\": [\"/view/repository\"]\n },\n {\"groupname\": \"RepositoryDelete\",\n \"group_description\": \"RepositoryDelete\",\n \"permissions\": [\"/repository/delete/~\"],\n \"routes\": []\n }, \n {\"groupname\": \"SystemAdministration\",\n \"group_description\": \"SystemAdministration\",\n \"permissions\": [\"/server/restart\"],\n \"routes\": []\n }\n]\n\n# list for Email Template reference data\nEMAIL_TEMPLATE = [\n {\n \"templateid\": 1,\n \"html\": \"passwordreset.html\",\n \"subject\": \"Password Reset Alert\"\n },\n {\n \"templateid\": 2,\n \"html\": \"deploymentcompleted.html\",\n \"subject\": \"Deployment Manager-Deployment Request Status\"\n },\n {\n \"templateid\": 3,\n \"html\": \"clonecompleted.html\",\n \"subject\": \"Deployment Manager-Clone Request Status\"\n },\n {\n \"templateid\": 4,\n \"html\": \"pullcompleted.html\",\n \"subject\": \"Deployment Manager-Pull Request Status\"\n },\n {\n \"templateid\": 5,\n \"html\": \"pushcompleted.html\",\n \"subject\": \"Deployment Manager-Push Request Status\"\n },\n {\n \"templateid\": 6,\n \"html\": \"syncstatus.html\",\n \"subject\": \"SyncServices - Status\"\n },\n {\n \"templateid\": 7,\n \"html\": \"newsync.html\",\n \"subject\": \"SyncServices - New Entry\"\n },\n {\n \"templateid\": 8,\n \"html\": \"forgotreset.html\",\n \"subject\": \"Password Reset Alert\"\n },\n {\n \"templateid\": 9,\n \"html\": \"pushstatus.html\",\n \"subject\": \"Push Status Alert\"\n },\n {\n \"templateid\": 10,\n \"html\": \"distributioncompleted.html\",\n \"subject\": \"DistributionCenterService - Status\"\n },\n {\n \"templateid\": 11,\n \"html\": \"newdistribution.html\",\n \"subject\": \"DistributionSyncServices - New Entry\"\n },\n {\n \"templateid\": 12,\n \"html\": \"comparedistribution.html\",\n \"subject\": \"DistributionSyncServices - Processed Entry\"\n },\n {\n \"templateid\": 13,\n \"html\": \"groupdeploymentcomplete.html\",\n \"subject\": \"groupdeploymentcompleted\",\n },\n {\n \"templateid\": 14,\n \"html\": \"newuser.html\",\n \"subject\": \"New user created\",\n },\n {\n \"templateid\": 15,\n \"html\": \"newtoolproposal.html\",\n \"subject\": \"New Tool Proposal-Approval Required\",\n },\n {\n \"templateid\": 16,\n \"html\": \"toolproposalapproved.html\",\n \"subject\": \"Your Proposed Tool was approved !\",\n },\n {\n \"templateid\": 17,\n \"html\": \"newtoolproposalforuser.html\",\n \"subject\": \"New Tool Proposal was received\",\n },\n {\n \"templateid\": 18,\n \"html\": \"toolproposalrejected.html\",\n \"subject\": \"Your Proposed Tool was rejected !\",\n }]\n\n# list for Machine Type reference data\nMACHINE_TYPE = [\n {\"type\": \"Production\"},\n {\"type\": \"IUT\"},\n {\"type\": \"UT\"},\n {\"type\": \"ST\"},\n {\"type\": \"Value Package Master\"},\n {\"type\": \"Other\"},\n {\"type\": \"UAT\"}, \n {\"type\": \"SIT\"},\n {\"type\": \"PET\"},\n {\"type\": \"eDPM\"}]\n\n# list for Dummy Accounts reference data\nACCOUNT = [\n {\"mps_version\": \"mps 1\",\n \"status\": \"1\",\n \"name\": \"Test\"\n }]\n\n# list for Dummy Deployment Unit Type s reference data\nDEPLOYMENT_UNIT_TYPE = [\n {\"name\": \"Fast Track\"},\n {\"name\": \"Hot Fix\"},\n {\"name\": \"Version\"}]\n\n# DeploymentUnitApprovalStatus\nDEPLOYMENT_UNIT_APPROVAL_STATUS = [\n {\"name\": \"Created\"},\n {\"name\": \"Tested\"},\n {\"name\": \"Certified\"}]\n\n\n# FLEX_ATTRIBUTES\nFLEX_ATTRIBUTES =[\n {\n \"name\": \"compTypes\",\n \"title\": \"Component Type\",\n \"type\": \"Select\",\n \"entity\": \"DeploymentUnit\",\n \"default_value\": \"\",\n \"description\": \"Application component type (CRM-BACKEND, CRM-CLIENT)\",\n \"is_mandatory\": False,\n \"is_active\": True,\n \"valid_values\": [\n \"CRM-DB\",\n \"CRM-BACKEND\",\n \"CRM-CLIENT\",\n \"OMS-DB\",\n \"OMS-BACKEND\"\n ]\n },\n {\n \"name\": \"compTypes\",\n \"title\": \"Component Type\",\n \"type\": \"MultiSelect\",\n \"entity\": \"Machine\",\n \"default_value\": \"\",\n \"description\": \"Application component type (CRM-BACKEND, CRM-CLIENT)\",\n \"is_mandatory\": False,\n \"is_active\": True,\n \"valid_values\": [\n \"CRM-DB\",\n \"CRM-BACKEND\",\n \"CRM-CLIENT\",\n \"OMS-DB\",\n \"OMS-BACKEND\"\n ]\n }\n]\n\n# Connect routes and routes group\nUSERS = [\n {\n \"status\": \"active\",\n \"accountid\": \"Test\",\n \"employeeid\": \"99999\",\n \"roleid\": \"Admin\",\n \"user\": \"Admin\",\n \"password\": default_admin_password,\n \"email\": \"testAdmin@amdocs.com\"\n },\n {\n \"status\": \"active\",\n \"accountid\": \"Test\",\n \"employeeid\": \"99999\",\n \"roleid\": \"SuperAdmin\",\n \"user\": \"SuperAdmin\",\n \"password\": default_admin_password,\n \"email\": \"SuperAdmin@amdocs.com\"\n },\n {\n \"status\": \"active\",\n \"accountid\": \"Test\",\n \"employeeid\": \"99999\",\n \"roleid\": \"Operator\",\n \"user\": \"Operator\",\n \"password\": \"12345\",\n \"email\": \"testOperator@amdocs.com\"\n },\n {\n \"status\": \"active\",\n \"accountid\": \"Test\",\n \"employeeid\": \"99999\",\n \"roleid\": \"Guest\",\n \"user\": \"Guest\",\n \"password\": \"guest\",\n \"email\": \"testGuest@amdocs.com\"\n },\n {\n \"status\": \"active\",\n \"accountid\": \"Test\",\n \"employeeid\": \"99999\",\n \"roleid\": \"DPMsysCI\",\n \"user\": \"DPMsysCI\",\n \"password\": \"dpmsysci\",\n \"email\": \"testDPMsysCI@amdocs.com\"\n }\n]\n\nCONFIG_DATA = [\n {\n \"name\": \"Mailer\",\n \"configid\": 1,\n \"debug\": \"False\",\n \"host\": \"umg.corp.amdocs.com\",\n \"server\": \"umg.corp.amdocs.com\",\n \"port\": \"587\",\n \"tls\": \"True\",\n \"ssl\": \"False\",\n \"username\": \"\",\n \"password\": \"\",\n \"defaultsender\": \"DeploymentManager-DoNotReply@amdocs.com\",\n \"socketip\": \"127.0.0.1\",\n \"socketport\": \"8080\",\n \"field_types\": [\n {\"name\": \"username\", \"type\": \"textbox\"},\n {\"name\": \"tls\", \"type\": \"dropdown\",\n \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"password\", \"type\": \"password\"},\n {\"name\": \"port\", \"type\": \"number\"},\n {\"name\": \"defaultsender\", \"type\": \"textbox\"},\n {\"name\": \"debug\", \"type\": \"dropdown\",\n \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"server\", \"type\": \"textbox\"},\n {\"name\": \"ssl\", \"type\": \"dropdown\",\n \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"host\", \"type\": \"textbox\"},\n {\"name\": \"socketip\", \"type\": \"textbox\"},\n {\"name\": \"port\", \"type\": \"number\"}\n ]\n },\n {\n \"name\": \"MailerService\",\n \"intervalGiven\": \"1\",\n \"configid\": 2,\n \"enable\": \"true\",\n \"type\": \"interval\",\n \"hrs\": \"00\",\n \"min\": \"00\",\n \"field_types\": [\n {\"name\": \"intervalGiven\", \"type\": \"number\"},\n {\"name\": \"enable\", \"type\": \"dropdown\",\n \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"type\", \"type\": \"dropdown\",\n \"available_values\": [\"interval\", \"scheduled\"]},\n {\"name\": \"hrs\", \"type\": \"number\"},\n {\"name\": \"min\", \"type\": \"number\"}\n ]\n },\n {\n \"name\": \"DeploymentRequestService\",\n \"intervalGiven\": \"0.5\",\n \"noOfThreads\": \"2\",\n \"configid\": 3,\n \"enable\": \"true\",\n \"type\": \"interval\",\n \"hrs\": \"00\",\n \"min\": \"00\",\n \"skipDeploymentInd\":\"true\",\n \"machineMatchingInd\":\"false\",\n \"enable_callback\": \"false\",\n \"callback_timeout\": 30,\n \"field_types\": [\n {\"name\": \"intervalGiven\", \"type\": \"number\"},\n {\"name\": \"noOfThreads\", \"type\": \"number\"},\n {\"name\": \"enable\", \"type\": \"dropdown\",\n \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"type\", \"type\": \"dropdown\",\n \"available_values\": [\"interval\", \"scheduled\"]},\n {\"name\": \"hrs\", \"type\": \"number\"},\n {\"name\": \"min\", \"type\": \"number\"},\n {\"name\": \"skipDeploymentInd\", \"type\": \"dropdown\",\n \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"machineMatchingInd\", \"type\": \"dropdown\",\n \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"enable_callback\", \"type\": \"dropdown\", \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"callback_timeout\", \"type\": \"number\"}\n ]\n },\n {\n \"name\": \"CloneRequestService\",\n \"intervalGiven\": \"0.5\",\n \"noOfThreads\": \"2\",\n \"configid\": 4,\n \"enable\": \"true\",\n \"type\": \"interval\",\n \"hrs\": \"00\",\n \"min\": \"00\",\n \"field_types\": [\n {\"name\": \"intervalGiven\", \"type\": \"number\"},\n {\"name\": \"noOfThreads\", \"type\": \"number\"},\n {\"name\": \"enable\", \"type\": \"dropdown\",\n \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"type\", \"type\": \"dropdown\",\n \"available_values\": [\"interval\", \"scheduled\"]},\n {\"name\": \"hrs\", \"type\": \"number\"},\n {\"name\": \"min\", \"type\": \"number\"}\n ]\n },\n {\n \"name\": \"AppLogger\",\n \"loggingLevel\": \"TRACE\",\n \"configid\": 5,\n \"enable\": \"true\",\n \"log_to_console\": \"true\",\n \"backupCount\" : 0,\n \"logFormat\" :\"%(asctime)s[%(levelname)-5.5s]%(message)s\",\n \"dateFormat\":\"%d-%m-%Y %H:%M:%S\",\n \"field_types\": [{\"name\": \"enable\", \"type\": \"dropdown\", \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"loggingLevel\", \"type\": \"dropdown\",\n \"available_values\":\n ['CRITICAL', 'ERROR', 'WARNING', 'INFO', 'DEBUG', 'NOTSET', 'TRACE']},\n {\"name\": \"logFormat\", \"type\": \"textbox\"},\n {\"name\": \"dateFormat\", \"type\": \"textbox\"},\n {\"name\": \"log_to_console\", \"type\": \"dropdown\", \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"backupCount\", \"type\": \"number\"}\n ]\n },\n {\n \"name\": \"AuthService\",\n \"expiration\": \"6000\",\n \"configid\": 6,\n \"enable_ldap\":\"false\",\n \"allow_multi_user_session\":\"false\",\n \"ldap_server\": \"ldap://raappdc1.corp.amdocs.com:3268\",\n \"ldap_base_dn\": \"DC=corp,DC=amdocs,DC=com\",\n \"email_domain\": \"@amdocs.com\",\n \"admin_role_groups\": \"eDPMDev\",\n \"operator_role_groups\": \"\",\n \"field_types\": [{\"name\": \"expiration\", \"type\": \"number\"},\n {\"name\": \"enable_ldap\", \"type\": \"dropdown\", \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"ldap_server\", \"type\": \"textbox\"},\n {\"name\": \"ldap_base_dn\", \"type\": \"textbox\"},\n {\"name\": \"email_domain\", \"type\": \"textbox\"},\n {\"name\": \"admin_role_groups\", \"type\": \"textbox\"},\n {\"name\": \"operator_role_groups\", \"type\": \"textbox\"},\n {\"name\": \"allow_multi_user_session\", \"type\": \"dropdown\", \"available_values\": [\"true\", \"false\"]}]\n },\n {\n \"name\": \"CloneAccountServiceDetails\",\n \"gitlab_token\": \"oiV1nt2VWEFtUJZfgM8F\",\n \"gitlab_user\": \"vpadmin\",\n \"gitlab_password\": \"Unix11!!\",\n \"local_jenkins_job_path\": \"/var/lib/jenkins/jobs/\",\n \"jenkins_version\": \"1.651.3\",\n \"git_lab_rest_api_url\": \"http://illin4467:80/api/v3/\",\n \"remote_dpm_port\": \"8000\",\n \"configid\": 7,\n \"target_artifact_auth_repo_type\": \"nexus2:2.14.2_01\",\n \"target_dpm_user\": \"admin\",\n \"target_dpm_password\" :\"12345\",\n \"account_gitlab_password\":\"vpadmin123\",\n \"field_types\": [{\"name\": \"gitlab_token\", \"type\": \"textbox\"},\n {\"name\": \"gitlab_user\", \"type\": \"textbox\"},\n {\"name\": \"gitlab_password\", \"type\": \"password\"},\n {\"name\": \"local_jenkins_job_path\", \"type\": \"textbox\"},\n {\"name\": \"git_lab_rest_api_url\", \"type\": \"textbox\"},\n {\"name\": \"remote_dpm_port\", \"type\": \"number\"},\n {\"name\": \"target_dpm_user\", \"type\": \"textbox\"},\n {\"name\": \"account_gitlab_password\", \"type\": \"password\"},\n {\"name\": \"target_dpm_password\", \"type\": \"password\"},\n {\"name\": \"jenkins_version\", \"type\": \"dropdown\", \"available_values\": [\"1.651.3\", \"2.140\",\"2.149\"]},\n {\"name\": \"target_artifact_auth_repo_type\",\"type\": \"dropdown\", \"available_values\": [\"nexus2:2.14.2_01\", \"nexus3:3.12.0\"]},\n ]\n },\n {\n \"name\": \"JenkinsAuth\",\n \"jenkins_user\": \"vpadmin\",\n \"jenkins_pass\": \"vpadmin\",\n \"configid\": 8,\n \"field_types\": [{\"name\": \"jenkins_user\", \"type\": \"textbox\"},\n {\"name\": \"jenkins_pass\", \"type\": \"password\"},\n ]\n },\n {\n \"name\": \"SyncServices\",\n \"intervalGiven\": \"10\",\n \"enable\": \"false\",\n \"full_sync_flag\": \"true\",\n \"distribution_list\": \"admin@amdocs.com\",\n \"configid\": 9,\n \"type\": \"scheduled\",\n \"hrs\": \"00\",\n \"min\": \"00\",\n \"enable_callback\": \"false\",\n \"callback_timeout\": 30,\n \"field_types\": [{\"name\": \"intervalGiven\", \"type\": \"number\"},\n {\"name\": \"full_sync_flag\",\n \"type\": \"dropdown\", \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"enable\", \"type\": \"dropdown\",\n \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"distribution_list\", \"type\": \"email\"},\n {\"name\": \"type\", \"type\": \"dropdown\",\n \"available_values\": [\"interval\", \"scheduled\"]},\n {\"name\": \"hrs\", \"type\": \"number\"},\n {\"name\": \"min\", \"type\": \"number\"},\n {\"name\": \"enable_callback\",\n \"type\": \"dropdown\", \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"callback_timeout\", \"type\": \"number\"}\n ]\n },\n {\n \"name\": \"PullServices\",\n \"intervalGiven\": \"10\",\n \"configid\": 10,\n \"enable\": \"false\",\n \"timeout\": \"600\", # 5 mins,\n \"type\": \"scheduled\",\n \"hrs\": \"00\",\n \"min\": \"00\",\n \"count_of_files\": \"2\",\n \"field_types\": [{\"name\": \"intervalGiven\", \"type\": \"number\"},\n {\"name\": \"enable\", \"type\": \"dropdown\", \\\n \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"timeout\", \"type\": \"number\"},\n {\"name\": \"type\", \"type\": \"dropdown\", \\\n \"available_values\": [\"interval\", \"scheduled\"]},\n {\"name\": \"hrs\", \"type\": \"number\"},\n {\"name\": \"min\", \"type\": \"number\"}\n ]\n },\n {\n \"name\": \"PushServices\",\n \"intervalGiven\": \"10\",\n \"configid\": 11,\n \"enable\": \"false\",\n \"remote_machine_import_path\": remote_sync_import_path,\n \"type\": \"scheduled\",\n \"hrs\": \"00\",\n \"min\": \"00\",\n \"count_of_files\": \"2\",\n \"allow_split\": \"true\",\n \"field_types\": [{\"name\": \"intervalGiven\", \"type\": \"number\"},\n {\"name\": \"enable\", \"type\": \"dropdown\",\n \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"remote_machine_import_path\", \"type\": \"textbox\"},\n {\"name\": \"type\", \\\n \"type\": \"dropdown\", \"available_values\": [\"interval\", \"scheduled\"]},\n {\"name\": \"hrs\", \"type\": \"number\"},\n {\"name\": \"min\", \"type\": \"number\"},\n {\"name\": \"count_of_files\", \"type\": \"number\"},\n {\"type\": \"dropdown\", \"name\": \"allow_split\",\n \"available_values\": [\"true\", \"false\"]}\n ]\n },\n {\n \"name\": \"DistributionCenterService\",\n \"hrs\": \"00\",\n \"min\": \"00\",\n \"configid\": 12,\n \"enable\": \"false\",\n \"distribution_list\": \"admin@amdocs.com\",\n \"remote_machine_import_path\": remote_distribution_import_path,\n \"type\": \"scheduled\",\n \"intervalGiven\": \"10\",\n \"field_types\": [{\"name\": \"intervalGiven\", \"type\": \"number\"},\n {\"name\": \"enable\", \"type\": \"dropdown\",\n \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"remote_machine_import_path\", \"type\": \"textbox\"},\n {\"name\": \"distribution_list\", \"type\": \"email\"},\n {\"name\": \"type\", \\\n \"type\": \"dropdown\", \"available_values\": [\"interval\", \"scheduled\"]},\n {\"name\": \"hrs\", \"type\": \"number\"},\n {\"name\": \"min\", \"type\": \"number\"}\n ]\n },\n {\n \"name\": \"DistributionSyncServices\",\n \"intervalGiven\": \"10\",\n \"enable\": \"false\",\n \"distribution_list\": \"admin@amdocs.com\",\n \"configid\": 13,\n \"type\": \"scheduled\",\n \"hrs\": \"00\",\n \"min\": \"00\",\n \"field_types\": [{\"name\": \"intervalGiven\", \"type\": \"number\"},\n {\"name\": \"enable\", \"type\": \"dropdown\",\n \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"distribution_list\", \"type\": \"email\"},\n {\"name\": \"type\", \\\n \"type\": \"dropdown\", \"available_values\": [\"interval\", \"scheduled\"]},\n {\"name\": \"hrs\", \"type\": \"number\"},\n {\"name\": \"min\", \"type\": \"number\"}\n ]\n },\n {\n \"name\": \"ContributionCenterService\",\n \"intervalGiven\": \"10\",\n \"enable\": \"false\",\n \"git_path\": base_path + \"git\",\n \"distribution_list\": \"admin@amdocs.com\",\n \"configid\": 14,\n \"type\": \"scheduled\",\n \"hrs\": \"00\",\n \"min\": \"00\",\n \"field_types\": [{\"name\": \"intervalGiven\", \"type\": \"number\"},\n {\"name\": \"enable\", \"type\": \"dropdown\",\n \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"git_path\", \"type\": \"textbox\"},\n {\"name\": \"distribution_list\", \"type\": \"email\"},\n {\"name\": \"type\", \\\n \"type\": \"dropdown\", \"available_values\": [\"interval\", \"scheduled\"]},\n {\"name\": \"hrs\", \"type\": \"number\"},\n {\"name\": \"min\", \"type\": \"number\"}\n ]\n }, \n {\n \"enable\" : \"false\",\n \"buildolderthandays\" : 30,\n \"olderthandays\" : 30,\n \"field_types\" : [ \n {\n \"type\" : \"number\",\n \"name\" : \"intervalGiven\"\n }, \n {\n \"type\" : \"dropdown\",\n \"name\" : \"enable\",\n \"available_values\" : [ \n \"true\", \n \"false\"\n ]\n }, \n {\n \"type\" : \"dropdown\",\n \"name\" : \"RemoveActualArtifacts\",\n \"available_values\" : [ \n \"true\", \n \"false\"\n ]\n }, \n {\n \"type\" : \"number\",\n \"name\" : \"buildcount\"\n }, \n {\n \"type\" : \"number\",\n \"name\" : \"olderthandays\"\n }, \n {\n \"type\" : \"dropdown\",\n \"name\" : \"type\",\n \"available_values\" : [ \n \"interval\", \n \"scheduled\"\n ]\n }, \n {\n \"type\" : \"number\",\n \"name\" : \"hrs\"\n }, \n {\n \"type\" : \"number\",\n \"name\" : \"min\"\n }, \n {\n \"type\" : \"number\",\n \"name\" : \"buildolderthandays\"\n }, \n {\n \"type\" : \"checkbox\",\n \"name\" : \"EntitiesToHandle\",\n \"available_values\" : [ \n \"Logos\", \n \"MediaFiles\", \n \"Emails\", \n \"GuestUsers\", \n \"GitPush\", \n \"Sync\", \n \"Distribition\", \n \"InactiveBuild\", \n \"ActiveBuild\", \n \"DeployementRequestlogs\", \n \"CloneRequestLogs\", \n \"OldData\",\n \"Auditing\"\n ]\n }\n ],\n \"EntitiesToHandle\" : {\n \"12\" : True,\n \"11\" : True,\n \"10\" : True,\n \"1\" : True,\n \"0\" : True,\n \"3\" : True,\n \"2\" : True,\n \"5\" : True,\n \"4\" : True,\n \"7\" : True,\n \"6\" : True,\n \"9\" : True,\n \"8\" : True\n },\n \"intervalGiven\" : 10,\n \"name\" : \"CleanerServices\",\n \"min\" : 0,\n \"RemoveActualArtifacts\" : \"false\",\n \"hrs\" : 0,\n \"configid\" : 15,\n \"type\" : \"scheduled\",\n \"buildcount\" : 30\n },\n {\n \"name\": \"AuditingServices\",\n \"olderthandays\": \"30\",\n \"enable\": \"true\",\n \"configid\": 19,\n \"field_types\": [{\"name\": \"enable\", \"type\": \"dropdown\",\n \"available_values\": [\"true\", \"false\"]},\n {\"name\": \"olderthandays\", \"type\": \"number\"}]\n },\n {\n \"name\": \"ProposedToolService\",\n \"support_details\": \"vpadmin@amdocs.com\",\n \"gitpath\": base_path + \"git\",\n \"jenkinspath\": base_path + \"jenkins\",\n \"package\": \"amdocs.aio\",\n \"reponame\": \"vp_builds\",\n \"configid\": 20,\n \"enable\": \"true\",\n \"field_types\": [{\"name\": \"support_details\", \"type\": \"textbox\"},\n {\"name\": \"gitpath\", \"type\": \"textbox\"},\n {\"name\": \"jenkinspath\", \"type\": \"textbox\"},\n {\"name\": \"package\", \"type\": \"textbox\"},\n {\"name\": \"reponame\", \"type\": \"textbox\"},\n {\"name\": \"enable\", \"type\": \"dropdown\",\n \"available_values\": [\"true\", \"false\"]}]\n },\n {\n \"name\": \"FabricService\",\n \"configid\": 21,\n \"command_timeout\": 60*60,\n \"field_types\": [{\"name\": \"command_timeout\", \"type\": \"number\"}]\n },\n {\n \"name\": \"Deployment\",\n \"configid\": 22,\n \"entity_exposed_attributes\": \"name\",\n \"machine_exposed_attributes\": \"machine_name\",\n \"field_types\": [{\"name\": \"entity_exposed_attributes\", \"type\": \"textbox\"},\n {\"name\": \"machine_exposed_attributes\", \"type\": \"textbox\"}]\n },\n {\n \"name\": \"MachineGroup\",\n \"configid\": 23,\n \"deployment_details_count_to_display\": 30,\n \"field_types\": [{\"name\": \"deployment_details_count_to_display\", \"type\": \"textbox\"}] \n }\n]\n\n\nEXITPOINTPLUGINS = [\n {\n \"repo_provider\": \"Yum\",\n \"plugin_name\": \"YumSyncPlugin\",\n \"type\": \"sync\",\n \"repo_user\": \"admin\",\n \"repo_password\": \"admin123\",\n \"repo_url\": \"http://\"+default_nexus_container_name+\":8081/nexus/service/local/artifact/maven/content\"\n },\n {\n \"repo_provider\": \"Docker\",\n \"plugin_name\": \"DockerSyncPlugin\",\n \"type\": \"sync\"\n },\n {\n \"plugin_name\": \"DefaultDeploymentPlugin\",\n \"type\": \"deployment\"\n },\n {\n \"plugin_name\": \"DirectNexusDeploymentPlugin\",\n \"type\": \"deployment\"\n },\n {\n \"plugin_name\": \"WindowsDeploymentPlugin\",\n \"type\": \"deployment\"\n },\n\t{\n \"plugin_name\": \"DefaultSudoDeploymentPlugin\",\n \"type\": \"deployment\"\n },\n {\n \"plugin_name\": \"DefaultNexus2RepositoryPlugin\",\n \"type\": \"repository\"\n },\n {\n \"plugin_name\": \"SWPPPCustomNexus2RepositoryPlugin\",\n \"type\": \"repository\"\n },\n {\n \"plugin_name\": \"DefaultNexus3RepositoryPlugin\",\n \"type\": \"repository\"\n },\n {\n \"plugin_name\": \"DummyRepositoryPlugin\",\n \"type\": \"repository\"\n },\n {\n \"plugin_name\": \"JfrogArtifactoryRepositoryPlugin\",\n \"type\": \"repository\"\n },\n {\n \"plugin_name\": \"SWPPPCustomDeploymentPlugin\",\n \"type\": \"deployment\"\n }\n]\n\nREPOSITORY_DATA=[\n {\n \"name\":\"DefaultNexus2Repository\", \n \"repo_user\": \"admin\",\n \"repo_pass\": \"admin123\",\n \"upload_protocol\": \"http\", #\"http\", \"mvn\",\"filesystem\"\n \"upload_type\": \"single\", # \"single\",\"bulk\"\n \"base_url\": \"http://\"+default_nexus_container_name+\":8081/nexus/repository\",\n \"file_path_url\": \"http://\"+default_nexus_container_name+\":8081/nexus/content/repositories\",\n \"http_url\": \"http://\"+default_nexus_container_name+\":8081/nexus/service/local/artifact/maven/content\",\n \"mvn_url\": \"http://\"+default_nexus_container_name+\":8081/nexus/content/repositories/\",\n \"list_all_repositories_url\": \"http://\"+default_nexus_container_name+\":8081/nexus/service/local/repositories\",\n \"create_repo_url\": \"http://\"+default_nexus_container_name+\":8081/nexus/service/local/repositories\", \n \"repo_path\": base_path+\"nexus/storage\",\n \"force_upload\":\"false\", #\"true\",\"false\"\n \"additional_artifacts_upload\":\"true\", #\"true\",\"false\"\n \"handler\" : \"DefaultNexus2RepositoryPlugin\",\n \"is_default_repo_ind\": \"true\"\n },\n {\n \"name\":\"DefaultNexus3Repository\",\n \"repo_user\": \"admin\",\n \"repo_pass\": \"admin123\",\n \"upload_protocol\": \"http\", #\"http\", \"mvn\"\n \"upload_type\": \"single\",# \"single\"\n \"base_url\": \"http://\"+default_nexus_container_name+\":8081/repository\",\n \"file_path_url\": \"http://\"+default_nexus_container_name+\":8081/repository\",\n \"mvn_url\": \"http://\"+default_nexus_container_name+\":8081/repository/\",\n \"list_all_repositories_url\": \"http://\"+default_nexus_container_name+\":8081/service/rest/beta/repositories\",\n \"add_script_url\": \"http://\"+default_nexus_container_name+\":8081/service/rest/v1/script\",\n \"run_script_url\": \"http://\"+default_nexus_container_name+\":8081/service/rest/v1/script/~/run\",\n \"remove_script_url\": \"http://\"+default_nexus_container_name+\":8081/service/rest/v1/script/~\", \n \"force_upload\":\"false\", #\"true\",\"false\"\n \"additional_artifacts_upload\":\"true\", #\"true\",\"false\"\n \"handler\" : \"DefaultNexus3RepositoryPlugin\",\n \"is_default_repo_ind\": \"false\"\n },\n {\n \"name\":\"JfrogArtifactoryRepository\",\n \"repo_user\": \"admin\",\n \"repo_pass\": \"password\",\n \"base_url\": \"http://vp_jfrog_artifactory:9045/artifactory\",\n \"additional_artifacts_upload\":\"true\", #\"true\",\"false\" \n \"handler\" : \"JfrogArtifactoryRepositoryPlugin\",\n \"is_default_repo_ind\": \"false\"\n },\n {\n \"name\":\"SWPPPKeyStoreRepository\",\n \"host\": \"kvstore\",\n \"port\": \"6379\",\n \"user\": \"redisadmin\",\n \"pass\": \"Unix11!\",\n \"additional_artifacts_upload\":\"false\", #\"true\",\"false\" \n \"handler\" : \"DummyRepositoryPlugin\",\n \"is_default_repo_ind\": \"false\"\n }\n ]","sub_path":"server/DBUtil/InitData/InitDataCommon.py","file_name":"InitDataCommon.py","file_ext":"py","file_size_in_byte":73031,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"580451494","text":"import requests\nimport re\nimport bs4\nfrom bs4 import BeautifulSoup\nimport bs4\nimport pymysql\n\nheaders ={\n 'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:75.0) Gecko/20100101 Firefox/75.0'\n}\n\n#全局变量\nglobal mid,eid,oid\nmid = int(0)\neid = int(0)\noid = int(0)\n\nmuseum_info = {}\n\nclass ConnMysql(object):\n def __init__(self):\n # 连接数据库\n self.db = pymysql.connect(host='39.97.241.101',\n port=3306,\n database='testsitedb',\n user='root',\n password='root',\n charset='utf8')\n self.cursor = self.db.cursor()\n def insert(self,dict1):\n global mid,eid,oid\n # 将数据添加到数据库中的movie表中\n sql_1 = \"insert into museums(name,imgurl,mobile,address,introduction,opentime) values(%s,%s,%s,%s,%s,%s)\"\n for i in dict1[\"1\"]: \n data_1 = [i[\"name\"],i[\"img\"],i['number'],i['location'],i['description'],i['opentime']]\n try:\n self.cursor.execute(sql_1,data_1)\n self.db.commit() # 提交操作\n except:\n self.db.rollback()\n sql_2 = \"insert into exhibitions(name,imgurl,introduction,mname) values(%s,%s,%s,%s)\"\n for i in dict1[\"2\"]:\n data_2 = [i[\"name\"],i[\"img\"],i['description'],i[\"mname\"]]\n try:\n self.cursor.execute(sql_2,data_2)\n self.db.commit() # 提交操作\n except:\n self.db.rollback()\n\n sql_3 = \"insert into collections(name,imgurl,introduction,mname) values(%s,%s,%s,%s)\"\n for i in dict1[\"3\"]:\n data_3 = [i[\"name\"],i[\"img\"],i['description'],i[\"mname\"]]\n try:\n self.cursor.execute(sql_3,data_3)\n self.db.commit() # 提交操作\n except:\n self.db.rollback()\n\n sql_4 = \"insert into educations(name,imgurl,introduction,time,mname) values(%s,%s,%s,%s,%s)\"\n for i in dict1[\"4\"]:\n data_4 = [i[\"name\"],i[\"img\"],i['description'],i['time'],i[\"mname\"]]\n try:\n self.cursor.execute(sql_4,data_4)\n self.db.commit() # 提交操作\n except:\n self.db.rollback()\n\n self.db.close()\n def dataselect(self, issue, db_table):\n try:\n sql = \"SELECT '%s' FROM %s \" % (issue, db_table)\n self.cursor.execute(sql)\n self.db.commit() # 提交操作\n except:\n self.db.rollback()\n finally:\n return issue\ndef save_data(dict_data):\n # 存数据库\n database = ConnMysql()\n database.insert(dict_data)\n print(\"数据保存\")\n\ndef get_text(url):\n try:\n res = requests.get(url)\n res.raise_for_status()\n res.encoding = res.apparent_encoding\n return res.text\n except:\n return \"\"\n\ndef get_soup(url):\n text = get_text(url)\n soup = BeautifulSoup(text,\"html.parser\")\n return soup\n\ndef get_soup1(url):\n res = requests.get(url,headers = headers)\n res.encoding = 'utf-8'\n soup = BeautifulSoup(res.text,\"html.parser\")\n return soup\n\ndef get_brief(url):\n brief ={}\n soup = get_soup1(url)\n print(\"------博物馆简介------\")\n brief = soup.find('div',id = 'j-shareAbstract',style = 'display: none')\n description = brief.text\n brief[\"description\"] = description \n #print(description)\n div = soup.find('div',attrs={'class':'abstract_main'})\n #img = div.find('img',attrs={'width':'250'})\n #src = img[\"src\"]\n print(\"------参观信息------\")\n visit = soup.find('table',class_ ='abstract_tbl')\n info = visit.find_all('tr')\n for tag in info:\n title = tag.find('th',class_ = 'base-info-card-title')\n #print(title.text+\":\",end=\"\")\n texts = tag.find('div',class_ = 'base-info-card-value').find(text=True).strip()\n #print(texts)\n brief[\"name\"] = \"江汉关博物馆\"\n brief[\"img\"] = \"http://www.jhgmuseum.com/upload/20180903103310kSns.jpg\"\n brief[\"location\"] = \"江汉关博物馆，位于武汉市汉口沿江大道129号 武汉国民政府旧址纪念馆（汉口南洋大楼），位于汉口中山大道708号 詹天佑故居博物馆，位于汉口洞庭街65号\"\n brief[\"number\"] = \" 预约电话：027-82880866\"\n brief[\"opentime\"] = \"开放时间：周二至周日9:00-17:00(16:30停止入馆)，周一闭馆整休。\" \n return brief\n\ndef visit(url):\n soup = get_soup1(url)\n div = soup.find('div',attrs={'class':'screen noteArea'})\n print(\"------博物馆简介------\")\n p = div.find_all('p')\n brief = \"\"\n for tag in p:\n brief = brief+tag.text\n print(brief)\n print(\"------参观信息------\")\n div = soup.find('div',attrs={'class':'museumInfo'})\n print(div.text)\n visit = \"江汉关博物馆，位于武汉市汉口沿江大道129号 武汉国民政府旧址纪念馆（汉口南洋大楼），位于汉口中山大道708号 詹天佑故居博物馆，位于汉口洞庭街65号\"\n print(visit)\n\ndef show(url):\n exhibition = {}\n home = \"http://www.jhgmuseum.com\"\n soup = get_soup1(url)\n div = soup.find('div',attrs={'class':'article'})\n p = div.find_all('p')\n main = \"\"\n for tag in p:\n main = main+tag.text\n exhibition[\"description\"] = main\n #print(main)\n img = div.find_all('img')\n i = 0\n for tag in img:\n i = i+1\n src = home+tag[\"src\"]\n exhibition[\"img\"] = src\n #print(\"展览图示:\"+src)\n #if i == 4:\n break\n exhibition[\"mname\"] = \"江汉关博物馆\"\n return exhibition\n\ndef education(url):\n edu = {}\n home = \"http://www.jhgmuseum.com\"\n soup = get_soup1(url)\n div = soup.find('div',attrs={'class':'article'})\n p = div.find_all('p')\n main = \"\"\n for tag in p:\n main = main+tag.text\n edu[\"description\"] = main\n #print(main)\n img = div.find_all('img')\n i = 0\n for tag in img:\n i = i+1\n src = home+tag[\"src\"]\n edu[\"img\"] = src\n #print(\"活动写照:\"+src)\n #if i == 3:\n break\n edu[\"mname\"] = \"江汉关博物馆\"\n return edu\n\nurl = \"https://baike.sogou.com/v139691332.htm?fromTitle=%E6%B1%9F%E6%B1%89%E5%85%B3%E5%8D%9A%E7%89%A9%E9%A6%86\"\nx = get_brief(url)\na = []\na.append(x)\n\nexhibitions = []\nprint(\"------展览陈列------\")\nurl = \"http://www.jhgmuseum.com/section-50.html\"\nx = show(url)\nx[\"name\"] = \"武汉国民政府旧址\"\nexhibitions.append(x)\nprint(\"\\n\")\nurl = \"http://www.jhgmuseum.com/section-49.html\"\nx = show(url)\nx[\"name\"] = \"詹天佑故居\"\nexhibitions.append(x)\nprint(\"\\n\")\nurl = \"http://www.jhgmuseum.com/section-27.html\"\nx = show(url)\nx[\"name\"] = \"《江汉朝宗——武汉城市现代化历程》\"\nexhibitions.append(x)\nprint(\"\\n\")\n\neducational = []\nprint(\"------教育活动------\")\nurl = \"http://www.jhgmuseum.com/article-614.html\"\nz = education(url)\nz[\"name\"] = \"出彩江汉关 | 踏青迎春度清明，缅怀先祖寄哀思\"\nz[\"time\"] = \"2019年04月08日\"\neducational.append(z)\nprint(\"\\n\")\nurl = \"http://www.jhgmuseum.com/article-580.html\"\nz = education(url)\nz[\"name\"] = \"江汉关博物馆送中秋知识进小学\"\nz[\"time\"] = \"2018年09月21日\"\neducational.append(z)\nprint(\"\\n\")\nurl = \"http://www.jhgmuseum.com/article-388.html\"\nz = education(url)\nz[\"name\"] = \"无诗书 不青春——江汉关博物馆开展“悦读青春·再识鲁迅”五四专题活动\"\nz[\"time\"] = \"2018年05月04日\"\neducational.append(z)\nprint(\"\\n\")\nurl = \"http://www.jhgmuseum.com/article-390.html\"\nz = education(url)\nz[\"name\"] = \"落梅生春度新年\"\nz[\"time\"] = \"2018年01月14日\"\neducational.append(z)\nprint(\"\\n\")\n\nmuseum_info[\"1\"] = a\nmuseum_info[\"2\"] = exhibitions\nmuseum_info[\"3\"] = []\nmuseum_info[\"4\"] = educational\n\nsave_data(museum_info)","sub_path":"src/博物馆爬取陈润/江汉关博物馆.py","file_name":"江汉关博物馆.py","file_ext":"py","file_size_in_byte":7894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"476401087","text":"#!/usr/bin/python3\n\"\"\"Fabric script\"\"\"\nfrom fabric.api import *\nfrom datetime import datetime\nimport os\nenv.hosts = ['35.185.2.183', '54.145.7.110']\n\n\ndef do_pack():\n \"\"\"Create folder and file\"\"\"\n time = datetime.now().strftime(\"%Y%m%d%H%M%S\")\n local(\"mkdir -p versions\")\n file = \"versions/web_static_{}.tgz\" .format(time)\n var = local(\"tar -cvzf {} web_static\" .format(file))\n if var.succeeded:\n return file\n else:\n return None\n\n\ndef do_deploy(archive_path):\n \"\"\"Deploy\"\"\"\n if os.path.exists(archive_path) is False:\n return False\n try:\n file = archive_path.split(\"/\")[-1]\n ext = file.split(\".\")[0]\n path = \"/data/web_static/releases/\"\n put(archive_path, '/tmp/')\n run('mkdir -p {}{}/'.format(path, ext))\n run('tar -xzf /tmp/{} -C {}{}/'.format(file, path, ext))\n run('rm /tmp/{}'.format(file))\n run('mv {}{}/web_static/* {}{}/'.format(path, ext, path, ext))\n run('rm -rf {}{}/web_static'.format(path, ext))\n run('rm -rf /data/web_static/current')\n run('ln -s {}{}/ /data/web_static/current'.format(path, ext))\n return True\n except:\n return False\n\n\ndef deploy():\n \"\"\"Full deployment\"\"\"\n archive_path = do_pack()\n if archive_path is None:\n return False\n return do_deploy(archive_path)\n","sub_path":"3-deploy_web_static.py","file_name":"3-deploy_web_static.py","file_ext":"py","file_size_in_byte":1348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"636290689","text":"import unittest\n\nfrom cupy import sparse\nfrom cupy import testing\n\nimport pytest\n\n\n@testing.parameterize(*testing.product({\n 'format': ['csr', 'csc'],\n 'density': [0.1, 0.4, 0.9],\n 'dtype': ['float32', 'float64', 'complex64', 'complex128'],\n 'n_rows': [25, 150],\n 'n_cols': [25, 150]\n}))\n@testing.with_requires('scipy>=1.4.0')\n@testing.gpu\nclass TestIndexing(unittest.TestCase):\n\n def _run(self, maj, min=None, format=None):\n\n # Skipping tests that are only supported in one\n # format for now.\n if format is not None and format != self.format:\n pytest.skip()\n\n a = sparse.random(self.n_rows, self.n_cols,\n format=self.format,\n density=self.density)\n\n # sparse.random doesn't support complex types\n # so we need to cast\n a = a.astype(self.dtype)\n\n expected = a.get()\n\n if min is not None:\n expected = expected[maj, min]\n actual = a[maj, min]\n else:\n expected = expected[maj]\n actual = a[maj]\n\n if sparse.isspmatrix(actual):\n actual.sort_indices()\n expected.sort_indices()\n\n testing.assert_array_equal(\n actual.indptr, expected.indptr)\n testing.assert_array_equal(\n actual.indices, expected.indices)\n testing.assert_array_equal(\n actual.data, expected.data)\n else:\n testing.assert_array_equal(\n actual, expected)\n\n def test_major_slice(self):\n self._run(slice(5, 9))\n self._run(slice(9, 5))\n\n def test_major_all(self):\n self._run(slice(None))\n\n def test_major_scalar(self):\n self._run(10)\n self._run(-10)\n\n def test_major_slice_minor_slice(self):\n self._run(slice(1, 5), slice(1, 5))\n\n def test_major_slice_minor_all(self):\n self._run(slice(1, 5), slice(None))\n self._run(slice(5, 1), slice(None))\n\n def test_major_slice_with_step(self):\n\n # CSR Tests\n self._run(slice(1, 20, 2), slice(1, 5, 1),\n format='csr')\n self._run(slice(20, 1, 2), slice(1, 5, 1),\n format='csr')\n self._run(slice(1, 15, 2), slice(1, 5, 1),\n format='csr')\n self._run(slice(15, 1, 5), slice(1, 5, 1),\n format='csr')\n self._run(slice(1, 15, 5), slice(1, 5, 1),\n format='csr')\n self._run(slice(20, 1, 5), slice(None),\n format='csr')\n self._run(slice(1, 20, 5), slice(None),\n format='csr')\n\n # CSC Tests\n self._run(slice(1, 5, 1), slice(1, 20, 2),\n format='csc')\n self._run(slice(1, 5, 1), slice(20, 1, 2),\n format='csc')\n self._run(slice(1, 5, 1), slice(1, 15, 2),\n format='csc')\n self._run(slice(1, 5, 1), slice(15, 1, 5),\n format='csc')\n self._run(slice(None), slice(20, 1, 5),\n format='csc')\n self._run(slice(None), slice(1, 20, 5),\n format='csc')\n\n def test_major_scalar_minor_slice(self):\n self._run(5, slice(1, 5))\n\n def test_major_scalar_minor_all(self):\n self._run(5, slice(None))\n\n def test_major_scalar_minor_scalar(self):\n self._run(5, 5)\n\n def test_major_all_minor_scalar(self):\n self._run(slice(None), 5)\n\n def test_major_all_minor_slice(self):\n self._run(slice(None), slice(5, 10))\n\n def test_major_all_minor_all(self):\n self._run(slice(None), slice(None))\n\n def test_ellipsis(self):\n self._run(Ellipsis)\n self._run(Ellipsis, 1)\n self._run(1, Ellipsis)\n self._run(Ellipsis, slice(None))\n self._run(slice(None), Ellipsis)\n self._run(Ellipsis, slice(1, None))\n self._run(slice(1, None), Ellipsis)\n\n def test_bad_indexing(self):\n with pytest.raises(IndexError):\n self._run(\"foo\")\n\n with pytest.raises(IndexError):\n self._run(2, \"foo\")\n\n with pytest.raises(ValueError):\n self._run([1, 2, 3], [1, 2, 3, 4])\n","sub_path":"tests/cupyx_tests/scipy_tests/sparse_tests/test_index.py","file_name":"test_index.py","file_ext":"py","file_size_in_byte":4186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"246409272","text":"from network import HexNetwork\nfrom Hex_game import HexGame,chess\nimport numpy as np\nfrom numpy.random import choice\nimport torch\n\n\"\"\"\nclass: azalea_Player\nparams:\n net: pytorch net\n\"\"\"\nclass Azalea_Player:\n\n def __init__(self,net):\n self.name = 'azalea'\n self.net = net\n \"\"\"\n introduction： use pytorch net calculate the advantage in each place\\\n and get the legal move\n params:\n game: the game play\n competitive: if choose the best choice\n debug: if display game process\n returns:\n game_data: game play data for train\n move: the right move number\n \"\"\"\n def getMove(self,game,competitive = False,debug = False):\n\n if not (game.size == 11):\n raise Exception('Error: azalea player size must be 11')\n game_data = []\n\n legal_moves = []\n legalBoard = game.legalBoard()\n if(game.turn == chess.blue):\n legalBoard = legalBoard.T\n\n for i in range(game.size):\n for j in range(game.size):\n if(legalBoard[i][j] == True):\n legal_moves.append(i*game.size + j + 1)\n legal_moves = np.reshape(legal_moves,(1,len(legal_moves)))\n\n #board = game.board.reshape((1, game.size * game.size))\n if(game.turn == chess.red):\n board = game.board.reshape((1,game.size*game.size))\n else:\n board = (-game.board).T.reshape((1,game.size*game.size))\n azaleaBoard = np.zeros(np.shape(board),dtype=int)\n\n for i in range(game.size * game.size):\n if(board[0][i] == 1):\n azaleaBoard[0][i] = 1\n elif(board[0][i] == 0):\n azaleaBoard[0][i] = 0\n elif(board[0][i] == -1):\n azaleaBoard[0][i] = 2\n else:\n raise Exception('Error: undefined chess type')\n\n batch = {\n 'board': torch.tensor(azaleaBoard, device='cpu', dtype=torch.int32),\n 'moves': torch.tensor(legal_moves, device='cpu', dtype=torch.int64)\n }\n\n output = self.net.run(batch)\n prior_prob = np.exp(output['moves_logprob']\n .cpu().numpy())\n #prior_prob = self.formatPros(prior_prob)\n value = output['value'].cpu().numpy()\n #print(value)\n probs = self.formatPros(game,prior_prob,legal_moves)\n probs = probs.reshape((game.size, game.size))\n game_data.append((board,probs,value))\n if (game.turn == chess.blue):\n probs = probs.T\n if (debug == True):\n print(game)\n move = self.moveLegal(game, probs, competitive)\n return game_data,move\n\n \"\"\"\n introduction： find the seemly move \n params:\n game: the game play\n probs: the price of each place\n competitive: if choose the best place\n returns:\n the seemly move\n \"\"\"\n def moveLegal(self, game, probs, competitive=False):\n probs_list = probs.reshape((game.size * game.size))\n if (competitive == True):\n moveNum = np.argmax(probs_list)\n move = [moveNum // game.size,moveNum % game.size]\n return move\n else:\n move = choice(len(probs_list),1 , p=probs_list)\n move = [move[0] // game.size, move[0] % game.size]\n return move\n\n \"\"\"\n introduction： format the probability numpy\n params:\n game: the game play\n probs: the price of each place\n legal_moves: where\n returns:\n the seemly move\n \"\"\"\n def formatPros(self,game,probs,legal_moves):\n probs_format = np.zeros((1,game.size * game.size))\n normalize_probs = probs\n for i in range(legal_moves.size):\n probs_format[0][legal_moves[0][i] - 1] = normalize_probs[0][i]\n probs_format = probs_format.reshape((game.size * game.size))\n probs_format = probs_format / np.sum(probs_format)\n return probs_format\n\n'''\n#azalea player test\nnet = HexNetwork()\nnet.load('/home/reget/project/AlphaHex/azalea.policy.pth')\nplayer = azalea_Player(net)\nfor i in range(10):\n game = HexGame(11)\n while(game.isComplet() == False): \n x = int(input())\n y = int(input())\n game.move([x,y])\n print(game)\n data,move = player.getMove(game,competitive= False);\n game.move(move)\n print(game)\n'''","sub_path":"py_train_model/azalea_player.py","file_name":"azalea_player.py","file_ext":"py","file_size_in_byte":4381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"20237497","text":"## Simplify the backpropagation procedure and Chain Rule\n\nimport numpy as np\n\n## Definition of Sigmoid function\ndef Sig(x):\n return 1/(1 + np.exp(-x))\n\nx = -1.0\ny = 1.0\nz = 0.0\nstep = 0.01\n\nq = x * x + y * y + z * z\nf = Sig(q)\nprint(f)\nprint('\\n')\n\ndf = 1\ndq = (1 - f) * f * df\ndx = (2 * x) * dq\ndy = (2 * y) * dq\ndz = (2 * z) * dq\n\nx = x + step * dx\ny = y + step * dy\nz = z + step * dz\n\nq = x * x + y * y + z * z\nf = Sig(q)\nprint(dx)\nprint(dy)\nprint(dz)\nprint(x)\nprint(y)\nprint(z)\nprint(f)\n","sub_path":"NN/NN5.py","file_name":"NN5.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"561013995","text":"#!/usr/bin/env python3\n\n\"\"\" A python version of Vectorize1.R, sums all elements in a matrix\nusing loop or vectorization\"\"\"\n\n__appname__ = 'Vectorize1.py'\n__author__ = 'Hanyun Zhang (hanyun.zhang18@imperial.ac.uk)'\n__version__ = '0.0.1'\n\n# Import \nimport numpy as np\n\n# Create a 1000 * 1000 array\nM = np.random.random_sample((1000,1000))\n\n\ndef SumAllElements(M):\n\t\"\"\" Sum all elements in a matrix \"\"\"\n\tTot = 0\n\tfor i in range(M.shape[0]):\n\t\tfor j in range(M.shape[1]):\n\t\t\tTot = Tot + M[i][j]\n\treturn Tot\n\n## Calculate sum using loop\n# Record the time taken\nfrom timeit import default_timer as timer\nstart = timer()\ntotal = SumAllElements(M)\nend = timer()\ntimetaken = end - start\nprint (\"Looping sum is \", total)\nprint (\"Looping sum takes: \", timetaken)\n\n## Calculate sum with vectorization\nstart = timer()\ntotal = sum(map(sum, M))\nend = timer()\ntimetaken = end - start\nprint (\"Vectorized sum is \", total)\nprint (\"Vectorized sum takes: \", timetaken)\n\n\n\t\n\t\n\n","sub_path":"CodeSolution/Vectorize1.py","file_name":"Vectorize1.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"570984394","text":"# Define todays date and run the code to populate the data base.\n\nTODAYS_DATE = \"2018-09-22\"\n\n############################################################################################3\n\nfrom firebase import firebase\n\n# input: date \"YYYY-MM-DD\"\ndef games_today(date):\n f = firebase.FirebaseApplication('https://brobet-221407.firebaseio.com', None)\n\n games = f.get('Group/SchedulesBySeason/', None)\n for game in games:\n getString = 'Group/SchedulesBySeason/' + game\n gameDate = f.get(getString, \"Day time\")\n\n try:\n if gameDate[0:10] == date:\n HomeName = f.get('Group/SchedulesBySeason/' + game , \"Home Team ID\")\n AwayName = f.get('Group/SchedulesBySeason/' + game, \"Away Team ID\")\n pushString = HomeName + \" vs. \" + AwayName # ex. 'ARZST vs. WASH'\n\n f = firebase.FirebaseApplication('https://brobet-221407.firebaseio.com', None)\n f.patch(\"TodaysGame/\", {game : pushString})\n except: \n print(\"Skipped missing piece of data\")\n return 0\n\n# For populating database:\ngames_today(TODAYS_DATE)\n","sub_path":"current_day_games.py","file_name":"current_day_games.py","file_ext":"py","file_size_in_byte":1125,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"68873452","text":"import socket\nimport struct\nimport time\n\nclass MasterEtherCAT_v2:\n def __init__(self,NickName):\n poat = 0x88A4\n self.self = socket.socket(socket.PF_PACKET,socket.SOCK_RAW)\n timeval = struct.pack('ll', 0,1)\n self.self.setsockopt(socket.SOL_SOCKET,socket.SO_RCVTIMEO,timeval)\n self.self.setsockopt(socket.SOL_SOCKET,socket.SO_SNDTIMEO,timeval)\n self.self.setsockopt(socket.SOL_SOCKET, socket.SO_DONTROUTE, 1)\n self.self.settimeout(100)\n self.self.bind((NickName,poat))\n\n def build_socket(self,CMD,IDX,ADP,ADO,C,NEXT,IRQ,DATA,WKC):\n pduflame =[0]*(len(DATA)+13)\n pduflame[0] = CMD # CMD (1 byte)\n pduflame[1] = IDX # IDX (1 byte)\n pduflame[2] = (ADP&0xFF) # ADP (2 byte)\n pduflame[3] = (ADP&0xFF00)>>8 \n pduflame[4] = (ADO&0xFF) # ADO (2 byte)\n pduflame[5] = (ADO&0xFF00)>>8\n pduflame[6] = (len(DATA)&0xFF) # LEN (2 byte)\n pduflame[7] = (len(DATA)&0xFF00)>>8 \n pduflame[8] = (IRQ&0xFF) # IRQ (2 byte)\n pduflame[9] = (0x01&NEXT)<<16 | (0x01&C)<<15 | (IRQ&0x7F00)>>8 # IRQ (2 byte)\n for i in range(len(DATA)):\n #print ('[{:d}]: 0x{:02x}'.format(i,pduflame[i+10]))\n pduflame[10+i] = DATA[i]\n pduflame[10+len(DATA)] = (WKC&0xFF) # WKC (2 byte)\n pduflame[11+len(DATA)] = (WKC&0xFF00)>>8 # WKC (2 byte)\n #for i in range(len(scoket)):\n #print ('[%d]: 0x{:02x}'.format(scoket[i]) % (i))\n return pduflame\n def socket_write(self,pduflame):\n #----------------------------------------------------#\n send_mac=[0]*6\n send_mac[0:6] = 0xff,0xff,0xff,0xff,0xff,0xff\n receive_mac=[0]*6\n receive_mac[0:6] = 0x01,0x01,0x01,0x01,0x01,0x01\n self_head =[0]*2\n self_head[0] = 0x88\n self_head[1] = 0xA4\n #----------------------------------------------------#\n frame = [0]*2\n frame[0] = len(pduflame)\n frame[1] = 0x10 | ((0x700&len(pduflame))>>8)\n #----------------------------------------------------#\n scoket=[]\n scoket.extend(send_mac)\n scoket.extend(receive_mac)\n scoket.extend(self_head)\n scoket.extend(frame)\n scoket.extend(pduflame)\n #----------------------------------------------------#\n self.self.send(bytes(self_scoket))\n\n\n def socket_read(self):\n #time.sleep(0.1)\n recv = self.self.recv(1023)\n pduflame=[0]*len(recv)\n for i in range(len(recv)):\n if(i>=16):\n #print ('[{:d}]: 0x{:02x}'.format(i-16,recv[i]))\n pduflame[i-16] = recv[i]\n offset = 0\n cnt = 0\n buff = []\n while 1:\n if(i > offset):\n CMD = pduflame[0+offset] # CMD (1 byte)\n IDX = pduflame[1+offset] # IDX (1 byte)\n ADP = pduflame[2+offset] | (pduflame[3+offset]<<8) # ADP (2 byte)\n ADO = pduflame[4+offset] | (pduflame[5+offset]<<8) # ADO (2 byte)\n LEN = pduflame[6+offset] | (pduflame[7+offset]<<8) # LEN (2 byte)\n IRQ = pduflame[8+offset] | (pduflame[9+offset]<<8) # IRQ (2 byte)\n DATA = [0] * LEN\n for i in range(LEN):\n #print ('[{:d}]: 0x{:02x}'.format(i,pduflame[10+i]))\n DATA[i] = pduflame[10+offset+i]\n WKC = pduflame[9+offset+LEN+1] | (pduflame[9+offset+LEN+2]<<8) # WKC (2 byte)\n buff.append({CMD,IDX,ADP,ADO,LEN,IRQ,DATA,WKC})\n cnt = cnt+1\n offset = 9+LEN+2\n else:\n break \n #print(\"-\"*30)\n #print(\"CMD= 0x{:02x}\".format(CMD))\n #print(\"IDX= 0x{:02x}\".format(IDX))\n #print(\"ADP= 0x{:04x}\".format(ADP))\n #print(\"ADO= 0x{:04x}\".format(ADO))\n #print(\"LEN= 0x{:04x}\".format(LEN))\n #print(\"IRQ= 0x{:04x}\".format(IRQ))\n #for i in range(LEN):\n # print ('DATA[%d]: 0x{:02X}'.format(DATA[i]) % (i))\n #print(\"WKC= 0x{:04x}\".format(WKC))\n return buff\n \n def APRD(self,IDX,ADP,ADO,DATA):\n CMD = 0x01 # APRD\n C = 0\n NEXT = 0\n IRQ = 0x0000\n WKC = 0x0000\n return self.build_socket(CMD,IDX,ADP,ADO,C,NEXT,IRQ,DATA,WKC)\n\n def FPRD(self,IDX,ADP,ADO,DATA):\n CMD = 0x04 # FPRD\n C = 0\n NEXT = 0\n IRQ = 0x0000\n WKC = 0x0000\n return self.build_socket(CMD,IDX,ADP,ADO,C,NEXT,IRQ,DATA,WKC)\n\n def BRD(self,IDX,ADP,ADO,DATA):\n CMD = 0x07 # BRD\n C = 0\n NEXT = 0\n IRQ = 0x0000\n WKC = 0x0000\n return self.build_socket(CMD,IDX,ADP,ADO,C,NEXT,IRQ,DATA,WKC)\n\n def LRD(self,IDX,ADP,ADO,DATA):\n CMD = 0x0A # LRD\n C = 0\n NEXT = 0\n IRQ = 0x0000\n WKC = 0x0000\n return self.build_socket(CMD,IDX,ADP,ADO,C,NEXT,IRQ,DATA,WKC)\n\n def APWR(self,IDX,ADP,ADO,DATA):\n CMD = 0x02 # APWR\n C = 0\n NEXT = 0\n IRQ = 0x0000\n WKC = 0x0000\n return self.build_socket(CMD,IDX,ADP,ADO,C,NEXT,IRQ,DATA,WKC)\n\n def FPWR(self,IDX,ADP,ADO,DATA):\n CMD = 0x05 # FPWR\n C = 0\n NEXT = 0\n IRQ = 0x0000\n WKC = 0x0000\n return self.build_socket(CMD,IDX,ADP,ADO,C,NEXT,IRQ,DATA,WKC)\n\n def BWR(self,IDX,ADP,ADO,DATA):\n CMD = 0x08 # BWR\n C = 0\n NEXT = 0\n IRQ = 0x0000\n WKC = 0x0000\n return self.build_socket(CMD,IDX,ADP,ADO,C,NEXT,IRQ,DATA,WKC)\n\n def LWR(self,IDX,ADP,ADO,DATA):\n CMD = 0x0B # LWR\n C = 0\n NEXT = 0\n IRQ = 0x0000\n WKC = 0x0000\n return self.build_socket(CMD,IDX,ADP,ADO,C,NEXT,IRQ,DATA,WKC)\n\n def APRW(self,IDX,ADP,ADO,DATA):\n CMD = 0x03 # APRW\n C = 0\n NEXT = 0\n IRQ = 0x0000\n WKC = 0x0000\n return self.build_socket(CMD,IDX,ADP,ADO,C,NEXT,IRQ,DATA,WKC)\n\n def FPRW(self,IDX,ADP,ADO,DATA):\n CMD = 0x06 # FPRW\n C = 0\n NEXT = 0\n IRQ = 0x0000\n WKC = 0x0000\n return self.build_socket(CMD,IDX,ADP,ADO,C,NEXT,IRQ,DATA,WKC)\n\n def BRW(self,IDX,ADP,ADO,DATA):\n CMD = 0x09 # BRW\n C = 0\n NEXT = 0\n IRQ = 0x0000\n WKC = 0x0000\n return self.build_socket(CMD,IDX,ADP,ADO,C,NEXT,IRQ,DATA,WKC)\n\n def LRW(self,IDX,ADP,ADO,DATA):\n CMD = 0x0C # LRW\n C = 0\n NEXT = 0\n IRQ = 0x0000\n WKC = 0x0000\n return self.build_socket(CMD,IDX,ADP,ADO,C,NEXT,IRQ,DATA,WKC)\n\n def ARMW(self,IDX,ADP,ADO,DATA):\n CMD = 0x0D # ARMW\n C = 0\n NEXT = 0\n IRQ = 0x0000\n WKC = 0x0000\n return self.build_socket(CMD,IDX,ADP,ADO,C,NEXT,IRQ,DATA,WKC)\n def FRMW(self,IDX,ADP,ADO,DATA):\n CMD = 0x0E # FRMW\n C = 0\n NEXT = 0\n IRQ = 0x0000\n WKC = 0x0000\n return self.build_socket(CMD,IDX,ADP,ADO,C,NEXT,IRQ,DATA,WKC)\n\n def EEPROM_SetUp(self,ADP):\n self.ADP = ADP\n self.APWR(IDX=0x00,ADP=self.ADP,ADO=0x0500,DATA=[0x02])\n self.socket_read()\n #time.sleep(0.1)\n self.APWR(IDX=0x00,ADP=self.ADP,ADO=0x0500,DATA=[0x00])\n self.socket_read()\n #time.sleep(0.1)\n \n def EEPROM_Stasus(self,enable=0x00,command=0x00):\n d = command<<8 | enable\n self.APWR(IDX=0x00,ADP=self.ADP,ADO=0x0502,DATA=[d&0xFF,(d>>8)&0xFF])\n #time.sleep(0.05)\n (DATA,WKC) = self.socket_read()\n #time.sleep(0.05)\n while True:\n self.APRD(IDX=0x00,ADP=self.ADP,ADO=0x0502,DATA=[0x00,0x00])\n #time.sleep(0.05)\n (DATA,WKC) = self.socket_read()\n #time.sleep(0.05)\n #print(\"S= 0x{:04x}\".format(DATA[0]|DATA[1]<<8))\n d = DATA[0]&0xFF | DATA[1]<<8\n if d&0x8000 == 0:\n break\n #time.sleep(0.1)\n return (DATA,WKC)\n \n def EEPROM_AddrSet(self,addr=0x0000):\n self.APWR(IDX=0x00,ADP=self.ADP,ADO=0x0504,DATA=[addr&0xFF,(addr>>8)&0xFF])\n #time.sleep(0.05)\n (DATA,WKC) = self.socket_read()\n #time.sleep(0.05)\n return (DATA,WKC)\n \n def EEPROM_Read(self):\n self.APRD(IDX=0x00,ADP=self.ADP,ADO=0x0508,DATA=[0x00,0x00])\n #time.sleep(0.05)\n (DATA,WKC) = self.socket_read()\n #time.sleep(0.05)\n return (DATA,WKC)\n\n def EEPROM_Write(self,data):\n self.APWR(IDX=0x00,ADP=self.ADP,ADO=0x0508,DATA=[data&0xFF,(data>>8)&0xFF])\n #time.sleep(0.05)\n (DATA,WKC) = self.socket_read()\n #time.sleep(0.05)\n return (DATA,WKC)\n def EthereCAT_Reset(self):\n ADDR = 0x0041 # Reset レジスタ\n data = 0x0052 # 'R'\n self.APWR(IDX=0x00,ADP=self.ADP,ADO=ADDR,DATA=[data&0xFF,(data>>8)&0xFF])\n (DATA,WKC) = self.socket_read()\n ADDR = 0x0041 # Reset レジスタ\n data = 0x0045 # 'E'\n self.APWR(IDX=0x00,ADP=self.ADP,ADO=ADDR,DATA=[data&0xFF,(data>>8)&0xFF])\n (DATA,WKC) = self.socket_read()\n ADDR = 0x0041 # Reset レジスタ\n data = 0x0053 # 'S'\n self.APWR(IDX=0x00,ADP=self.ADP,ADO=ADDR,DATA=[data&0xFF,(data>>8)&0xFF])\n (DATA,WKC) = self.socket_read()\n return (DATA,WKC)\n ","sub_path":"beta/pyEtherCAT/MasterEtherCAT_v2.py","file_name":"MasterEtherCAT_v2.py","file_ext":"py","file_size_in_byte":9438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"359039619","text":"\nfrom project2 import Model\nimport pandas as pd\nimport numpy as np\n\ndata=pd.read_excel('AllData.xlsx')\n\n\n# Use pandas to convert the excel sheet into a 2D numpy array.\ndata=pd.DataFrame.to_numpy(data)\n\n# Suppose the array is in a variable named “data”.\nerr = 0\nfor i in set(data[:, 0]):\n train = data[data[:, 0] != i]\n test = data[data[:, 0] == i]\n X_train, y_train = train[:, 2:], train[:, 1]\n X_test, y_test = test[:, 2:], test[:, 1]\n m = Model() # a new model is used for each CV iteration\n # m = ModelPyTorch() # a new model is used for each CV iteration\n m.train(X_train, y_train)\n y_pred = m.predict(X_test)\n err += np.sum(y_pred != y_test)\n\ntotal_accuracy = 1 - err/len(data)\n\nprint(\"Total Accuracy: \"+ str(round(total_accuracy*100,1))+\"%\")","sub_path":"CSC7343-Deeplearningfinalproject/evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"80859541","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom metartface.items import MetartfaceItem\nfrom selenium import webdriver\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom time import sleep\n\n\nclass MetartSpider(scrapy.Spider):\n name = 'metart'\n allowed_domains = []\n # start_urls = ['http://met-art-faces.com/']\n\n def start_requests(self):\n yield scrapy.Request('http://met-art-faces.com/', self.face_parse, dont_filter=True)\n\n def face_parse(self, response):\n faces = response.xpath('//*[@id=\"content1\"]/div/div/h2/a/@href').extract()\n for face in faces:\n # print(\"=\"*100)\n # print(\"The face is: \",face)\n # print(\"=\" * 100)\n yield scrapy.Request(face, callback=self.parse_bigimg_address, dont_filter=True)\n next_page = response.xpath('//a[@class=\"nextpostslink\"]/@href').extract_first()\n yield scrapy.Request(next_page, callback=self.face_parse)\n\n def parse_bigimg_address(self, response):\n big_imgs = response.xpath('//div/p/a/@href').extract()\n for big_img in big_imgs:\n yield scrapy.Request(big_img, callback=self.parse)\n\n def parse(self, response):\n item = MetartfaceItem()\n img = response.url\n if \"img.yt\" in img:\n driver = webdriver.Chrome()\n driver.get(img)\n sleep(1)\n click_btn = driver.find_element_by_xpath('//*[@id=\"continuebutton\"]')\n ActionChains(driver).click(click_btn).perform()\n z = driver.find_element_by_xpath('//div[3]/a')\n true_url = z.get_attribute(name=\"href\")\n driver.close()\n elif img.endswith('jpg'):\n true_url = response.xpath('//*[@id=\"body\"]/center/p[2]/img/@src').extract_first()\n elif \"imagehosting.pro\" in img:\n true_url = response.xpath('//*[@id=\"container\"]/a/img/@src').extract_first()\n else:\n pass\n item['clear_url'] = true_url\n yield item\n","sub_path":"metartface/metartface/spiders/metart.py","file_name":"metart.py","file_ext":"py","file_size_in_byte":1991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"312849525","text":"#!/usr/bin/env python\n\"\"\"\nScript Header\n\n$Id: cmCC_3pcc_stress_bulk_conf_call.py\n\nCopyright (c) 2016-2017 Cisco Systems, Inc.\n\nReferences:\n Tph10312585c\n\nTest cases:\n test0101_bulk_conf_call_stress\n\nTopology:\n 1. 3 3pcc phones\n 2. 4 user ids\n (if using TLS, 4 asterisk user ids should be reserved)\n\nNotes:\n\nKnown Bugs:\n\n\"\"\"\n\nimport tng\nimport logging\nfrom tng.api import concurrent\nfrom tng_sl.contrib.setup_helper import SetupHelpersTestCase\nfrom tng_sl.device.endpoint.synergylite.synergylite_3pcc_extended \\\n import wait_for_ccapi_call_states, wait_for_registration_states\nfrom cmCC_3pcc_stress_base import StressCallBase\n\n\nlog = logging.getLogger('BulkConfCallStress')\n\n\nclass BulkConfCallStress(StressCallBase, SetupHelpersTestCase):\n helper_num_devices = 3\n\n @classmethod\n def setUpClass(cls):\n super(BulkConfCallStress, cls).setUpClass()\n\n if any([p.hardware == 'BigEasyVideo' for p in cls.devices]):\n concurrent([\n p.ccapi.set_param_value for p in cls.devices],\n {'Video': '0'})\n if cls.transport == 'TLS':\n log.info(\"Use Asterisk users when transport is TLS\")\n cls.proxy = cls.asterisk_info['as_ip_addr']\n cls.user_id1 = cls.asterisk_info['userID1']\n cls.user_id2 = cls.asterisk_info['userID2']\n cls.user_id3 = cls.asterisk_info['userID3']\n else:\n cls.certificate = ''\n\n cls.user_id4 = (\n cls.asterisk_info['userID4'] if cls.transport == 'TLS'\n else cls.phone_data['userID4'])\n\n def phone1_register_line():\n phone1_pvpairs = {\n 'Proxy[1]': cls.proxy,\n 'Proxy[{}]'.format(cls.phone1_line_num): cls.proxy,\n 'User ID[1]': cls.user_id1,\n 'User ID[{}]'.format(cls.phone1_line_num): cls.user_id4,\n 'SIP Transport[1]': cls.transport,\n 'SIP Transport[{}]'.format(\n cls.phone1_line_num): cls.transport,\n 'Call Appearances Per Line': cls.call_appearance,\n 'Custom CA Rule': cls.certificate}\n cls.oPhone1.ui.set_param_value(phone1_pvpairs)\n\n def phone2_register_line():\n phone2_pvpairs = {\n 'Proxy[1]': cls.proxy,\n 'User ID[1]': cls.user_id2,\n 'SIP Transport[1]': cls.transport,\n 'Call Appearances Per Line': cls.call_appearance,\n 'Custom CA Rule': cls.certificate}\n cls.oPhone2.ui.set_param_value(phone2_pvpairs)\n\n def phone3_register_line():\n phone3_pvpairs = {\n 'Proxy[1]': cls.proxy,\n 'User ID[1]': cls.user_id3,\n 'SIP Transport[1]': cls.transport,\n 'Call Appearances Per Line': cls.call_appearance,\n 'Custom CA Rule': cls.certificate}\n cls.oPhone3.ui.set_param_value(phone3_pvpairs)\n\n concurrent([\n phone1_register_line, phone2_register_line,\n phone3_register_line])\n wait_for_registration_states(\n (cls.oPhone1, cls.oPhone1, cls.oPhone2, cls.oPhone3),\n ('REGISTERED', 'REGISTERED', 'REGISTERED', 'REGISTERED'),\n (1, cls.phone1_line_num, 1, 1))\n\n # TIMS ID: Tph10312585c\n # Author: Jingming Xu (jingmxu@cisco.com)\n # Description and Test Steps:\n\n # 1. Register phone A first and last line, Phone B, C registered\n # 2. Make bulk conference call using Phone A as bridge\n # 2. Repeat the test with phoneA last line.\n\n # Verify:\n # 1. DUT won't hang.\n # 2. Conference won't drop\n\n # Topology:\n # 1. 3 phones\n\n def test0101_bulk_conf_call_stress(self):\n log.info(\"Start of test0101_bulk_conf_call_stress\")\n\n for codec in self.codecs.split(','):\n self.set_codec(codec)\n self.mpp_stress.run_stress_test(\n self.conf_call_stress, 1,\n call_rounds=int(self.call_rounds),\n test_name='{}_{}'.format(self._testMethodName, codec))\n self.mpp_stress.run_stress_test(\n self.conf_call_stress, self.phone1_line_num,\n call_rounds=int(self.call_rounds),\n test_name='{}_{}'.format(self._testMethodName, codec))\n\n log.info(\"End of test0101_bulk_conf_call_stress\")\n\n\ndef main():\n tng.api.runner()\n\nif __name__ == '__main__':\n tng.run(main)\n","sub_path":"common/Stress/cmCC_3pcc_stress_bulk_conf_call.py","file_name":"cmCC_3pcc_stress_bulk_conf_call.py","file_ext":"py","file_size_in_byte":4444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"55970906","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n# author:caozy time:19-1-15\nfrom django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n # url(r'^categories/$',views.CategoryView.as_view(),name='cagegories'),\n #/goods/categories/(?P\\d+)/hotskus/\n url(r'^categories/(?P\\d+)/hotskus/$',views.HotSKUListAPIView.as_view(),name='hot'),\n url(r'^categories/(?P\\d+)/skus/$',views.SKUListView.as_view(),name='list'),\n\n]\n\nfrom rest_framework.routers import DefaultRouter\n\nrouter=DefaultRouter()\nrouter.register('search',views.SKUSearchViewSet,base_name='skus_search')\nurlpatterns+=router.urls","sub_path":"mall/apps/goods/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"378611298","text":"# -*- coding: utf-8 -*-\n'''\n1. 明确目的\n2. 找到数据对应的网站\n3. 分析网页结构找到数据所在的标签位置\n\n 模拟http请求，向服务器发送这个请求，获取到服务器返回给我们的html\n 用正则表达式提取我们要的数据（名字，人气）\n 框架： Beautiful Soup 、 Scrapy\n 爬虫、反爬虫、反反爬虫\n Ip 代理Ip\n'''\nfrom urllib import request\nimport ssl\nimport re\n\nssl._create_default_https_context = ssl._create_unverified_context\n\nclass Spider():\n url = 'https://www.huya.com/l'\n root_pattern = '([\\s\\S]*?)'\n name_pattern = '([\\s\\S]*?)'\n number_pattern = '([\\s\\S]*?)'\n\n # 获取要爬取的html，并对其进行编码设置\n def __fetch_content(self):\n r = request.urlopen(Spider.url)\n # bytes\n htmls = r.read()\n htmls = str(htmls, encoding='utf-8')\n return htmls\n\n # 对html进行分析，通过正则表达式返回要用对数据\n def __analysis(self, htmls):\n root_htmls = re.findall(Spider.root_pattern, htmls)\n\n anchors = []\n for html in root_htmls:\n name = re.findall(Spider.name_pattern, html)\n number = re.findall(Spider.number_pattern, html)\n # 格式 {'name': ['卡尔'], 'number': ['609.7万']}\n anchor = {'name' : name, 'number' : number}\n anchors.append(anchor)\n # print(anchors)\n \n return anchors\n\n # 将数据格式转化为字典返回\n def __refine(self, anchors):\n l = lambda anchor: {'name' : anchor['name'][0].strip(),\n 'number' : anchor['number'][0]}\n # 格式 {'name': '卡尔', 'number': '609.7万'}\n return map(l, anchors)\n\n # 排序方法 降序排列\n def __sort(self, anchors):\n anchors = sorted(anchors, key=self.__sort_seed, reverse=True)\n # print(anchors)\n return anchors\n\n # 人数排序处理\n def __sort_seed(self, anchor):\n r = re.findall('\\d*', anchor['number'])\n number = float(r[0])\n if '万' in anchor['number']:\n number *= 10000\n return number\n \n # 展示打印方法\n def __show(self, anchors):\n for rank in range(0, len(anchors)):\n print('rank:' + str(rank + 1) + \n ' : ' + anchors[rank]['name'] + \n ' ' + anchors[rank]['number'])\n\n # 入口方法 主方法\n def go(self):\n htmls = self.__fetch_content()\n anchors = self.__analysis(htmls)\n anchors = list(self.__refine(anchors))\n anchors = self.__sort(anchors)\n self.__show(anchors)\n\nspider = Spider()\nspider.go()\n","sub_path":"basic-learn/原生爬虫/analysis.py","file_name":"analysis.py","file_ext":"py","file_size_in_byte":2718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"136196235","text":"\"\"\"\nModule for adding tasks to an independent data store\n\"\"\"\n\n\ndef add_task(name, time, task_store):\n \"\"\"\n Updates a given data store with a new task & estimated time to complete\n\n task: (string) name/short description of the task\n time: (int) esitmated number of minutes to complete the task\n task_store: (list) a list of objects representing each task currently in the list\n\n returns: (list) a new data store with all previous objects plus the new task object\n \"\"\"\n\n task = {\n 'name': name,\n 'time': time\n }\n\n return task_store + [task]\n","sub_path":"add_task.py","file_name":"add_task.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"220266093","text":"\"\"\"\nThis script adds a foreign key to the form_values table in the galaxy_user table\n\"\"\"\n\nimport logging\n\nfrom sqlalchemy import (\n Column,\n ForeignKey,\n Integer,\n MetaData\n)\n\nfrom galaxy.model.migrate.versions.util import (\n add_column,\n drop_column\n)\n\nlog = logging.getLogger(__name__)\nmetadata = MetaData()\n\n\ndef upgrade(migrate_engine):\n print(__doc__)\n metadata.bind = migrate_engine\n metadata.reflect()\n\n col = Column(\"form_values_id\", Integer, ForeignKey('form_values.id', name='user_form_values_id_fk'), index=True)\n add_column(col, 'galaxy_user', metadata, index_name='ix_galaxy_user_form_values_id')\n\n\ndef downgrade(migrate_engine):\n metadata.bind = migrate_engine\n metadata.reflect()\n\n drop_column('form_values_id', 'galaxy_user', metadata)\n","sub_path":"lib/galaxy/model/migrate/versions/0025_user_info.py","file_name":"0025_user_info.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"285953532","text":"import pickle\nimport numpy as np\n\nclass CustomPredictor(object):\n \n columns = ['UX_UI', 'Documentation', 'Performance', 'Bugs', 'Feature_Request',\n 'Price', 'Customer_Support', 'Onboarding', 'Reporting',\n 'Alerts_Notification', 'Value_Prop']\n \n def __init__(self, models):\n self.models = models\n\n def predict(self, instances, **kwargs):\n results = []\n for instance in instances:\n for index, model in enumerate(self.models):\n prediction_proba = model.predict_proba(instance)[0].tolist()\n results.append({\"column\": self.columns[index], \"prediction\": str(model.predict(instance)[0]), \"probabilities_0\": str(prediction_proba[0]), \"probabilities_1\": str(prediction_proba[1])})\n return results\n\n @classmethod\n def from_path(cls, model_dir):\n models = []\n for i in cls.columns:\n models.append(pickle.load(open(model_dir + '/model_'+ i +'.pkl', 'rb')))\n return cls(models)\n","sub_path":"notebooks/GCP/multiTag/src/ensemble.py","file_name":"ensemble.py","file_ext":"py","file_size_in_byte":999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"649858859","text":"# https://leetcode.com/problems/word-search-ii\n\n# Create a Trie and add all the words to it\n# Walk through the array\n# For each letter we need three queues:\n# One for nodes in which the letter could be a child\n# One for coordinates of the letter\n# One for sets of coordinates of the letters before it that are considered part of the word (initially an empty)\n# While these qs exist (they should all be the same length):\n# Deque the front of each.\n# Iterating through the neighbours, if the neighbour hasn't already been used in the word\n# and could be the next letter in the word\n# Enqueue:\n# The node in which the neighbouring letter could be a child\n# The coordinates of the neighbouring letter\n# The set of coordinates of letters that could precede the neighbouring letter in the word\n# Then ask if the current letters node.val exists.\n# If it does then add to the output the word in \"words\" at index = node.val\n# Set node.val to None so that the word won't be added again if it is found again\n\n# Unfortunately this function is quite memory intensive because:\n# It stores a set of all the preceding letters for each of the letters in the queue\n# so worst case space complexity of O(n^2)\n\n# Included below, a recursive solution. I originally attempted a recursive solution\n# but couldn't work out an reliable way of keeping track of \"used\" across call stacks.\n# so borrowed a trick I found in some of the LC discussions to mark letters as \"#\"\n# while they were part of a prospective path\n\n# And below that a recursive version that uses a HashMap for a trie, found in LC submissions\n\n# Time complexity O(wk + (nm)^2)\n# for adding w words of length k to a trie\n# and iterating through an n*m board\n# where the recursive function calls could be made on all nm elements before a base case is reached (in worst case)\n\nclass TrieNode:\n def __init__(self):\n self.children = {}\n self.val = None\n\ndef addToTrie(word, node, num):\n for char in word:\n if char not in node.children:\n node.children[char] = TrieNode()\n node = node.children[char]\n node.val = num\n\n# Helper functions for debugging\n# def findInTrie(word, node):\n# for char in word:\n# if char not in node.children:\n# return None\n# node = node.children[char]\n# return node.val\n\n# def printTrieWord(word, node):\n# for char in word:\n# print(node.children, node.val)\n# node = node.children[char]\n\n# Helper function to return tuples which are the coords of the neighbours of a letter\ndef neighbours(r, c, maxR, maxC):\n return [tup for tup in [(r, c + 1), (r - 1, c), (r, c - 1), (r + 1, c)] if (0 <= tup[0] < maxR) and (0 <= tup[1] < maxC)]\n \nclass Solution:\n def findWords(self, board: List[List[str]], words: List[str]) -> List[str]:\n output = []\n if not board or not words:\n return output\n \n root = TrieNode()\n for wordNum, word in enumerate(words):\n addToTrie(word, root, wordNum)\n \n rows = len(board)\n cols = len(board[0])\n \n for row in range(rows):\n for col in range(cols):\n letter = board[row][col]\n if letter in root.children:\n nodeQ = [root.children[letter]]\n coordQ = [(row, col)]\n usedQ = [{(row, col)}]\n while nodeQ:\n curNode = nodeQ.pop(0)\n curCoord = coordQ.pop(0)\n curUsed = usedQ.pop(0)\n for nb in neighbours(curCoord[0],curCoord[1], rows, cols):\n if (nb[0], nb[1]) not in curUsed and board[nb[0]][nb[1]] in curNode.children:\n nodeQ.append(curNode.children[board[nb[0]][nb[1]]])\n coordQ.append((nb[0], nb[1]))\n nextUsed = curUsed | {(nb[0], nb[1])}\n usedQ.append(nextUsed)\n if curNode.val != None:\n output.append(words[curNode.val])\n curNode.val = None\n \n return output\n\n#\n# class TrieNode:\n# def __init__(self):\n# self.children = {}\n# self.isComplete = False\n#\n#\n# def addToTrie(word, node):\n# for char in word:\n# if char not in node.children:\n# node.children[char] = TrieNode()\n# node = node.children[char]\n# node.isComplete = True\n#\n#\n# def dfs(board, node, i, j, path, otpt):\n# if node.isComplete:\n# otpt.append(path)\n# node.isComplete = False\n# if not (0 <= i < len(board)) or not (0 <= j < len(board[0])):\n# return\n# temp = board[i][j]\n# node = node.children.get(temp)\n# if not node:\n# return\n# board[i][j] = \"#\"\n# dfs(board, node, i + 1, j, path + temp, otpt)\n# dfs(board, node, i, j + 1, path + temp, otpt)\n# dfs(board, node, i - 1, j, path + temp, otpt)\n# dfs(board, node, i, j - 1, path + temp, otpt)\n# board[i][j] = temp\n#\n#\n# class Solution:\n# def findWords(self, board: List[List[str]], words: List[str]) -> List[str]:\n# root = TrieNode()\n# for word in words:\n# addToTrie(word, root)\n#\n# output = []\n# for row in range(len(board)):\n# for col in range(len(board[0])):\n# dfs(board, root, row, col, \"\", output)\n#\n# return output\n\n\nclass Solution:\n def findWords(self, board: List[List[str]], words: List[str]) -> List[str]:\n\n # build a trie using hashtable\n word_key = '$'\n trie = {}\n for word in words:\n node = trie\n for letter in word:\n node = node.setdefault(letter, {})\n node[word_key] = word\n\n # starting from each cell\n rows, cols = len(board), len(board[0])\n matchWords = []\n\n def backtrack(r, c, parent):\n letter = board[r][c]\n currNode = parent[letter]\n\n word_match = currNode.pop(word_key, False)\n if word_match: matchWords.append(word_match)\n board[r][c] = '#'\n for x, y in [(-1, 0), (1, 0), (0, 1), (0, -1)]:\n r1, c1 = r + x, c + y\n if r1 < 0 or r1 >= rows or c1 < 0 or c1 >= cols: continue\n if not board[r1][c1] in currNode:\n continue\n backtrack(r1, c1, currNode)\n board[r][c] = letter\n\n if not currNode:\n parent.pop(letter)\n\n for r in range(rows):\n for c in range(cols):\n if board[r][c] in trie:\n backtrack(r, c, trie)\n return matchWords\n\ndef findWords(self, board, words):\n # make trie\n trie = {}\n for w in words:\n t = trie\n for c in w:\n if c not in t:\n t[c] = {}\n t = t[c]\n t['#'] = '#'\n self.res = set()\n self.used = [[False] * len(board[0]) for _ in range(len(board))]\n for i in range(len(board)):\n for j in range(len(board[0])):\n self.find(board, i, j, trie, '')\n return list(self.res)\n\n\ndef find(self, board, i, j, trie, pre):\n if '#' in trie:\n self.res.add(pre)\n if i < 0 or i >= len(board) or j < 0 or j >= len(board[0]):\n return\n if not self.used[i][j] and board[i][j] in trie:\n self.used[i][j] = True\n self.find(board, i + 1, j, trie[board[i][j]], pre + board[i][j])\n self.find(board, i, j + 1, trie[board[i][j]], pre + board[i][j])\n self.find(board, i - 1, j, trie[board[i][j]], pre + board[i][j])\n self.find(board, i, j - 1, trie[board[i][j]], pre + board[i][j])\n self.used[i][j] = False\n\n\n\n","sub_path":"Strings/findWords.py","file_name":"findWords.py","file_ext":"py","file_size_in_byte":7911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"235751135","text":"\"\"\"\nProject Euler #97: Large non-Mersenne prime\n\"\"\"\n# pylint-disable: invalid-names\n\ndef main():\n \"\"\"Main program.\"\"\"\n import sys\n test_cases = int(sys.stdin.readline())\n modulus = 1000000000000\n result = 0\n for _ in range(test_cases):\n a, b, c, d = [int(s) % modulus for s in sys.stdin.readline().split()]\n\n # moduluar exponentiation of b^c\n x = 1\n while c > 0:\n if c % 2 == 1:\n x = (x * b) % modulus\n c = c >> 1\n b = (b * b) % modulus\n\n solution = (((a * x) % modulus) + d) % modulus\n result = (result + solution) % modulus\n\n print(\"%012d\" % result)\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"097/python/problem097.py","file_name":"problem097.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"469171334","text":"\"\"\"API calls for the annotator\"\"\"\n\nimport json\n\nimport hug\nimport os\nimport random\nfrom tinydb import Query\n\nfrom dataprocessing.meta.load import selected_uids\nfrom utils import database_user, field_from_all_tables, get_uid_length, \\\n logger, wrap_uid_response, wrap_annotation_response\n\nlog = logger(\"Annotate\")\n\n\n@hug.get('/annotations')\ndef get_all_annotations():\n return field_from_all_tables(\"annotations\", \"annotation\")\n\n\n@hug.get('/annotations/count')\ndef get_all_annotations_count():\n return len(field_from_all_tables(\"annotations\", \"annotation\"))\n\n\n@hug.get('/annotations/current')\ndef get_current_annotation(user: hug.types.text):\n db = database_user(\"annotations\", user)\n annotations = db.search(~Query().annotation.exists())\n\n if len(annotations) == 0:\n return get_new_annotation # Pass function over to hug decorator\n else:\n return wrap_uid_response(annotations[0][\"uid\"])\n\n\n@hug.get('/annotations/new')\ndef get_new_annotation(user: hug.types.text):\n db_user = database_user(\"annotations\", user)\n\n # If there is a annotation without actual annotation, redirect to current\n if len(db_user.search(~Query().annotation.exists())) > 0:\n log.debug(\"Request _new_, redirect to _current_\")\n return get_current_annotation # Pass function over to hug decorator\n\n # Is this user supposed to be reviewing another user\n review_couples = json.load(open('/home/kay/projects/dash/api/review_config.json', 'r'))\n reviewing = user in review_couples\n\n # Get all uids that have to be annotated\n if reviewing:\n user_to_review = review_couples[user]\n db_to_review = database_user(\"annotations\", user_to_review)\n annotations_to_review = db_to_review.search(Query().annotation.exists())\n uids_all = set([x[\"uid\"] for x in annotations_to_review])\n else:\n uids_all = set(selected_uids())\n\n # Get uids that have been annotated\n if reviewing:\n uids_done = set([a[\"uid\"] for a in db_user.all()])\n else:\n uids_done = set([a[\"uid\"] for a in field_from_all_tables(\"annotations\", \"annotation\")])\n\n # noinspection PyTypeChecker\n uids_not_done = uids_all.difference(uids_done)\n\n if len(uids_not_done) == 0:\n return \"Done\"\n\n # Pick uid\n new_uid = random.choice(list(uids_not_done))\n db_user.insert({\"uid\": new_uid, \"len\": get_uid_length(new_uid)})\n\n return wrap_uid_response(new_uid)\n\n\n@hug.get('/annotations/done')\ndef get_done_annotation(user: hug.types.text, uid: hug.types.text):\n db = database_user(\"annotations\", user)\n Annotation = Query()\n return wrap_annotation_response(db.search((Annotation.uid == uid) & (Annotation.annotation.exists()))[0])\n\n\n@hug.get('/annotations/done/all')\ndef get_all_done_annotations(user: hug.types.text):\n db = database_user(\"annotations\", user)\n return db.search(Query().annotation.exists())\n\n\n@hug.get('/annotations/done/count')\ndef get_all_done_annotations_count(user: hug.types.text):\n db = database_user(\"annotations\", user)\n return len(db.search(Query().annotation.exists()))\n\n\n@hug.post('/annotations')\ndef post_annotation(user, uid, annotation):\n annotation = json.loads(annotation)\n db = database_user(\"annotations\", user)\n db.update({\n \"annotation\": annotation\n }, Query().uid == uid)\n return \"Ok\"\n\n\n@hug.post('/annotations/rejects')\ndef post_annotation_rejection(user, uid):\n db = database_user(\"annotations\", user)\n db.update({\n \"annotation\": \"\",\n \"rejection\": True\n }, Query().uid == uid)\n return \"Ok\"\n","sub_path":"web/api/annotator.py","file_name":"annotator.py","file_ext":"py","file_size_in_byte":3562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"383792292","text":"#Дан прямоугольник с размерами 543 130 мм. Сколько квадратов со стороной 130 мм можно отрезать от него?\na = 543\nb = 130\n\nS1 = a * b\nS2 = b**2\nC = a // b\n\nprint('Площадь прямоугольника =', S1, 'мм')\nprint('Площадь квадрата =', S2, 'мм')\nprint('Полных квадратов поместиться:', C)\n\n","sub_path":"zlatopolskiy/chapterTwo/2.07.py","file_name":"2.07.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"239532323","text":"from flask import Flask, render_template, request, redirect\nfrom content import *\n\napp = Flask(__name__)\n\n@app.route('/')\ndef entries():\n return render_template('index.html', entries=get_all_entries())\n\n@app.route('/entry/')\ndef entry(name):\n return render_template('entry.html', entry=get_entry(name))\n \n@app.route('/about')\ndef about():\n return render_template('about.html')\n\n@app.route('/admin/add', methods=['POST', 'GET'])\n@app.route('/admin/edit/', methods=['POST', 'GET'])\ndef add_entry(name=None):\n if request.method == 'POST':\n save_entry(request.form['name'],\n request.form['title'],\n request.form['content'])\n return redirect('/entry/{}'.format(request.form['name']))\n if name:\n entry = get_entry(name)\n else:\n entry = None\n return render_template('entry_form.html', entry=entry)\n\n@app.route('/admin/delete/', methods=['POST', 'GET'])\ndef delete(name):\n if request.method == 'POST':\n remove_entry(name)\n return redirect('/')\n entry = get_entry(name)\n return render_template('entry_delete.html', entry=entry)","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"363754171","text":"\n\nfrom xai.brain.wordbase.nouns._screenplay import _SCREENPLAY\n\n#calss header\nclass _SCREENPLAYS(_SCREENPLAY, ):\n\tdef __init__(self,): \n\t\t_SCREENPLAY.__init__(self)\n\t\tself.name = \"SCREENPLAYS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"screenplay\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_screenplays.py","file_name":"_screenplays.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"279709276","text":"#Author: Marta CICCARELLA\n#Univeristé Paris Diderot\n#Projet court: Septembre\n\n#import libraries\nimport numpy as np\nfrom scipy.spatial import distance\nfrom sys import argv\nimport sys\nfrom importSequence import *\nfrom matrix import *\nfrom translate import *\nfrom MCsearch import *\nfrom animation import *\n\n## 0\n\nprint (\"This is the name of the script: \", sys.argv[0])\nprint (\"This script takes as argument: \" , sys.argv[1])\nprint (\"The number of MCsteps are: \" , sys.argv[2])\nname_file = sys.argv[1]\nphi = sys.argv[2]\n\nsaving =input(\"Do you want to save plot ? [y/n]\")\n\n## 1st STEP \n\n# import the sequence\nseq = import_sequence(name_file) #from library importSequence\n\n# print the sequence \nprint(\"I am folding the sequence:\", convert(seq))\n\n\n\n## 2nd STEP\n\n# build a matrix\nmatrix = build_matrix(seq)\n\n# compute coordinates of the extended sequence in the matrix\nc = coordinates_extended(seq)\n#print(\"The coordinates of starting extended conformation are\", c)\n\n# plot the protein in extended configuration\nplot_configuration(c,seq,\"plot_initial.png\")\n\n\n\n## 3rd STEP\n\n# MC search\n\nlist_c,E_list = MCsearch(phi, c, seq)\nc1=list_c[-1]\n#print(\"The coordinates of final optimized conformation are\", c1)\n\n# graph of Energy optimization\n\nfig, ax = plt.subplots(figsize=(6, 6)) \nplt.plot(range(len(E_list)),E_list, 'x', ls='-',c='k')\nplt.ylabel(\"E\")\nplt.xlabel(r\"$\\phi$\")\nax.xaxis.set_major_locator(MultipleLocator(int(phi)/10))\nplt.savefig(\"Energy_vs_loops.png\")\n\n# save graph of final configuration\n\nif saving in [\"y\",\"Y\",\"yes\"]:\n plot_configuration(c1,seq,\"plot_final.png\")\n print(\"The plot of the final conformation is in /code\")\nelse:\n show_configuration(c1, seq) \n plt.show()\n \n#evolution_plot(list_c, seq) \n\n\n\n\n\n\n\n\n","sub_path":"code/MCsearch_for_protein_folding.py","file_name":"MCsearch_for_protein_folding.py","file_ext":"py","file_size_in_byte":1741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"548146436","text":"# -*- mode: python -*-\na = Analysis(['serve.py'],\n pathex=['C:\\\\Users\\\\JBAnderson\\\\Desktop\\\\django_pyinstaller_main\\\\django_pyinstaller'],\n hiddenimports=[],\n hookspath=None,\n runtime_hooks=None)\n\ndef Datafiles(*filenames, **kw):\n import os\n \n def datafile(path, strip_path=True):\n parts = path.split('/')\n path = name = os.path.join(*parts)\n if strip_path:\n name = os.path.basename(path)\n return name, path, 'DATA'\n\n strip_path = kw.get('strip_path', True)\n return TOC(\n datafile(filename, strip_path=strip_path)\n for filename in filenames\n if os.path.isfile(filename))\n\ndb = Datafiles('rango.sqlite3', strip_path=False)\ntemplates = Datafiles('templates/rango/index.html', 'templates/rango/base.html', 'templates/rango/log_in.html', \n 'templates/rango/about.html', 'templates/rango/add_category.html', 'templates/rango/add_page.html', 'templates/rango/category.html', 'templates/rango/register.html', strip_path=False)\nstatic = Datafiles('static/css/bootstrap.min.css', 'static/css/bootstrap.css', 'static/css/bootstrap-responsive.css',\n 'static/css/bootstrap-fluid-adj.css', 'static/css/bootstrap-responsive.min.css', \n\t\t\t\t 'static/images/about-us.png', 'static/images/sweetDR2.jpg', 'static/img/glyphicons-halflings.png', \n\t\t\t\t 'static/img/glyphicons-halflings-white.png', 'static/js/bootstrap.js', 'static/js/bootstrap.min.js', 'static/js/jquery-2.1.1.min.js', strip_path=False)\n\npyz = PYZ(a.pure)\nexe = EXE(pyz,\n a.scripts,\n exclude_binaries=True,\n name='serve.exe',\n debug=False,\n strip=None,\n upx=True,\n console=True )\ncoll = COLLECT(exe,\n a.binaries,\n a.zipfiles,\n a.datas,\n\t\t\t db,\n\t\t\t templates,\n\t\t\t static,\n strip=None,\n upx=True,\n name='serve')\n","sub_path":"Standalone_win_single_dir/source/serve.spec","file_name":"serve.spec","file_ext":"spec","file_size_in_byte":1977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"30337217","text":"# -*- coding: utf-8 -*-\n\nimport cPickle as pkl\nfrom itertools import izip\n\nimport numpy as np\nimport h5py\n\n\nclass Hdf5DataIterator(object):\n def __init__(self, path):\n hdf5File = h5py.File(path, \"r\")\n self.data = hdf5File[\"data\"]\n self.image_dim = self.data.shape[1]\n\n def __iter__(self):\n for x in self.data:\n yield x\n\n def __len__(self):\n return len(self.data)\n\n\nclass CocoStyleWrapper(object):\n def __init__(self, \n path_image_names, path_sents, path_feats,\n vocab):\n self.POOL_SIZE = 10000\n\n self.path_image_names = path_image_names\n self.path_sents = path_sents\n self.path_feats = path_feats\n\n iter_feats = Hdf5DataIterator(path_feats)\n self.image_dim = iter_feats.image_dim\n\n count = 0\n for s in open(path_image_names):\n count += 1\n self.n_data = count\n\n count = 0\n for s in open(path_sents):\n count += 1\n assert self.n_data == count\n\n assert self.n_data == len(iter_feats)\n \n if isinstance(vocab, dict):\n self.vocab = vocab\n else:\n self.vocab = pkl.load(open(vocab, \"rb\"))\n\n self.ivocab = {i:w for w,i in self.vocab.items()}\n \n self.current_iterator = izip(open(self.path_image_names), open(self.path_sents), iter_feats)\n self.data_i = 0\n \n self.pool_image_names = np.zeros((self.POOL_SIZE,), dtype=\"O\")\n self.pool_sents = np.zeros((self.POOL_SIZE,), dtype=\"O\")\n self.pool_feats = np.zeros((self.POOL_SIZE, self.image_dim), dtype=np.float32)\n self.fill_pool(batch_index=None)\n\n def __len__(self):\n return self.n_data\n\n def __iter__(self):\n for n, s, f in izip(open(self.path_image_names), open(self.path_sents), Hdf5DataIterator(self.path_feats)):\n yield n.strip(), self.split(s.decode(\"utf-8\").strip()), f\n\n def split(self, s):\n s = s.split()\n s = [self.vocab[w] for w in s]\n return s\n\n def next_sample(self):\n self.data_i += 1\n if self.data_i > self.n_data:\n # self.current_iterator.close()\n self.current_iterator = izip(open(self.path_image_names), open(self.path_sents), Hdf5DataIterator(self.path_feats))\n self.data_i = 1\n \n n, s, f = self.current_iterator.next()\n return n.strip(), self.split(s.decode(\"utf-8\").strip()), f \n\n def next_batch(self, size):\n batch_index = np.random.choice(self.POOL_SIZE, size=size)\n batch_image_names = self.pool_image_names[batch_index]\n batch_sents = self.pool_sents[batch_index]\n batch_feats = self.pool_feats[batch_index]\n self.fill_pool(batch_index)\n return batch_image_names, batch_sents, batch_feats\n\n def fill_pool(self, batch_index=None):\n if batch_index is None:\n for pool_i in xrange(self.POOL_SIZE):\n n, s, f = self.next_sample()\n self.pool_image_names[pool_i] = n\n self.pool_sents[pool_i] = s\n self.pool_feats[pool_i] = f\n else:\n for pool_i in batch_index:\n n, s, f = self.next_sample()\n self.pool_image_names[pool_i] = n\n self.pool_sents[pool_i] = s\n self.pool_feats[pool_i] = f\n\n def random_sample(self):\n target_index = np.random.randint(0, self.n_data)\n current_index = 0\n for n, s, f in izip(open(self.path_src), open(self.path_tgt), Hdf5DataIterator(self.path_feats)):\n if current_index == target_index:\n n = n.strip()\n s = self.split(s.decode(\"utf-8\").strip())\n return n, s, f\n current_index += 1\n \n \n","sub_path":"corpus_wrapper/CocoStyleWrapper.py","file_name":"CocoStyleWrapper.py","file_ext":"py","file_size_in_byte":3801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"397731174","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\nimport datetime\nimport os\n\n\ndef new_day_new_folder():\n path = []\n for piece in 'y%Y/m%m/d%d'.split('/'):\n path.append(datetime.datetime.strftime(datetime.datetime.now(), piece))\n tmp = os.sep.join(path)\n\n try:\n os.makedirs(tmp)\n except OSError:\n if not os.path.isdir(tmp):\n raise\n\n tmp = tmp + os.sep + '__init__.py'\n if not os.path.exists(tmp):\n with open(tmp, 'a'):\n os.utime(tmp, None)\n\n\ndef main():\n new_day_new_folder()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"ludejo_py27/new_day_new_folder.py","file_name":"new_day_new_folder.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"580565537","text":"import logging\n\nfrom github.GithubException import UnknownObjectException\nfrom sretoolbox.utils import (\n retry,\n threaded,\n)\n\nimport reconcile.aws_support_cases_sos as aws_sos\nfrom reconcile import queries\nfrom reconcile.github_users import init_github\nfrom reconcile.utils import git_secrets\nfrom reconcile.utils.aws_api import AWSApi\n\nQONTRACT_INTEGRATION = \"github-scanner\"\n\n\ndef strip_repo_url(repo_url):\n return repo_url.rstrip(\"/\").replace(\".git\", \"\")\n\n\n@retry(max_attempts=6)\ndef get_all_repos_to_scan(repos):\n logging.info(\"getting full list of repos\")\n all_repos = []\n all_repos.extend([strip_repo_url(r) for r in repos])\n g = init_github()\n for r in repos:\n logging.debug(\"getting forks: {}\".format(r))\n repo_name = r.replace(\"https://github.com/\", \"\")\n try:\n repo = g.get_repo(repo_name)\n forks = repo.get_forks()\n all_repos.extend([strip_repo_url(f.clone_url) for f in forks])\n except UnknownObjectException:\n logging.error(\"not found {}\".format(r))\n\n return all_repos\n\n\ndef run(dry_run, gitlab_project_id=None, thread_pool_size=10):\n accounts = queries.get_aws_accounts()\n settings = queries.get_app_interface_settings()\n with AWSApi(thread_pool_size, accounts, settings=settings) as aws:\n existing_keys = aws.get_users_keys()\n existing_keys_list = [\n key\n for user_key in existing_keys.values()\n for keys in user_key.values()\n for key in keys\n ]\n logging.info(\"found {} existing keys\".format(len(existing_keys_list)))\n\n app_int_github_repos = queries.get_repos(server=\"https://github.com\")\n all_repos = get_all_repos_to_scan(app_int_github_repos)\n logging.info(\"about to scan {} repos\".format(len(all_repos)))\n\n results = threaded.run(\n git_secrets.scan_history,\n all_repos,\n thread_pool_size,\n existing_keys=existing_keys_list,\n )\n all_leaked_keys = [key for keys in results for key in keys]\n\n deleted_keys = aws_sos.get_deleted_keys(accounts)\n keys_to_delete = [\n {\"account\": account, \"key\": key}\n for key in all_leaked_keys\n for account, user_keys in existing_keys.items()\n if key in [uk for uks in user_keys.values() for uk in uks]\n and key not in deleted_keys[account]\n ]\n aws_sos.act(dry_run, gitlab_project_id, accounts, keys_to_delete)\n","sub_path":"reconcile/github_scanner.py","file_name":"github_scanner.py","file_ext":"py","file_size_in_byte":2404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"98928254","text":"# -*- coding: utf-8 -*-\nimport configparser\nimport datetime\nimport random\nimport re\nimport time\n\nfrom mastodon import Mastodon, StreamListener\nfrom pytz import timezone\n\nfrom Yu import KotohiraMemory, KotohiraUtil, YuChan\n\nconfig = configparser.ConfigParser()\nconfig.read('config/config.ini')\n\nmastodon = Mastodon(\n access_token='config/accesstoken.txt',\n api_base_url=config['instance']['address']\n)\n\n# ローカルタイムラインのリスナー\nclass local_listener(StreamListener):\n def on_update(self, status):\n try:\n # Botアカウントは応答しないようにする\n if status['account']['bot'] == True:\n return\n\n # 自分のトゥートは無視\n if config['user']['me'] == status['account']['acct']:\n return\n\n # トゥート内のHTMLタグを除去\n txt = KotohiraUtil.h2t(status['content'])\n\n # 自分宛てのメンションはここのリスナーでは無視する（ユーザー絵文字の場合は例外）\n isMeMention = re.search('(?!.*:)@({}+)(?!.*:)'.format(config['user']['me']), txt)\n if isMeMention:\n return\n \n # データベース初期化\n memory = KotohiraMemory(showLog=config['log'].getboolean('enable'))\n\n # レイちゃんが知ってるユーザーか調べる\n # 知らない場合はレイちゃんは記憶しますっ！\n isknown = memory.select('known_users', status['account']['id'])\n if len(isknown) == 0:\n now = datetime.datetime.now(timezone('Asia/Tokyo'))\n dt = now.strftime(\"%Y-%m-%d %H:%M:%S%z\")\n memory.insert('known_users', status['account']['id'], status['account']['acct'], dt)\n memory.insert('fav_rate', status['account']['id'])\n memory.insert('updated_users', status['account']['id'], dt)\n print('覚えたっ！： @{0}'.format(status['account']['acct']))\n newUser = True\n # トゥートカウントが10以下の場合は新規さん向けの挨拶しますっ！\n if status['account']['statuses_count'] <= 10:\n print('新規さん！: @{0}'.format(status['account']['acct']))\n mastodon.status_reblog(status['id'])\n time.sleep(0.5)\n mastodon.toot('新規さんっ！はじめましてっ！琴平レイって言うよっ！\\nよろしくねっ！\\n\\n:@{0}: @{0}'.format(status['account']['acct']))\n else:\n newUser = False\n\n # NGワードを検知した場合は弾いて好感度下げ\n if YuChan.ngWordHook(txt):\n print('変なことを言っちゃダメ～！！(*`ω´*): @{0}'.format(status['account']['acct']))\n memory.update('fav_rate', -5, status['account']['id'])\n YuChan.unfollow_attempt(status['account']['id'])\n return\n\n # 名前\n nameDic = memory.select('nickname', status['account']['id'])\n if len(nameDic) == 0:\n # ニックネームが指定されていない場合は基の名前を使用する\n # 名前が設定されていない場合はユーザーIDを使用する\n if status['account']['display_name'] == '':\n name = status['account']['acct']\n else:\n # デコードして、\\u202e(文字が逆さまになるやつ)を削除して戻してどーん\n dpname = status['account']['display_name'].encode('unicode-escape')\n dpname = dpname.replace(b\"\\\\u202e\", b'')\n name = dpname.decode('unicode-escape')\n else:\n # ニックネームが設定されている場合はそちらを優先\n name = nameDic[0][2]\n name = re.sub(r'(?!.*:)@([a-zA-Z0-9_]+)(?!.*:)', '', name)\n\n # 名前に語尾がない場合は付け足す\n if re.search(r'(さん|ちゃん|どの|殿|くん|君|様|さま|教授|たん|きゅん)$', name) == None:\n name += \"さん\"\n\n # 正規表現チェック\n calledYuChan = re.search(r'(琴平|ことひら|コトヒラ|ｺﾄﾋﾗ|れい|れぃ|レイ|レィ|ﾚｲ|ﾚｨ|:@' + config['user']['me'] + ':)', txt)\n otherNick = re.search(r'^:@([a-zA-Z0-9_]+):\\sの(あだ名|あだな|ニックネーム)[:：は]\\s?(.+)', txt)\n nick = re.search(r'^(あだ(名|な)|ニックネーム)[:：は]\\s?(.+)', txt)\n iBack = re.search(r'(帰宅|ただいま|帰った|帰還)(?!.*(する|します|しちゃう|しよう|中|ちゅう|してる))', txt)\n goodNight = re.search(r'寝(ます|る|マス)([よかぞね]?|[...。うぅー~!・]+)$|^寝(ます|る|よ)[...。うぅー~!・]*$|寝(ます|る|マス)(.*)[ぽお]や[ユすしー]|(レイ|ﾕｳ|れい|ことひら|コトヒラ|ｺﾄﾋﾗ)(ちゃん)?(.*)[ぽお]や[ユすしー]', txt)\n seeYou = re.search(r'((行|い)って(きます|くる)|ノシ|ﾉｼ)', txt)\n passage = re.search(r'(通過|つうか|ツウカ)(?!.*(おめ|した))', txt)\n sinkiSagi = re.search(r'(新規|しんき)(です|だよ|なのじゃ)', txt)\n nullPoint = re.search(r'(ぬるぽ|ヌルポ|ﾇﾙﾎﾟ|[nN][uU][lL]{2}[pP][oO])', txt)\n notNicoFri = re.search(r'(にこふれ|ニコフレ|ﾆｺﾌﾚ)', txt)\n sad = re.search(r'((泣|な)いてる|しくしく|シクシク|ｼｸｼｸ|ぐすん|グスン|ｸﾞｽﾝ|ぶわっ|ブワッ|ﾌﾞﾜｯ)', txt)\n noNow = re.search(r'(いまのなし|イマノナシ|ｲﾏﾉﾅｼ)', txt)\n writeDict = re.search(r'^:@[a-zA-Z0-9_]+:(さん|くん|君|殿|どの|ちゃん)?はこんな人[:：]', txt)\n writeMemo = re.search(r'^(メモ|めも|[Mm][Ee][Mm][Oo])[:：](.+)', txt)\n \n # レイちゃん etc... とか呼ばれたらふぁぼる\n if calledYuChan:\n print('呼ばれたっ！：@{0} < {1}'.format(status['account']['acct'], txt))\n mastodon.status_favourite(status['id'])\n # 好感度ちょいアップ\n memory.update('fav_rate', 1, status['account']['id'])\n\n # 投票型のトゥートだったら投票する（期限切れでないかつ投票してないこと）\n if status['poll'] != None:\n if status['poll']['expired'] == False and not ('voted' in status['poll'] and status['poll']['voted'] == True):\n voteOptions = status['poll']['options']\n \n # NGワードを検知した場合は弾いて好感度下げ\n for voteSection in voteOptions:\n if YuChan.ngWordHook(voteSection['title']):\n print('変なことを言っちゃダメ～！！！！(*`ω´*): @{0}'.format(status['account']['acct']))\n memory.update('fav_rate', -5, status['account']['id'])\n del memory\n return\n \n # ここで投票する場所を抽選\n voteChoose = random.randint(0, len(voteOptions) - 1)\n mastodon.poll_vote(status['poll']['id'], voteChoose)\n # 投票したものをトゥートする\n print('投票っ！：@{0} => {1}'.format(status['account']['acct'], status['poll']['options'][voteChoose]['title']))\n mastodon.status_post('レイは「{0}」がいいと思うっ！\\n\\n{1}'.format(status['poll']['options'][voteChoose]['title'], status['url']))\n\n elif otherNick:\n # 他人のニックネームの設定\n YuChan.set_otherNickname(txt, status['id'], status['account']['id'], status['account']['acct'], status['visibility'], memory)\n\n elif nick:\n # ニックネームの設定\n YuChan.set_nickname(txt, status['id'], status['account']['id'], status['account']['acct'], status['visibility'], memory)\n\n elif iBack:\n # おかえりとか言ったら実行\n if YuChan.msg_hook('wel_back', 600, \":@{0}: {1}、おかえりなさいっ！\".format(status['account']['acct'], name), status, memory):\n print('おかえりっ！：@{0} < {1}'.format(status['account']['acct'], txt))\n\n elif goodNight:\n # おやすみですっ！\n if YuChan.msg_hook('good_night', 600, \":@{0}: {1}、おやすみなさいっ！🌙\".format(status['account']['acct'], name), status, memory):\n print('おやすみっ！:@{0} < {1}'.format(status['account']['acct'], txt))\n\n elif seeYou:\n # いってらっしゃいなのですっ！\n if YuChan.msg_hook('see_you', 600, \":@{0}: {1}、いってらっしゃ～いっ！🚪\".format(status['account']['acct'], name), status, memory):\n print('いってらっしゃいっ！:@{0} < {1}'.format(status['account']['acct'], txt)) \n\n elif passage:\n # 通過 とか言ったら阻止しちゃうよっ！\n if YuChan.msg_hook('passage', 300, \"阻止っ！！(*`ω´*)\", status, memory):\n print('阻止っ！：@{0} < {1}'.format(status['account']['acct'], txt))\n\n elif sinkiSagi and status['account']['statuses_count'] > 10:\n # 新規詐欺見破りっ！ \n if YuChan.msg_hook('sin_sagi', 10800, \"新規詐欺はいけませんっ！！(*`ω´*)\", status, memory):\n print('新規詐欺っ！:@{0} < {1}'.format(status['account']['acct'], txt))\n \n elif nullPoint:\n # ぬるぽって、言ったら■━⊂( ･∀･)彡ｶﾞｯ☆`Дﾟ)\n if YuChan.msg_hook('null_point', 180, \":gaxtsu:\", status, memory):\n print('がっ：@{0} < {1}'.format(status['account']['acct'], txt))\n\n elif notNicoFri:\n # ニコフレじゃないよっ！\n if YuChan.msg_hook('not_nikofure', 10800, \"ここはニコフレじゃないよっ！！レイ丼だよっ！(*`ω´*)\", status, memory):\n print('レイ丼だよっ！：@{0} < {1}'.format(status['account']['acct'], txt))\n\n elif sad:\n # よしよしっ\n if YuChan.msg_hook('yoshiyoshi', 180, \"(´･ω･`)ヾ(･ω･｡)ﾖｼﾖｼ\", status, memory):\n print('よしよしっ：@{0} < {1}'.format(status['account']['acct'], txt))\n\n elif noNow:\n # いまのなしは封印ですっ！\n if YuChan.msg_hook('no_now', 180, \"いまのなしは封印だよっ！！(*`ω´*)\", status, memory):\n print('いまのなしは封印だよっ！：@{0} < {1}'.format(status['account']['acct'], txt))\n\n elif writeDict:\n # 辞書登録っ\n # (実装中)\n # YuChan.update_userdict()\n pass\n \n elif writeMemo:\n # メモの書き込みっ\n memoBody = re.sub(r'^(メモ|めも|[Mm][Ee][Mm][Oo])[:：]', '', txt, 1)\n mastodon.status_reblog(status['id'])\n print('メモっ！：@{0} < {1}'.format(status['account']['acct'], txt))\n res = YuChan.write_memo(status['account']['acct'], memoBody, status['id'], memory)\n if res == False:\n mastodon.status_post('@{}\\n長い過ぎてまとめられそうにないよ・・・'.format(status['account']['acct']), in_reply_to_id=status['id'])\n\n # 最終更新を変更\n now = datetime.datetime.now(timezone('Asia/Tokyo'))\n dt = now.strftime(\"%Y-%m-%d %H:%M:%S%z\")\n # ２重更新防策\n if not newUser:\n updated_at = memory.select('updated_users', status['account']['id'])[0]\n updatedAtRaw = datetime.datetime.strptime(updated_at[2], '%Y-%m-%d %H:%M:%S%z')\n greetableTime = updatedAtRaw + datetime.timedelta(hours=3)\n shouldGreet = now >= greetableTime\n # 3時間以上更新がなかった場合は挨拶する\n if shouldGreet:\n time.sleep(0.5)\n if now.hour < 12 and now.hour >= 5:\n print(\"おはようっ！：@{0} < {1}\".format(status['account']['acct'], txt))\n mastodon.toot(\"\"\":@{1}: {0}、おはようっ！🌄\"\"\".format(name, status['account']['acct']))\n elif now.hour >= 12 and now.hour < 17:\n print(\"こんにちはっ！：@{0} < {1}\".format(status['account']['acct'], txt))\n mastodon.toot(\"\"\":@{1}: {0}、こんにちはっ！☀\"\"\".format(name, status['account']['acct']))\n elif now.hour >= 17 and now.hour < 5:\n print(\"こんばんはっ！：@{0} < {1}\".format(status['account']['acct'], txt))\n mastodon.toot(\"\"\":@{1}: {0}、こんばんはっ！🌙\"\"\".format(name, status['account']['acct']))\n\n YuChan.drill_count(status['account']['acct'], name, status['account']['statuses_count'])\n\n # 最終更新を変更\n memory.update('updated_users', dt, status['account']['id'])\n\n except Exception as e:\n # Timelines.pyの方へエラーを送出させる\n raise e\n finally: # 必ず実行\n try:\n del memory # データベースロック防止策、コミットする\n except NameError: # 定義されていなくてもエラーを出さない\n pass\n\n def on_delete(self, status_id):\n try:\n # メモのトゥートが削除されたらデータベースから削除する\n if YuChan.cancel_memo(status_id):\n print(f\"メモ削除っ！: {str(status_id)}\")\n except Exception as e: # 上と同じ\n raise e\n finally:\n try:\n del memory\n except NameError:\n pass\n","sub_path":"Yu/listener/local.py","file_name":"local.py","file_ext":"py","file_size_in_byte":14542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"96584670","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport datetime\nfrom django.utils.timezone import utc\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('website', '0030_auto_20150919_2256'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='construct',\n name='constructid',\n field=models.IntegerField(default=datetime.datetime(2015, 9, 19, 15, 4, 21, 488782, tzinfo=utc), unique=True, max_length=20),\n preserve_default=False,\n ),\n ]\n","sub_path":"website/migrations/0031_construct_constructid.py","file_name":"0031_construct_constructid.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"326045480","text":"# Minecraft Turtle Example - Spiral\nfrom mcpi import block\nimport random\nimport ministack\n\n# get players position\npos = ministack.getPlayerPosition(\"jerry\")\n\n# create minecraft turtle\nsteve = ministack.createTurtle()\n\nsteve.speed(10)\nsteve.up(5)\nfor step in range(0, 1000):\n random_color = [i for i in range(0, 16)]\n steve.penblock(block.WOOL.id, random.choice(random_color))\n steve.forward(2)\n steve.right(10)","sub_path":"MC_turtle_examples/example_spiral.py","file_name":"example_spiral.py","file_ext":"py","file_size_in_byte":422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"69748908","text":"import datetime\nimport functools\nfrom typing import Dict\n\nimport httpx\n\nfrom app import config\n\n# This feature requires an API KEY - get yours free @ www.weatherapi.com\n\nASTRONOMY_URL = \"http://api.weatherapi.com/v1/astronomy.json\"\nNO_API_RESPONSE = \"No response from server\"\n\n\n@functools.lru_cache(maxsize=128, typed=False)\nasync def get_data_from_api(formatted_date: str, location: str) \\\n -> Dict[str, int]:\n \"\"\" get the relevant astronomical data by calling the \"weather api\" API.\n Args:\n formatted_date (date) - relevant date.\n location (str) - location name.\n Returns:\n response_json (json dict) including:\n relevant part (data / error) of the JSON returned by the API.\n Success (bool)\n ErrorDescription (str) - error message.\n \"\"\"\n input_query_string = {'key': config.ASTRONOMY_API_KEY, 'q': location,\n 'dt': formatted_date}\n output = {}\n try:\n async with httpx.AsyncClient() as client:\n response = await client.get(ASTRONOMY_URL,\n params=input_query_string)\n except httpx.HTTPError:\n output[\"Success\"] = False\n output[\"ErrorDescription\"] = NO_API_RESPONSE\n return output\n if response.status_code != httpx.codes.OK:\n output[\"Success\"] = False\n output[\"ErrorDescription\"] = NO_API_RESPONSE\n return output\n output[\"Success\"] = True\n try:\n output.update(response.json()['location'])\n return output\n except KeyError:\n output[\"Success\"] = False\n output[\"ErrorDescription\"] = response.json()['error']['message']\n return output\n\n\nasync def get_astronomical_data(requested_date: datetime.datetime,\n location: str) -> Dict[str, int]:\n \"\"\" get astronomical data (Sun & Moon) for date & location -\n main function.\n Args:\n requested_date (date) - date requested for astronomical data.\n location (str) - location name.\n Returns: dictionary with the following entries:\n Status - success / failure.\n ErrorDescription - error description (relevant only in case of error).\n location - relevant location values(relevant only in case of success).\n name, region, country, lat, lon etc.\n astronomy - relevant astronomy values, all time in local time -\n (relevant only in case of success):\n sunrise, sunset, moonrise, moonset, moon_phase, moon_illumination.\n \"\"\"\n formatted_date = requested_date.strftime('%Y-%m-%d')\n return await get_data_from_api(formatted_date, location)\n","sub_path":"app/internal/astronomy.py","file_name":"astronomy.py","file_ext":"py","file_size_in_byte":2635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"150487045","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.linux-x86_64/egg/taurus/qt/qtgui/button/taurusbutton.py\n# Compiled at: 2019-08-19 15:09:29\n\"\"\"This module provides a taurus QPushButton based widgets\"\"\"\nfrom __future__ import print_function\nfrom builtins import map\nfrom builtins import str\nfrom future.utils import string_types\nfrom taurus.external.qt import Qt, compat\nfrom taurus.core.taurusbasetypes import LockStatus, TaurusLockInfo\nfrom taurus.core.taurusdevice import TaurusDevice\nfrom taurus.qt.qtgui.base import TaurusBaseWidget\nfrom taurus.core.util import eventfilters\nfrom taurus.qt.qtgui.dialog import ProtectTaurusMessageBox\n__all__ = [\n 'TaurusLauncherButton', 'TaurusCommandButton', 'TaurusLockButton']\n__docformat__ = 'restructuredtext'\n\nclass _ButtonDialog(Qt.QDialog):\n _widget = None\n deleteWidgetOnClose = False\n\n def __init__(self, parent=None):\n Qt.QDialog.__init__(self, parent, Qt.Qt.WindowTitleHint)\n l = Qt.QVBoxLayout()\n self.setLayout(l)\n self.previousWidgetConfig = None\n return\n\n def setWidget(self, widget):\n oldWidget = self.widget()\n if oldWidget is not None:\n try:\n self._widget.setModel(None)\n except:\n pass\n\n oldWidget.hide()\n oldWidget.setParent(None)\n oldWidget.deleteLater()\n if widget is not None:\n self.layout().addWidget(widget)\n self._widget = widget\n return\n\n def widget(self):\n return self._widget\n\n def closeEvent(self, event):\n if self.deleteWidgetOnClose:\n try:\n self.previousWidgetConfig = self.widget().createConfig()\n except:\n self.previousWidgetConfig = None\n\n self.setWidget(None)\n Qt.QDialog.closeEvent(self, event)\n return\n\n\nclass TaurusLauncherButton(Qt.QPushButton, TaurusBaseWidget):\n \"\"\"This class provides a button that launches a modeless dialog containing\n a specified Taurus widget which gets the same model as the button.\n The button does not use the model directly. Instead it passes it to the\n associated widget.\n\n Code examples::\n\n # a button that launches a TaurusAttrForm when clicked\n button = TaurusLauncherButton(widget = TaurusAttrForm())\n button.setModel('a/b/c') #a device name, which will be set at the TaurusAttrForm when clicking\n\n # a button that launches a taurusLabel (whose model is an attribute: 'a/b/c/attrname')\n button = TaurusLauncherButton(widget = TaurusLabel())\n button.setModel('a/b/c/attrname') # attr name, which will be set at the TaurusLabel when clicking\n\n \"\"\"\n _widgetClassName = ''\n _args = []\n _kwargs = {}\n _deleteWidgetOnClose = True\n _icon = None\n _text = None\n\n def __init__(self, parent=None, designMode=False, widget=None, icon=None, text=None):\n \"\"\"Constructor\n\n :param parent: (Qt.QWidget or None) parent of this widget\n :param designMode: (bool) flag for Qt designer\n :param widget: (Qt.QWidget) a QWidget that will be shown when clicking\n in the button\n :param icon: (Qt.QIcon) icon for the button\n :param text: (str) the button text (if None passed, the widget's class name\n is used) \"\"\"\n name = self.__class__.__name__\n self.call__init__wo_kw(Qt.QPushButton, parent)\n self.call__init__(TaurusBaseWidget, name, designMode=designMode)\n self._dialog = _ButtonDialog(self)\n if icon is None and self._icon is not None:\n icon = Qt.QIcon(self._icon)\n if icon is not None:\n self.setIcon(Qt.QIcon(icon))\n if text is not None:\n self._text = text\n if isinstance(widget, Qt.QWidget):\n self._deleteWidgetOnClose = False\n self.setWidget(widget)\n elif widget is not None:\n self._widgetClassName = widget\n self.clicked.connect(self.onClicked)\n self.setDefault(False)\n self.setAutoDefault(False)\n self.insertEventFilter(eventfilters.IGNORE_CHANGE_AND_PERIODIC)\n self._updateText()\n return\n\n def getModelClass(self):\n \"\"\"see :meth:`TaurusBaseComponent.getModelClass`. Note that in the case of\n :class:`TaurusLauncherButton`, the class is completely dependent on the\n widget's class\"\"\"\n try:\n return self.widget().getModelClass()\n except:\n return TaurusBaseWidget.getModelClass(self)\n\n def setText(self, text):\n \"\"\"Sets the text of the button. see :meth:`Qt.QPushButton.setText`\"\"\"\n self._text = text\n Qt.QPushButton.setText(self, text)\n\n def getWidgetClassName(self):\n return self._widgetClassName\n\n def setWidgetClassName(self, className, args=None, kwargs=None):\n self._widgetClassName = str(className)\n if args is not None:\n self._args = args\n if kwargs is not None:\n self._kwargs = kwargs\n self._updateText()\n return\n\n def resetWidgetClassName(self, className, args=None, kwargs=None):\n self.setWidgetClassName(self.__class__._widgetClassName)\n\n def createWidget(self):\n from taurus.qt.qtgui.util import TaurusWidgetFactory\n klass = TaurusWidgetFactory().getWidgetClass(self._widgetClassName)\n widget = klass(*self._args, **self._kwargs)\n self.setWidget(widget)\n if self._dialog.previousWidgetConfig is not None:\n try:\n widget.applyConfig(self._dialog.previousWidgetConfig)\n except Exception as e:\n self.warning('Cannot apply previous configuration to widget. Reason: %s', repr(e))\n\n return\n\n def widget(self):\n return self._dialog.widget()\n\n def setWidget(self, widget):\n \"\"\"sets the widget that will be shown when clicking the button\n\n :param widget: (Qt.QWidget)\n \"\"\"\n self._dialog.setWidget(widget)\n self._updateText()\n\n def displayValue(self, v):\n \"\"\"see :meth:`TaurusBaseComponent.displayValue`\"\"\"\n if self._text is not None:\n return self._text\n else:\n TaurusBaseWidget.displayValue(self, v)\n return\n\n def getDisplayValue(self):\n \"\"\"see :meth:`TaurusBaseComponent.getDisplayValue`\"\"\"\n if self._text is not None:\n return self._text\n else:\n if self.widget() is not None:\n return self.widget().__class__.__name__\n return self._widgetClassName or '---'\n\n def _updateText(self):\n Qt.QPushButton.setText(self, self.getDisplayValue())\n\n def onClicked(self):\n \"\"\"\n Slot called when the button is clicked.\n Note that the dialog will only be created once. Subsequent clicks on\n the button will only raise the existing dialog\"\"\"\n if self.widget() is None:\n self.createWidget()\n self.widget().setModel(self.getModelName())\n self._dialog.deleteWidgetOnClose = self._deleteWidgetOnClose\n self._dialog.setWindowTitle(str(self.getModelName()))\n self._dialog.show()\n self._dialog.raise_()\n return\n\n @classmethod\n def getQtDesignerPluginInfo(cls):\n return {'group': 'Taurus Buttons', \n 'icon': 'designer:pushbutton.png', \n 'module': 'taurus.qt.qtgui.button', \n 'container': False}\n\n Model = Qt.pyqtProperty('QString', TaurusBaseWidget.getModel, TaurusBaseWidget.setModel, TaurusBaseWidget.resetModel)\n UseParentModel = Qt.pyqtProperty('bool', TaurusBaseWidget.getUseParentModel, TaurusBaseWidget.setUseParentModel, TaurusBaseWidget.resetUseParentModel)\n widgetClassName = Qt.pyqtProperty('QString', getWidgetClassName, setWidgetClassName, resetWidgetClassName)\n\n\nclass TaurusCommandButton(Qt.QPushButton, TaurusBaseWidget):\n \"\"\"This class provides a button that executes a tango command on its device.\n\n Code examples::\n\n # a button that executes the \"status\" command for the 'a/b/c' device server\n button = TaurusCommandButton(command = 'Status', icon='logos:taurus.png')\n button.setModel('a/b/c')\n\n # a button that executes the \"exec\" command for the 'a/b/c' device server with one parameter\n button = TaurusCommandButton(command = 'Status', parameters=['2+2'],icon='logos:taurus.png')\n button.setModel('a/b/c')\n\n .. seealso:: :class:`TaurusCommandsForm` provides a good example of use of\n TaurusCommandButton (including managing the return value) \"\"\"\n commandExecuted = Qt.pyqtSignal(compat.PY_OBJECT)\n\n def __init__(self, parent=None, designMode=False, command=None, parameters=None, icon=None, text=None, timeout=None):\n \"\"\"Constructor\n\n :param parent: (Qt.QWidget or None) parent of this widget\n :param designMode: (bool) flag for Qt designer\n :param command: (str) the name of the command to be executed\n :param parameters: (sequence) the list of parameteres. Default value is None meaning no parameters\n :param icon: (Qt.QIcon) icon for the button\n :param text: (str) the button text (if None passed, `command` is used)\n :param timeout: (float) the command timeout (in seconds)\n \"\"\"\n name = self.__class__.__name__\n if command is None:\n command = ''\n if parameters is None:\n parameters = []\n self._command = command\n self._parameters = parameters\n self._timeout = timeout\n self._customText = text\n self.call__init__wo_kw(Qt.QPushButton, parent)\n self.call__init__(TaurusBaseWidget, name, designMode=designMode)\n if icon is not None:\n self.setIcon(Qt.QIcon(icon))\n self.setCustomText(text)\n self.setDefault(False)\n self.setAutoDefault(False)\n self.clicked.connect(self.onClicked)\n return\n\n def getDisplayValue(self):\n \"\"\"see :meth:`TaurusBaseComponent.displayValue`\"\"\"\n if self._customText is not None:\n return self._customText\n else:\n if len(self._command) == 0:\n return '---'\n modelobj = self.getModelObj()\n if modelobj is None or not hasattr(modelobj, self._command):\n return '---'\n return self._command\n\n @ProtectTaurusMessageBox(title='Unexpected error when executing command')\n def onClicked(self, value=0):\n return self._onClicked()\n\n def _onClicked(self):\n \"\"\"Slot called when the button is clicked. It executes the command with\n parameters. It may issue a warning if the command is flagged as\n dangerous. On successful execution, it returns the command result and it\n emits a \"commandExecuted\" signal with the result as well.\n\n :return: The result of the command. The type depends on the command. It\n may be None.\n\n .. seealso:: :meth:`setCommand`, :meth:`setParameters`, :meth:`TaurusBaseComponent.isDangerous`\n \"\"\"\n self.debug('launch command %s' % str(self._command))\n if len(self._command) == 0:\n return\n else:\n modelobj = self.getModelObj()\n if modelobj is None or not hasattr(modelobj, self._command):\n self.warning('Device %s does not implement command %s' % (\n modelobj, self._command))\n return\n if self.isDangerous() and not self.getForceDangerousOperations():\n result = Qt.QMessageBox.question(self, 'Potentially dangerous action', '%s\\nProceed?' % self.getDangerMessage(), Qt.QMessageBox.Ok | Qt.QMessageBox.Cancel, Qt.QMessageBox.Ok)\n if result != Qt.QMessageBox.Ok:\n return\n orig_timeout = modelobj.get_timeout_millis()\n try:\n try:\n if self._timeout is not None:\n modelobj.set_timeout_millis(int(self._timeout * 1000))\n result = modelobj.command_inout(self._command, self._castParameters(self._parameters, self._command, modelobj))\n except Exception as e:\n self.error('Unexpected error when executing command %s of %s: %s' % (\n self._command, modelobj.getNormalName(), str(e)))\n raise\n\n finally:\n modelobj.set_timeout_millis(orig_timeout)\n\n self.commandExecuted.emit(result)\n return result\n\n def _castParameters(self, parameters=None, command=None, dev=None):\n \"\"\"Internal method used to cast the command paramters to the appropriate\n type required for the given command\n\n :param parameters: (sequence) a sequence of parameters. If None is\n passed, the currently set parameters are used.\n :param command: (str) the command name. If None is passed, the currently\n set command is used.\n :param dev: (taurus.core.taurusdevice.TaurusDevice) the device on which the command is\n executed. If None is passed, the current model is used.\n\n :return: (sequence or scalar) a sequence of parameters (or a scalar if only one parameter)\n \"\"\"\n import PyTango\n if parameters is None:\n parameters = self._parameters\n if command is None:\n command = self._command\n if dev is None:\n dev = self.getModelObj()\n try:\n param_type = dev.command_query(command).in_type\n except Exception as e:\n self.warning('Cannot get parameters info for command %s:%s' % (command, str(e)))\n return parameters\n\n if param_type == PyTango.CmdArgType.DevVoid:\n return\n else:\n if PyTango.is_int_type(param_type, True):\n cast_type = int\n elif PyTango.is_float_type(param_type, True):\n cast_type = float\n elif param_type == PyTango.CmdArgType.DevVarStringArray or param_type == PyTango.CmdArgType.DevString:\n cast_type = str\n elif param_type == PyTango.CmdArgType.DevVarBooleanArray or param_type == PyTango.CmdArgType.DevBoolean:\n cast_type = bool\n else:\n self.info('Unsupported parameters type (%s). Casting to \"str\"' % str(param_type))\n cast_type = str\n if PyTango.is_scalar_type(param_type):\n if parameters:\n return cast_type(parameters[0])\n else:\n return parameters\n\n else:\n return list(map(cast_type, parameters))\n return\n\n def setCommand(self, commandName):\n \"\"\"sets the command to be executed when the button is clicked\n\n :param commandName: (str or None) the command name\n \"\"\"\n if commandName is None:\n self._command = ''\n else:\n self._command = str(commandName)\n self._setText(self.getDisplayValue())\n return\n\n def getCommand(self):\n \"\"\"returns the command name to be executed when the button is clicked\n\n :return: (str or None) the command name\n \"\"\"\n return self._command\n\n def resetCommand(self):\n \"\"\"equivalent to self.setCommand(None)\"\"\"\n self.setCommand('')\n\n def setParameters(self, parameters):\n \"\"\"\n Sets the parameters to be used on command execution.\n\n :param parameters: (sequence) a sequence of parameters. If the\n elements of the sequence are not of the right type\n required for the parameter, an automatic conversion\n will be attempted on execution time. As a special\n case, if parameters is a string not starting and\n ending in quote characters, it will be splitted on\n whitespace to obtain a sequence of parameters. If\n it is a string starting and ending with quotes, the\n quotes will be removed and the quoted text will not\n be splitted.\n \"\"\"\n if isinstance(parameters, string_types):\n parameters = str(parameters).strip()\n if parameters[0] in ('\"', \"'\") and parameters[0] == parameters[(-1)]:\n parameters = [\n parameters[1:-1]]\n else:\n parameters = parameters.split()\n self._parameters = parameters\n\n def getParameters(self):\n \"\"\"returns the parameters to be used on command execution\n\n :param parameters: (sequence)\n \"\"\"\n return self._parameters\n\n def resetParameters(self):\n \"\"\"Equivalent to setParameters([])\n \"\"\"\n self.setParameters([])\n\n def setCustomText(self, customText=None):\n \"\"\"Sets a custom text for the button (by default it is the command name)\n\n :param customText: (str or None) the custom text. If None passed, it\n will use the command name\n \"\"\"\n self._customText = customText\n self._setText(self.getDisplayValue())\n\n def getCustomText(self):\n \"\"\"Returns the custom text of the buttom, or None if no custom text is\n used\n \"\"\"\n return self._customText\n\n def resetCustomText(self):\n \"\"\"Equivalent to setCustomText(None)\"\"\"\n self.setCustomText(None)\n return\n\n @Qt.pyqtSlot(float)\n @Qt.pyqtSlot(int)\n def setTimeout(self, timeout):\n \"\"\"Sets the number of seconds to wait for the result of the command.\n\n .. seealso:: :meth:`PyTango.DeviceProxy.command_inout`\n\n :param timeout: (float) the command timeout in seconds\n (timeout <0 or timeout=None disables the timeout)\n \"\"\"\n if timeout < 0:\n timeout = None\n self._timeout = timeout\n return\n\n def getTimeout(self):\n \"\"\"\n Returns the number of seconds to wait for the result of the command\n (or -1 if timeout is disabled)\n \"\"\"\n ret = self._timeout\n if ret is None or ret < 0:\n ret = -1\n return ret\n\n def resetTimeout(self):\n \"\"\"Equivalent to setTimeout(None)\"\"\"\n self.setTimeout(None)\n return\n\n @classmethod\n def getQtDesignerPluginInfo(cls):\n return {'group': 'Taurus Buttons', \n 'icon': 'designer:pushbutton.png', \n 'module': 'taurus.qt.qtgui.button', \n 'container': False}\n\n Model = Qt.pyqtProperty('QString', TaurusBaseWidget.getModel, TaurusBaseWidget.setModel, TaurusBaseWidget.resetModel)\n UseParentModel = Qt.pyqtProperty('bool', TaurusBaseWidget.getUseParentModel, TaurusBaseWidget.setUseParentModel, TaurusBaseWidget.resetUseParentModel)\n Command = Qt.pyqtProperty('QString', getCommand, setCommand, resetCommand)\n Parameters = Qt.pyqtProperty('QStringList', getParameters, setParameters, resetParameters)\n DangerMessage = Qt.pyqtProperty('QString', TaurusBaseWidget.getDangerMessage, TaurusBaseWidget.setDangerMessage, TaurusBaseWidget.resetDangerMessage)\n CustomText = Qt.pyqtProperty('QString', getCustomText, setCustomText, resetCustomText)\n Timeout = Qt.pyqtProperty('double', getTimeout, setTimeout, resetTimeout)\n\n\nclass TaurusLockButton(Qt.QPushButton, TaurusBaseWidget):\n _LOCK_MAP = {LockStatus.Unlocked: 'extra_icons:lock_unlocked.svg', LockStatus.Locked: 'extra_icons:lock_locked_unpreviledged.svg', \n LockStatus.LockedMaster: 'extra_icons:lock_locked.svg', \n LockStatus.Unknown: 'extra_icons:lock_unknown.svg'}\n\n def __init__(self, parent=None, designMode=False):\n self._lock_info = TaurusLockInfo()\n name = self.__class__.__name__\n self.call__init__wo_kw(Qt.QPushButton, parent)\n self.call__init__(TaurusBaseWidget, name, designMode=designMode)\n self.toggled.connect(self.on_toggle)\n self.setCheckable(True)\n self.setAutoTooltip(False)\n self.insertEventFilter(eventfilters.IGNORE_ALL)\n self.update_button()\n\n @classmethod\n def getQtDesignerPluginInfo(cls):\n return {'group': 'Taurus Buttons', \n 'icon': 'designer:pushbutton.png', \n 'module': 'taurus.qt.qtgui.button', \n 'container': False}\n\n def getModelClass(self):\n return TaurusDevice\n\n def setModel(self, model):\n TaurusBaseWidget.setModel(self, model)\n self.update_button()\n\n def get_lock_info(self, cache=False):\n dev = self.getModelObj()\n if dev is not None:\n self._lock_info = dev.getLockInfo(cache=cache)\n return self._lock_info\n\n def update_button(self, lock_info=None):\n if lock_info is None:\n lock_info = self.get_lock_info()\n status = lock_info.status\n self.setIcon(Qt.QIcon(self._LOCK_MAP[status]))\n self.setDown(status in (LockStatus.Locked, LockStatus.LockedMaster))\n self.setToolTip(lock_info.status_msg)\n self.update()\n return lock_info\n\n def _on_toggle(self, down):\n dev = self.getModelObj()\n if down:\n dev.lock()\n else:\n dev.unlock()\n self.update_button()\n\n def on_toggle(self, down):\n try:\n self._on_toggle(down)\n except:\n import sys\n from taurus.qt.qtgui.dialog import TaurusMessageBox\n msgbox = TaurusMessageBox(*sys.exc_info())\n msgbox.setWindowTitle('Error locking device')\n if self.update_button().status == LockStatus.Locked:\n msgbox.setText(self._lock_info.status_msg)\n msgbox.exec_()\n\n model = Qt.pyqtProperty('QString', TaurusBaseWidget.getModel, setModel, TaurusBaseWidget.resetModel)\n\n\ndef lockButtonMain():\n import sys, taurus.qt.qtgui.application\n Application = taurus.qt.qtgui.application.TaurusApplication\n app = Application.instance()\n owns_app = app is None\n if owns_app:\n import taurus.core.util.argparse\n parser = taurus.core.util.argparse.get_taurus_parser()\n parser.usage = '%prog [options] '\n app = Application(sys.argv, cmd_line_parser=parser, app_name='Taurus lock button demo', app_version='1.0', org_domain='Taurus', org_name='Tango community')\n args = app.get_command_line_args()\n if len(args) == 0:\n w = demo()\n else:\n models = list(map(str.lower, args))\n w = Qt.QWidget()\n layout = Qt.QGridLayout()\n w.setLayout(layout)\n for model in models:\n lock_button = TaurusLockButton()\n lock_button.model = model\n layout.addWidget(lock_button)\n\n w.show()\n if owns_app:\n sys.exit(app.exec_())\n else:\n return w\n return\n\n\ndef commandButtonMain():\n import sys\n from taurus.qt.qtgui.application import TaurusApplication\n app = TaurusApplication(cmd_line_parser=None)\n form = TaurusCommandButton(parent=None, designMode=False, command='DevBoolean', parameters=[\n 123], icon='logos:taurus.png', text='launch: DevBoolean 123')\n form.setModel('sys/tg_test/1')\n form.setDangerMessage('Booo scary command!!\\n Maybe you should think twice!')\n\n def f(*a):\n print(a)\n\n form.commandExecuted.connect(f)\n form.show()\n sys.exit(app.exec_())\n return\n\n\ndef launcherButtonMain():\n import sys\n from taurus.qt.qtgui.application import TaurusApplication\n app = TaurusApplication(cmd_line_parser=None)\n\n class MyButton(TaurusLauncherButton):\n _widgetClassName = 'TaurusPlot'\n _icon = 'logos:taurus.png'\n _text = 'show'\n\n form = MyButton()\n form.setModel('sys/tg_test/1/wave')\n form.show()\n sys.exit(app.exec_())\n return\n\n\ndef main():\n lockButtonMain()\n\n\ndef demo():\n \"\"\"Lock button\"\"\"\n lock_button = TaurusLockButton()\n lock_button.model = 'sys/tg_test/1'\n return lock_button\n\n\nif __name__ == '__main__':\n launcherButtonMain()","sub_path":"pycfiles/taurus-4.6.1-py2.7/taurusbutton.py","file_name":"taurusbutton.py","file_ext":"py","file_size_in_byte":24255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"613963642","text":"import inspect, copy\ndef deepcopyDecorator(pos_to_drop=0, *kw_to_drop):\n \"\"\"\n The decorator it creates deepcopies all the parametes except the firs 'pos_to_drop' ones and\n the keyword parameters with key in 'kw_to_drop'\n \"\"\"\n\n def deepcopyIntermediate(func):\n \"\"\"\n Takes the function and returns a function with the same signature but deepcopied args\n \"\"\"\n sig = inspect.signature(func)\n pars = list(sig.parameters)\n kws_not_copy = copy.deepcopy(list(kw_to_drop))\n\n for i in range(pos_to_drop):\n kws_not_copy.append(pars[i])\n\n def f(*args, **kwargs):\n argsNew = args[:pos_to_drop] + copy.deepcopy(args[pos_to_drop:])\n kwargsNew = dict()\n for k,v in kwargs.items():\n if k not in kws_not_copy:\n v = copy.deepcopy(v)\n kwargsNew[k]=v\n\n return func(*argsNew, **kwargsNew)\n\n f.__signature__=sig\n f.__name__ = func.__name__\n return f\n return deepcopyIntermediate\n","sub_path":"Preparatory/general.py","file_name":"general.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"575073478","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, division, print_function\n\nimport warnings\n\nfrom pytest import raises\n\n\ndef assert_deprecations(w, *msgs):\n\n assert len(w) == len(msgs)\n\n for i in range(len(w)):\n assert issubclass(w[i].category, DeprecationWarning)\n assert msgs[i] in str(w[i].message)\n\n\nclass TestToBeRemovedIn20:\n\n def test_type_info_action_links_deprecated(self, allwarnings):\n from kotti.resources import TypeInfo\n from kotti.util import LinkParent\n\n my_item = object()\n with warnings.catch_warnings(record=True) as wngs:\n # If there's a last LinkParent item, we'll assume that is\n # the action menu.\n TypeInfo(edit_links=[LinkParent('foo', [])],\n action_links=[my_item])\n assert wngs[0].category == DeprecationWarning\n\n with raises(ValueError):\n # If there's no last LinkParent item, we'll raise an\n # error, since we can't do anything useful with the link.\n TypeInfo(edit_links=[], action_links=[my_item])\n\n def test_security_has_permission(self, allwarnings):\n with warnings.catch_warnings(record=True) as w:\n from kotti.security import has_permission\n __ = has_permission # pyflakes\n assert_deprecations(w, \"deprecated as of Kotti 1.0\")\n\n def test_object_after_delete(self, allwarnings):\n with warnings.catch_warnings(record=True) as w:\n from kotti.events import ObjectAfterDelete\n __ = ObjectAfterDelete # pyflakes\n assert_deprecations(w, \"Kotti 0.10\")\n\n def test_is_root_permission(self, allwarnings):\n with warnings.catch_warnings(record=True) as w:\n from kotti.views.util import is_root\n __ = is_root # pyflakes\n assert_deprecations(w, \"deprecated as of Kotti 1.0\")\n\n def test_uploaded_file_response(self, allwarnings):\n with warnings.catch_warnings(record=True) as w:\n from kotti.views.file import UploadedFileResponse\n __ = UploadedFileResponse # pyflakes\n assert_deprecations(w, \"removed in Kotti 2.0.0\")\n\n def test_image_deprecations(self, allwarnings):\n with warnings.catch_warnings(record=True) as w:\n from kotti.interfaces import IImage\n from kotti.resources import Image\n from kotti.views.edit.content import ImageAddForm\n from kotti.views.edit.content import ImageEditForm\n from kotti.views.image import _load_image_scales\n from kotti.views.image import image_scales\n from kotti.views.image import ImageView\n from kotti.views.image import includeme\n __ = (IImage, Image, _load_image_scales, image_scales, ImageView,\n includeme, ImageAddForm, ImageEditForm) # pyflakes\n assert_deprecations(w, *('kotti_image', ) * 8)\n\n def test_translate_titles_deprecated(self, allwarnings):\n from kotti.views.edit import _translate_titles\n\n with warnings.catch_warnings(record=True) as w:\n info = [\n {'data': {'title': u\"_(u'Private')\"}, 'title': u\"_(u'Private')\", },\n {'data': {'title': u\"_(u'Public')\"}, 'title': u\"_(u'Public')\", },\n ]\n _translate_titles(info)\n assert_deprecations(w, \"removed in Kotti 2.0.0\")\n\n def test_testing_root_factory(self, allwarnings):\n with warnings.catch_warnings(record=True) as w:\n from kotti.testing import TestingRootFactory\n __ = TestingRootFactory # pyflakes\n assert_deprecations(w, \"will be no longer available starting with \"\n \"Kotti 2.0.0.\")\n","sub_path":"kotti/tests/test_deprecated.py","file_name":"test_deprecated.py","file_ext":"py","file_size_in_byte":3755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"101985455","text":"import pathlib\nimport inspect\nimport gdsfactory as gf\n\n\nfilepath = pathlib.Path(__file__).parent.absolute() / \"components.rst\"\n\nskip = {\n \"LIBRARY\",\n \"circuit_names\",\n \"component_factory\",\n \"component_names\",\n \"container_names\",\n \"component_names_test_ports\",\n \"component_names_skip_test\",\n \"component_names_skip_test_ports\",\n \"dataclasses\",\n \"library\",\n \"waveguide_template\",\n \"extend_ports_list\",\n}\n\nskip_plot = [\n \"component_lattice\",\n \"component_sequence\",\n]\n\nskip_settings = {\"vias\"}\n\n\nwith open(filepath, \"w+\") as f:\n f.write(\n \"\"\"\n\nHere is a list of generic component factories that you can customize for your fab or use it as an inspiration to build your own.\n\n\nComponents\n=============================\n\"\"\"\n )\n\n for name in sorted(gf.components.factory.keys()):\n if name in skip or name.startswith(\"_\"):\n continue\n print(name)\n sig = inspect.signature(gf.components.factory[name])\n kwargs = \", \".join(\n [\n f\"{p}={repr(sig.parameters[p].default)}\"\n for p in sig.parameters\n if isinstance(sig.parameters[p].default, (int, float, str, tuple))\n and p not in skip_settings\n ]\n )\n if name in skip_plot:\n f.write(\n f\"\"\"\n\n{name}\n----------------------------------------------------\n\n.. autofunction:: gdsfactory.components.{name}\n\n\"\"\"\n )\n else:\n f.write(\n f\"\"\"\n\n{name}\n----------------------------------------------------\n\n.. autofunction:: gdsfactory.components.{name}\n\n.. plot::\n :include-source:\n\n import gdsfactory as gf\n\n c = gf.components.{name}({kwargs})\n c.plot()\n\n\"\"\"\n )\n","sub_path":"docs/write_components_doc.py","file_name":"write_components_doc.py","file_ext":"py","file_size_in_byte":1758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"475549771","text":"import pandas as pd\n\nunames = ['user_id', 'gender', 'age', 'ocupation', 'zip']\nusers = pd.read_table('/Users/gregoryknowles/Desktop/Python Data Analysis/ml-1m/users.dat', \\\n\tsep = '::', header = None, names = unames)\t\n\t\nrnames = ['user_id', 'movie_id', 'rating', 'timestamp']\nratings = pd.read_table('/Users/gregoryknowles/Desktop/Python Data Analysis/ml-1m/ratings.dat', \\\n\tsep = '::', header = None, names = rnames)\n\t\nmnames = ['movie_id', 'title', 'genres']\nmovies = pd.read_table('/Users/gregoryknowles/Desktop/Python Data Analysis/ml-1m/movies.dat', \\\n\tsep = '::', header = None, names = mnames)\n\t\nusers[:5]\nratings[:5]\nmovies[:5]\n\n# merge data sets together, pandas infers cols to join on from name \ndata = pd.merge(pd.merge(ratings, users),movies)\n\n# aggregating by one or more attributes \n# use pivot_table method\nmean_ratings = data.pivot_table('rating', index = 'title', columns = 'gender', \\\n\taggfunc = 'mean')\nmean_ratings[:5]\n\n# filter to movies that received at least 250 ratings\nratings_by_title = data.groupby('title').size()\nratings_by_title[:10]\n\nactive_titles = ratings_by_title.index[ratings_by_title >= 250]\n\n# then use index to select rows from mean_ratings\nmean_ratings = mean_ratings.ix[active_titles]\n\ntop_female_ratings = mean_ratings.sort_values(by='F', ascending=False)\n\nmean_ratings['diff'] = mean_ratings['M'] - mean_ratings['F']\nsorted_by_diff = mean_ratings.sort_values(by='diff')\n# reverse order of rows\nsorted_by_diff[::-1][:15]\n\nrating_std_by_title = data.groupby('title')['rating'].std()\nrating_std_by_title = rating_std_by_title.ix[active_titles]\nrating_std_by_title.order(ascending=False)[:10]","sub_path":"Python/Python Data Analysis/pandas_movielens.py","file_name":"pandas_movielens.py","file_ext":"py","file_size_in_byte":1631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"589668722","text":"# -*- coding:utf-8 -*- \n# \n# @Version : 1.0 \n# @Time : 2018/1/222 16:34\n# @Author : YuepengWang \n# @File : dictionary.py\n# \n# 此脚本为一个dictionary演示脚本\n\nav_catalog = {\n \"欧美\": {\n \"www.youporn.com\": [\"很多免费的,世界最大的\", \"质量一般\"],\n \"www.pornhub.com\": [\"很多免费的,也很大\", \"质量比yourporn高点\"],\n \"letmedothistoyou.com\": [\"多是自拍,高质量图片很多\", \"资源不多,更新慢\"],\n \"x-art.com\": [\"质量很高,真的很高\", \"全部收费,屌比请绕过\"]\n },\n \"日韩\": {\n \"tokyo-hot\": [\"质量怎样不清楚,个人已经不喜欢日韩范了\", \"听说是收费的\"]\n },\n \"大陆\": {\n \"1024\": [\"全部免费,真好,好人一生平安\", \"服务器在国外,慢\"]\n }\n}\n\nav_catalog[\"大陆\"][\"1024\"][1] = \"可以在国内做镜像\"\nprint(av_catalog)\n\nav_catalog.setdefault(\"大\", {\"www.baidu.com\":[1, 2]}) # 有这个指定主键的情况下，保留原主键的值；没有这个指定的主键，创建一个新的主键，将这个值写给这个主键\nprint(av_catalog)\n\ninfo = {\n 'stu1101': \"TengLan Wu\",\n 'stu1102': \"LongZe Luola\",\n 'stu1103': \"XiaoZe Maliya\",\n}\n\nb = {\n 'stu1101': \"Alex\",\n 1: 3,\n 2: 5\n}\n\nprint(info.values())\nprint(info.items())\nprint(info.keys())\n\ninfo.update(b)\nprint(info)\nc = dict.fromkeys([6, 7, 8], [1, {\"name\": \"alex\"}, 444])\nprint(c)\nc[7][1]['name'] = \"Jack Chen\"\nprint(c)\n\nprint(info.items())\n\n# info['stu1104'] 这种写法如果元素不存在会报错\nprint(info.get('stu1103'))\n\nprint('stu1103' in info) #info.has_key(\"1103\") in py2.x\n\nprint(info[\"stu1101\"])\ninfo[\"stu1101\"] =\"武藤兰\"\ninfo[\"stu1104\"] =\"CangJingkong\"\n\ndel info[\"stu1101\"]\ninfo.pop(\"stu1102\")\ninfo.popitem()\nprint(info)\n\ninfo1 = {\n 'stu1101': \"TengLan Wu\",\n 'stu1102': \"LongZe Luola\",\n 'stu1103': \"XiaoZe Maliya\",\n}\n\nfor i in info1:\n print(i, info1[i])\n\nfor k, v in info1.items():\n print(k, v)\n\n","sub_path":"python/python3.6/day2/dictionary.py","file_name":"dictionary.py","file_ext":"py","file_size_in_byte":1965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"429889642","text":"import datetime\nimport hashlib\nimport time\nimport pymongo\nfrom fake_useragent import UserAgent\nimport requests\nimport json\nimport MySQLdb\n\n# UA池\nuser_agent = str(UserAgent().random)\n\nheaders = {\n 'User-Agent': user_agent\n}\n\n# 13位时间戳\ntime_13 = str(int(time.time() * 1000))\ntime_10 = str(int(time.time() * 10))\n\n\ndef md_5(data):\n m = hashlib.md5()\n m.update(data.encode('utf-8'))\n return m.hexdigest()\n\n\ndef get_json(url, headers, params):\n res = requests.post(url, headers=headers, data=json.dumps(params)).json()\n return res\n\n\ndef datetime_tostring(dt):\n return dt.strftime(\"%Y-%m-%d %H:%M:%S\")\n\n\ndef string_todatetime(st):\n return datetime.datetime.strptime(st, \"%Y-%m-%d %H:%M:%S\")\n\n\ndef jsonp_to_json(st):\n json_res = st.split('(', 1)[1].rsplit(')', 1)[0]\n return json.loads(json_res)\n\n\ndef get_cookie(url):\n headers = {\n 'User-Agent': user_agent\n }\n res = requests.get(url, headers=headers)\n cookies = dict(res.cookies)\n cookie = ''\n for k in cookies.keys():\n cookie = cookie + k + '=' + cookies[k] + ';'\n return cookie\n\n\nclient = pymongo.MongoClient(host='localhost', port=27017)\ndb = client.pachong\n\n\nclass BaseMongo(object):\n \"\"\"\n mongo工具类\n \"\"\"\n @staticmethod\n def insert_one(collection, data):\n \"\"\"直接使用insert() 可以插入一条和插入多条 不推荐 明确区分比较好\"\"\"\n res = collection.insert_one(data)\n return res.inserted_id\n\n @staticmethod\n def insert_many(collection, data_list):\n res = collection.insert_many(data_list)\n return res.inserted_ids\n\n @staticmethod\n def find_one(collection, data, data_field={}):\n if len(data_field):\n res = collection.find_one(data, data_field)\n else:\n res = collection.find_one(data)\n return res\n\n @staticmethod\n def find_many(collection, data, data_field={}):\n \"\"\" data_field 是指输出 操作者需要的字段\"\"\"\n if len(data_field):\n res = collection.find(data, data_field)\n else:\n res = collection.find(data)\n return res\n\n @staticmethod\n def update_one(collection, data_condition, data_set):\n \"\"\"修改一条数据\"\"\"\n res = collection.update_one(data_condition, data_set)\n return res\n\n @staticmethod\n def update_many(collection, data_condition, data_set):\n \"\"\" 修改多条数据 \"\"\"\n res = collection.update_many(data_condition, data_set)\n return res\n\n @staticmethod\n def delete_many(collection, data):\n res = collection.delete_many(data)\n return res\n\n @staticmethod\n def delete_one(collection, data):\n res = collection.delete_one(data)\n return res\n\n\nclass MysqlHelper():\n \"\"\"\n mysql工具类\n \"\"\"\n def __init__(self, host, port, db, user, passwd, charset='utf8'):\n self.host = host\n self.port = port\n self.db = db\n self.user = user\n self.passwd = passwd\n self.charset = charset\n\n def connect(self):\n self.conn = MySQLdb.connect(host=self.host, port=self.port, db=self.db, user=self.user, passwd=self.passwd,\n charset=self.charset)\n self.cursor = self.conn.cursor()\n\n def close(self):\n self.cursor.close()\n self.conn.close()\n\n def get_one(self, sql, params=()):\n result = None\n try:\n self.connect()\n self.cursor.execute(sql, params)\n result = self.cursor.fetchone()\n self.close()\n except Exception as e:\n print(e)\n return result\n\n def get_all(self, sql, params=()):\n list = ()\n try:\n self.connect()\n self.cursor.execute(sql, params)\n list = self.cursor.fetchall()\n self.close()\n except Exception as e:\n print(e)\n return list\n\n def insert(self, sql, params=()):\n return self.__edit(sql, params)\n\n def update(self, sql, params=()):\n return self.__edit(sql, params)\n\n def delete(self, sql, params=()):\n return self.__edit(sql, params)\n\n def __edit(self, sql, params):\n count = 0\n try:\n self.connect()\n count = self.cursor.execute(sql, params)\n self.conn.commit()\n self.close()\n except Exception as e:\n print(e)\n return count\n","sub_path":"Tools/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":4442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"650779768","text":"import pandas as pd\r\nimport numpy as np\r\nimport argparse\r\n\r\n\r\ndef create_parser():\r\n parser = argparse.ArgumentParser(\r\n description='Aid')\r\n parser.add_argument('-d', '--directory', metavar='input', required=True,\r\n help='path')\r\n return parser\r\n\r\n\r\ndef parse_arguments(parser):\r\n args = parser.parse_args()\r\n return args\r\n\r\n\r\ndef execute_parser():\r\n parser = create_parser()\r\n return parse_arguments(parser)\r\n\r\n\r\nargs = execute_parser()\r\npath = args.directory\r\n\r\nspecies_df = pd.read_csv(path, sep=',', decimal=\".\")\r\n\r\n\r\nspecies_df['Genus != Species ?'] = np.where(species_df['Genus'].apply(lambda x: x.split(' ')[0]) != species_df['Species&strain'].apply(\r\n lambda x: x.split(' ')[0]), 'Distinct!', '')\r\n\r\nspecies_df['Species&strainFixed'] = np.where(species_df['Genus != Species ?'].str.contains(\"Distinct!\"), species_df['Genus'] + ' ' +\r\n species_df['Species&strain'].apply(lambda x: ' '.join(x.split(' ')[1:])), species_df['Species&strain'])\r\n\r\nnew_names = [\"Taxonomy_Id\", \"Main_genome_description\", \"Genomes_mean_size\", \"Genes_mean_size\", \"Num_genes\", \"Num_unique_genes\", \"Num_genes_+strand\",\r\n \"Num_genes_-strand\", \"Superkingdom\", \"Phylum\", \"Class\", \"Order\", \"Family\", \"Genus\", \"Species&strain\", 'Genus != Species ?', 'Species&strainFixed', \"Genomes\"]\r\nspecies_df = species_df[new_names]\r\n\r\n\r\nnew_path = path.replace('.csv', '')\r\nwith pd.ExcelWriter(f'{new_path}_fix_genus_species.xlsx', engine='xlsxwriter') as writer:\r\n species_df.to_excel(writer, sheet_name='species_all',\r\n index=None, header=True)\r\n\r\n # Get the xlsxwriter workbook and worksheet objects.\r\n workbook = writer.book\r\n worksheet = writer.sheets['species_all']\r\n\r\n # Get the dimensions of the dataframe.\r\n (max_row, max_col) = species_df.shape\r\n\r\n # Create a list of column headers, to use in add_table().\r\n column_settings = []\r\n for header in species_df.columns:\r\n column_settings.append({'header': header})\r\n\r\n # Add the table.\r\n worksheet.add_table(0, 0, max_row, max_col - 1,\r\n {'columns': column_settings})\r\n\r\n # Make the columns wider for clarity.\r\n worksheet.set_column(9, 13, 18)\r\n worksheet.set_column(14, 14, 45)\r\n worksheet.set_column(16, 16, 45)\r\n","sub_path":"variant_analysis/fix_genus_species.py","file_name":"fix_genus_species.py","file_ext":"py","file_size_in_byte":2343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"352053769","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 ('index', '0003_auto_20161228_1641'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='orden',\n name='Accesorios',\n field=models.CharField(max_length=250),\n ),\n migrations.AlterField(\n model_name='orden',\n name='Estado',\n field=models.CharField(choices=[('PROCESANDO', 'Procesando'), ('ESPERA', 'En espera'), ('TERMINADO', 'Terminado'), ('ENTREGADO', 'Entregado')], default='PROCESANDO', max_length=20),\n ),\n ]\n","sub_path":"index/migrations/0004_auto_20161228_1649.py","file_name":"0004_auto_20161228_1649.py","file_ext":"py","file_size_in_byte":699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"210804538","text":"import mosaic.utilities.mosaicLogging as mlog \nimport nose\n\ndef _mosaicUnitTests(base):\n class mosaicUnitTests(base):\n log=mlog.mosaicLogging().getLogger(__name__)\n\n description = \"run the MOSAIC unit test suite.\"\n user_options = [\n ('algorithms','a', 'run algorithmic tests'),\n ('apps','p', 'run top-level application tests'),\n ('segment','s', 'run time-series segmentation tests'),\n ('dependencies', 'd', 'test MOSAIC dependency versions'),\n ('modules', 'm', 'test MOSAIC modules'),\n ('trajio', 't', 'test MOSAIC I/O'),\n ('mdio', 'i', 'test MOSAIC MDIO'),\n ('mosaicweb', 'w', 'test MOSAIC web')\n ]\n\n def initialize_options(self):\n self.algorithms=0\n self.apps=0\n self.segment=0\n self.dependencies=0\n self.modules=0\n self.trajio=0\n self.mdio=0\n self.mosaicweb=0\n\n def finalize_options(self):\n pass\n\n def run(self):\n try:\n testList=[]\n testargs=['mosaic']\n\n if self.algorithms:\n mosaicUnitTests.log.debug(\"Running algorithm unit tests\")\n testList.extend(['mosaic/tests/adept2State_Test.py', 'mosaic/tests/adept_Test.py', 'mosaic/tests/cusum_Test.py'])\n if self.apps:\n mosaicUnitTests.log.debug(\"Running top-level applications unit tests\")\n testList.extend(['mosaic/tests/apps_Test.py'])\n if self.segment:\n mosaicUnitTests.log.debug(\"Running event segmentation unit tests\")\n testList.extend(['mosaic/tests/eventPartition_Test.py', 'mosaic/tests/eventPartitionParallel_Test.py'])\n if self.dependencies:\n mosaicUnitTests.log.debug(\"Running dependency unit tests\")\n testList.extend(['tests/dependencyVersion_Test.py'])\n if self.modules:\n mosaicUnitTests.log.debug(\"Running module import unit tests\")\n testList.extend(['mosaic/tests/import_Tests.py'])\n if self.trajio:\n mosaicUnitTests.log.debug(\"Running module trajectory I/O unit tests\")\n testList.extend(['mosaic/tests/trajio_Test.py'])\n if self.mdio:\n mosaicUnitTests.log.debug(\"Running module metadata I/O unit tests\")\n testList.extend(['mosaic/tests/mdio_Test.py']) \n if self.mosaicweb:\n mosaicUnitTests.log.debug(\"Running module Mosaic web unit tests\")\n testList.extend(['mosaicweb/tests/status_Test.py', 'mosaicweb/tests/session_Test.py'])\n\n if self.verbose:\n mosaicUnitTests.log.debug(\"Running verbose unit tests\")\n testargs.extend(['-v'])\n else:\n testargs.extend([])\n \n testargs.extend(testList)\n\n return nose.main(argv=testargs)\n except:\n raise\n\n return mosaicUnitTests","sub_path":"tests/mosaicUnitTests.py","file_name":"mosaicUnitTests.py","file_ext":"py","file_size_in_byte":3319,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"290199953","text":"import os\nimport platform\nimport sys\nimport subprocess\nmenu=\"\\033[41;1;37m\"\ncorPadrao=\"\\033[0m\"\npreto=\"\\033[0;30m\"\nvermelho=\"\\033[0;31m\"\nverde=\"\\033[0;32m\"\nmarrom=\"\\033[0;33m\"\nazul=\"\\033[0;34m\"\npurple=\"\\033[0;35m\"\ncyan=\"\\033[0;36m\"\ncinzaClaro=\"\\033[0;37m\"\npretoCinza=\"\\033[1;30m\"\nvermelhoClaro=\"\\033[1;31m\"\nverdeClaro=\"\\033[1;32m\"\namarelo=\"\\033[1;33m\"\nazulClaro=\"\\033[1;34m\"\npurpleClaro=\"\\033[1;35m\"\ncyanClaro=\"\\033[1;36m\"\nbranco=\"\\033[1;37m\"\nfim=\"\\033[0m\"\n\ndef criarusuario():\n\tsubprocess.call(\"sudo groupadd badmanager 1>/dev/null 2>/dev/null\", shell=True)\n\tusuario = input(verde + \"Ingresa nombre de usuario: \" + fim)\n\tsenha = input(verde + \"contrasena: \" + fim)\n\tvalidade = input(verde + \"Quiere agregar fecha de expiracion \" + usuario + \"? (s/n) \" + fim)\n\tif validade == \"s\":\n\t\tvalidade = input(verde + \"Escriba fecha: \" + fim)\n\t\tsubprocess.call(\"sudo useradd -g badmanager -M -N -s /bin/false \" + usuario + \" -e \" + validade, shell=True)\n\t\tsubprocess.call(\"sudo bash /etc/BadManager/criarusuario/pass.sh \" + senha, shell=True)\n\t\tsys.path.insert(0, \"/etc/BadManager/\")\n\t\tfrom limite import limite\n\t\tlimite.deflimite()\n\t\tip = subprocess.call(\"echo Ip: $(ip addr | grep '/19' | awk '{print $4}')\", shell=True)\n\t\tprint(cyan + \"Usuario \" + usuario + \" Creado!\" + fim)\n\t\tprint(cyan + \"Dados: \" + fim)\n\t\tprint(cyan + \"Usuario: \" + usuario + fim)\n\t\tprint(cyan + \"Contrasena: \" + senha + fim)\n\t\tprint(cyan + \"Caducidad: \" + validade + fim)\n\t\treturn True\n\telse:\n\t\tsubprocess.call(\"sudo useradd -M -N -s /bin/false \" + usuario, shell=True)\n\t\tsubprocess.call(\"sudo bash /etc/BadManager/criarusuario/pass.sh \" + senha + \" \" + usuario, shell=True)\n\t\tip = subprocess.call(\"echo Ip: $(ip addr | grep '/19' | awk '{print $4}')\", shell=True)\n\t\tprint(cyan + \"Usuario \" + usuario + \" Creado!\" + fim)\n\t\tprint(cyan + \"Dados:\\n\" + fim)\n\t\tprint(cyan + \"Usuario: \" + usuario + fim)\n\t\tprint(cyan + \"Contrasena: \" + senha + fim)\n\t\treturn True\n","sub_path":"criarusuario/criarusuario.py","file_name":"criarusuario.py","file_ext":"py","file_size_in_byte":1922,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"214748226","text":"import json\nimport urllib3\nfrom flask import Flask, request, redirect, send_from_directory, jsonify\nfrom werkzeug.utils import secure_filename\nfrom flask_cors import CORS\nfrom fileutil import save_local, upload_s3, list_local, list_s3, add_file, get_files, allowed_file\n\nupload_folder = 'media'\n\napp = Flask(__name__)\nCORS(app)\napp.config['UPLOAD_FOLDER'] = upload_folder\n\nhttp = urllib3.PoolManager()\nip = http.request('GET', '169.254.169.254/latest/meta-data/public-ipv4').data.decode()\n\n@app.route('/')\ndef root():\n return send_from_directory('web', 'index.html')\n\n@app.route('/')\ndef webfiles(filename):\n return send_from_directory(\"web\", filename)\n\n@app.route('/upload', methods=['GET', 'POST'])\ndef upload_file():\n if request.method == 'POST':\n if 'file' not in request.files:\n return 'No file part'\n\n file = request.files['file']\n if file.filename == '':\n return 'No selected file'\n if file and allowed_file(file.filename):\n filename = secure_filename(file.filename)\n# MAKE CHANGES HERE!\n add_file(file)\n #save_local(file)\n #upload_s3(file, \"john-selfie\")\n# ------------------\n return f'Received {filename}'\n return\n\n@app.route('/listmedia')\ndef list_media():\n# MAKE CHANGES HERE!\n media_files = get_files()\n #media_files = list_local()\n #media_files = list_s3('john-selfie')\n# ------------------\n return media_files\n\n@app.route('/media/')\ndef uploaded_file(filename):\n return send_from_directory(app.config['UPLOAD_FOLDER'], filename)\n","sub_path":"Lab/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"401684794","text":"from re import sub\ndef printer_error(s):\n # your code\n # 字符串只接受小写字母a-m\n error_char=len(sub(\"[a-m]\",'',s))\n # for i in range(len(s)):\n # if ord(s[i])>=97 and ord(s[i])<=109:\n # pass\n # else:\n # error_char+=1\n\n return('{}/{}'.format(len(sub(\"[a-m]\",'',s)),len(s)))\n\n\nprint(printer_error('aaaaaaaaaaaaaaaabbbbbbbbbbbbbbbbbbmmmmmmmmmmmmmmmmmmmxyz'))\n ","sub_path":"7Kyu/PrinterErrors.py","file_name":"PrinterErrors.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"654443837","text":"from ..core import adata\nfrom ..core import atable\nfrom astropy.io import fits\nfrom astropy import log\nimport numpy as np\nimport os\nimport astropy.units as u\nfrom astropy.wcs import wcs\n\n\ndef HDU_to_adata(hdu):\n data=hdu.data\n meta=hdu.header\n mask=np.isnan(data)\n # Hack to correct wrong uppercased units generated by CASA\n try:\n bscale=meta['BSCALE']\n except KeyError:\n bscale=1.0\n try:\n bzero=meta['BZERO']\n except KeyError:\n bzero=0.0\n try:\n bsu=meta['BUNIT']\n bsu=bsu.lower()\n bsu=bsu.replace(\"jy\",\"Jy\")\n bunit=u.Unit(bsu,format=\"fits\")\n except KeyError:\n bunit=u.Unit(\"u.Jy/u.beam\")\n for k in meta.keys():\n if k.startswith('PC00'):\n meta.pop(k) \n\n\n mywcs=wcs.WCS(meta)\n # Create astropy units\n if len(data.shape) == 4:\n # Put data in physically-meaninful values, and remove stokes\n # TODO: Stokes is removed by summing (is this correct? maybe is averaging?)\n log.info(\"4D data detected: assuming RA-DEC-FREQ-STOKES (like CASA-generated ones), and dropping STOKES\")\n data=data.sum(axis=0)*bscale+bzero\n mywcs=mywcs.dropaxis(3)\n elif len(data.shape) == 3:\n log.info(\"3D data detected: assuming RA-DEC-FREQ\")\n data=data*bscale+bzero\n else:\n log.error(\"Only 3D data allowed (or 4D in case of polarization)\")\n raise TypeError\n\n # META Fixing\n #TODO..\n\n return adata.AData(data,mywcs,meta,bunit)\n\n\ndef HDU_to_atable(hdu):\n log.warning(\"FITS Table ---> ATable not implemented Yet\")\n #return atable.ATable(data=hdu.data,meta=hdu.header)\n\n\ndef load_fits_to_ws(path,name,ws):\n log.info(\"Loading \"+name+\".fits\")\n hdulist = fits.open(path)\n counter=0\n for hdu in hdulist:\n if isinstance(hdu,fits.PrimaryHDU) or isinstance(hdu,fits.ImageHDU):\n log.info(\"Processing HDU \"+str(counter)+\" (Image)\")\n ndd=HDU_to_adata(hdu)\n ide=name+\"-\"+str(counter)\n ws[ide]=ndd\n counter+=1\n\n elif isinstance(hdu,fits.BinTableHDU) or isinstance(hdu,fits.TableHDU):\n log.info(\"Processing HDU \"+str(counter)+\" (Table)\")\n ntt=HDU_to_atable(hdu.data,meta=hdu.header)\n ide=name+\"-\"+str(counter)\n ws[ide]=ntt\n else:\n log.warning(\"HDU type not recognized, ignoring \"+hdu.name+\" (\"+counter+\")\")\n counter+=1\n\n#TODO: support more filetypes\ndef load_hdf5_to_ws(path,name,ws):\n log.warning(\"HDF5 format not supported yet. Ignoring file \"+name+\".hdf5\")\ndef load_votable_to_ws(path,name,ws):\n log.warning(\"VOTable format not supported yet. Ignoring file \"+name+\".xml\")\ndef load_ascii_to_ws(path,name,ws):\n log.warning(\"ASCII format not supported yet. Ignoring file \"+name)\n\ndef load_to_ws(path,ws):\n filename=os.path.basename(path)\n name,ext=os.path.splitext(filename)\n if ext == '.fits':\n load_fits_to_ws(path,name,ws)\n elif ext == '.hdf5':\n load_hdf5_to_ws(path,name,ws)\n elif ext == '.xml':\n load_votable_to_ws(path,name,ws)\n else:\n load_ascii_to_ws(path,name,ws)\n\ndef load_to_cont(path,cont):\n filename=os.path.basename(path)\n name,ext=os.path.splitext(filename)\n if ext == '.fits':\n load_fits_to_cont(path,name,cont)\n elif ext == '.hdf5':\n (path,name,cont)\n elif ext == '.xml':\n votable_consumer(path,name,cont)\n else:\n ascii_consumer(path,name,cont)\n\ndef save_from_cont(path,cont):\n filename=os.path.basename(path)\n name,ext=os.path.splitext(filename)\n if ext == '.fits':\n save_fits_from_cont(path,cont)\n else:\n log.warning(\"We only support saving in fits format for the moment\")\n\n\n#TODO: support more filetypes\ndef load_hdf5_to_cont(path,name,cont):\n log.warning(\"HDF5 format not supported yet. Ignoring file \"+name+\".hdf5\")\ndef load_votable_to_cont(path,name,cont):\n log.warning(\"VOTable format not supported yet. Ignoring file \"+name+\".xml\")\ndef load_ascii_to_cont(path,name,cont):\n log.warning(\"ASCII format not supported yet. Ignoring file \"+name)\n\ndef save_fits_from_cont(filepath,acont):\n if acont.primary == None:\n phdu=fits.PrimaryHDU()\n else:\n phdu=acont.primary.get_hdu(True)\n nlist=[phdu]\n count=0\n for elm in acont.adata:\n count+=1\n hdu=elm.get_hdu()\n hdu.header['EXTNAME'] = 'SCI'\n hdu.header['EXTVER'] = count\n nlist.append(hdu)\n count=0\n for elm in acont.atable:\n count+=1\n hdu=elm.get_hdu()\n hdu.header['EXTNAME'] = 'TAB'\n hdu.header['EXTVER'] = count\n nlist.append(hdu)\n hdulist = fits.HDUList(nlist)\n hdulist.writeto(filepath,clobber=True)\n\ndef load_fits_to_cont(filePath,name,acont):\n hdulist = fits.open(filePath)\n for counter,hdu in enumerate(hdulist):\n if isinstance(hdu,fits.PrimaryHDU) or isinstance(hdu,fits.ImageHDU):\n log.info(\"Processing HDU \"+str(counter)+\" (Image)\")\n try:\n ndd=HDU_to_adata(hdu)\n if isinstance(hdu,fits.PrimaryHDU):\n acont.primary = ndd\n acont.adata.append(ndd)\n except TypeError:\n log.info(str(counter)+\" (Image) wasn't an Image\")\n\n if isinstance(hdu, fits.BinTableHDU):\n table = HDU_to_atable(hdu)\n acont.atable.append(table)\n if acont.primary is None:\n if len(acont.adata)==0:\n acont.primary = acont.atable[0]\n else:\n acont.primary = acont.adata[0]\n\n","sub_path":"acalib/io/formats.py","file_name":"formats.py","file_ext":"py","file_size_in_byte":5511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"235473746","text":"#!/usr/bin/env python3\n\n\nfrom ev3dev2.motor import OUTPUT_A, OUTPUT_B, OUTPUT_C\n\nfrom track3r_rc_tank_ev3dev2 import Track3r\n\n\nclass Track3rWithSpinner(Track3r):\n def __init__(\n self,\n left_motor_port: str = OUTPUT_B, right_motor_port: str = OUTPUT_C,\n medium_motor_port: str = OUTPUT_A):\n super().__init__(\n left_motor_port=left_motor_port, right_motor_port=right_motor_port,\n medium_motor_port=medium_motor_port)\n \n self.remote.on_channel1_beacon = self.spin\n\n\n def spin(self, state):\n if state:\n self.medium_motor.run_forever(speed_sp=50)\n\n else:\n self.medium_motor.stop()\n\n \nif __name__ == '__main__':\n TRACK3R_WITH_SPINNER = Track3rWithSpinner()\n\n TRACK3R_WITH_SPINNER.main()\n","sub_path":"Computing-Platforms/EV3/Home-Edition/Core-Robots/Track3r/Track3r-5-RCTank-with-Spinner.EV3Dev2.py","file_name":"Track3r-5-RCTank-with-Spinner.EV3Dev2.py","file_ext":"py","file_size_in_byte":808,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"571521123","text":"from django.urls import path\n\nfrom . import views\n\napp_name = 'tekmovanja'\n\nurlpatterns = [\n path('index', views.index, name='index'), \n #path('test', views.IndexView.as_view(), name='index'),\n path('login',views.login, name='login'),\n path('registration',views.registration, name='registration'),\n path('/sola/', views.sola, name='sola'),\n path('auth/', views.auth, name='auth'),\n path('tekmovanja',views.tekmovanja,name='tekmovanja'),\n path('tek_prijava',views.tek_prijava,name='prijava'),\n path('tek_odjava',views.tek_odjava,name='odjava'),\n path('izpis',views.izpis, name='izpis'),\n path('register/',views.register, name='register'),\n path('test/',views.test, name='test'),\n path('izpis_sola/',views.izpis_sola, name='izpis_sola'),\n path('profil/',views.profil, name='profil'),\n path('profil_update/',views.profil_update, name='profil_update'),\n \n \n \n \n]","sub_path":"tekmovanja/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"305022068","text":"import pandas as pd\n\ntitanic = pd.read_csv(\"train.csv\")\n\n# to get inital domain knowledge\nprint(titanic.head(5))\nprint(titanic.describe())\n\n'''\n PassengerId Survived Pclass \\\n0 1 0 3 \n1 2 1 1 \n2 3 1 3 \n3 4 1 1 \n4 5 0 3 \n\n Name Sex Age SibSp \\\n0 Braund, Mr. Owen Harris male 22.0 1 \n1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n2 Heikkinen, Miss. Laina female 26.0 0 \n3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n4 Allen, Mr. William Henry male 35.0 0 \n\n Parch Ticket Fare Cabin Embarked \n0 0 A/5 21171 7.2500 NaN S \n1 0 PC 17599 71.2833 C85 C \n2 0 STON/O2. 3101282 7.9250 NaN S \n3 0 113803 53.1000 C123 S \n4 0 373450 8.0500 NaN S \n'''\n##################\n'''\n PassengerId Survived Pclass Age SibSp \\\ncount 891.000000 891.000000 891.000000 714.000000 891.000000 \nmean 446.000000 0.383838 2.308642 29.699118 0.523008 \nstd 257.353842 0.486592 0.836071 14.526497 1.102743 \nmin 1.000000 0.000000 1.000000 0.420000 0.000000 \n25% 223.500000 0.000000 2.000000 NaN 0.000000 \n50% 446.000000 0.000000 3.000000 NaN 0.000000 \n75% 668.500000 1.000000 3.000000 NaN 1.000000 \nmax 891.000000 1.000000 3.000000 80.000000 8.000000 \n\n Parch Fare \ncount 891.000000 891.000000 \nmean 0.381594 32.204208 \nstd 0.806057 49.693429 \nmin 0.000000 0.000000 \n25% 0.000000 7.910400 \n50% 0.000000 14.454200 \n75% 0.000000 31.000000 \nmax 6.000000 512.329200 \n'''\n\nprint(titanic.info())\n'''\n\nInt64Index: 891 entries, 0 to 890\nData columns (total 12 columns):\nPassengerId 891 non-null int64\nSurvived 891 non-null int64\nPclass 891 non-null int64\nName 891 non-null object\nSex 891 non-null object\nAge 714 non-null float64\nSibSp 891 non-null int64\nParch 891 non-null int64\nTicket 891 non-null object\nFare 891 non-null float64\nCabin 204 non-null object\nEmbarked 889 non-null object\ndtypes: float64(2), int64(5), object(5)\n'''\n\n####################################\n#\t\tDATA CLEANING\t\t\t####\n####################################\n\ntitanic[\"Age\"] = titanic[\"Age\"].fillna(titanic[\"Age\"].median())\n\n# decide to drop particular feature since insignificant amout of data \n# only got 204 examples out of 891 total\nprint (\"Cabin column count: \")\nprint (titanic[\"Cabin\"].count())\n# >> drop Cabin, Name, Ticket\n\n# CONVERT SEX COLUMN TO NUMERIC\n# Find all the unique genders -- the column appears to contain only male and female.\nprint(titanic[\"Sex\"].unique())\n# Replace all the occurences of male with the number 0.\ntitanic.loc[titanic[\"Sex\"] == \"male\", \"Sex\"] = 0\ntitanic.loc[titanic[\"Sex\"] == \"female\", \"Sex\"] = 1\n\n\n#CONVERT EMBARKED COLUMN TO NUMERIC\n# Find all the unique values for \"Embarked\".\nprint(titanic[\"Embarked\"].unique())\ntitanic[\"Embarked\"] = titanic[\"Embarked\"].fillna(\"S\")\n\ntitanic.loc[titanic[\"Embarked\"] == \"S\", \"Embarked\"] = 0\ntitanic.loc[titanic[\"Embarked\"] == \"C\", \"Embarked\"] = 1\ntitanic.loc[titanic[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n\nprint(\"Done data cleaning with train set\")\n\n####################################\n#\t\tLINEAR REGRESSION\t\t####\n####################################\n\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.cross_validation import KFold\n\n# only use these coluns for prediction\npredictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]\n\n# Initialize algorithm \nalg = LinearRegression()\n\ncv = KFold(titanic.shape[0], n_folds=3, random_state = 1)\n\n# results = []\n# applicable for single column data only\n# for traincv, testcv in cv:\n# probas = alg.fit(train[traincv], target[traincv]).predict_proba(train[testcv])\n# results.append(llfun(target[testcv], [x[1] for x in probas]) )\n\npredictions = []\nfor train, test in cv:\n\t# only using the predictors column on rows in the training folds\n\ttrain_predictors = (titanic[predictors].iloc[train])\n\n\ttrain_target = titanic[\"Survived\"].iloc[train]\n\n\talg.fit(train_predictors, train_target)\n\n\ttest_predictions = alg.predict(titanic[predictors].iloc[test])\n\tpredictions.append(test_predictions)\n\nprint (\"Done training & predicting in linear regression\")\n# print (type(predictions))\n# print (len(predictions[2]))\n# predictions divided into 3 folds of 297 in length, total 891 examples\n\n\n####################################\n#\t\tVALIDATION \t\t\t\t####\n####################################\nimport numpy as np\npredictions = np.concatenate(predictions,axis = 0)\n\n# Map predictions to outcomes (only possible outcomes are 1 and 0)\npredictions[predictions > .5] = 1\npredictions[predictions <=.5] = 0\n\naccuracy = sum(predictions[predictions == titanic[\"Survived\"]])/len(predictions)\n\nprint (\"Accuracy for linear regression: \")\nprint (accuracy)\n\n####################################\n#\t\tLOGISTIC REGRESSION\t\t####\n####################################\n\nfrom sklearn import cross_validation\nfrom sklearn.linear_model import LogisticRegression\n\n# Initialize our algorithm\nalg = LogisticRegression(random_state=1)\n# Compute the accuracy score for all the cross validation folds. (much simpler than what we did before!)\nscores = cross_validation.cross_val_score(alg, titanic[predictors], titanic[\"Survived\"], cv=3)\n# Take the mean of the scores (because we have one for each fold)\nprint()\nprint (\"Accuracy for logistic regression: \")\nprint(scores.mean())\n\n\n####################################\n#\t\tPROCESS TEST SET\t\t####\n####################################\n\n\ntitanic_test = pd.read_csv(\"test.csv\")\ntitanic_test[\"Age\"] = titanic_test[\"Age\"].fillna(titanic[\"Age\"].median())\n\ntitanic_test.loc[titanic_test[\"Sex\"]==\"male\", \"Sex\"] = 0\ntitanic_test.loc[titanic_test[\"Sex\"]==\"female\", \"Sex\"] = 1\n \ntitanic_test.loc[titanic_test[\"Embarked\"] == \"S\", \"Embarked\"] = 0\ntitanic_test.loc[titanic_test[\"Embarked\"] == \"C\", \"Embarked\"] = 1\ntitanic_test.loc[titanic_test[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n \ntitanic_test[\"Fare\"] = titanic_test[\"Fare\"].fillna(titanic[\"Fare\"].median())\nprint(\"Done cleaning test set\")\n\n####################################\n#\t\t\tSUBMISSION\t\t\t####\n####################################\nalg = LogisticRegression(random_state=1)\nalg.fit(titanic[predictors],titanic[\"Survived\"])\npredictions = alg.predict(titanic_test[predictors])\n\n# create new datafram with predictions for Kaggle submssion\nsubmission = pd.DataFrame({\n\t\t\"PassengerId\": titanic_test[\"PassengerId\"],\n\t\t\"Survived\": predictions\n\t})\n\nsubmission.to_csv(\"Submission/kaggle2.csv\", index=False)\nprint (\"Logistic regression prediction written to csv\")\n\n","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":7212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"590086962","text":"\n\"\"\"\n======================COPYRIGHT/LICENSE START==========================\n\nChainLinkPopup.py: GUI window for linking chains from original format file to chains in data model\n\nCopyright (C) 2005 Wim Vranken (European Bioinformatics Institute)\n\n=======================================================================\n\nThis library is free software; you can redistribute it and/or\nmodify it under the terms of the GNU Lesser General Public\nLicense as published by the Free Software Foundation; either\nversion 2.1 of the License, or (at your option) any later version.\n \nA copy of this license can be found in ../../../../license/LGPL.license\n \nThis library is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nLesser General Public License for more details.\n \nYou should have received a copy of the GNU Lesser General Public\nLicense along with this library; if not, write to the Free Software\nFoundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA\n\n\n======================COPYRIGHT/LICENSE END============================\n\nfor further information, please contact :\n\n- CCPN website (http://www.ccpn.ac.uk/)\n- PDBe website (http://www.ebi.ac.uk/pdbe/)\n\n- contact Wim Vranken (wim@ebi.ac.uk)\n=======================================================================\n\nIf you are using this software for academic purposes, we suggest\nquoting the following references:\n\n===========================REFERENCE START=============================\nR. Fogh, J. Ionides, E. Ulrich, W. Boucher, W. Vranken, J.P. Linge, M.\nHabeck, W. Rieping, T.N. Bhat, J. Westbrook, K. Henrick, G. Gilliland,\nH. Berman, J. Thornton, M. Nilges, J. Markley and E. Laue (2002). The\nCCPN project: An interim report on a data model for the NMR community\n(Progress report). Nature Struct. Biol. 9, 416-418.\n\nWim F. Vranken, Wayne Boucher, Tim J. Stevens, Rasmus\nH. Fogh, Anne Pajon, Miguel Llinas, Eldon L. Ulrich, John L. Markley, John\nIonides and Ernest D. Laue (2005). The CCPN Data Model for NMR Spectroscopy:\nDevelopment of a Software Pipeline. Proteins 59, 687 - 696.\n\n===========================REFERENCE END===============================\n\"\"\"\nimport Tkinter\n\nimport string\n\nfrom memops.universal.Io import joinPath\n\nfrom memops.universal.Util import returnStrings\nfrom memops.universal.Util import returnInt\n\nfrom memops.gui.Label import Label\nfrom memops.gui.Util import createDismissHelpButtonList\nfrom memops.gui.PulldownMenu import PulldownMenu\nfrom memops.gui.ScrolledListbox import ScrolledListbox\nfrom memops.gui.MessageReporter import showError\n\nfrom ccp.format.general.Util import getSeqAndInsertCode\n\nfrom ccpnmr.format.general.Io import getHelpUrlDir\n\nfrom ccpnmr.format.gui.BasePopup import TemporaryBasePopup\n\nclass ChainLinkPopup(TemporaryBasePopup):\n \n help_url = joinPath(getHelpUrlDir(),'ChainLink.html')\n\n def __init__(self, parent, ccpChainLabelDict, ccpChainSeqIdCodes, formatChains, formatChainList, formatChainDict, defaultFormatChain):\n\n # Constructor doesn't do much except call body\n # The parent is self.parent (parent of the popup)\n \n self.chain = None\n\n self.formatChains = formatChains\n self.ccpChainLabelDict = ccpChainLabelDict\n self.ccpChainSeqIdCodes = ccpChainSeqIdCodes\n self.formatChainList = formatChainList\n self.formatChainDict = formatChainDict\n self.localFormatChainDict = {}\n \n self.defaultFormatChain = defaultFormatChain\n \n self.ccpChainSeqIdDict = {}\n self.ccpChainSeqCodeDict = {}\n \n project = self.ccpChainLabelDict.values()[0].root\n\n # modal = true means that it won't continue unless this one returns value\n TemporaryBasePopup.__init__(self, parent=parent, title=\"Project '%s': \" % project.name +'Link chains', modal=False, transient=True)\n\n def body(self, master):\n \n #\n # Setup header \n #\n\n row = 0\n\n label = Label(master, text= \"Data model\", fg = 'blue')\n label.grid(row=row, column=0, columnspan = 2, sticky=Tkinter.EW)\n\n label = Label(master, text= \"Information from external file\")\n label.grid(row=row, column=2, columnspan = 2, sticky=Tkinter.EW)\n\n row += 1\n\n label = Label(master, text= \"Ccp chain code\", fg = 'blue')\n label.grid(row=row, column=0, sticky=Tkinter.W)\n\n label = Label(master, text= \"Sequence Id (code) start\", fg = 'blue')\n label.grid(row=row, column=1, sticky=Tkinter.W)\n\n label = Label(master, text= \"Format chain code\")\n label.grid(row=row, column=2, sticky=Tkinter.W)\n\n label = Label(master, text= \"Sequence code start\")\n label.grid(row=row, column=3, sticky=Tkinter.W)\n\n #\n # Setup list of ccp chains and selection menus for other chains\n # \n \n self.formatChainMenu = {}\n\n self.ccpCodeLow = {}\n self.formatChainCodeLow = {}\n self.formatRemoveCode = {}\n self.formatRemoveLabel = {}\n\n ccpChainLabels = self.ccpChainLabelDict.keys()\n ccpChainLabels.sort()\n \n ccpChainSeqIdLabels = {}\n \n for ccpChainLabel in ccpChainLabels:\n \n ccpChainSeqIdLabels[ccpChainLabel] = []\n self.ccpChainSeqIdDict[ccpChainLabel] = {}\n self.ccpChainSeqCodeDict[ccpChainLabel] = {}\n \n #\n # Make a dict to list codes with Ids for ccp\n #\n \n for i in range(0,len(self.ccpChainSeqIdCodes[ccpChainLabel][0])):\n seqId = self.ccpChainSeqIdCodes[ccpChainLabel][0][i]\n (seqCode,seqInsertCode) = self.ccpChainSeqIdCodes[ccpChainLabel][1][i]\n \n label = \"%s (%s)\" % (seqId,seqCode + string.strip(seqInsertCode))\n ccpChainSeqIdLabels[ccpChainLabel].append(label)\n self.ccpChainSeqIdDict[ccpChainLabel][label] = seqId\n self.ccpChainSeqCodeDict[ccpChainLabel][label] = seqCode\n \n #\n # Currently only linking start in ccp chain (ONLY ONE!) to start in chain read in from file\n # Assuming sequential order thereafter.\n #\n\n for ccpChainLabel in ccpChainLabels:\n\n row = row + 1\n\n label = Label(master, text= ccpChainLabel, fg = 'blue')\n label.grid(row=row, column=0, sticky=Tkinter.W)\n\n self.ccpCodeLow[ccpChainLabel] = ScrolledListbox(master, initial_list = ccpChainSeqIdLabels[ccpChainLabel],width = 4, height = 4, xscroll = False)\n self.ccpCodeLow[ccpChainLabel].grid(row=row, column=1, sticky=Tkinter.EW)\n\n self.formatChainMenu[ccpChainLabel] = PulldownMenu(master, entries = self.formatChainList, selected_index = self.formatChainList.index(self.defaultFormatChain))\n self.formatChainMenu[ccpChainLabel].grid(row=row, column=2, sticky=Tkinter.EW)\n\n self.formatChainCodeLow[ccpChainLabel] = ScrolledListbox(master, initial_list = [], width = 4, height = 4, xscroll = False)\n self.formatChainCodeLow[ccpChainLabel].grid(row=row, column=3, sticky=Tkinter.EW)\n \n self.formatRemoveLabel[ccpChainLabel] = Label(master, text = \"\", fg = 'red')\n self.formatRemoveCode[ccpChainLabel] = Tkinter.Button(master, text = \"Remove\", command = (lambda x = ccpChainLabel, row = row: self.removeSeqCode(x,row)))\n\n self.formatRemoveLabel[ccpChainLabel].grid(row=row, column=4, sticky=Tkinter.N)\n self.formatRemoveLabel[ccpChainLabel].grid_forget()\n \n self.formatRemoveCode[ccpChainLabel].grid(row=row, column=4, sticky=Tkinter.S)\n\n self.formatChainMenu[ccpChainLabel].callback = (lambda x = -1, y = self.defaultFormatChain, z = ccpChainLabel, row = row: self.updateSeqCodes(x,y,z,row))\n\n row = row + 1\n\n texts = [ 'OK' ]\n commands = [ self.ok ] # This calls 'ok' in BasePopup, this then calls 'apply' in here\n buttons = createDismissHelpButtonList(master, texts=texts, commands=commands, dismiss_text = 'Exit', help_url=self.help_url)\n buttons.grid(row=row, column=0, columnspan = 5)\n\n def removeSeqCode(self,ccpChainLabel,row):\n\n selection = self.formatChainMenu[ccpChainLabel].getSelected()\n \n if selection != 'Do not link':\n \n chainDictKey = (ccpChainLabel,selection)\n \n (formatChainCode,seqCodesList) = self.localFormatChainDict[chainDictKey]\n \n selectedItems = self.formatChainCodeLow[ccpChainLabel].getSelectedItems()\n \n for selectedItem in selectedItems:\n seqCodeKey = getSeqAndInsertCode(selectedItem)\n \n try:\n seqCodeIndex = seqCodesList.index(seqCodeKey)\n seqCodesList.pop(seqCodeIndex)\n except:\n pass\n \n self.updateSeqCodes(-1,selection,ccpChainLabel,row)\n \n def updateSeqCodes(self,formatChainIndex,formatChainText,ccpChainLabel,row):\n\n if formatChainText == 'Do not link':\t \n \n self.formatChainCodeLow[ccpChainLabel].setItems([])\n \n else:\n \n chainDictKey = (ccpChainLabel,formatChainText)\n \n if not self.localFormatChainDict.has_key(chainDictKey):\n (formatChainCode,seqCodesList) = self.formatChainDict[formatChainText]\n self.localFormatChainDict[chainDictKey] = (formatChainCode,seqCodesList[:])\n \n (formatChainCode,seqCodesList) = self.localFormatChainDict[chainDictKey]\n \n self.formatChainCodeLow[ccpChainLabel].clear()\n \n seqCodeJumps = 0\n oldSeqCode = seqCodesList[0][0] - 1\n oldSeqInsertCode = seqCodesList[0][1]\n \n for (seqCode,seqInsertCode) in seqCodesList:\n self.formatChainCodeLow[ccpChainLabel].append(str(seqCode) + seqInsertCode)\n \n if oldSeqInsertCode == seqInsertCode and oldSeqCode + 1 != seqCode:\n seqCodeJumps = 1\n \n oldSeqInsertCode = seqInsertCode\n oldSeqCode = seqCode\n \n self.formatChainCodeLow[ccpChainLabel].setSelectedItems([str(seqCodesList[0][0]) + seqCodesList[0][1]])\n \n #\n # Try to match on sequence code...\n #\n \n foundMatch = 0\n \n (formatFirstSeqCode,formatSeqInsertCode) = seqCodesList[0]\n matchKey = (str(formatFirstSeqCode),formatSeqInsertCode)\n\n if matchKey in self.ccpChainSeqIdCodes[ccpChainLabel][1]:\n matchKey = str(formatFirstSeqCode) + string.strip(formatSeqInsertCode)\n for label in self.ccpChainSeqCodeDict[ccpChainLabel].keys():\n if self.ccpChainSeqCodeDict[ccpChainLabel][label] == matchKey:\n self.ccpCodeLow[ccpChainLabel].setSelectedItems([label])\n foundMatch = 1\n break\n \n if not foundMatch:\n \n #\n # Try to match on sequence id...\n # This could all be more intelligent but leave for now.\n #\n \n if str(formatFirstSeqCode) in self.ccpChainSeqIdCodes[ccpChainLabel][0]:\n for label in self.ccpChainSeqIdDict[ccpChainLabel].keys():\n if self.ccpChainSeqIdDict[ccpChainLabel][label] == str(formatFirstSeqCode):\n self.ccpCodeLow[ccpChainLabel].setSelectedItems([label])\n foundMatch = 1\n break\n\n #\n # Set 'remove' if seqInsertCodes present, and print warning\n #\n \n multipleSeqInsertCodes = 0\n for chainDictKey in self.localFormatChainDict.keys():\n (tempFormatChainCode,tempSeqCodesList) = self.localFormatChainDict[chainDictKey]\n if formatSeqInsertCode != tempSeqCodesList[0][1]:\n multipleSeqInsertCodes += 1\n \n #\n # Set user warning messages...\n #\n \n text = \"\"\n if multipleSeqInsertCodes:\n text += \"Warning: insertion codes present\"\n if seqCodeJumps:\n text += \"Warning: jumps in seqCode - assumed sequential\"\n \n self.formatRemoveLabel[ccpChainLabel].grid_forget()\n if text:\n self.formatRemoveLabel[ccpChainLabel].set(text)\n self.formatRemoveLabel[ccpChainLabel].grid(row=row, column=4, sticky=Tkinter.N) \n\n def apply(self):\n \n self.chainDict = {}\n \n for ccpChainLabel in self.ccpChainLabelDict.keys():\n \n chain = self.ccpChainLabelDict[ccpChainLabel]\n selection = self.formatChainMenu[ccpChainLabel].getSelected()\n \n if selection != 'Do not link':\n \n chainDictKey = (ccpChainLabel,selection)\n (formatChainCode,seqCodesList) = self.localFormatChainDict[chainDictKey]\n \n selectedItems = self.ccpCodeLow[ccpChainLabel].getSelectedItems()\n \n if not selectedItems:\n showError(\"No sequence ID selected\",\"Please select a sequence ID on the left side for chain %s!\" % ccpChainLabel)\n return False\n \n ccpCodeLow = returnInt(self.ccpChainSeqIdDict[ccpChainLabel][selectedItems[0]])\n\n (formatChainSeqCodeLow,seqInsertCode) = getSeqAndInsertCode(self.formatChainCodeLow[ccpChainLabel].getSelectedItems()[0])\n\n exportSeqCodesList = seqCodesList[:]\n\n for (tempSeqCode,tempSeqInsertCode) in seqCodesList:\n if tempSeqCode == formatChainSeqCodeLow and tempSeqInsertCode == seqInsertCode:\n break\n else:\n exportSeqCodesList.pop(0)\n \n self.chainDict[chain] = [formatChainCode,ccpCodeLow,exportSeqCodesList]\n \n return True\n","sub_path":"ccpnmr2.4/python/ccpnmr/format/gui/ChainLinkPopup.py","file_name":"ChainLinkPopup.py","file_ext":"py","file_size_in_byte":13017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"408309337","text":"import sys\nimport time\t\nimport os\t\nimport subprocess\t\nfrom csv import reader\t\nfrom pyspark.sql.functions import col\nfrom time import ctime, strptime\n\nimport predictor_path#path configuration\nsys.path.append(predictor_path.main_path)\nfrom feature_extractor import FeatureExtractor\t\nimport functions\t\nfrom models import XGBoostModel, SVRModel\t\nimport env\t\n\n#Util\ndef file_line_len(fname):\n with open(fname) as f:\n for i, l in enumerate(f):\n pass\n return i+1\n\ndef filter_by_membership(spark, df, col_name, l):\n df = df[col(col_name).isin(l)]\n return df\n\ndef make_csv_reader_wo_header(file_path):\n f = open(file_path)\n csv_reader = reader(f, delimiter=',')\n header = next(csv_reader, None)\n return header, csv_reader\n \n#0. Argument check\nif len(sys.argv) < 2:\n print(\"Argument erorr: Test year is missed. You have to give the target test year as first argument.\")\n sys.exit()\nif len(sys.argv) < 3:\n print(\"Argument warning: Any output path is not set. Output will be stored in default path.\")\n\npredictor_name = os.path.basename((sys.argv[0])[:-3])\ntest_year = int(sys.argv[1])\nfile_prefix = predictor_name + \"_\" + str(test_year)\nprint(predictor_name + \" starts\")\nprint(str(test_year) + \" TEST\")\n\n\n#1. Prepare test / train data\nall_players_file_path = predictor_path.raw_file_path\n\"\"\"1-1. Test file configuraton, creation\t\nNote: Test file must contain the column of Name, WAR, playerid, age, ServiceTime\t\n\"\"\"\t\ntest_file_name = file_prefix + \"_test_data.csv\"\ntest_file_path = os.path.join(predictor_path.test_path, test_file_name)\t\n\ntrain_file_name = file_prefix + \"_train_data.csv\"\ntrain_file_path = os.path.join(predictor_path.train_path, train_file_name)\n\n#def filter_test_data(spark, df):\t\n# df = df.filter(df.Season == test_year)\n# df = df.filter(df.ServiceTime > 1)\n# return df\t\ndef filter_test_data(spark, df):\n new_df = df.filter(df.Season == test_year).select(df.playerid).withColumnRenamed(\"playerid\", \"tmpid\")\n df = df.filter(df.Season == (test_year-1)).join(new_df, df.playerid == new_df.tmpid, \"inner\").drop(\"tmpid\")\n return df\ndef filter_train_data(spark, df):\n df = df.filter(df.Season < test_year)\n return df\n\n#test data\nprint(\"\\033[31m\" + \"Testing Year: \" + str(test_year) + \"\\033[0m\")\nprint(\"\\033[31m\" + \"Test data preparation\" + \"\\033[0m\")\nif not os.path.exists(test_file_path):\t\n fe = FeatureExtractor()\t\n fe.raw_to_df(all_players_file_path)\t\n fe.df_update(filter_test_data)\n fe.dump_df(test_file_path)\t\n\n#train data\nprint(\"\\033[31m\" + \"Train data preparation\" + \"\\033[0m\")\nif not os.path.exists(train_file_path):\t\n fe = FeatureExtractor()\t\n fe.raw_to_df(all_players_file_path)\t\n fe.df_update(filter_train_data)\t\n fe.dump_df(train_file_path)\t\n\n\"\"\"1-2. Load test file\t\n\"\"\"\ntest_header, test_csv_reader = make_csv_reader_wo_header(test_file_path)\n\n\"\"\"Prediction Phase\t\nBaseline Principle: If ServiceTime of one player in test_year is greater or equal than 4, make cluster information using recent 4 ages(X-3 ~ X).\t\nOtherwise, use recent Y ages where Y is ServiceTime of one player in test_year\t\n\"\"\"\t\n#output file setting\nif not os.path.exists(os.path.join(predictor_path.output_path, predictor_name)):\n os.mkdir(os.path.join(predictor_path.output_path, predictor_name))\n\nif len(sys.argv)==3:\n result_file_path = sys.argv[2]\nelse:\n parsed_time = strptime(ctime())\n result_file_name = file_prefix + \"_\" + str(parsed_time.tm_year) + \"_\" + str(parsed_time.tm_mon) + \"_\" + str(parsed_time.tm_mday) + \"_\" + str(parsed_time.tm_hour) + \"_\" + str(parsed_time.tm_min) + \".csv\"\n result_file_path = os.path.join(predictor_path.output_path, predictor_name, result_file_name)\nf = open(result_file_path, \"w\")\nf.write('id,real,pred\\n')\nf.close()\n\n#Iteration starts \nfor row in test_csv_reader:\t\n #2. Get cluster information\t\n test_name = str(row[test_header.index(\"Name\")])\t\n test_id = int(row[test_header.index(\"playerid\")])\t\n test_WAR = float(row[test_header.index(\"WAR\")])\t\n test_ServiceTime = int(row[test_header.index(\"ServiceTime\")])\t\n test_age = int(row[test_header.index(\"Age\")])\n test_isStarter = int(row[test_header.index(\"isStarter\")])\n\n print(\"\\033[31m\" +\"++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\")\n print(\"Player Name: \" + test_name)\n print(\"Age: \" + str(test_age))\n print(\"Active Years: \" + str(test_ServiceTime) + \"\\033[0m\")\n print(\"\")\n \"\"\"2-1. Set age range from ServiceTime.\t\n Note: Here is the place that set the range of data to cluster players!\n \"\"\"\n range_years = 4\n age_high = test_age\t\n if test_ServiceTime >= range_years: \t\n age_low = test_age - (range_years-1)\n else:\t\n age_low = test_age - (test_ServiceTime-1)\n\n \"\"\"2-2. Create proper WAR enumeration csv.\t\n Note: data since test_year should be discarded.\t\n \"\"\"\n print(\"\\033[31m\" + \"Age \" + str(age_low) + \"~\" + str(age_high) + \" data will be used to make prediction\" + \"\\033[0m\")\n WAR_by_age_file_name = file_prefix + \"_WAR_enumerated_by_age_from_\" + str(age_low) + \"_to_\" + str(age_high) + \".csv\"\t\n WAR_by_age_file_path = os.path.join(predictor_path.internal_path, WAR_by_age_file_name)\n if not os.path.exists(WAR_by_age_file_path):\t\n fe = FeatureExtractor()\t\n fe.raw_to_df(train_file_path)\t\n fe.df_update(functions.WAR_enumeration_by_age)\t\n age_range = [\"WAR\" + str(i) for i in range(age_low, age_high+1)]\n cols = [\"Name\", \"playerid\"] + age_range \t\n fe.df_update(functions.selection, cols)\n fe.df_update(functions.null_remover)\n fe.dump_df(WAR_by_age_file_path)\t\n\n #3. Run Rscript to create csv containing cluster information\t\n cluster_csv_file_name = file_prefix + \"_test_cluster_by_age_from_\" + str(age_low) + \"_to_\" + str(age_high) + \".csv\"\t\n cluster_csv_file_path = os.path.join(predictor_path.internal_path, cluster_csv_file_name)\n\n if not os.path.exists(cluster_csv_file_path):\t\n lines = file_line_len(WAR_by_age_file_path)\n if lines > 100:\n cluster_num = 15\n elif lines > 7:\n cluster_num = int(lines / 7)\n else:\n print(\"Sample is too small. Player Name: \" + test_name)\n continue\n subprocess.call([\"Rscript\", os.path.join(predictor_path.main_path, \"R/kml.R\"), str(age_high - age_low + 1), WAR_by_age_file_path,\t\n cluster_csv_file_path, str(cluster_num)], shell=False)\t\n #4. Filter players in same cluster\t\n \"\"\"4-1. get clutser of testing player\t\n Note: From here, all generated csv files are temporary\t\n \"\"\"\t\n fe = FeatureExtractor()\t\n fe.raw_to_df(cluster_csv_file_path)\t\n fe.df_update(filter_by_membership, \"playerid\", test_id)\n \n tmp_file_path = os.path.join(predictor_path.internal_path, \"tmp.csv\")\n fe.dump_df(tmp_file_path)\t\n\n test_cluster = 'A' \n _, tmp_reader = make_csv_reader_wo_header(tmp_file_path)\n row = next(tmp_reader, None) \n if row==None:#missing data\n print(\"Missing data. Player Name: \" + test_name)\n continue\n test_cluster = row[-1]\t\n \n\n print(\"\\033[31m\" + test_name + \" is in Cluster \" + test_cluster + \"\\033[0m\")\n\n \"\"\"4-2 filter by cluster\t\n \"\"\"\t\n fe = FeatureExtractor()\t\n fe.raw_to_df(cluster_csv_file_path)\t\n fe.df_update(filter_by_membership, \"Cluster\", test_cluster)\t\n \n tmp_same_cluster_file_path = os.path.join(predictor_path.internal_path, \"tmp_same_cluster.csv\")\n fe.dump_df(tmp_same_cluster_file_path)\n\n #5. Make prediction using multiple hypothesis!\n \"\"\"5-2-1. Double Clustering\n \"\"\"\n tmp_double_cluster_file_name = \"tmp_double_cluster.csv\"\n tmp_double_cluster_file_path = os.path.join(predictor_path.internal_path, tmp_double_cluster_file_name)\n \n lines = file_line_len(tmp_same_cluster_file_path)\n if lines > 35:\n cluster_num = 7\n elif lines > 5:\n cluster_num = int(lines / 5)\n else:\n cluster_num = 1\n subprocess.call([\"Rscript\", os.path.join(predictor_path.main_path, \"R/kml.R\"), str(age_high - age_low + 1), tmp_same_cluster_file_path,\t\n tmp_double_cluster_file_path, str(cluster_num)], shell=False)\t\n\n \"\"\"5-2-2. get clutser of testing player\t\n \"\"\"\t\n\n fe = FeatureExtractor()\t\n fe.raw_to_df(tmp_double_cluster_file_path)\t\n fe.df_update(filter_by_membership, \"playerid\", test_id)\t\n \n tmp_file_path = os.path.join(predictor_path.internal_path, \"tmp.csv\")\n fe.dump_df(tmp_file_path)\t\n \n test_cluster = 'A' \n _, tmp_reader = make_csv_reader_wo_header(tmp_file_path)\n row = next(tmp_reader, None) \n \n if row==None:#missing data\n print(\"Missing data. Player Name: \" + test_name)\n continue\n test_cluster = row[-1]\t\n\n \"\"\"filter by cluster\t\n \"\"\"\t\n fe = FeatureExtractor()\t\n fe.raw_to_df(tmp_double_cluster_file_path)\t\n fe.df_update(filter_by_membership, \"Cluster\", test_cluster)\n\n tmp_same_double_cluster_file_name = \"tmp_same_double_cluster.csv\"\n tmp_same_double_cluster_file_path = os.path.join(predictor_path.internal_path, tmp_same_double_cluster_file_name)\n fe.dump_df(tmp_same_double_cluster_file_path)\n\n\n \"\"\"5-2-3. Preparing train data with major_cluster\n \"\"\"\n fe = FeatureExtractor()\n fe.raw_to_df(tmp_same_double_cluster_file_path)\n fe.df_update(functions.selection, \"playerid\")\n \n tmp_major_ids_file_name = \"tmp_major_ids.csv\"\n tmp_major_ids_file_path = os.path.join(predictor_path.internal_path, tmp_major_ids_file_name)\n fe.dump_df(tmp_major_ids_file_path)\n \n #Filtering train data by playerid(excerpted from cluser information) and age\n _, tmp_major_ids_reader = make_csv_reader_wo_header(tmp_major_ids_file_path)\n id_list = []\n for row in tmp_major_ids_reader:\n print(type(row[0]))\n id_list.append(int(float(row[0])))\n \n ###IMPORTANT###\n id_list.remove(test_id)\n ###############\n\n fe = FeatureExtractor()\n fe.raw_to_df(train_file_path)\n fe.df_update(filter_by_membership, \"playerid\", id_list)\n fe.df_update(filter_by_membership, \"Age\", age_high)\n #IP ratio filtering!\n def filter_by_IPratio(spark, df):\n df = df.filter(df.ratioIP > 0.7)\n df = df.filter(df.ratioIP < 1.3)\n return df\n fe.df_update(filter_by_IPratio)\n\n ###Split reilver, starter\n def filter_by_isStarter(spark, df):\n df = df.filter(df.isStarter == test_isStarter)\n return df\n fe.df_update(filter_by_isStarter)\n #################################\n\n tmp_train_file_name = \"tmp_train.csv\"\n tmp_train_file_path = os.path.join(predictor_path.internal_path, tmp_train_file_name) \n fe.dump_df(tmp_train_file_path)\n if file_line_len(tmp_train_file_path)==1:\n print(\"Lack of good training data. Passed\")\n continue\n \n \"\"\"5-0-5. Preparing test data\n \"\"\"\n fe = FeatureExtractor()\n fe.raw_to_df(test_file_path)\n fe.df_update(filter_by_membership, \"playerid\", test_id)\n \n tmp_test_file_name = \"tmp_test.csv\"\n tmp_test_file_path = os.path.join(predictor_path.internal_path, tmp_test_file_name) \n fe.dump_df(tmp_test_file_path)\n\n \"\"\"5-0-6. Run XGBoost\n \"\"\"\n xgbm = XGBoostModel()\n param_map = {\n \"feature_start_index\":env.feature_start_index,\n \"features_num\":env.features_num,\n \"label_index\":env.label_index,\n \"id_index\":env.id_index,\n \"WAR_index\":env.WAR_index,\n \"metric\":\"rmse\"\n }\n from random import randint\n xgbm.train(tmp_train_file_path, param_map, 1000, randint(0,100)) \n xgbm.test(tmp_test_file_path, param_map) \n xgbm.dump_output(result_file_path , mode=\"a\", header=False) \n\n#6. Report Result\n\"\"\"6-1. Raw Result\"\"\"\ndef join_result(spark, df):\n new_df = spark.read.format(\"com.databricks.spark.csv\").option(\"header\",\"true\").option(\"inferSchema\",\"true\").load(test_file_path).select([\"Name\",\"playerid\"])\n df = df.join(new_df, new_df.playerid == df.id).select([\"Name\", \"playerid\", \"real\", \"pred\"])\n return df\n\nfe = FeatureExtractor()\nfe.raw_to_df(result_file_path)\nfe.df_update(join_result)\nfe.dump_df(result_file_path)\n\"\"\"6-2. Distribution Similarity \"\"\"\n\n\"\"\"6-3. Reference: ZiPs \"\"\"\n#End of Iteration\n","sub_path":"predictor/kml4_strp.py","file_name":"kml4_strp.py","file_ext":"py","file_size_in_byte":12255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"490268045","text":"import numpy\nfrom gnuradio import gr\n \nclass square3_ff(gr.sync_block):\n \" Squaring block \" \n def __init__(self):\n gr.sync_block.__init__(\n self,\n name = \"square3_ff\",\n in_sig = [numpy.float32], # Input signature: 1 float at a time\n out_sig = [numpy.float32], # Output signature: 1 float at a time\n )\n \n def work(self, input_items, output_items):\n output_items[0][:] = input_items[0] * input_items[0] # Only works because numpy.array\n return len(output_items[0])\n","sub_path":"gr-openavionics/python/square3_ff.py","file_name":"square3_ff.py","file_ext":"py","file_size_in_byte":538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"348545437","text":"import aiohttp\nimport discord\nimport re\n\nfrom wzrd.bot import config, data\nfrom discord.ext import commands\n\nasync def get_json(method, params):\n request = 'http://ws.audioscrobbler.com/2.0/?method='\n request += method\n for param in params:\n request += param\n request += '&format=json'\n\n async with aiohttp.ClientSession() as cs:\n async with cs.get(request) as r:\n return await r.json()\n\nasync def get_period(period):\n if period == 'weekly' or period == 'week' or period == 'w':\n return '7day'\n elif period == 'monthly' or period == 'month' or period == 'm':\n return '1month'\n elif period == 'yearly' or period == 'year' or period == 'y':\n return '12month'\n elif period == 'overall' or period == \"all\" or period == 'a':\n return 'overall'\n\nasync def get_limit(size):\n l, w = size.split('x')\n limit = int(l)*int(w)\n return str(limit)\n\nasync def make_lastfm_embed(track, user_info, author):\n embed = discord.Embed(description=discord.Embed.Empty, title='See more recent tracks', url=user_info[\"user\"][\"url\"])\n embed.set_author(name='last.fm')\n embed.set_thumbnail(url=track['image'][2]['#text'])\n embed.add_field(name=f'\"{track[\"name\"]}\"', value=f'{track[\"artist\"][\"#text\"]} | *{track[\"album\"][\"#text\"]}*', inline=False)\n embed.set_footer(text=f'{user_info[\"user\"][\"name\"]} has {user_info[\"user\"][\"playcount\"]} scrobbles.', icon_url=user_info[\"user\"][\"image\"][0][\"#text\"])\n return embed\n\n\nclass MusicCog(commands.Cog, name='Music', command_attrs=dict(hidden=False)):\n def __init__(self, bot, data):\n self.bot = bot\n self.data = data\n self.lastfm_key = config[\"lastfm_key\"]\n\n @commands.command(brief='Show your latest scrobble.')\n async def fm(self, ctx):\n await ctx.channel.trigger_typing()\n try:\n user = self.data['users'][str(ctx.message.author.id)]['last.fm']\n except KeyError:\n return await ctx.send('You have not set your last.fm username. Use `.fmset` to set it.')\n\n tracks = await get_json('user.getrecenttracks', ['&api_key='+self.lastfm_key, '&user='+user, '&limit=1'])\n track = dict(tracks['recenttracks']['track'][0])\n\n user_info = await get_json('user.getinfo', ['&api_key='+self.lastfm_key, '&user='+user])\n\n embed = await make_lastfm_embed(track, user_info, ctx.author)\n await ctx.send(embed=embed)\n\n @commands.command(brief='Set your last.fm username.')\n async def fmset(self, ctx, username=None):\n if username is None:\n return await ctx.send('You did not provide a username.')\n\n user = await get_json('user.getinfo', ['&api_key='+self.lastfm_key, '&user='+username, '&limit=1'])\n\n if 'error' in user:\n return await ctx.send('That user doesn\\'t exist on last.fm.')\n\n self.data['users'][str(ctx.message.author.id)]['last.fm'] = username\n\n await ctx.send(f'Set your last.fm username to `{username}`.')\n\n @commands.command(brief='Search YouTube for your latest scrobble.')\n async def fmyt(self, ctx):\n await ctx.channel.trigger_typing()\n try:\n user = self.data[\"users\"][str(ctx.message.author.id)][\"last.fm\"]\n except KeyError:\n return await ctx.send('You have not set your last.fm username. Use `.fmset` to set it.')\n\n tracks = await get_json('user.getrecenttracks', ['&api_key='+self.lastfm_key, '&user='+user, '&limit=1'])\n track = dict(tracks['recenttracks']['track'][0])\n artist = str(track['artist']['#text'])\n title = str(track['name'])\n\n request = f'https://www.youtube.com/results?search_query={artist.replace(\" \", \"+\")}+{title.replace(\" \", \"+\")}'\n async with aiohttp.ClientSession() as cs:\n async with cs.get(request) as r:\n response = await r.text()\n\n try:\n addr = re.findall(r'href=\\\"\\/watch\\?v=(.{11})', response)[0]\n except IndexError:\n await ctx.send(f'Couldn\\'t find a YouTube video for \"{title}\" by {artist}.')\n return\n\n await ctx.send(f'YouTube video for \"{title}\" by {artist}:')\n await ctx.send(f'http://www.youtube.com/watch?v={addr}')\n\n\ndef setup(bot):\n bot.add_cog(MusicCog(bot, data))\n","sub_path":"wzrd/cogs/music.py","file_name":"music.py","file_ext":"py","file_size_in_byte":4275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"591180959","text":"import networkx as nx\nimport numpy as np\nimport torch\nimport os\n\nfrom graphs.no_rotation import index_to_position_no_orientation\n\n# config = {\"model\":{\"number_outputs\":8}}\n# indices = torch.tensor([[382,24],[957,162],[222,850]])\n# positions = torch.empty(3,6)\n# for i,pair in enumerate(indices):\n# for j,index in enumerate(pair):\n# positions[i,3*j:3*(j+1)] = torch.tensor(index_to_position_no_orientation(index))\n# terminate_penalty = 10\n\ndef calc_action_penalties_graph(indices,config, terminate_penalty):\n if config[\"model\"][\"number_outputs\"] == 8:\n action_to_index = {'stay':0, 'pos x': 1, 'neg x': 2, 'pos y': 3, 'neg y': 4,'pos z': 5, 'neg z': 6, 'term': 7}\n graph_path = os.path.dirname(os.path.realpath(__file__)) + '/../../graphs/no_rotation.gpickle'\n elif config[\"model\"][\"number_outputs\"] == 10:\n graph_path = os.path.dirname(os.path.realpath(__file__)) + '/../../graphs/rotation.gpickle'\n action_to_index = {'stay':0, 'pos x': 1, 'neg x': 2, 'pos y': 3, 'neg y': 4,\n 'pos z': 5, 'neg z': 6, 'rot +': 7, 'rot -': 8, 'term': 9}\n G = nx.read_gpickle(graph_path)\n total_number_images = config[\"data\"][\"number_images\"]\n n_actions = config[\"model\"][\"number_outputs\"]\n action_penalties = torch.empty(indices.shape[0],n_actions).cuda()\n for i,pair in enumerate(indices):\n penalties = -1000*torch.ones(n_actions).cuda()\n if pair[0] == pair[1]:\n penalties[-1] = 0.\n for adj_node in (G[pair[0].item()]):\n # Try out all nodes except terminate one\n if adj_node < total_number_images:\n action_index = action_to_index[G[pair[0].item()][adj_node][0]['action']]\n penalties[action_index] = nx.shortest_path_length(G,adj_node,pair[1].item() + total_number_images)\n penalties[penalties==-1000] = torch.max(penalties)\n penalties = (penalties - torch.min(penalties)) * 0.1\n\n if pair[0] != pair[1]:\n penalties[-1] = terminate_penalty\n\n action_penalties[i] = penalties\n\n return action_penalties\n\n ","sub_path":"image_to_action/code/losses/graphs.py","file_name":"graphs.py","file_ext":"py","file_size_in_byte":2094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"268766307","text":"from tkinter import *\n\nws = Tk()\nws.title('PythonGuides')\nws.geometry('400x300')\nws.config(bg='#84BF04')\n\nmessage ='''\nDear Reader,\n\n Thank you for giving your\n Love and Support to PythonGuides.\n PythonGuides is now available on \n YouTube with the same name.\n\nThanks & Regards,\nTeam PythonGuides '''\n\ntext_box = Text(\n ws,\n height=12,\n width=40\n)\ntext_box.pack(expand=True)\ntext_box.insert('end', message)\ntext_box.config(state='disabled')\n\nws.mainloop()","sub_path":"TkinterSample1.py","file_name":"TkinterSample1.py","file_ext":"py","file_size_in_byte":475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"617428956","text":"import socket\ntarget_host = \"www.google.com\"\ntarget_port = 80\n\n# The AF_INET parameter is saying we are going to use a standard IPv4 address or hostname, and SOCK_STREAM indicates that this will be a TCP client.\n\nclient = socket.socket(socket.AF_INET, socket.SOCK_STREAM) #create a socket\n\n# We then connect the client to the server and send it some data\n\nclient.connect((target_host,target_port)) #connect the client\n\n# The last step is to receive some data back and print out the response\n\nclient.send(\"GET / HTTP/1.1\\r\\nHost: google.com\\r\\n\\r\\n\") #send some data\n\n# This is the simplest form of a TCP client, but the one you will write most often. \n","sub_path":"tcp_client.py","file_name":"tcp_client.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"541558489","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Oct 29 20:43:47 2019\r\n\r\n@author: Jakub Bławat\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport matplotlib as mpl\r\nimport random\r\n\r\nrandom.seed(42693)\r\n\r\n# Question 1 \r\n\r\n# 1)\r\n\r\n######################################################################\r\n## We draw 10^7 observations from joint normal distribution and use \r\n# numpy.random.multivariate_normal(mean, cov[, size, check_valid, tol])\r\n# to generate variables\r\n######################################################################\r\n\r\nmean = [-0.5, -0.5]\r\ncov = [[1, 0], [0, 1]] #we need to have identity matrix as a variance-covariance matrix to assure no correlation\r\nlog_k, log_z = np.random.multivariate_normal(mean, cov, 10**7).T\r\n\r\n\r\n\r\n# Plotting k,z in logs\r\n\r\nmpl.pyplot.scatter(log_k, log_z, s=1, color='blue')\r\nmpl.pyplot.show()\r\n\r\n\r\nk = np.exp(log_k)\r\nz = np.exp(log_z)\r\n\r\nprint(\"Average k:\", k.mean())\r\nprint(\"Avergae z:\", z.mean())\r\n\r\n# Plotting k,z in levels\r\n\r\nmpl.pyplot.scatter(k, z, s=1, color='blue')\r\nmpl.pyplot.show()\r\n\r\n# 2)\r\n\r\ngamma = 0.8\r\n\r\ndef y(cap,s):\r\n output = pow(s,1-gamma)*pow(cap,gamma)\r\n return output\r\n\r\nprod = y(k, z)\r\n\r\nagg_prod = sum(prod)\r\nprint(\"Aggregate production:\", agg_prod)\r\nprint(\"Average production:\", prod.mean())\r\n\r\n# 3)\r\nk_opt = np.array(sorted(k, reverse = True))\r\nz_opt = np.array(sorted(z, reverse = True))\r\n\r\nprod_opt = y(k_opt, z_opt)\r\nagg_prod_opt = sum(prod_opt)\r\ncapital_opt = sum(k_opt)\r\n\r\nprint(\"Aggregate optimal production:\", agg_prod_opt)\r\nprint(\"Average optimal production:\", prod_opt.mean())\r\n\r\n\r\n#4)\r\n# Computing optimal allociations against the data\r\nk_atd = k_opt - k\r\n\r\n#5)\r\n# Reallocation problem\r\nagg_prod = sum(prod)\r\nprod_gain = agg_prod_opt - agg_prod\r\nprod_gain_percent = (agg_prod_opt/agg_prod - 1) * 100\r\nprod_gain_pc = prod_gain/(10**7)\r\n\r\nprint(\"Production gain nominally:\", prod_gain)\r\nprint(\"Production gain percentage:\", prod_gain_percent)\r\nprint(\"Production gain per capita:\", prod_gain_pc)\r\n\r\n#6)\r\n# Same code but with different correlations:\r\n\r\n########################### For correaltion equal to 0.5:######################\r\nmean_a = [-0.5, -0.5]\r\ncov_a = [[1, 0.5], [0.5, 1]] \r\nlog_k_a, log_z_a = np.random.multivariate_normal(mean_a, cov_a, 10**7).T\r\n\r\n\r\nmpl.pyplot.scatter(log_k_a, log_z_a, s=1, color='blue')\r\nmpl.pyplot.show()\r\n\r\n\r\nk_a = np.exp(log_k_a)\r\nz_a = np.exp(log_z_a)\r\n\r\nprint(\"Average k:\", k_a.mean())\r\nprint(\"Avergae z:\", z_a.mean())\r\n\r\n# Plotting k,z in levels\r\n\r\nmpl.pyplot.scatter(k_a, z_a, s=1, color='blue')\r\nmpl.pyplot.show()\r\n\r\n# 6.1.2)\r\ndef y(cap,s):\r\n output = pow(s,1-gamma)*pow(cap,gamma)\r\n return output\r\n\r\nprod_a = y(k_a, z_a)\r\n\r\nagg_prod_a = sum(prod_a)\r\nprint(\"Aggregate production:\", agg_prod_a)\r\nprint(\"Average production:\", prod_a.mean())\r\n\r\n# 6.1.3)\r\nk_a_opt = np.array(sorted(k_a, reverse = True))\r\nz_a_opt = np.array(sorted(z_a, reverse = True))\r\n\r\nprod_opt_a = y(k_a_opt, z_a_opt)\r\nagg_prod_opt_a = sum(prod_opt_a)\r\ncapital_opt_a = sum(k_a_opt)\r\n\r\nprint(\"Aggregate optimal production:\", agg_prod_opt_a)\r\nprint(\"Average optimal production:\", prod_opt_a.mean())\r\n\r\n\r\n#6.1.4)\r\n# Computing optimal allociations against the data\r\nk_a_atd = k_a_opt - k_a\r\n\r\n#6.1.5)\r\n# Reallocation problem\r\nagg_prod_a = sum(prod_a)\r\nprod_gain_a = agg_prod_opt_a - agg_prod_a\r\nprod_gain_percent_a = (agg_prod_opt_a/agg_prod_a - 1) * 100\r\nprod_gain_pc_a = prod_gain_a/(10**7)\r\n\r\nprint(\"Production gain nominally:\", prod_gain_a)\r\nprint(\"Production gain percentage:\", prod_gain_percent_a)\r\nprint(\"Production gain per capita:\", prod_gain_pc_a)\r\n\r\n\r\n############################### For correaltion equal to -0.5:#################\r\n\r\nmean_b = [-0.5, -0.5]\r\ncov_b = [[1, -0.5], [-0.5, 1]] \r\nlog_k_b, log_z_b = np.random.multivariate_normal(mean_b, cov_b, 10**7).T\r\n\r\n\r\nmpl.pyplot.scatter(log_k_b, log_z_b, s=1, color='blue')\r\nmpl.pyplot.show()\r\n\r\n\r\nk_b = np.exp(log_k_b)\r\nz_b = np.exp(log_z_b)\r\n\r\nprint(\"Average k:\", k_b.mean())\r\nprint(\"Avergae z:\", z_b.mean())\r\n\r\n# Plotting k,z in levels\r\n\r\nmpl.pyplot.scatter(k_b, z_b, s=1, color='blue')\r\nmpl.pyplot.show()\r\n\r\n# 6.2.2)\r\ndef y(cap,s):\r\n output = pow(s,1-gamma)*pow(cap,gamma)\r\n return output\r\n\r\nprod_b = y(k_b, z_b)\r\n\r\nagg_prod_b = sum(prod_b)\r\nprint(\"Aggregate production:\", agg_prod_b)\r\nprint(\"Average production:\", prod_b.mean())\r\n\r\n# 6.2.3)\r\nk_b_opt = np.array(sorted(k_b, reverse = True))\r\nz_b_opt = np.array(sorted(z_b, reverse = True))\r\n\r\nprod_opt_b = y(k_b_opt, z_b_opt)\r\nagg_prod_opt_b = sum(prod_opt_b)\r\ncapital_opt_b = sum(k_b_opt)\r\n\r\nprint(\"Aggregate optimal production:\", agg_prod_opt_b)\r\nprint(\"Average optimal production:\", prod_opt_b.mean())\r\n\r\n\r\n#6.2.4)\r\n# Computing optimal allociations against the data\r\nk_b_btd = k_b_opt - k_b\r\n\r\n#6.2.5)\r\n# Reallocation problem\r\n\r\nagg_prod_b = sum(prod_b)\r\nprod_gain_b = agg_prod_opt_b - agg_prod_b\r\nprod_gain_percent_b = (agg_prod_opt_b/agg_prod_b - 1) * 100\r\nprod_gain_pc_b = prod_gain_b/(10**7)\r\n\r\nprint(\"Production gain nominally:\", prod_gain_b)\r\nprint(\"Production gain percentage:\", prod_gain_percent_b)\r\nprint(\"Production gain per capita:\", prod_gain_pc_b)","sub_path":"HW5/Exercise_2.py","file_name":"Exercise_2.py","file_ext":"py","file_size_in_byte":5070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"214356997","text":"import urllib.request, urllib.parse\nimport json\n\nclass SlackBot:\n def __init__(self):\n self.url = 'https://hooks.slack.com/services/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'\n\n def sendMessage(self, message):\n header = {\"Content-Type\": \"application/json\"}\n payload={\"text\": message}\n\n data = json.dumps(payload)\n req = urllib.request.Request(self.url, bytes(data.encode('utf-8')))\n resp = urllib.request.urlopen(req)\n","sub_path":"notifications/slack/bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"155767255","text":"# Copyright (c) 2014 Rackspace Hosting.\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\n# implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom django.urls import reverse\nfrom django.urls import reverse_lazy\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom horizon import exceptions\nfrom horizon import forms\nfrom horizon import tables\nfrom horizon import tabs\nfrom horizon import workflows\n\nfrom solumdashboard.api.client import client as solumclient\nfrom solumdashboard.applications import forms as app_forms\nfrom solumdashboard.applications import tables as app_tables\nimport solumdashboard.applications.tabs as _tabs\nfrom solumdashboard.applications import workflows as update_flow\n\n\nclass IndexView(tables.DataTableView):\n table_class = app_tables.ApplicationsTable\n template_name = 'applications/index.html'\n page_title = _(\"Applications\")\n\n def get_data(self):\n try:\n solum = solumclient(self.request)\n apps = solum.apps.list()\n except Exception:\n apps = []\n exceptions.handle(\n self.request,\n _('Unable to retrieve apps.'))\n return apps\n\n\nclass CreateView(forms.ModalFormView):\n form_class = app_forms.CreateForm\n template_name = 'applications/create.html'\n modal_header = _(\"Create Application\")\n page_title = _(\"Create Application\")\n submit_url = reverse_lazy('horizon:solum:applications:create')\n success_url = reverse_lazy(\"horizon:solum:applications:index\")\n\n\nclass ScaleView(forms.ModalFormView):\n form_class = app_forms.ScaleForm\n template_name = \"applications/scale.html\"\n modal_header = _(\"Scale Application\")\n page_title = _(\"Scale Application\")\n submit_url = reverse_lazy('horizon:solum:applications:scale')\n success_url = reverse_lazy(\"horizon:solum:applications:index\")\n failure_url = reverse_lazy(\"horizon:solum:applications:index\")\n\n def get_context_data(self, **kwargs):\n context = super(ScaleView, self).get_context_data(**kwargs)\n context[\"application_id\"] = self.kwargs[\"application_id\"]\n return context\n\n def get_initial(self):\n application_id = self.kwargs['application_id']\n return {'application_id': application_id}\n\n\nclass UpdateView(workflows.WorkflowView):\n workflow_class = update_flow.UpdateApplicationClass\n success_url = \"horizon:solum:applications:index\"\n classes = (\"ajax-modal\")\n\n def get_context_data(self, **kwargs):\n context = super(UpdateView, self).get_context_data(**kwargs)\n context[\"application_id\"] = self.kwargs[\"application_id\"]\n return context\n\n def _get_object(self, *args, **kwargs):\n application_id = self.kwargs['application_id']\n solum = solumclient(self.request)\n app = solum.apps.find(name_or_id=application_id)\n return app\n\n def get_initial(self):\n app = self._get_object()\n return {'application_id': app.id}\n\n\nclass DetailView(tabs.TabView):\n template_name = 'applications/detail.html'\n tab_group_class = _tabs.AppDetailsTabs\n page_title = \"{{ app.name|default:app.id }}\"\n\n def get_context_data(self, **kwargs):\n context = super(DetailView, self).get_context_data(**kwargs)\n application_id = self.kwargs['application_id']\n app = None\n try:\n solum = solumclient(self.request)\n app = solum.apps.find(name_or_id=application_id)\n except Exception:\n INDEX_URL = 'horizon:solum:applications:index'\n exceptions.handle(\n self.request,\n _('Unable to retrieve application details.'),\n redirect=reverse(INDEX_URL))\n context[\"app\"] = app\n table = app_tables.ApplicationsTable(self.request)\n context[\"actions\"] = table.render_row_actions(app)\n return context\n\n\nclass LaunchView(forms.ModalFormView):\n form_class = app_forms.LaunchForm\n template_name = \"applications/launch.html\"\n modal_header = _(\"Launch Application\")\n page_title = _(\"Launch Application\")\n success_url = reverse_lazy(\"horizon:solum:applications:index\")\n failure_url = reverse_lazy(\"horizon:solum:applications:index\")\n\n def get_context_data(self, **kwargs):\n context = super(LaunchView, self).get_context_data(**kwargs)\n context[\"application_id\"] = self.kwargs[\"application_id\"]\n return context\n\n def get_initial(self):\n application_id = self.kwargs['application_id']\n return {'application_id': application_id}\n","sub_path":"solumdashboard/applications/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"177458001","text":"from BeautifulSoup import BeautifulSoup\nimport urllib\nimport re\n\ndef openpage(webpage):\n\t\"\"\"\n\tPass a results url from the speedtest.bbmax.co.uk website and get back a \n\ttuple containing test date, download speed & upload speed in kbps\n\ti.e http://www.speedtest.bbmax.co.uk/results.php?t=1316531280&v=13764727 gives\n\t('2011-09-20 16:08:00', '8249', '825')\n\t\"\"\"\n\ttry:\n\t\tpage=BeautifulSoup(urllib.urlopen(webpage))\n\t\ttest_date=page.find(text=re.compile(\"Date of Speed Test\")).findNext('td').renderContents()\n\t\tdownload_speed=page.find(text=re.compile(\"Download Speed\")).findNext('td').renderContents()\n\t\tupload_speed=page.find(text=re.compile(\"Upload Speed\")).findNext('td').renderContents()\n\n\t\t#findall returns a list so select the first entry\n\t\tregex=re.compile(\"^[0-9]+\") #match digits at the start of a string\n\t\tregex2=re.compile(\"^\\d\\d\\d\\d-\\d\\d-\\d\\d.\\d\\d.\\d\\d.\\d\\d$\") #match date\n\t\tdownloadspeed=regex.findall(download_speed)[0]\n\t\tuploadspeed=regex.findall(upload_speed)[0]\n\t\t#just check to see if the data is what we expect it to be\n\t\tif not regex.match(downloadspeed):\n\t\t\traise RuntimeError()\n\t\telse:\n\t\t\tif not regex.match(uploadspeed):\n\t\t\t\traise RuntimeError()\n\t\t\telse:\n\t\t\t\tif not regex2.match(test_date):\n\t\t\t\t\traise RuntimeError()\n\t\treturn test_date, downloadspeed, uploadspeed\n\texcept:\n\t\traise RuntimeError('Problem getting speed test results')\n\n#print openpage('http://www.speedtest.bbmax.co.uk/results.php?t=1316531280&v=13764727')\n\n","sub_path":"modules/grab_speedtest_data.py","file_name":"grab_speedtest_data.py","file_ext":"py","file_size_in_byte":1442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"606078948","text":"# 编写一个程序，通过已填充的空格来解决数独问题。 \n# \n# 一个数独的解法需遵循如下规则： \n# \n# \n# 数字 1-9 在每一行只能出现一次。 \n# 数字 1-9 在每一列只能出现一次。 \n# 数字 1-9 在每一个以粗实线分隔的 3x3 宫内只能出现一次。 \n# \n# \n# 空白格用 '.' 表示。 \n# \n# \n# \n# 一个数独。 \n# \n# \n# \n# 答案被标成红色。 \n# \n# Note: \n# \n# \n# 给定的数独序列只包含数字 1-9 和字符 '.' 。 \n# 你可以假设给定的数独只有唯一解。 \n# 给定数独永远是 9x9 形式的。 \n# \n# Related Topics 哈希表 回溯算法\n\n\n# leetcode submit region begin(Prohibit modification and deletion)\n# import heapq\n# from collections import namedtuple\nclass Solution:\n def solveSudoku(self, board: List[List[str]]) -> None:\n \"\"\"\n Do not return anything, modify board in-place instead.\n \"\"\"\n rows = [set(range(1, 10)) for _ in range(9)] # 初始化可以用来填空的数字\n cols = [set(range(1, 10)) for _ in range(9)]\n boxes = [set(range(1, 10)) for _ in range(9)]\n\n empty = [] # 记录需要填空的位置\n for i in range(9):\n for j in range(9):\n if board[i][j] != '.':\n num = int(board[i][j])\n rows[i].remove(num)\n cols[j].remove(num)\n boxes[i // 3 * 3 + j // 3].remove(num)\n else:\n empty.append((i, j))\n\n\n\n def backtrack(index=0):\n if index == len(empty):\n return True\n i, j = empty[index]\n b = i // 3 * 3 + j // 3\n for num in rows[i] & cols[j] & boxes[b]:\n rows[i].remove(num)\n cols[j].remove(num)\n boxes[b].remove(num)\n board[i][j] = str(num)\n if backtrack(index + 1):\n return True\n rows[i].add(num)\n cols[j].add(num)\n boxes[b].add(num)\n return False\n\n backtrack(0)\n\n# leetcode submit region end(Prohibit modification and deletion)\n","sub_path":"Week07/code/[37]解数独.py","file_name":"[37]解数独.py","file_ext":"py","file_size_in_byte":2171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"77675224","text":"from math import e\nimport matplotlib.pyplot as plt\n\n\ndef sigmoid(x):\n \"\"\"\n Sigmoid function. Accepts either single value or lists.\n :param x: single value or list.\n :return: single value or list.\n \"\"\"\n if isinstance(x, list):\n out = []\n for elem in x:\n out.append(sigmoid(elem))\n return out\n else:\n return 1 / (1 + e ** (-x))\n\n\ndef d_sigmoid(x):\n \"\"\"\n Derivative of sigmoid function. Accepts either single value or lists.\n :param x: single value or list.\n :return: single value or list.\n \"\"\"\n if isinstance(x, list):\n out = []\n for elem in x:\n out.append(d_sigmoid(elem))\n return out\n else:\n sig = sigmoid(x)\n return sig * (1 - sig)\n\n\nif __name__ == '__main__':\n x_interval = [i/6 for i in range(-60, 60)]\n y_interval = sigmoid(x_interval)\n delta_y_interval = d_sigmoid(x_interval)\n plt.plot(x_interval, delta_y_interval)\n plt.plot(x_interval, y_interval)\n","sub_path":"SigmoidFunc.py","file_name":"SigmoidFunc.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"362055208","text":"#cobra.core.Solution.py\n##########################\n#BEGIN Class Solution\n#\nfrom .Object import Object\nclass Solution(Object):\n \"\"\"Stores the solution from optimizing a cobra.Model. This is\n used to provide a single interface to results from different\n solvers that store their values in different ways.\n\n NOTE: This class might be deprecated in favor of associating the\n values with the Reactions and Metabolites in the cobra.Model.\n\n f: The objective value\n \n the_time: Float. Sometimes indicates how long it took to solve a\n problem. As this is typically negligible and not used in cobra pie,\n it might be deprecated.\n \n the_solver: A string indicating which solver package was used.\n\n x: List or Array of the values from the primal.\n\n x_dict: A dictionary of reaction ids that maps to the primal values.\n\n y: List or Array of the values from the dual.\n\n y_dict: A dictionary of reaction ids that maps to the dual values.\n \n \"\"\"\n def __init__(self, the_f, x=None,\n x_dict=None, y=None, y_dict=None,\n the_solver=None, the_time=0, status='NA'):\n Object.__init__(self, the_f)\n self.solver = the_solver\n self.f = the_f\n self.x = x\n self.x_dict = x_dict\n self.status = status\n self.y = y\n self.y_dict = y_dict\n def dress_results(self, model):\n \"\"\"Attaches results from FBA simulations to the Model's Reactions and\n Metabolites.\n\n model: The model that matches the Solution.\n\n \"\"\"\n [setattr(k, 'x', v) for k, v in zip(model.reactions, self.x)];\n [setattr(k, 'y', v) for k, v in zip(model.metabolites, self.y)];\n#\n#END Class Solution\n#########################\n","sub_path":"bin/python/usr/lib/python2.7/site-packages/cobra/core/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":1748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"380991500","text":"#!/usr/bin/env python3\n\"\"\"包含所有读图函数\"\"\"\n\n\nimport pandas as pd\nimport cartopy.io.shapereader as cread\n\n\ndef GetCountiesFromShp(province_list=None):\n shp_read_info = cread.Reader('../CHN_shps/gadm36_CHN_3.shp')\n read_list = []\n if province_list == None:\n # read 全国所有\n df_cols = ['province', 'city', 'county', 'type', 'x', 'y']\n for rcd, geo in zip(shp_read_info.records(), shp_read_info.geometries()):\n read_list.append(read_rcd(rcd.attributes, geo))\n else:\n for rcd, geo in zip(shp_read_info.records(), shp_read_info.geometries()):\n if not is_prov_ignore(rcd_attributes, province_list):\n read_list.append(read_rcd(rcd.attributes, geo))\n df = pd.DataFrame(read_list, columns=df_cols)\n return df\n\n \ndef read_rcd(shp3_rcd_attr, geo):\n read = []\n # province\n prov_name = shp3_rcd_attr['NL_NAME_1'].split('|')[0]\n read.append(prov_name)\n # city\n city_name = shp3_rcd_attr['NL_NAME_2']\n read.append(city_name)\n # conty, if null add cityname\n conty_name = shp3_rcd_attr['NL_NAME_3']\n conty_name = [conty_name, city_name][conty_name == None or conty_name == '']\n read.append(conty_name)\n\n # type use empty\n read.append(None)\n # x\n read.append(geo.centroid.x)\n # y\n read.append(geo.centroid.y)\n return read\n\ndef is_prov_ignore(rcd_attributes, ignore_list):\n if rcd_attributes['NL_NAME_1'].split('|')[0] in ignore_list:\n return True\n else:\n return False\n\n\nclass CompanyRegistryMatcher:\n def __init__(self, province=None):\n self.area_df = pd.read_pickle('./org_data/country_wild_conuties_df.pkl')\n self.similar_match_dict = {\n '龙江': '黑龙江',\n }\n self.sino_list = ['中粮', '供销总社']\n\n def get_match(self, company):\n # if matched, return registry name\n # forloop matching from county to province\n for area in self.area_df.iloc[:, 2]:\n area = area.split('|')[-1]\n if area in company:\n return area\n elif self.__match_with_short_name(company, area[:-1]):\n return area\n \n # match with sino name\n for i in self.sino_list:\n if i in company:\n return i\n\n # return other\n return 'other'\n\n def __match_with_short_name(self, company, area_short):\n \n if area_short in self.similar_match_dict:\n return self.__similar_name_match(company, area_short, self.similar_match_dict[area_short])\n \n else:\n return area_short in company\n\n\n\n def __similar_name_match(self, company, k1, k2):\n # return is company matched with specified similar dict, \n # eg: '龙江上有公司' with '龙江' return True \n \n if k1 in company and k2 in a:\n str_split = company.split(k2)\n for i in str_split:\n if k1 in i:\n return True # 在切割完的部分里还有,则的确是\n return False\n elif k1 in company:\n return True\n else:\n return False\n\n\n","sub_path":"models/toolkits.py","file_name":"toolkits.py","file_ext":"py","file_size_in_byte":3196,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"441334568","text":"import asyncio\nimport json\nfrom sys import argv\nfrom time import time, sleep\nimport math\nimport numpy as np\n\nfrom attractive_field import AttractiveField as af\nfrom repulsive_field import RepulsiveField as rf\nfrom tangential_field import TangentialField as tf\nfrom random_field import RandomField as ranf\nfrom wheel_speed import WheelSpeed\nfrom a_star import AStar\nfrom rrt_search import RRTSearch\n\ndef main(host, port):\n loop = asyncio.get_event_loop()\n reader, writer = loop.run_until_complete(\n asyncio.open_connection(host, port))\n print(reader.readline())\n\n def do(command):\n print('>>>', command)\n writer.write(command.strip().encode())\n res = loop.run_until_complete(reader.readline()).decode().strip()\n print('<<<', res)\n print()\n return res\n\n def follow_waypoint(waypoint_position, goal, tag_radius):\n max_force = 5\n waypoint_radius = tag_radius * 2\n res = do('where others')\n others_dic = json.loads(res)\n del others_dic[goal]\n del others_dic['time']\n field_dic = {}\n waypoint = 'waypoint'\n field_dic[waypoint] = af(waypoint_radius, 10, max_force, True)\n #for obstacle_key in others_dic:\n #field_dic[obstacle_key] = rf(tag_radius, 100, max_force, True)\n ws = WheelSpeed(float(max_force))\n\n not_in_radius = True\n\n while(not_in_radius):\n res = do('where robot')\n robot_dic = json.loads(res)\n res = do('where others')\n others_dic = json.loads(res)\n if ('orientation' in robot_dic):\n robot_direction = robot_dic['orientation']\n robot_position = robot_dic['center']\n force = [0, 0]\n force = np.add(force, field_dic[waypoint].get_vector(robot_position, waypoint_position))\n for tag_key in others_dic:\n if (tag_key != 'time' and tag_key in field_dic):\n tag_position = others_dic[tag_key]['center']\n field = field_dic[tag_key]\n force = np.add(force, field.get_vector(robot_position, tag_position))\n speed = ws.get_wheel_speed(force)\n ws.adjust_speed_for_rotation(speed, robot_direction, force)\n do('speed ' + str(speed[0]) + ' '+ str(speed[1]))\n distance_to_goal = math.sqrt((waypoint_position[0] - robot_position[0]) ** 2 + (waypoint_position[1] - robot_position[1]) ** 2)\n not_in_radius = distance_to_goal > waypoint_radius\n #do('speed 0 0')\n\n def solve_maze(search_strategy, field_dim, goal, unit_length):\n res = do('where others')\n others_dic = json.loads(res)\n corner1 = others_dic[goal]['corners'][0]\n corner3 = others_dic[goal]['corners'][2]\n tag_radius = round(math.sqrt(((corner3[0] - corner1[0]) / 2) ** 2 + ((corner3[1] - corner1[1]) / 2) ** 2) * 1.5)\n robot_radius = tag_radius\n\n robot_dic = {}\n while (not 'orientation' in robot_dic):\n res = do('where robot')\n robot_dic = json.loads(res)\n robot_position = robot_dic['center']\n robot_position = [round(robot_position[0]), round(robot_position[1])]\n\n others_dic = {}\n while (not goal in others_dic):\n res = do('where others')\n others_dic = json.loads(res)\n goal_position = others_dic[goal]['center']\n goal_position = [round(goal_position[0]), round(goal_position[1])]\n del others_dic[goal]\n del others_dic['time']\n obstacle_pos = []\n for obstacle_key in others_dic:\n center = others_dic[obstacle_key]['center']\n obstacle_pos.append([round(center[0]), round(center[1])])\n searcher = search_strategy(field_dim, tag_radius, robot_radius)\n waypoints = searcher.get_path(robot_position, goal_position, obstacle_pos, unit_length)\n\n for waypoint in waypoints:\n follow_waypoint(waypoint, goal, tag_radius)\n\n do('speed 0 0')\n\n do('param kp 25')\n do('param ki .5')\n do('param kd .5')\n\n field_dim = [1920, 1080]\n goal = '6'\n unit_length = 50\n search_strategy = AStar\n\n solve_maze(search_strategy, field_dim, goal, unit_length)\n\n writer.close()\n\nif __name__ == '__main__':\n main(*argv[1:])\n","sub_path":"controller_old.py","file_name":"controller_old.py","file_ext":"py","file_size_in_byte":4376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"204140296","text":"# 별 용도는 없음. 그저 단축키 뽑아쓰는게 목적\n\n# 게임상 기본값 설정\ncpu_count = 20\nmax_distance = 6\n\n# 캐리어가 본진 진입시 자원 넣을 곳 최대거리.\nMAX_DROP_DISTANCE = 10\n\n# 잡다한거\n# ERR_NOT_ENOUGH_RESOURCES + 크립이 뭔갈 들고있다.\nERR_NOT_ENOUGH_RESOURCES_AND_CARRYING_SOMETHING = -101\n# 단순 초기화 용도\nERR_UNDONE_CONSTANT = -200\n\nhome_room = 'home_room'\nlab_type = 'lab_type'\nmax_energy = 'max_energy'\nmax_range_to_container = 4\nmax_upgraders = 'max_upgraders'\nmineral_type = 'mineral_type'\nrepair_targets = 'repair_targets'\nroom_lvl = 'room_lvl'\n\n# STRUCTURE_CONTAINER/LINKS\nfor_upgrade = 'for_upgrade'\nfor_harvest = 'for_harvest'\nfor_store = 'for_store'\nreceived_time = 'received_time'\n\n# 게임 내 리모트 명칭들\ndefenders = 'defenders'\ndisplay = 'display'\ninit_road = 'init_road'\nhave_walls = 'have_walls'\noptions = 'options'\nremotes = 'remotes'\nrepair = 'repair'\nrepair_done = 'repair_done'\nreset = 'reset'\nstop_fixer = 'stop_fixer'\n\n# 스폰·링크에 현황판 위치\ncenter = 'center'\nleft = 'left'\nright = 'right'\n\n# 자원 관련\nenergy = 'energy'\nhaul_all = 'haul_all'\nhaul_all_but_energy = 'haul_all_but_energy'\nhaul_resource = 'haul_resource'\nminerals = 'minerals'\nresources = 'resources'\nkeeper = 'keeper'\n\n# 따옴표 귀찮음...\nignoreCreeps = 'ignoreCreeps'\n\n# 캐리어 관련\nto_home = 'to_home'\nto_pickup = 'to_pickup'\n\n","sub_path":"src/_custom_constants.py","file_name":"_custom_constants.py","file_ext":"py","file_size_in_byte":1417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"278357469","text":"from textwrap import fill\nfrom item import Item\nfrom player import Player\nfrom room import Room\n\n# Declare all the rooms\n\nroom = {\n 'outside': Room(\"Outside Cave Entrance\",\n \"North of you, the cave mount beckons\", {'n': 'foyer'}),\n\n 'foyer': Room(\"Foyer\", \"\"\"Dim light filters in from the south. Dusty\npassages run north and east. Propped against the west wall lies the decaying remains\nof a long lost explorer.\"\"\", {'s': 'outside', 'n': 'overlook', 'e': 'narrow'},\n['sword']),\n\n 'overlook': Room(\"Grand Overlook\", \"\"\"A steep cliff appears before you, falling\ninto the darkness. Ahead to the north, a light flickers in\nthe distance, but there is no way across the chasm.\"\"\", {'s': 'foyer'}, ['potion']),\n\n 'narrow': Room(\"Narrow Passage\", \"\"\"The narrow passage bends here from west\nto north. The smell of gold permeates the air.\"\"\", {'w': 'foyer', 'n': 'treasure'},\n['feather', 'nuts']),\n\n 'treasure': Room(\"Treasure Chamber\", \"\"\"You've found the long-lost treasure\nchamber! Sadly, it has already been completely emptied by\nearlier adventurers. The only exit is to the south.\"\"\", {'s': 'narrow'}, ['mushroom']),\n}\n\nitems = {\n 'sword': Item(\"Stone Sword\", \"A heavy and primitive stone sword\"),\n\n 'potion': Item(\"Potion Bottle\", \"\"\"A effervescent bottle of glowing green liquid\n with a \"Drink Me\" label\"\"\"),\n\n 'feather': Item(\"Eagle Feather\", \"\"\"The tail feather of a golden eagle split nearly\n in half.\"\"\"),\n\n 'nuts': Item(\"Mixed Nuts\", \"A pouch full of mixed nuts still in their shell\"),\n\n 'mushroom': Item(\"Miracle Cap\", \"\"\"A phosphorescent mushroom, beautiful but smells like \n rotten fish.\"\"\")\n}\n#\n# Main\n#\n\n# Make a new player object that is currently in the 'outside' room.\n\n# Write a loop that:\n#\n# * Prints the current room name\n# * Prints the current description (the textwrap module might be useful here).\n# * Waits for user input and decides what to do.\n#\n# If the user enters a cardinal direction, attempt to move to the room there.\n# Print an error message if the movement isn't allowed.\n#\n# If the user enters \"q\", quit the game.\n\n\nplayer = Player(\"outside\")\n\nuser_input = \"\"\n\nwhile user_input != \"q\":\n \n location = player.get_location()\n current_room = room[location]\n \n print(current_room.name)\n print(fill(current_room.description))\n \n if len(current_room.inventory) == 0:\n print(\"The room contains no items.\")\n else:\n print(\"The room contains the following items:\", \", \".join(current_room.inventory))\n\n prompt_txt = \"\"\"\n You may take/drop an item by entering take [item name] or drop [item name].\n Enter i to get a list of items you are carrying. \n Enter a direction n/s/e/w to move to another room.\n Enter q to quit: \n \"\"\"\n user_input = input(prompt_txt).lower()\n split_txt = user_input.split()\n \n if len(split_txt) < 2:\n if user_input == \"i\":\n if len(player.inventory) == 0:\n print(\"Your pockets are currently empty.\")\n else:\n print(\"You are carrying: \", \", \".join(player.inventory))\n for item in player.inventory:\n print(items[item])\n elif user_input in current_room.exits:\n next_room = current_room.exits[user_input]\n player.set_location(next_room)\n elif user_input != 'q':\n print(\"That is not a valid input. Please try again.\")\n else:\n print(\"Thank you for playing my adventure game.\")\n \n elif len(split_txt) >= 2:\n if split_txt[0] == \"take\":\n if split_txt[1] in current_room.inventory:\n current_room.take_item(split_txt[1])\n player.take_item(split_txt[1])\n items[split_txt[1]].on_take()\n else:\n print(\"That item is not located in this room.\")\n elif split_txt[0] == \"drop\":\n if split_txt[1] in player.inventory:\n current_room.drop_item(split_txt[1])\n player.drop_item(split_txt[1])\n items[split_txt[1]].on_drop()\n else:\n print(\"You are not carrying this item.\")\n\n \n\n\n","sub_path":"src/adv.py","file_name":"adv.py","file_ext":"py","file_size_in_byte":4178,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"414493075","text":"import pygame as pg\r\nfrom settings import *\r\n\r\nclass Camera():\r\n def __init__(self, game, width, height):\r\n self.cam = pg.Rect(0, 0, width, height)\r\n self.width = width\r\n self.height = height\r\n self.game = game\r\n\r\n def apply(self, entity):\r\n return entity.rect.move(self.cam.topleft)\r\n\r\n def update(self, target):\r\n self.x = -target.rect.centerx + int(WIDTH/2)\r\n\r\n # So the camera doesn't move if you are on the far left side of the map\r\n self.x = min(0, self.x) # x is negative, unless on the wall (see first line in Camera.update)\r\n\r\n # So the camera doesn't move if you are on the far right side of the map\r\n self.x = max(-(self.game.map_width - WIDTH), self.x)\r\n \r\n self.cam = pg.Rect(self.x, 0, self.width, self.height)\r\n","sub_path":"Pygame boss fight simulator/camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"602964526","text":"from django.conf.urls import patterns, url, include\nfrom . import views\nfrom tastypie.api import Api\nfrom .api import EventRecommendResource,EventHottestResource,EventLatestResource\n\nurlpatterns = patterns('',\n url(r'^$', views.EventListView.as_view(), name='event_list'),\n url(r'^(?P\\d+)/$', views.EventDetailView.as_view(), \n name='event_detail'),\n url(r'^event_create/$', views.EventCreateView.as_view(), \n name='event_create'),\n url(r'^event_update/(?P\\d+)/$', views.EventUpdateView.as_view(), \n name='event_update'),\n url(r'^event_delete/(?P\\d+)/$', views.EventDeleteView.as_view(), \n name='event_delete'),\n url(r'^event_comment_create/(?P\\d+)/$', views.event_comment_create, name='event_comment_create'),\n url(r'^event_rsvp/(?P\\d+)/$', views.event_rsvp, name='event_rsvp'),\n url(r'^event_rsvp_remove/(?P\\d+)/$', \n views.event_rsvp_remove, name='event_rsvp_remove'),\n url(r'^event_image_create/(?P\\d+)/$', views.event_image_create, \n name='event_image_create'),\n url(r'^event_image_delete/(?P\\d+)/$', \n views.EventImageDeleteView.as_view(), name='event_image_delete'),\n url(r'^event_image_create_api/(?P\\d+)/$', views.EventImageCreateAPIView.as_view(), \n name='event_image_create_api'),\n url(r'^image_collection$', views.Event_Image_Collection.as_view(), name='event_image_collection'),\n url(r'^app/$',views.app_download),\n)\n\n\n\neventapi = Api(api_name='v02')\neventapi.register(EventRecommendResource())\neventapi.register(EventHottestResource())\neventapi.register(EventLatestResource())\n\n# urlpatterns+=patterns('',\n# url(r'^api/', include(v02_api.urls)),\n# )","sub_path":"events/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"232215075","text":"# -*- coding: utf-8 -*-\nfrom django.core.management.base import BaseCommand\n\nimport feedparser\nfrom bs4 import BeautifulSoup\n# from digest.management.commands import fresh_google_check\nfrom digest.management.commands.import_news import _get_http_data_of_url, \\\n apply_parsing_rules, parsing\nfrom digest.models import AutoImportResource, Item\n\ntry:\n from urllib.request import urlopen\nexcept ImportError:\n from urllib2 import urlopen\n\n\ndef get_tweets():\n \"\"\"Импорт твитов пользователя.\"\"\"\n dsp = []\n for src in AutoImportResource.objects.filter(type_res='twitter',\n in_edit=True):\n url = urlopen(src.link)\n soup = BeautifulSoup(url)\n url.close()\n\n num = 0\n\n print('\\n\\n' + '=' * 25)\n print(' ' + src.name)\n print('=' * 25 + '\\n')\n resource = src.resource\n excl = src.excl.split(', ')\n print('Исключения:')\n print('-' * 25)\n for i in excl:\n print(i)\n print('\\n')\n print('Распарсенные твитты:')\n print('-' * 25)\n for p in soup.findAll('p', 'ProfileTweet-text js-tweet-text u-dir'):\n try:\n tw_lnk = p.find('a', 'twitter-timeline-link').get(\n 'data-expanded-url')\n\n for i in excl:\n if tw_lnk.find(i) > -1 and i != '':\n excl_link = True\n else:\n excl_link = False\n\n if not excl_link and p.contents[0].find(src.incl) > -1:\n num = num + 1\n tw_txt = p.contents[0].replace(src.incl, '')\n print(str(num) + '. excl:' + str(excl_link) + ' ' + tw_txt\n + '--- ' + tw_lnk)\n dsp.append([tw_txt, tw_lnk, resource])\n except:\n pass\n print('-' * 25)\n return dsp\n\n\ndef get_rss(**kwargs):\n for src in AutoImportResource.objects.filter(type_res='rss',\n in_edit=False):\n print('\\n\\n' + '=' * 25)\n print(' ' + src.name)\n print('=' * 25 + '\\n')\n\n num = 0\n rssnews = feedparser.parse(src.link)\n for n in rssnews.entries:\n\n # title = u'[!] %s' % n.title if fresh_google_check(\n # n.title,\n # debug=True) else n.title\n #\n title = n.title\n\n http_code, content, _ = _get_http_data_of_url(n.link)\n\n item_data = {\n 'title': title,\n 'link': n.link,\n 'http_code': http_code,\n 'content': content,\n 'description': n.summary,\n 'resource': src.resource,\n }\n data = apply_parsing_rules(item_data, **kwargs) if kwargs.get(\n 'query_rules') else {}\n item_data.update(data)\n\n print_str = ''\n print_str += 'status: %s' % item_data['status'] if (\n 'status' in item_data) else ''\n print_str += 'tags: %s' % item_data['tags'] if ('tags' in\n item_data) else ''\n print_str += 'section: %s' % item_data['section'] if (\n 'section' in item_data) else ''\n print(print_str)\n try:\n lastnews = Item.objects.get(link=item_data.get('link'))\n except Item.DoesNotExist:\n num += 1\n print('%d: Title: %s (%s)' %\n (num, item_data.get('title'), item_data.get('link')))\n # print src.resource\n\n\nclass Command(BaseCommand):\n\n args = 'no arguments!'\n help = u''\n\n def handle(self, *args, **options):\n '''\n Основной метод - точка входа\n '''\n print(get_tweets())\n print(parsing(get_rss))\n","sub_path":"digest/management/commands/test_import_news.py","file_name":"test_import_news.py","file_ext":"py","file_size_in_byte":3985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"649228760","text":"#\n# Categorization of graph operations into NodeType's\n# Copyright EAVISE\n#\nimport enum\nfrom .._imports import onnx\n\n__all__ = ['NodeType', 'get_node_type', 'add_nodetype_operation']\n\n\nclass NodeType(enum.Enum):\n \"\"\" Enum containing all different types of operation in the dependency graph. \"\"\"\n CONV = enum.auto() #: Convolution operation\n BATCHNORM = enum.auto() #: BatchNorm operation\n CONCAT = enum.auto() #: Concat operation on channel dimension\n ELEMW_OP = enum.auto() #: Element-wise operation (eg. add)\n IGNORE = enum.auto() #: Node that does not need to be modified and does not change output dims (eg. relu)\n IGNORE_STOP = enum.auto() #: Ignore node and stop dependency chain\n\n\ndef get_node_type(element):\n op_type = element.op_type\n if op_type == 'ATen':\n attr = onnx.helper.printable_attribute(element.attribute[0])\n attr = attr.split('=')[-1].strip()[1:-1]\n\n for key, value in node_type_map_aten.items():\n if attr in value:\n return key\n\n raise NotImplementedError(f'ATen: {attr}')\n else:\n for key, value in node_type_map_onnx.items():\n if op_type in value:\n return key\n\n raise NotImplementedError(element.op_type)\n\n\ndef add_nodetype_operation(name, node_type, aten=True):\n \"\"\" Add a graph operation to a specific NodeType. |br|\n Adding all possible ONNX/ATen graph operations to our list is not feasible,\n and thus we test our algorithms only with the models in this library.\n\n If you get the following error, whilst generating the dependency map of your network,\n it means you use an operation which we have not added to our list of known operations:\n\n .. code:: bash\n\n Cannot prune [{layer}], unimplemented dependency [{operation}]\n\n You can then add your operation to a certain :class:`~lightnet.prune.dependency.NodeType` with this function.\n If the operation name starts with **\"ATen:\"**,\n add the operation without this prefix and set the ``aten`` argument to **True**.\n Otherwise, set ``aten`` to **False**.\n\n Args:\n name (str): Name of the operation in the graph (dont forget to remove \"ATen:\" if necessary)\n node_type (NodeType): To which type to add\n aten (bool, optional): Whether to operation is an ATen or ONNX operation in the graph; Default **True**\n\n Note:\n We would strongly appreciate it if anyone with unimplemented operations, could open an issue on our gitlab.\n That way, we can manually add it to our list and grow it organically based on usage!\n \"\"\"\n if aten:\n node_type_map_aten[node_type].add(name)\n else:\n node_type_map_onnx[node_type].add(name)\n\n\n# Default maps\nnode_type_map_aten = {\n NodeType.CONV: {\n '_convolution',\n },\n NodeType.BATCHNORM: {\n 'batch_norm',\n },\n NodeType.CONCAT: {\n 'cat',\n },\n NodeType.ELEMW_OP: {\n 'add',\n },\n NodeType.IGNORE: {\n 'leaky_relu',\n 'relu',\n 'hardtanh',\n 'adaptive_avg_pool2d',\n 'max_pool2d',\n 'cummax',\n 'upsample_nearest2d',\n 'replication_pad2d',\n 'flip',\n },\n NodeType.IGNORE_STOP: {\n 'size',\n },\n}\n\n\nnode_type_map_onnx = {\n NodeType.CONV: set(),\n NodeType.BATCHNORM: set(),\n NodeType.CONCAT: set(),\n NodeType.ELEMW_OP: set(),\n NodeType.IGNORE: set(),\n NodeType.IGNORE_STOP: {\n 'Concat',\n },\n}\n","sub_path":"lightnet/prune/dependency/_type.py","file_name":"_type.py","file_ext":"py","file_size_in_byte":3496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"15815925","text":"from robot.api import logger\nfrom robot.libraries.BuiltIn import BuiltIn\nfrom decorators_for_robot_functionalities import *\nimport sys\nimport os\nsys.path.append(os.path.join(os.path.dirname(__file__), '..', '../libraries/common'))\nfrom test_constants import * # noqa\nimport common_utils # noqa\n\n\nex = BuiltIn().get_library_instance('execute_command')\nSTACK_INFOS = BuiltIn().get_library_instance('stack_infos')\n\n\ndef tc_002_pod_health_check():\n steps = ['step1_check_componentstatus',\n 'step2_check_kubelet_is_running',\n 'step3_check_apiserver_is_running',\n 'step4_check_all_kubernetes_pod',\n 'step5_check_services_with_systemctl']\n common_utils.keyword_runner(steps)\n\n\n@pabot_lock(\"health_check_1\")\n@pabot_lock(\"health_check_2\")\ndef step1_check_componentstatus():\n stdout = ex.execute_unix_command(\"kubectl get componentstatus -o json | jq .items[].conditions[].type\")\n logger.console('\\n')\n for line in stdout.split('\\n'):\n if \"Healthy\" in line:\n logger.console(line)\n else:\n raise Exception(line)\n\n\n@robot_log\ndef check_container_is_running(name, nodes):\n for key in nodes:\n stdout = ex.execute_unix_command_on_remote_as_user(\"docker ps --filter status=running --filter name=\" + name +\n \" | grep -v pause | grep \" + name + \" | wc -l \", nodes[key])\n if stdout == '1':\n logger.console(\"\\n\" + name + \" container is running on node \" + key + \".\")\n else:\n stdout = ex.execute_unix_command_on_remote_as_user(\"docker ps | grep -v pause | grep \" + name, nodes[key])\n raise Exception(name + \"container is NOT running on node \" + key + \"\\n\" + stdout)\n\n\n@robot_log\ndef check_program_is_running(name, nodes):\n for key in nodes:\n stdout = ex.execute_unix_command_on_remote_as_user(\"ps -aux | grep '\" + name + \"' | grep -v 'color' | wc -l \",\n nodes[key])\n if stdout == '1':\n logger.console(\"\\n\" + name + \" is running on node \" + key + \".\")\n else:\n stdout = ex.execute_unix_command_on_remote_as_user(\"ps -aux | grep '\" + name + \"' | grep -v 'color'\",\n nodes[key])\n raise Exception(name + \" is NOT running on node \" + key + \"\\n\" + stdout)\n\n\ndef step2_check_kubelet_is_running():\n all_nodes = STACK_INFOS.get_all_nodes()\n check_program_is_running(\"/kubelet \", all_nodes)\n check_program_is_running(\"/kubelet_healthcheck.sh\", all_nodes)\n\n\ndef step3_check_apiserver_is_running():\n crf_nodes = STACK_INFOS.get_crf_nodes()\n check_container_is_running(\"kube-apiserver\", crf_nodes)\n\n\n@pabot_lock(\"health_check_1\")\ndef step4_check_all_kubernetes_pod():\n LOG_DIR = os.path.join(os.path.dirname(__file__))\n command = \"kubectl get po -n kube-system | tail -n +2 | grep -vP 'Running\"\n for pod in pods_skipped:\n command += '|'+pod\n command += \"'\"\n stdout = ex.execute_unix_command(command, fail_on_non_zero_rc=False, skip_prompt_in_command_output=True)[0]\n if not stdout:\n logger.console(\"\\nAll kubernetes PODs are running.\")\n return\n for line in stdout.split(\"\\n\"):\n line = line.split()\n command = \"kubectl logs --namespace \" + line[0] + \" \" + line[1]\n filename = \"tc004_step1_\" + line[1] + \".log\"\n common_utils.gather_logs(command, filename, LOG_DIR)\n raise Exception(stdout)\n\n\ndef step5_check_services_with_systemctl():\n all_nodes = STACK_INFOS.get_all_nodes()\n command = \"systemctl status | grep -E 'State: running|Jobs: 0 queued|Failed: 0 units' | grep -v grep\"\n for key in all_nodes:\n logger.console(key)\n stdout = \"\\nsystemctl status output:\\n\" + ex.execute_unix_command_on_remote_as_user(command, all_nodes[key])\n if all(x in stdout for x in [\"State: running\", \"Jobs: 0 queued\", \"Failed: 0 units\"]):\n logger.console(stdout)\n else:\n # cat is needed here to remove the coloring of the systemctl for the robot logs\n failedservices = ex.execute_unix_command_on_remote_as_user(\"systemctl --failed | cat\", all_nodes[key])\n # TODO: cloud-final.service fails with unknown reason\n if any(service in failedservices for service in services_skipped):\n stdout = stdout + \"\\n\" + ex.execute_unix_command_on_remote_as_user(\"systemctl --failed | cat\",\n all_nodes[key])\n logger.console(stdout)\n else:\n stdout = stdout + \"\\n\" + ex.execute_unix_command_on_remote_as_user(\"systemctl --failed | cat\",\n all_nodes[key])\n raise Exception(stdout)\n","sub_path":"testcases/basic_func_tests/tc_002_pod_health_check.py","file_name":"tc_002_pod_health_check.py","file_ext":"py","file_size_in_byte":4910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"502133840","text":"import os\n\n# Backward compatibility - for now, allow POSTGRES_* env if set, or use standard PG*\nPOSTGRES_DB = os.getenv('POSTGRES_DB') or os.environ['PGDATABASE']\nPOSTGRES_USER = os.getenv('POSTGRES_USER') or os.environ['PGUSER']\nPOSTGRES_PASSWORD = os.getenv('POSTGRES_PASSWORD') or os.environ['PGPASSWORD']\nPOSTGRES_HOST = os.getenv('POSTGRES_HOST') or os.environ['PGHOST']\nPOSTGRES_PORT = os.getenv('POSTGRES_PORT') or os.getenv('PGPORT') or '5432'\n\n'''Path to Wikidata dump from /import folder'''\nDUMP = 'latest-all.json.gz'\n\n'''Max number of lines to read from dump'''\nLIMIT = 100000000\n# LIMIT = 1000000\n\n'''OSM tables to fetch Wikidata for'''\nOSM_TABLES = [\n 'osm_aerodrome_label_point',\n 'osm_peak_point',\n 'osm_city_point',\n 'osm_continent_point',\n 'osm_country_point',\n 'osm_island_point',\n 'osm_island_polygon',\n 'osm_state_point',\n 'osm_poi_point',\n 'osm_poi_polygon',\n 'osm_marine_point',\n 'osm_water_polygon',\n 'osm_waterway_linestring'\n]\n\n'''Table with imported wikidata'''\nTABLE_NAME = 'wd_names'\n","sub_path":"docker/import-wikidata/wikidata/cfg.py","file_name":"cfg.py","file_ext":"py","file_size_in_byte":1052,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"361485978","text":"import cv2\nimport base64\nimport json\nimport numpy as np\nimport boto3\nimport uuid\nimport io\n\ndef convert_b64_string_to_bynary(s):\n \"\"\"base64をデコードする\"\"\"\n return base64.b64decode(s.encode(\"UTF-8\"))\n\ndef base64_to_cv2(image_base64):\n \"\"\"base64 image to cv2\"\"\"\n image_bytes = base64.b64decode(image_base64)\n np_array = np.fromstring(image_bytes, np.uint8)\n image_cv2 = cv2.imdecode(np_array, cv2.IMREAD_COLOR)\n return image_cv2\n\n\ndef cv2_to_base64(image_cv2):\n \"\"\"cv2 image to base64\"\"\"\n image_bytes = cv2.imencode('.jpg', image_cv2)[1].tostring()\n image_base64 = base64.b64encode(image_bytes).decode()\n return image_base64\n\ndef nurie_filter(img):\n neiborhood24 = np.ones((5, 5), dtype=np.uint8)\n dilated = cv2.dilate(img, neiborhood24, iterations=1)\n diff = cv2.absdiff(dilated, img)\n contour = 255 - diff\n img_gray = cv2.cvtColor(contour, cv2.COLOR_BGR2GRAY)\n return img_gray\n\ndef post_s3(img, putname):\n s3 = boto3.client('s3')\n s3.upload_file(\n Filename = img,\n Bucket = 'nurie',\n Key = putname,\n ExtraArgs={\"ContentType\": \"image/jpeg\", \"ACL\":\"public-read\"}\n )\n\n\n\ndef check_r18(img):\n client=boto3.client('rekognition')\n result, buf = cv2.imencode('.jpg', img)\n response = client.detect_moderation_labels(Image={'Bytes':buf.tobytes()})\n if len(response['ModerationLabels']) == 0:\n return True\n return False\n\ndef lambda_handler(event, context):\n # requestbodeyの中のjsonはeventに辞書型に変化されて保��されている\n # なので、eventの中には {\"mypng\": \"base64でエンコードされた文字列\"}が入力される想定。\n base_64ed_image = event['mypng']\n save_flag = event['saveflag']\n\n \n # バケット作成を作成してbynary変換して保存する。\n cvimg = base64_to_cv2(base_64ed_image)\n \n if check_r18(cvimg):\n putname = \"Moderation/\" + str(uuid.uuid4()) + \".jpg\"\n else:\n putname = \"NoModeration/\" + str(uuid.uuid4()) + \".jpg\"\n \n anime = nurie_filter(cvimg)\n tmpname = '/tmp/tmp.jpg'\n cv2.imwrite(tmpname, anime)\n \n if save_flag == \"True\":\n post_s3(tmpname, putname)\n\n body = cv2_to_base64(anime)\n \n # とりあえずOKを返す。\n return body","sub_path":"lambda_function.py","file_name":"lambda_function.py","file_ext":"py","file_size_in_byte":2287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"122538064","text":"from floodsystem.station import MonitoringStation\n# import new MonitoringStation class\n\n# -----------------------------------------------------------------------------\n# EXERCISE 2B\n\n\ndef stations_level_over_threshold(stations, tol):\n \"\"\"Returns a list of tuples, each holding i) a station (object) which is\n above the input water level tol; and ii) the relative water level at\n the station\"\"\"\n\n floodstations = [] # creates empty lists for output\n floodlevels = []\n\n for i in range(len(stations)): # iterate through stations\n\n # Data type checks\n if type(stations[i]) is not MonitoringStation:\n raise TypeError(\"ERROR: Station is not a MonitoringStation\")\n if type(stations[i].name) is not str:\n raise TypeError(\"ERROR: Station 'name' attribute is not a string\")\n if type(tol) is not float:\n raise TypeError(\"ERROR: Tol is not a float\")\n if tol < 0 or tol > 1:\n raise ValueError(\"ERROR: Tol is not between 0.0 and 1.0\")\n\n if (isinstance((stations[i].relative_water_level()), float)\n # ^checks station for valid water level value\n and stations[i].relative_water_level() > tol):\n # ^checks height of water\n floodstations.append(stations[i].name)\n floodlevels.append(stations[i].relative_water_level())\n # ^adds to lists\n\n floods = [] # create output list\n if ((floodstations is not None) # checks for station list contents\n and (floodlevels is not None)): # checks for level list contents\n floods = list(zip(floodstations, floodlevels)) # creates output list\n floods.sort(key=lambda x: x[1], reverse=1) # sorts in reverse\n return floods\n# -----------------------------------------------------------------------------\n# EXERCISE 2C\n\n\ndef stations_highest_rel_level(stations, N):\n \"\"\"Returns a list containing the names of the N stations\n with the highest water level relative to the typical range\"\"\"\n\n names = [] # create list for names\n levels = [] # create list for levels\n\n for i in range(len(stations)): # iterate through stations\n\n if stations[i].relative_water_level() is not None:\n # ^checks for valid relative water level\n\n names.append(stations[i].name)\n levels.append(stations[i].relative_water_level())\n # ^adds names and levels to respective lists\n\n combined = list(zip(names, levels)) # combines names and levels\n combined.sort(key=lambda x: x[1], reverse=1) # sorts in reverse\n\n output = [] # create output list\n for i in range(N): # iterate up to N\n output.append(combined[i][0]) # add station name to output\n\n return output\n# -----------------------------------------------------------------------------\n","sub_path":"floodsystem/flood.py","file_name":"flood.py","file_ext":"py","file_size_in_byte":2870,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"399184733","text":"from typing import List, Optional\n\nfrom fastapi.encoders import jsonable_encoder\nfrom sqlalchemy.orm import Session\n\nfrom app.crud.base import CRUDBase\nfrom app.models.role import Role\nfrom app.schemas.role import RoleCreate, RoleUpdate\n\n\nclass CRUDRole(CRUDBase[Role, RoleCreate, RoleUpdate]):\n\n def get_by_uuid(self, db: Session, *, uuid: str) -> Optional[Role]:\n return db.query(Role).filter(Role.uuid == uuid).first()\n \n def create(\n self, db: Session, *, obj_in: RoleCreate\n ) -> Role:\n obj_in_data = jsonable_encoder(obj_in)\n db_obj = self.model(**obj_in_data)\n db.add(db_obj)\n db.commit()\n db.refresh(db_obj)\n return db_obj\n\n def get_multi(\n self, db: Session, *, skip: int = 0, limit: int = 100\n ) -> List[Role]:\n return (\n db.query(self.model)\n .offset(skip)\n .limit(limit)\n .all()\n )\n\n\nrole = CRUDRole(Role)\n","sub_path":"backend/app/app/crud/crud_role.py","file_name":"crud_role.py","file_ext":"py","file_size_in_byte":961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"313734314","text":"# -*- coding: utf-8 -*-\n\"\"\"\nRandomForest data format:\n wavelet representation:\n this is for i th channel:\n X = [wavelet data points of 3rd and 4th decomposition levels]\n y = [label]\n \n@author: Prophet X\n\"\"\"\n\n#scikit imports\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.tree import export_graphviz\nfrom sklearn.feature_selection import RFE\nfrom sklearn.feature_selection import SelectFromModel\n\n\n#pandas imports\nimport pandas as pd\nimport numpy as np\nimport joblib\nimport os\nimport datetime\n\n#import evaluators\nfrom sklearn import datasets, linear_model\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.metrics import classification_report,accuracy_score\nimport seaborn as sns\nfrom statistics import stdev\n\nfft_train = '../../dataset/transformed_data/wavelet_data/train' #wavelet training data directory\nfft_test = '../../dataset/transformed_data/wavelet_data/test' #wavelet test data directory \nmodel_root = \"../../models/rf_models/rf_model2/\" \n\nrandom_forests = []\nselectors = []\n \n# seperate random forest algorithms for 8 channels.\nfor channel in range(8):\n # create classification object.\n rfc = RandomForestClassifier(n_estimators=120, random_state=1,\n n_jobs=-1,\n max_features='log2',\n min_samples_leaf=2,\n min_samples_split=5,\n max_depth=10)\n random_forests.append(rfc)\n #create selector object.\n selector = SelectFromModel(estimator=random_forests[channel],threshold='mean')\n #selector = RFE(estimator = random_forests[channel], n_features_to_select=18, step=1)\n selectors.append(selector)\n\n# acquire the matrix related to training the algorithm\nfor channel in range(8):\n print('channel ',channel+1,' Training.')\n X = np.empty((0,60)) \n y = np.empty((0)) \n \n csv_count = 0 \n #-------------------------------------------------------create dataset-----------------------------------------------\n for root,dirs,files in os.walk(fft_train):\n for file in files:\n if file.endswith(\".csv\"):\n data_frame = pd.read_csv(root+'/'+file)\n \n filtered_df = data_frame[data_frame['channel']==(channel+1)]\n \n X = np.vstack( (X, np.array(filtered_df.drop(['channel','direction'],axis = 1) )) )\n y = np.hstack( (y, np.array(filtered_df['direction']) ) ).astype(np.int32) \n \n csv_count = csv_count+1\n \n if csv_count==0:\n print('No data for train')\n break;\n \n #feature selection.\n selector = selectors[channel].fit(X,y)\n print('estimator coefficient : ',selector.estimator_.feature_importances_)\n print('Threshold : ',selector.threshold_)\n \n transformed_X = selector.transform(X)\n \n random_forests[channel].fit(transformed_X,y)\n \n score = np.average(cross_val_score(random_forests[channel],transformed_X,y, cv=10))\n print('shape : ',X.shape)\n print('cvs : 10 fold: ',score)\n \n # save the models\n model_name = 'channel'+str(channel+1)+'.joblib'\n joblib.dump(random_forests[channel], model_root+model_name)\n\n#evaluations\ntotal_scores = [] \ntotal_accuracies = []\n \nfor channel in range(8):\n print('channel ',channel+1,' Testing.')\n X_test = np.empty((0,60))\n y_test = np.empty((0)) \n \n csv_count = 0 \n \n for root,dirs,files in os.walk(fft_test):\n for file in files:\n if file.endswith(\".csv\"):\n data_frame = pd.read_csv(root+'/'+file)\n \n filtered_df = data_frame[data_frame['channel']==(channel+1)]\n \n X_test = np.vstack( (X_test, np.array(filtered_df.drop(['channel','direction'],axis = 1) )) )\n y_test = np.hstack( (y_test, np.array(filtered_df['direction']) ) ).astype(np.int32) \n \n csv_count = csv_count + 1\n \n if csv_count == 0 :\n print('No files for test')\n break;\n \n #--------------------------------------------------------------------------------------------------------------------\n \n selector = selectors[channel]\n X_test = selector.transform(X_test)\n \n #------------------------------------------------predict for the test set-------------------------------------------\n start_time = datetime.datetime.now() \n predicted = random_forests[channel].predict(X_test)\n end_time = datetime.datetime.now()\n \n elapsed_time = end_time - start_time #elapsed time\n prediction_rate = float(elapsed_time.microseconds)/len(predicted) #time per one prediction\n print('elapsed time for prediction (us)',elapsed_time.microseconds,'\\n','time per one instance prediction(uV) ',prediction_rate)\n \n #cross validation score.\n print('Score for channel ',channel+1)\n \n #----------------------------------------------- calculate scores ---------------------------------------------------\n cv_scores = cross_val_score(random_forests[channel], X_test, y_test, cv=10)\n scores = np.average(cv_scores) #cross validation score\n std_dev = stdev(cv_scores) #standard deviation\n report = classification_report(y_test, predicted) #classification report\n accuracy = accuracy_score(y_test, predicted) #accuracies.\n \n total_scores.append(scores)\n total_accuracies.append(accuracy)\n print('shape : ',X_test.shape) \n \nprint(total_accuracies,'\\n',total_scores)\n \n\n\n","sub_path":"code/BCI_ai_src/trainer_evaluator/random_forest/rf_wt_ch_wise.py","file_name":"rf_wt_ch_wise.py","file_ext":"py","file_size_in_byte":5844,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"324460202","text":"from essentials_kit_management.interactors.storages.storage_interface \\\n import StorageInterface\nfrom essentials_kit_management.interactors.presenters.\\\n presenter_interface import PresenterInterface\nfrom essentials_kit_management.interactors.storages.dtos \\\n import FormMetricsDto\n\n\nclass GetFormsInteractor:\n\n def __init__(\n self, storage: StorageInterface,\n presenter: PresenterInterface\n ):\n self.storage = storage\n self.presenter = presenter\n\n def get_forms_as_list(self):\n form_list_dtos = self.storage.get_forms_details_as_list()\n\n form_metrics_dtos = \\\n self._get_form_metrics_dtos(form_list_dtos)\n\n response = self.presenter.get_forms_details_as_list_response(\n form_list_dtos=form_list_dtos,\n form_metrics_dtos=form_metrics_dtos\n )\n return response\n\n def _get_form_metrics_dtos(self, form_list_dtos):\n form_dtos = form_list_dtos.form_dtos\n ordered_item_dtos = form_list_dtos.ordered_item_dtos\n\n form_metrics_dtos = []\n for form_dto in form_dtos:\n form_id = form_dto.form_id\n form_metrics_dto = \\\n self._get_metrics_of_form_as_dto(form_id, ordered_item_dtos)\n form_metrics_dtos.append(form_metrics_dto)\n return form_metrics_dtos\n\n def _get_metrics_of_form_as_dto(self, form_id, ordered_item_dtos):\n ordered_item_dtos_of_form = self._get_filtered_ordered_items_of_form(\n form_id, ordered_item_dtos\n )\n \n \n items_count = \\\n self._get_items_count_of_form(form_id, ordered_item_dtos)\n estimated_cost = \\\n self._get_estimated_price(form_id, item_dtos)\n items_pending = \\\n self._get_items_yet_to_deliver(form_id, item_dtos)\n cost_incurred = \\\n self._get_cost_incurred(form_id, item_dtos)\n\n form_metrics_dto = FormMetricsDto(\n form_id=form_id, items_count=items_count,\n estimated_cost=estimated_cost, items_pending=items_pending,\n cost_incurred=cost_incurred\n )\n return form_metrics_dto\n\n def _get_filtered_ordered_items_of_form(form_id, ordered_item_dtos):\n ordered_item_dtos_of_form = [\n ordered_item_dto\n for ordered_item_dto in ordered_item_dtos\n ]\n\n\n def _get_items_count_of_form(self, form_id, ordered_item_dtos):\n items_count = 0\n for item_dto in item_dtos:\n is_item_in_form = self._is_a_valid_item_of_form(form_id, item_dto)\n if is_item_in_form:\n items_count = items_count + 1\n\n return items_count\n\n def _get_estimated_price(self, form_id, item_dtos):\n estimated_cost = 0\n for item_dto in item_dtos:\n is_item_in_form = self._is_a_valid_item_of_form(form_id, item_dto)\n if is_item_in_form:\n estimated_cost = estimated_cost + item_dto.item_price\n\n return estimated_cost\n\n @staticmethod\n def _is_a_valid_item_of_form(form_id, item_dto):\n is_item_in_form = item_dto.form_id == form_id\n return is_item_in_form\n\n def _get_items_yet_to_deliver(self, form_id, item_dtos):\n items_pending_count = 0\n for item_dto in item_dtos:\n is_item_in_form = self._is_a_valid_item_of_form(form_id, item_dto)\n is_not_delivered = not item_dto.is_deilvered\n is_pending_item = is_item_in_form and is_not_delivered\n if is_pending_item:\n items_pending_count = items_pending_count + 1\n\n return items_pending_count\n\n def _get_cost_incurred(self, form_id, item_dtos):\n cost_incurred = 0\n for item_dto in item_dtos:\n is_item_in_form = self._is_a_valid_item_of_form(form_id, item_dto)\n is_item_in_form_and_delivered = \\\n item_dto.is_delivered and is_item_in_form\n if is_item_in_form_and_delivered:\n cost_incurred = cost_incurred + item_dto.item_price\n\n return cost_incurred\n","sub_path":"essentials_kit_management/interactors/.~c9_invoke_bCEeb2.py","file_name":".~c9_invoke_bCEeb2.py","file_ext":"py","file_size_in_byte":4077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"513766006","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-10.11-x86_64/egg/saltcontainermap/extmods/lazy_yaml.py\n# Compiled at: 2017-11-28 12:39:26\nfrom __future__ import unicode_literals, absolute_import\nfrom dockermap.map.yaml import yaml\nfrom msgpack import ExtType\nfrom salt.utils.yamlloader import SaltYamlSafeLoader\n_ext_types = {}\n\ndef render(yaml_data, saltenv=b'base', sls=b'', **kws):\n config_get = __salt__[b'config.get']\n _ext_types[b'pillar'] = config_get(b'lazy_yaml.ext_code_pillar', 10)\n _ext_types[b'grain'] = config_get(b'lazy_yaml.ext_code_grain', 11)\n if config_get(b'lazy_yaml.skip_render', False):\n return yaml_data\n if not isinstance(yaml_data, basestring):\n yaml_data = yaml_data.read()\n data = yaml.load(yaml_data, Loader=SaltYamlSafeLoader)\n if data:\n return data\n return {}\n\n\ndef expand_pillar_lazy(loader, node):\n \"\"\"\n Substitutes a variable read from a YAML node with a MsgPack ExtType value referring to data stored in a pillar.\n\n :param loader: YAML loader.\n :type loader: yaml.loader.SafeLoader\n :param node: Document node.\n :type node: ScalarNode\n :return: Corresponding value stored in the pillar.\n :rtype: msgpack.ExtType\n \"\"\"\n val = loader.construct_scalar(node)\n return ExtType(_ext_types[b'pillar'], val.encode(b'utf-8'))\n\n\ndef expand_grain_lazy(loader, node):\n \"\"\"\n Substitutes a variable read from a YAML node with a MsgPack ExtType value referring to data stored in a grain.\n\n :param loader: YAML loader.\n :type loader: yaml.loader.SafeLoader\n :param node: Document node.\n :type node: ScalarNode\n :return: Corresponding value stored in the grain.\n :rtype: msgpack.ExtType\n \"\"\"\n val = loader.construct_scalar(node)\n return ExtType(_ext_types[b'grain'], val.encode(b'utf-8'))\n\n\nyaml.add_constructor(b'!pillar', expand_pillar_lazy, SaltYamlSafeLoader)\nyaml.add_constructor(b'!grain', expand_grain_lazy, SaltYamlSafeLoader)","sub_path":"pycfiles/salt_container_map-0.2.2-py2.7/lazy_yaml.py","file_name":"lazy_yaml.py","file_ext":"py","file_size_in_byte":2091,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"44403673","text":"import json\nimport datetime\nimport requests\n\nurl = \"http://uname.link/slack/log\"\ndef handler(event, context):\n data = event\n keywords = ['Message', 'body']\n code = 200\n try:\n for key in keywords:\n if key in event:\n data = event[key]\n break\n\n except Exception as e:\n data = str(e)\n print(str(e))\n code = 500\n finally:\n print(\"-------------\")\n print(data)\n print(\"-------------\")\n\n r = requests.post(url, data=json.dumps(data))\n print(r.content)\n\n return {'statusCode': code,\n 'body': json.dumps(data),\n 'headers': {'Content-Type': 'application/json'}}\n","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"48875031","text":"from socket import *\n\n\ndef send_msg(udp_socket):\n dest_ip = input(\"请输入对方ip:\")\n try:\n dest_port = int(input(\"请输入对方端口\"))\n except ValueError:\n print(ValueError)\n send_data = input(\"请输入要发送的数据:\")\n # udp_socket.sendto(send_data.encode('utf-8'), dest_addr)\n # udp_socket.sendto(b\"heheda\", dest_addr)\n udp_socket.sendto(send_data.encode('utf-8'), (dest_ip, dest_port))\n\n\ndef recv_msg(udp_socket):\n # udp_socket.sendto(b\"heheda\", dest_addr)\n recv_data = udp_socket.recvfrom(1024)\n print(\"%s:%s\" % (str(recv_data[1]), recv_data[0].decode(\"utf-8\")))\n\n\ndef main():\n udp_socket = socket(AF_INET, SOCK_DGRAM)\n local_addr = (\"\", 9999)\n udp_socket.bind(local_addr)\n # dest_addr = ('192.168.1.24', 9999)\n while True:\n print(\"------xxx聊天气xxx------\")\n print(\"1:发送消息\")\n print(\"2:接收消息\")\n print(\"0:退出系统\")\n op = input(\"请输入功能\")\n if op == \"1\":\n send_msg(udp_socket)\n elif op == \"2\":\n recv_msg(udp_socket)\n elif op == \"0\":\n break\n else:\n print(\"输入有误,请重新输入\")\n udp_socket.close()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"04_udp.py","file_name":"04_udp.py","file_ext":"py","file_size_in_byte":1261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"349632664","text":"import sys\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtWidgets import *\nimport numpy as np\nimport math\n\n\nclass PipeDrawing(object):\n\t'''\n\tThis class manages all the drawings of the pipe \n\tand the rendition of the simulation resutls\n\t'''\n\tcells = None\n\n\tdef __init__(self, graphicsView):\n\n\t\t# The view in which to draw\n\t\tself.view = graphicsView\n\n\t\t# The scene containing all the drawing\n\t\tsceneRect = QRectF(self.view.geometry()) \n\t\tself.scene = QGraphicsScene(sceneRect)\n\n\t\t# Appearance of different lines\n\t\tself.outerRectPen = QPen(Qt.DashLine)\n\t\tself.pipeLinePen = QPen(Qt.SolidLine)\n\t\tself.pipeFillingBrush = QBrush(Qt.Dense7Pattern)\n\t\tself.cellPen = QPen(Qt.DotLine)\n\t\tself.fillPen = QPen(Qt.NoPen)\n\n\tdef updateView(self):\n\t\t# The scene containing all the drawing\n\t\tsceneRect = QRectF(self.view.geometry())\n\t\tself.scene = QGraphicsScene(sceneRect)\n\n\t\t# Clear the scene\n\t\tself.scene.clear()\n\n\t\t# ReDraw rectangle\n\t\tself.drawOuterRect()\n\n\tdef drawOuterRect(self):\n\t\t''' \n\t\tDraws the outer rectangle, which will be the boundary of our system \n\t\t/!\\ Call this method before all other drawing methods, at best in init /!\\\n\t\t'''\n\t\t# Setup geometry\n\t\tbaseRect = QRectF(self.view.geometry())\n\t\tself.scene.setSceneRect(baseRect)\n\t\torigin = QPointF(baseRect.x()+20, baseRect.y()+20)\n\t\tsize = QSizeF(baseRect.width()-40, baseRect.height()-40)\n\t\touterRect = QRectF(origin, size)\n\t\tself.bounds = outerRect # Keep a trace of the rectangle in which we will draw the pipes\n\n\t\t# Draw the rectangle\n\t\tself.scene.addRect(outerRect, self.outerRectPen)\n\t\tself.view.setScene(self.scene)\n\n\n\tdef drawCells(self, vert, hor):\n\t\t'''\n\t\tDraws the discretization cells in the rectangle\n\t\tInputs:\n\t\t\t-> vert: number of cells to draw vertically\n\t\t\t-> hor: number of cells to draw horizontally\n\t\t'''\n\n\t\t# Initialization of return variables\n\t\tcoordinates_x = [self.bounds.x()]\n\t\tcoordinates_y = [self.bounds.y()]\n\n\t\t# Constants\n\t\tdx = self.bounds.width()/hor\n\t\tdy = self.bounds.height()/vert\n\t\ttop = self.bounds.y()\n\t\tbottom = top + self.bounds.height()\n\t\tleft = self.bounds.x()\n\t\tright = left + self.bounds.width()\n\n\t\t# We only draw the lines inside the outer rectangle\n\t\tvert = vert-1\n\t\thor = hor-1\n\n\t\t# Draw vertical lines\n\t\tfor i in range(hor):\n\t\t\tx = self.bounds.x() + (i+1)*dx\n\t\t\tcoordinates_x.append(x)\n\t\t\tstart = QPointF(x, top)\n\t\t\tend = QPointF(x, bottom)\n\t\t\tline = QLineF(start, end)\n\t\t\tself.scene.addLine(line, self.cellPen)\n\n\t\t# Draw horizontal ines\n\t\tfor j in range(vert):\n\t\t\ty = self.bounds.y() + (j+1)*dy\n\t\t\tcoordinates_y.insert(0,y)\n\t\t\tstart = QPointF(left, y)\n\t\t\tend = QPointF(right, y)\n\t\t\tline = QLineF(start, end)\n\t\t\tself.scene.addLine(line, self.cellPen)\n\n\t\tcoordinates_x.append(self.bounds.x()+self.bounds.width())\n\t\tcoordinates_y.insert(0, self.bounds.y()+self.bounds.height())\n\n\t\treturn (coordinates_x, coordinates_y)\n\n\n\tdef drawPipes(self, amount):\n\n\t\tpipeHeight = self.bounds.height()/amount/3\n\t\tdistance = self.bounds.height()/amount\n\t\tcenterLine = self.bounds.y() - distance/2\n\t\t\n\t\tfor i in range(amount):\n\t\t\tcenterLine = centerLine + distance\n\t\t\tself.drawPipe(centerLine, pipeHeight)\n\n\n\tdef drawPipe(self, centerLine, height):\n\t\t'''\n\t\tDraws a single pipe within the bounds of the outer rectangle\n\t\tThe inside of the pipe is darker than the outside so we can distinguish it better\n\n\t\tInputs:\n\t\t\t-> centerLine: the revolution axis y position of the pipe \n\t\t\t-> height: the height of the pipe\n\t\t'''\n\t\t# Setup geometry\n\t\thalf = height/2\n\t\ttop = centerLine-half \n\t\tbottom = centerLine+half\n\t\tstart = self.bounds.x()\n\t\tend = self.bounds.x()+self.bounds.width()\n\n\t\t# Draw the lines\n\t\ttopStart = QPointF(start, top)\n\t\ttopEnd = QPointF(end, top)\n\t\ttopLine = QLineF(topStart, topEnd)\n\t\tbottomStart = QPointF(start, bottom)\n\t\tbottomEnd = QPointF(end, bottom)\n\t\tbottomLine = QLineF(bottomStart, bottomEnd)\n\t\tself.scene.addLine(topLine, self.pipeLinePen)\n\t\tself.scene.addLine(bottomLine, self.pipeLinePen)\n\n\t\t# Fill the pipes\n\t\tbrushRect = QRectF(topStart, bottomEnd)\n\t\tself.scene.addRect(brushRect, QPen(Qt.NoPen), self.pipeFillingBrush)\n\n\n\tdef fillCells(self, coordinates, field):\n\n\t\tif self.cells is not None:\n\t\t\tfor i in range(1, len(self.cells)):\n\t\t\t\tself.scene.removeItem(self.cells[i])\n\t\t\tdel self.cells[:]\n\t\telse:\n\t\t\tself.cells = []\n\n\t\tcoordinates_x = coordinates[0]\n\t\tcoordinates_y = coordinates[1]\n\n\t\t#print(coordinates_x)\n\t\t#print(coordinates_y)\n\n\t\t# Find min and max and define color range\n\t\tmaxVal = field.max()\n\t\t# Bidouillage\n\t\tminVal = abs(field[1:,1:].min())\n\n\n\t\t# Draw each cell\n\t\tfor i in range(1,field.shape[1]):\n\t\t\tfor j in range(1,field.shape[0]):\n\n\t\t\t\t#print('(i,j) = (%i,%i)' %(i,j))\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t# Get rectangle coordinates\n\t\t\t\ttop = QPointF(coordinates_x[i-1], coordinates_y[j])\n\t\t\t\tbottom = QPointF(coordinates_x[i], coordinates_y[j-1])\n\t\t\t\trect = QRectF(top, bottom)\n\n\t\t\t\t#print(field[j,i])\n\n\t\t\t\t# Get right color\n\t\t\t\t''' /!\\ To be corrected !!! '''\n\t\t\t\tval = (field[j,i]-minVal)/(maxVal-minVal)/5 + 0.7\n\t\t\t\tcolor = QColor()\n\t\t\t\tcolor.setHsvF(val, 0.5,0.5,0.5)\n\t\t\t\tbrush = QBrush(color, Qt.Dense2Pattern)\n\n\t\t\t\t# Draw the rectangle\n\t\t\t\tcell = self.scene.addRect(rect, self.fillPen, brush)\n\t\t\t\tself.cells.append(cell)\n\n\nclass CutDrawing(object):\n\t'''\n\tThis class manages all the drawings of the cut graph\n\t'''\n\n\tdef __init__(self, graphicsView):\n\n\t\t# The view in which to draw\n\t\tself.view = graphicsView\n\n\t\t# The scene containing all the drawing\n\t\tsceneRect = QRectF(self.view.geometry()) \n\t\tself.scene = QGraphicsScene(sceneRect)\n\n\t\t# Appearance of different lines\n\t\tself.outerRectPen = QPen(Qt.DashLine)\n\t\tself.pipeLinePen = QPen(Qt.SolidLine)\n\t\tself.axisLine = QPen(Qt.DashDotLine)\n\t\tself.pipeFillingBrush = QBrush(Qt.Dense7Pattern)\n\n\tdef updateView(self):\n\t\t# The scene containing all the drawing\n\t\tsceneRect = QRectF(self.view.geometry())\n\t\tself.scene = QGraphicsScene(sceneRect)\n\n\t\t# Clear the scene\n\t\tself.scene.clear()\n\n\t\t# ReDraw rectangle\n\t\tself.drawOuterRect()\n\n\tdef drawOuterRect(self):\n\t\t''' \n\t\tDraws the outer rectangle, which will be the boundary of our system \n\t\t/!\\ Call this method before all other drawing methods, at best in init /!\\\n\t\t'''\n\t\t# Setup geometry\n\t\tbaseRect = QRectF(self.view.geometry())\n\t\tself.scene.setSceneRect(baseRect)\n\t\torigin = QPointF(baseRect.x()+20, baseRect.y()+20)\n\t\tsize = QSizeF(baseRect.width()-40, baseRect.height()-40)\n\t\touterRect = QRectF(origin, size)\n\n\t\t# Save Bound constants whith the margins\n\t\tmargin = 0.85\n\t\tself.viewTop = outerRect.y()+ outerRect.height()*(1-margin)/2\n\t\tself.viewBottom = self.viewTop + outerRect.height()*margin\n\t\tself.viewLeft = outerRect.x() + outerRect.width()*(1-margin)/2\n\t\tself.viewRight = self.viewLeft + outerRect.width()*margin\n\t\tself.viewHeight = outerRect.height()*margin\n\t\tself.viewWidth = outerRect.width()*margin\n\n\t\t# Draw the rectangle (inner)\n\t\tinnerRect = QRectF(QPointF(self.viewLeft,self.viewTop ), QSizeF(self.viewWidth, self.viewHeight))\n\t\tself.scene.addRect(innerRect, self.outerRectPen)\n\t\t# self.addPoint(QPointF(self.viewLeft, self.viewTop))\n\n\t\t# Draw the rectangle\n\t\tself.scene.addRect(outerRect, self.outerRectPen)\n\t\tself.view.setScene(self.scene)\n\n\tdef drawScheme(self, geom):\n\t\tself.geom = geom\n\n\t\t# Update graph\n\t\tself.updateView()\n\n\t\t# Setup ratio\n\t\tself.heightMax = self.geom['Ds']\n\t\tself.widthMax = self.geom['Ds']\n\n\t\tif self.viewHeight/self.heightMax < self.viewWidth/self.widthMax :\n\t\t\tself.ratio = self.viewHeight/self.heightMax\n\t\telse:\n\t\t\tself.ratio = self.viewWidth/self.widthMax\n\n\t\t#Change all inputs according to ratio\n\t\tself.shellDiam = self.geom['Ds']*self.ratio\n\t\tself.pipeDiam = self.geom['D']*self.ratio\n\t\tself.verPitch = self.geom['s']*self.ratio\n\t\tself.horPitch = self.geom['sh']*self.ratio\n\t\tself.Ncol = self.geom['Nt_col']\n\t\tself.Nrow = self.geom['Nt']\n\n\t\t# Draw shell\n\t\tself.drawShellCircle()\n\n\t\t# Draw the pipes\n\t\tself.drawPipesStaggered()\n\n\t\t# Draw axis\n\t\tself.drawAxis()\n\n\n\tdef drawShellCircle(self):\n\t\txHalfView = self.viewLeft + self.viewWidth/2\n\t\tyHalfView = self.viewTop + self.viewHeight/2\n\n\t\torigin = QPointF(xHalfView-self.shellDiam/2, yHalfView-self.shellDiam/2)\n\t\tsize = QSizeF(self.shellDiam, self.shellDiam)\n\t\tshellCircle = QRectF(origin, size)\n\n\t\tself.scene.addEllipse(shellCircle, self.pipeLinePen)\n\n\tdef drawAPipe(self, position):\n\t\tself.scene.addEllipse(position.x()-self.pipeDiam/2, position.y()-self.pipeDiam/2, \\\n\t\t\tself.pipeDiam, self.pipeDiam,\tself.pipeLinePen, self.pipeFillingBrush)\n\n\tdef drawPipesInline(self):\n\t\t# Start point\n\t\txHalfView = self.viewLeft + self.viewWidth/2\n\t\tyHalfView = self.viewTop + self.viewHeight/2\n\n\t\tif self.Ncol % 2 != 0:\n\t\t\txStart = xHalfView-(self.Ncol-1)/2*self.horPitch\n\t\telse:\n\t\t\txStart = xHalfView-(self.Ncol/2)*self.horPitch+self.horPitch/2\n\t\tif self.Nrow % 2 != 0:\n\t\t\tyStart = yHalfView-(self.Nrow-1)/2*self.verPitch\n\t\telse:\n\t\t\tyStart = yHalfView-(self.Nrow/2)*self.verPitch+self.verPitch/2\n\t\t\n\t\txPoint = xStart\n\t\tyPoint = yStart\n\n\t\tfor i in range(self.Ncol):\n\t\t\txPoint = xStart + self.horPitch*i\n\t\t\tfor j in range(self.Nrow):\n\t\t\t\tyPoint = yStart + self.verPitch*j\n\t\t\t\tpoint = QPointF(xPoint, yPoint)\n\t\t\t\tself.drawAPipe(point)\n\t\t\tyPoint = yStart \n\n\tdef drawPipesStaggered(self):\n\t\t# Start point\n\t\txHalfView = self.viewLeft + self.viewWidth/2\n\t\tyHalfView = self.viewTop + self.viewHeight/2\n\n\t\tif self.Ncol % 2 != 0:\n\t\t\txStart = xHalfView-(self.Ncol-1)/2*self.horPitch\n\t\telse:\n\t\t\txStart = xHalfView-(self.Ncol/2)*self.horPitch+self.horPitch/2\n\t\tif self.Nrow % 2 != 0:\n\t\t\tyStart = yHalfView-(self.Nrow-1)/2*self.verPitch\n\t\telse:\n\t\t\tyStart = yHalfView-(self.Nrow/2)*self.verPitch+self.verPitch/2\n\t\t\n\t\txPoint = xStart\n\t\tyPoint = yStart\n\t\tleft = True\n\n\t\ta = self.horPitch/self.pipeDiam\n\t\tb = self.verPitch/self.pipeDiam\n\n\t\t#Horizontal staggered detection :\n\t\tif (b >= 0.5*math.sqrt(2*a+1)):\n\t\t\tfor j in range(self.Nrow):\n\t\t\t\tyPoint = yStart + self.verPitch*j\n\t\t\t\tfor i in range(self.Ncol):\n\t\t\t\t\txPoint = xStart + self.horPitch*i\n\t\t\t\t\tpoint = QPointF(xPoint, yPoint)\n\t\t\t\t\tself.drawAPipe(point)\n\t\t\t\tif left:\n\t\t\t\t\txStart += self.horPitch/2\n\t\t\t\t\tleft = False\n\t\t\t\telse:\n\t\t\t\t\txStart -= self.horPitch/2\n\t\t\t\t\tleft = True\n\t\telse :\n\t\t\tfor i in range(self.Ncol):\n\t\t\t\t\txPoint = xStart + self.horPitch*i\n\t\t\t\t\tfor j in range(self.Nrow):\n\t\t\t\t\t\tyPoint = yStart + self.verPitch*j\n\t\t\t\t\t\tpoint = QPointF(xPoint, yPoint)\n\t\t\t\t\t\tself.drawAPipe(point)\n\t\t\t\t\tif left:\n\t\t\t\t\t\tyStart += self.verPitch/2\n\t\t\t\t\t\tleft = False\n\t\t\t\t\telse:\n\t\t\t\t\t\tyStart -= self.verPitch/2\n\t\t\t\t\t\tleft = True\n\n\t\n\n\tdef drawAxis(self):\n\t\txHalfView = self.viewLeft + self.viewWidth/2\n\t\tyHalfView = self.viewTop + self.viewHeight/2\n\n\t\t# y-axis\n\t\tstartLine = QPointF(xHalfView, self.viewTop)\n\t\tendLine = QPointF(xHalfView, self.viewBottom)\n\t\tline = QLineF(startLine, endLine)\n\t\tself.scene.addLine(line, self.axisLine)\n\n\t\t# x-axis\n\t\tstartLine = QPointF(xHalfView-self.shellDiam/2, yHalfView)\n\t\tendLine = QPointF(xHalfView+self.shellDiam/2, yHalfView)\n\t\tline = QLineF(startLine, endLine)\n\t\tself.scene.addLine(line, self.axisLine)\n\n\n\tdef addPoint(self, point):\n\t\tradius = 1.5\n\t\tself.scene.addEllipse(point.x()-radius, point.y()-radius, 2.0*radius, 2.0*radius,\\\n\t\t\tself.pipeLinePen)\n","sub_path":"drawing.py","file_name":"drawing.py","file_ext":"py","file_size_in_byte":11035,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"399153820","text":"# -*- coding: utf-8 -*-\n__author__ = 'admin'\n\n\nimport time\nimport random\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait\n# 登录脚本\nurl = 'http://portal.jc.yzw.cn.qa:8000/'\n# url = 'http://jctest.yzw.cn:16000/'\ndef login(user, pwd):\n\n driver = webdriver.Chrome()\n driver.get(url)\n # driver.maximize_window()\n driver.find_element_by_xpath('/html/body/div/div[3]/div/div/form/div[2]/input').send_keys(user)\n driver.find_element_by_xpath('/html/body/div/div[3]/div/div/form/div[3]/input').send_keys(pwd)\n driver.find_element_by_xpath('//*[@id=\"btnsubmit\"]').click()\n return driver\n\n# 报名\ndef sign_up(user, pwd, pid, sid):\n driver = login(user, pwd)\n time.sleep(1)\n url = 'http://vendor.yzw.cn.qa:8000/VendorPortal/Bidding/Detail?tenderSysNo='+pid+'&supplierSysNo='+sid\n # url = 'http://vendortest.yzw.cn:16000/VendorPortal/Bidding/Detail?tenderSysNo='+pid+'&supplierSysNo='+sid\n time.sleep(3)\n driver.get(url)\n time.sleep(2)\n try:\n driver.find_element_by_link_text('签收公告').click()\n time.sleep(2)\n except:\n pass\n try:\n driver.find_element_by_link_text('我要报名').click()\n except:\n driver.find_element_by_link_text('签收公告').click()\n time.sleep(4)\n driver.find_element_by_link_text('我要报名').click()\n time.sleep(1)\n driver.close()\n\n# 投标\ndef tender(user, pwd, pid, sid):\n driver = login(user, pwd)\n time.sleep(3)\n tender_url = 'http://vendor.yzw.cn.qa:8000/VendorPortal/Bidding/Detail?tenderSysNo='+pid+'&supplierSysNo='+sid\n # tender_url = 'http://vendortest.yzw.cn:16000/VendorPortal/Bidding/Detail?tenderSysNo='+pid+'&supplierSysNo='+sid\n time.sleep(2)\n driver.get(tender_url)\n time.sleep(2)\n try:\n driver.find_element_by_link_text('签收招标文件').click()\n time.sleep(2)\n driver.find_element_by_link_text('签收招标文件').click()\n time.sleep(2)\n except :\n pass\n time.sleep(3)\n driver.find_element_by_link_text('我的投标文件').click()\n time.sleep(2)\n driver.find_element_by_link_text('我的投标文件').click()\n time.sleep(2)\n el = driver.find_elements_by_class_name('input-sm')\n try:\n for i in range(len(el)):\n el[i].clear()\n el[i].send_keys(random.randint(-100, 200))\n time.sleep(1)\n except:\n for i in range(len(el)):\n el[i].clear()\n el[i].send_keys(random.randint(-100, 200))\n time.sleep(1)\n driver.find_element_by_xpath('//div/a/span[text()=\"投标\"]').click()\n time.sleep(1)\n #driver.close()\n\n\nif __name__ == '__main__':\n tender('sup100', '111111', '539202', '1004120')\n # login('1', '2')\n","sub_path":"yzwauto/login/login.py","file_name":"login.py","file_ext":"py","file_size_in_byte":2767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"338313188","text":"r'''\nFile: Blog\\app\\utils\\user_tools.py\nProject: Blog\nCreated Date: 2017-05-03 00:07:54\nAuthor: NaccOll\n-----\nModified By: NaccOll\nLast Modified: 2017-05-10 01:50:17\n-----\n'''\n#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n\n\nfrom flask_restful import reqparse\nfrom app.models import User\n\n\ndef user_exist(args: dict)->(dict, int, User or None):\n \"\"\"用户存在验证\"\"\"\n username = args.get(\"username\")\n user_id = args.get(\"id\")\n user = None\n if not username and not user_id:\n return {\"message\": \"请提供用户名或者用户ID\"}, 400, user\n\n if user_id:\n user = User.get_by_id(user_id)\n if not user:\n user = User.query.filter_by(username=username).first()\n if not user:\n return {\"message\": \"该用户不存在\"}, 404, user\n return {\"message\": \"该用户已存在\"}, 200, user\n","sub_path":"app/utils/user_tools.py","file_name":"user_tools.py","file_ext":"py","file_size_in_byte":834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"462752754","text":"import os\r\nfrom neuromancer import arg\r\nimport argparse\r\nfrom neuromancer import datasets\r\n\r\nparser = argparse.ArgumentParser()\r\nparser.add_argument('-gpu', type=int, default=None)\r\nargs = parser.parse_args()\r\nif args.gpu is not None:\r\n gpu = f'-gpu {args.gpu}'\r\n\r\np = arg.ArgParser(parents=[arg.log(), arg.opt(), arg.data(), arg.loss(), arg.lin(), arg.ssm()])\r\noptions = {k: v.choices for k, v in p._option_string_actions.items() if v.choices is not None and k != '-norm'}\r\nsystems = list(datasets.systems.keys())\r\nfor i, (k, v) in enumerate(options.items()):\r\n for j, opt in enumerate(v):\r\n print(k, opt)\r\n code = os.system(f'python ../train_scripts/system_id.py -norm Y '\r\n f'{k} {opt} -epochs 1 -nsteps 8 -verbosity 1 -nsim 128 -system {systems[(i*j) % len(systems)]}')\r\n assert code == 0, f'Failure on flag {k} with value {opt}.'","sub_path":"tests/test_system_id.py","file_name":"test_system_id.py","file_ext":"py","file_size_in_byte":885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"441876791","text":"from flask import Blueprint, render_template, request\nimport mysql.connector\n\nstaff_api = Blueprint('staff_api',__name__)\n\n#by Peter Pfoertsch,250741344\n#for CS3319 Assignment 3\n\n#Staff View\n@staff_api.route(\"/staff\")\ndef staff():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n \n query = (\"show tables\")\n cursor.execute(query)\n returnString = []\n for i in cursor:\n print(i)\n returnString.append(i)\n \n cursor.close()\n cnx.close()\n \n return render_template('staffIndex.html', data=returnString) \n \n#intermediate pages that set up the site to execute the right sql statement\n\n#edit is the add, it was originally going to be update, but I switched it over\n# for sake of consistency, I kept it edit, I know it's not good coding practice, but I had little time \n#it is not specified wether you should not allow someone to change primary key attributes, so I allowed them to change it\n@staff_api.route(\"/edit\")\ndef edit():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n \n #get all columns to show all categories\n type = str(request.args.get('type'))\n query = (\"show columns from %s\" % type)\n cursor.execute(query)\n \n returnString = []\n for i in cursor:\n print(i)\n returnString.append(i)\n \n cursor.close()\n cnx.close()\n \n #return template that gives input box for each condition and it's submit is the relevent add\n return render_template('edit.html', name=type,command=\"/add%s\" % type, data=returnString) \n\n#update is the page that sets up the update\n#you can only change one attribute at a time for a single tuple \n@staff_api.route(\"/update\")\ndef update():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n \n type = str(request.args.get('type'))\n key = str(request.args.get('key'))\n query = (\"show columns from %s\" % type)\n cursor.execute(query)\n \n returnString = []\n for i in cursor:\n print(i)\n returnString.append(i)\n \n cursor.close()\n cnx.close()\n \n return render_template('update.html', name=type,command=\"/modify%s\" % type, data=returnString,key=key)\n\n \n#I couldn't figure out how to make one show, add, delete and modify method for all tables, so I made each one seperately\n#they all have the same structure, with me first defining the basic structure and then adding in the relevant details from the sources\n#I sometimes get the data from the url, just to keep track of things better\n#movies methods\n@staff_api.route(\"/showMovie\")\ndef showMovie():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n \n query = (\"select idMovie,MovieName,MovieYear from Movie order by MovieName\")\n cursor.execute(query) \n \n returnString = [] \n for i in cursor:\n print(i)\n returnString.append(i)\n \n #add the columns you need\n query = (\"show columns from Movie\")\n cursor.execute(query)\n \n columns = []\n for i in cursor:\n print(i)\n columns.append(i)\n \n cursor.close()\n cnx.close()\n return render_template('table.html', type=\"Movie\", data=returnString, columns=columns)\n\n #add\n@staff_api.route(\"/addMovie\", methods=[\"POST\"])\ndef addMovie():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n insert_stmt = (\n \"INSERT INTO Movie(idMovie, MovieName, MovieYear) \"\n \"VALUES (%s,%s,%s)\"\n )\n data = (request.form['idMovie'], request.form['MovieName'], request.form['MovieYear'])\n print(data)\n cursor.execute(insert_stmt, data)\n \n cnx.commit()\n cursor.close()\n cnx.close()\n return render_template('success.html', type=\"Movie\")\n\n #delete\n@staff_api.route(\"/deleteMovie\", methods=[\"POST\"])\ndef deleteMovie():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n data = str(request.form['idMovie'])\n delete_stmt = (\"delete from Movie where idMovie=%s\" % data)\n \n #added try catch here because of the foreign key error\n try: \n cursor.execute(delete_stmt)\n except mysql.connector.errors.IntegrityError as e:\n print(e)\n return render_template('error.html', error=e)\n \n cnx.commit()\n cursor.close()\n cnx.close()\n \n return render_template('success.html', type=\"Movie\")\n \n #modify\n@staff_api.route(\"/modifyMovie\", methods=[\"POST\"])\ndef modifyMovie():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n modify_stmt = (\n \"update Movie set %s='%s' where idMovie='%s'\"\n ) % (request.form['attribute'], request.form['value'],request.form['idMovie'])\n print(modify_stmt)\n cursor.execute(modify_stmt)\n \n cnx.commit()\n cursor.close()\n cnx.close()\n \n return render_template('success.html', type=\"Movie\")\n\n#Genres methods\n@staff_api.route(\"/showGenre\")\ndef showGenre():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n \n query = (\"select Movie_idMovie, MovieName,Genre from Genre join Movie on Genre.Movie_idMovie=Movie.idMovie\")\n cursor.execute(query)\n \n returnString = []\n for i in cursor:\n print(i)\n returnString.append(i)\n query = (\"show columns from Genre\")\n cursor.execute(query)\n \n #get the columns for the table header\n columns = []\n for i in cursor:\n print(i)\n columns.append(i)\n \n cursor.close()\n cnx.close()\n return render_template('genreTable.html', type=\"Genre\",data=returnString, columns=columns)\n \n #add\n@staff_api.route(\"/addGenre\", methods=[\"POST\"])\ndef addGenre():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n insert_stmt = (\n \"INSERT INTO Genre(Genre, Movie_idMovie) \"\n \"VALUES (%s,%s)\"\n )\n data = (request.form['Genre'], request.form['Movie_idMovie'])\n print(data)\n cursor.execute(insert_stmt, data)\n \n cnx.commit()\n cursor.close()\n cnx.close()\n return render_template('success.html', type=\"Genre\")\n\n #delete\n@staff_api.route(\"/deleteGenre\", methods=[\"POST\"])\ndef deleteGenre():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n data = str(request.form['idGenre'])\n delete_stmt = (\"delete from Genre where Movie_idMovie='%s'\" % data)\n\n cursor.execute(delete_stmt)\n cnx.commit()\n cursor.close()\n cnx.close()\n \n return render_template('success.html', type=\"Genre\")\n \n#Rooms methods\n@staff_api.route(\"/showTheatreRoom\")\ndef showTheatreRoom():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n \n query = (\"select roomNumber,Capacity from TheatreRoom\")\n cursor.execute(query)\n \n returnString = []\n for i in cursor:\n print(i)\n returnString.append(i)\n \n query = (\"show columns from TheatreRoom\")\n cursor.execute(query)\n #get the columns for the table header \n columns = []\n for i in cursor:\n print(i)\n columns.append(i)\n \n cursor.close()\n cnx.close()\n return render_template('table.html', data=returnString, type=\"TheatreRoom\", columns=columns)\n \n #add\n@staff_api.route(\"/addTheatreRoom\", methods=[\"POST\"])\ndef addTheatreRoom():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n insert_stmt = (\n \"INSERT INTO TheatreRoom(RoomNumber, Capacity) \"\n \"VALUES (%s,%s)\"\n )\n data = (request.form['RoomNumber'], request.form['Capacity'])\n print(insert_stmt, data)\n \n cursor.execute(insert_stmt,data)\n cnx.commit()\n cursor.close()\n cnx.close()\n \n return render_template('success.html', type=\"TheatreRoom\")\n\n #delete\n@staff_api.route(\"/deleteTheatreRoom\", methods=[\"POST\"])\ndef deleteTheatreRoom():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n data = (request.form['idTheatreRoom'])\n delete_stmt = (\"delete from TheatreRoom where RoomNumber=%s\" % data)\n print(delete_stmt, data)\n cursor.execute(delete_stmt)\n \n cnx.commit()\n cursor.close()\n cnx.close()\n return render_template('success.html', type=\"TheatreRoom\")\n \n #modify\n@staff_api.route(\"/modifyTheatreRoom\", methods=[\"POST\"])\ndef modifyTheatreRoom():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n data = (request.form['attribute'], request.form['value'],request.form['idTheatreRoom'])\n modify_stmt = (\"update TheatreRoom set %s=%s where RoomNumber=%s\" % data)\n cursor.execute(modify_stmt)\n \n cnx.commit()\n cursor.close()\n cnx.close()\n return render_template('success.html', type=\"TheatreRoom\")\n \n#Showings methods\n@staff_api.route(\"/showShowing\")\ndef showShowing():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n \n query = (\"select idShowing,date_format(ShowingDateTime,'%m-%e-%Y %H:%i:%S'),Movie_idMovie,TheatreRoom_RoomNumber,TicketPrice \" \n \"from Showing order by ShowingDateTime\")\n cursor.execute(query)\n \n returnString = []\n for i in cursor:\n print(i)\n returnString.append(i)\n \n query = (\"show columns from Showing\")\n cursor.execute(query)\n #get the columns for the table header \n columns = []\n for i in cursor:\n print(i)\n columns.append(i) \n \n cursor.close()\n cnx.close()\n return render_template('table.html', data=returnString , type=\"Showing\", columns=columns)\n \n #add\n@staff_api.route(\"/addShowing\", methods=[\"POST\"])\ndef addShowing():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n insert_stmt = (\n \"INSERT INTO Showing(idShowing,ShowingDateTime,Movie_idMovie,TheatreRoom_RoomNumber,TicketPrice) \"\n \"VALUES (%s, %s, %s, %s, %s)\"\n )\n data = (request.form['idShowing'], request.form['ShowingDateTime'], \n request.form['Movie_idMovie'], request.form['TheatreRoom_RoomNumber'],\n request.form['TicketPrice'])\n cursor.execute(insert_stmt, data)\n \n cnx.commit()\n cursor.close()\n cnx.close()\n return render_template('success.html', type=\"Showing\")\n \n #delete\n@staff_api.route(\"/deleteShowing\", methods=[\"POST\"])\ndef deleteShowing():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n data = (request.form['idShowing'])\n delete_stmt = (\"delete from Showing where idShowing=%s\" % data)\n #added try catch here because of the foreign key error\n try: \n cursor.execute(delete_stmt)\n except mysql.connector.errors.IntegrityError as e:\n print(e)\n return render_template('error.html', error=e)\n \n cnx.commit()\n cursor.close()\n cnx.close()\n return render_template('success.html', type=\"Showing\")\n \n #modify\n@staff_api.route(\"/modifyShowing\", methods=[\"POST\"])\ndef modifyShowing():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n data = (request.form['attribute'], request.form['value'],request.form['idShowing'])\n modify_stmt = (\"update Showing set %s='%s' where idShowing='%s'\" % data)\n \n cursor.execute(modify_stmt)\n \n cnx.commit()\n cursor.close()\n cnx.close()\n return render_template('success.html', type=\"Showing\")\n \n#Customers methods\n@staff_api.route(\"/showCustomer\")\ndef showCustomer():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n \n query = (\"select idCustomer,LastName,FirstName,EmailAddress,cast(Sex as char(1)) from Customer order by LastName\")\n cursor.execute(query)\n \n returnString = []\n for i in cursor:\n print(i)\n returnString.append(i)\n \n query = (\"show columns from Customer\")\n cursor.execute(query)\n #get the columns for the table header \n columns = []\n for i in cursor:\n print(i)\n columns.append(i)\n \n cursor.close()\n cnx.close()\n return render_template('table.html', data=returnString , type=\"Customer\", columns=columns)\n \n #add\n@staff_api.route(\"/addCustomer\", methods=[\"POST\"])\ndef addCustomer():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre' )\n cursor = cnx.cursor()\n insert_stmt = (\n \"INSERT INTO Customer(idCustomer,FirstName,LastName,EmailAddress,Sex) \"\n \"VALUES (%s, %s, %s, %s, %s)\"\n )\n data = (request.form['idCustomer'], request.form['FirstName'], request.form['LastName'], request.form['EmailAddress'], request.form['Sex'])\n cursor.execute(insert_stmt, data)\n cnx.commit()\n cursor.close()\n cnx.close()\n return render_template('success.html', type=\"Customer\")\n\n #delete\n@staff_api.route(\"/deleteCustomer\", methods=[\"POST\"])\ndef deleteCustomer():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n delete_stmt = (\"delete from Customer where idCustomer=(%s)\")\n data = (request.form['idCustomer'],)\n cursor.execute(delete_stmt, data)\n cnx.commit()\n cursor.close()\n cnx.close()\n return render_template('success.html', type=\"Customer\")\n \n #modify\n@staff_api.route(\"/modifyCustomer\", methods=[\"POST\"])\ndef modifyCustomer():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n data = (request.form['attribute'], request.form['value'],request.form['idCustomer'])\n modify_stmt = (\"update Customer set %s='%s' where idCustomer='%s'\" % data)\n \n cursor.execute(modify_stmt)\n cnx.commit()\n cursor.close()\n cnx.close()\n return render_template('success.html', type=\"Customer\")\n \n#show Attendings\n@staff_api.route(\"/showAttend\")\ndef showAttend():\n cnx = mysql.connector.connect(user='root', database='MovieTheatre')\n cursor = cnx.cursor()\n \n query = (\"select idShowing,Rating,FirstName,LastName, date_format(ShowingDateTime,'%m-%e-%Y %H:%i:%S'), idMovie, MovieName from Attend \"\n \"join Customer on Customer_idCustomer=idCustomer \"\n \"join Showing on Showing_idShowing=idShowing \"\n \"join Movie on Movie_idMovie=idMovie \"\n \"order by Rating\")\n cursor.execute(query)\n \n returnString = []\n for i in cursor:\n print(i)\n returnString.append(i)\n \n cursor.close()\n cnx.close()\n return render_template('staticTable.html', data=returnString , type=\"Attend\")","sub_path":"staff.py","file_name":"staff.py","file_ext":"py","file_size_in_byte":14735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"526121826","text":"# -*- coding: utf-8 -*-\nfrom datetime import datetime\n# autenticação do simbolo para a jogada humano \n\ndef simb_humano():\n simbH= input(\"Olá humano, informe o simbolo que deseja utilizar para a partida: X ou O : \")\n while simbH!=\"X\" and simbH!=\"x\" and simbH!=\"O\" and simbH!=\"o\" :\n print (\"Ops! Simbolo inválido\")\n simbH= input(\"Informe um simbolo válido que deseja utilizar para a partida: X ou O : \")\n \n#sorteio\ndef sorteio ():\n now= datetime.now()\n a=now.minute\n if a%2==0:\n print(\"Quem inicia a partida, é você... Boa jogada\")\n else:\n print(\"Quem inicia a partida, é máquina. Observe.\")\n\n\n#Função para printar os simbolos no tabuleiro:\n\n \n \n \n \n\n \n \n \n \n\n","sub_path":"moodledata/vpl_data/429/usersdata/309/97666/submittedfiles/jogoDaVelha_BIB.py","file_name":"jogoDaVelha_BIB.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"431830520","text":"# -*- coding:utf-8 -*- \n'''\n__author__:liubin \n\n'''\n\n\nfrom selenium.webdriver.common.by import By\nfrom .base import BasePage\nimport time\nfrom common.logger import Log\nfrom common.getlogger import get_logger\n\nclass BuynowPage(BasePage):\n\n log = get_logger()\n\n\n #秒杀按钮\n buynow_button = (By.LINK_TEXT, \"秒杀活动\")\n\n iframe_id = 'mallmanage_index'\n\n #添加按钮\n\n addbuynow_button = (By.XPATH, '//*[@class=\"button button-primary mr10\"]')\n\n #x[0].click()\n\n # 添加秒杀\n\n vname_input = (By.ID, \"vname\")\n\n vquantity_input = (By.ID, \"vquantity\")\n\n productTitle_input = (By.ID, \"productTitle\")\n\n #productId_input = (By.NAME, \"54\")\n\n vupperLimit_input = (By.ID, \"vupperLimit\")\n\n vbuyNowPrice_input = (By.ID, \"vbuyNowPrice\")\n\n #提交\n\n submit_button = (By.XPATH, \"html/body/div[3]/div/table/tbody/tr[3]/td/div[2]/button[2]\")\n\n\n def __init__(self,driver):\n\n BasePage.__init__(self,driver)\n\n\n def open_buynowpage(self):\n '''点击打开秒杀页面'''\n self.click(self.buynow_button)\n self.log.info('打开秒杀界面')\n\n def switch_to_frame(self):\n '''切换到iframe'''\n self.switch_frame(self.iframe_id)\n self.log.info('切换到iframe')\n\n def click_add_btn(self):\n '''点击添加按钮'''\n self.click(self.addbuynow_button)\n self.log.info('点击添加秒杀')\n\n time.sleep(2)\n\n def add_buynow(self,vname,vquantity,proid,vupperLimit,vbuyNowPrice,starttime,endtime):\n '''添加秒杀'''\n\n # 输入活动名称\n\n self.send_keys(self.vname_input, vname)\n self.log.info('输入活动名')\n\n # 输入活动每人限购\n self.send_keys(self.vquantity_input, vquantity)\n self.log.info('输入每人限购')\n\n # 点击产品选择框按钮\n self.click(self.productTitle_input)\n\n # 点击添加按钮\n\n productId_input = (By.NAME, proid)\n self.click(productId_input)\n time.sleep(3)\n\n # 输入产品限购数量\n time.sleep(2)\n self.send_keys(self.vupperLimit_input, vupperLimit)\n time.sleep(3)\n\n # 输入秒杀价\n\n self.send_keys(self.vbuyNowPrice_input, vbuyNowPrice)\n\n # 输入活动开始结束时间\n\n js1 = \"document.getElementById('vstartTime').value=\\\"\" + starttime + \"\\\";\"\n js2 = \"document.getElementById('vendTime').value=\\\"\" + endtime + \"\\\";\"\n print(starttime)\n print(js1)\n\n self.js_execute(js1)\n self.js_execute(js2)\n\n time.sleep(3)\n\n # 活动提交\n\n self.click(self.submit_button)\n\n time.sleep(10)\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n","sub_path":"pages/buynow_page.py","file_name":"buynow_page.py","file_ext":"py","file_size_in_byte":2689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"403684041","text":"#!/usr/bin/env python\n# -*- coding:utf8 -*-\nimport pymysql\n\n\nclass SQLUtils(object):\n def __init__(self):\n print(\"init=====sql=\")\n self.dataBases = pymysql.connect(\"39.108.177.124\", \"wangtao\", \"PPYY0264\", \"ocr_data\", charset='utf8')\n self.cursor = self.dataBases.cursor()\n self.tbl_file = \"tbl_file\"\n self.tbl_ocr = \"tbl_ocr\"\n\n def exeSelect(self, tbl, where):\n sql = \"select * from \" + tbl + \" where \" + where\n self.cursor.execute(where)\n\n def addTableFile(self, fileBean):\n sql = \"insert into \" + self.tbl_file + \"(\" + fileBean.__getParaStr__() + \") values(\" + fileBean.__str__() + \")\";\n print(sql)\n try:\n self.cursor.execute(sql)\n self.dataBases.commit()\n return int(self.cursor.lastrowid)\n except:\n self.dataBases.rollback()\n print(\"exception====\")\n return -1\n\n def addTableOcr(self, OcrBean):\n sql = \"insert into \" + self.tbl_ocr + \"(\" + OcrBean.__getParaStr__() + \") values(%s,%s,%s,%s,%s,%s)\"\n print(sql)\n try:\n self.cursor.execute(sql, (OcrBean.content, OcrBean.file_id, OcrBean.key_word, OcrBean.type,\n OcrBean.search_url, OcrBean.create_date))\n self.dataBases.commit()\n return int(self.cursor.lastrowid)\n except Exception as e:\n self.dataBases.rollback()\n print(\"exception====\")\n return -1\n\n def closedAll(self):\n print(\"close-====\")\n self.cursor.close()\n\n def deleteFile(self, index):\n sql = \"DELETE FROM tbl_file WHERE _id=\" + index\n try:\n self.cursor.execute(sql)\n self.dataBases.commit()\n except:\n self.dataBases.rollback()\n print(\"exception====\")\n","sub_path":"sql/SQLUtils.py","file_name":"SQLUtils.py","file_ext":"py","file_size_in_byte":1833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"7471971","text":"\"\"\"\n--------------\n!! Be careful\n--------------\n### 1. Make a copy (take backup) of the SVN folder on your local drive\n### 2. Change \"Commit_State\" to \"Update\" if want to update the files\n Or change to \"Add\" if you are creating place holders or adding file afresh\n---------------------------------------------------------------\n\nHow does this script works\n---------------------------\nReads CSV file viz 'xha_SVN_list.csv' that contains the list of files to be commited to svn,\nAccess their respective \"SVN path\" from CSV data\nGets the file from \"Local_Path\" local source path given in CSV data\nand copies it to \"SVN_Path\"\n----------------------------\n\"\"\"\n\ndef csv_writer(data, path):\n \"\"\"\n Write data to a CSV file path\n \"\"\"\n with open(path, \"w\", newline='') as csv_file:\n writer = csv.writer(csv_file, delimiter=',')\n for line in data:\n writer.writerow(line)\n\n#----------------------------------------------------------------------\ndef csv_dict_reader(file_obj):\n firstTime =True\n \"\"\"\n Read a CSV file using csv.DictReader\n \"\"\"\n myList = []\n reader = csv.DictReader(file_obj, delimiter=',')\n\n for line in reader:\n d =[]\n keys, values = zip(*line.items())\n for i, k in enumerate(values):\n d.append(k)\n myList.append(d)\n return myList\n#----------------------------------------------------------------------\ndef copyFile (srcname,dstname):\n try:\n my_file = Path(dstname)\n if my_file.is_file():\n # file exists then remove it\n try:\n os.remove(dst_file)\n except PermissionError as exc:\n os.chmod(dst_file, stat.S_IWUSR)\n os.remove(dst_file)\n # Copy file\n result = copy2(srcname, dstname)\n if result.is_file():\n print(result.is_file())\n result = \"Copied\"\n return result\n\n except OSError as why:\n return errors.append((srcname, dstname, str(why)))\n # catch the Error from the recursive copytree so that we can\n # continue with other files\n except Error as err:\n return errors.extend(err.args[0])\n#----------------------------------------------------------------------\nif __name__ == \"__main__\":\n import sys\n import csv\n from operator import itemgetter\n import os\n from shutil import copy2\n from pathlib import Path\n\n #-----------------------------------------------------------\n # Values that needs to be chabged are bellow\n #-----------------------------------------------------------\n file_names = ['xha','halloweenusa']\n Local_soruce_dir = '/Users/anandihalli/Documents/01_Work/halloweenusa'\n #-----------------------------------------------------------\n # Values that should be constant across all the projects for user\n #-----------------------------------------------------------\n svn_dir = '/Users/anandihalli/Documents/SVN'\n #-----------------------------------------------------------\n # Values that some time needs to be changed\n #-----------------------------------------------------------\n reportFolder = 'Reports'\n filesToSVN_list = 'xha_SVN_list.csv'\n #-----------------------------------------------------------\n # Values that needs to be chabged end\n #-----------------------------------------------------------\n\n if (os.path.isdir(Path(Local_soruce_dir))):\n report_path = Local_soruce_dir +'/'+reportFolder\n if (os.path.isdir(Path(report_path))):\n csv_path = report_path +'/'+filesToSVN_list\n else:\n print(\"Not found \", report_path)\n sys.exit(1)\n else:\n print(\"Not found \", Local_soruce_dir)\n sys.exit(1)\n\n if (os.path.isdir(Path(svn_dir)) == False):\n print(\"Not found \", svn_dir)\n sys.exit(1)\n\n myList_SVN = []\n with open(csv_path) as f_obj:\n myList_SVN = csv_dict_reader(f_obj)\n\n reportData = [['File_Name','SVN_Path','Local_Path','Commit_State']]\n # report\n\n for val in myList_SVN:\n\n try:\n #file_tobe_copied = val[0]\n target_path = os.path.join(svn_dir + val[1])\n source_path = os.path.join(Local_soruce_dir + val[2]+'/'+val[0])\n #print(target_path,source_path)\n if 'Add' or 'Update' or 'Not Commited' == val[3] :\n my_folder = Path(target_path)\n if my_folder.is_dir():\n #print(\" ---- \",os.path.isfile(source_path),source_path)\n if(os.path.isfile(source_path)):\n #print(os.path.isfile(source_path))\n print(source_path)\n myResult = copyFile (source_path,target_path)\n\n reportData.append([val[0],val[1],val[2],myResult])\n else:\n reportData.append([val[0],val[1],val[2],\"Target Folder not found\"])\n else:\n reportData.append([val[0],val[1],val[2],val[3]])\n except:\n reportData.append([val[0],val[1],val[2],val[3] + \" Exception\"])\n pass\n\n path = report_path+\"/\"+file_names[0]+\"_SVN_commit_report.csv\"\n csv_writer(reportData, path)\n","sub_path":"Pyhton/Commit_to_SVN/Commit_To_SVN_v4.py","file_name":"Commit_To_SVN_v4.py","file_ext":"py","file_size_in_byte":5215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"552147658","text":"\"\"\"\nDQN and its variants\n------------------------\nWe implement Double DQN, Dueling DQN and Noisy DQN here.\nThe max operator in standard DQN uses the same values both to select and to\nevaluate an action by\nQ(s_t, a_t) = R_{t+1} + \\gamma * max_{a}Q_{tar}(s_{t+1}, a).\nDouble DQN propose to use following evaluation to address overestimation problem\nof max operator:\nQ(s_t, a_t) = R_{t+1} + \\gamma * Q_{tar}(s_{t+1}, max_{a}Q(s_{t+1}, a)).\nDueling DQN uses dueling architecture where the value of state and the advantage\nof each action is estimated separately.\nNoisy DQN propose to explore by adding parameter noises.\nReference:\n------------------------\n1. Double DQN\n Van Hasselt H, Guez A, Silver D. Deep reinforcement learning with double\n q-learning[C]//Thirtieth AAAI Conference on Artificial Intelligence. 2016.\n2. Dueling DQN\n Wang Z, Schaul T, Hessel M, et al. Dueling network architectures for deep\n reinforcement learning[J]. arXiv preprint arXiv:1511.06581, 2015.\n3. Noisy DQN\n Plappert M, Houthooft R, Dhariwal P, et al. Parameter space noise for\n exploration[J]. arXiv preprint arXiv:1706.01905, 2017.\nEnvironment:\n------------------------\nCartpole and Pong in OpenAI Gym\nRequirements:\n------------------------\ntensorflow>=2.0.0a0\ntensorlayer>=2.0.0\nTo run:\n------------------------\npython tutorial_DQN_variantes.py --mode=train\npython tutorial_DQN_variantes.py --mode=test --save_path=dqn_variants/8000.npz\n\"\"\"\nimport argparse\nimport datetime\nimport os\nimport operator\nimport random\nimport time\n\nimport gym\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport tensorflow as tf\n\nimport tensorlayer as tl\n\nfrom algorithm.rl.env.InstanceEnv import InstanceEnv\nfrom algorithm.rl.env.InstanceEnvCNN import InstanceEnvCNN\nfrom algorithm.rl.heuristic.HeuristicScheduleManager import HeuristicScheduleManager\nfrom instance.domain.solution.InstanceSolution import InstanceSolution\nfrom util.FileHandle import FileHandle\nfrom util.InstanceUtil import InstanceUtil\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--train', dest='train', action='store_true', default=True)\nparser.add_argument('--test', dest='test', action='store_true', default=False)\nparser.add_argument(\n '--save_path', default=None, help='folder to save if mode == train else model path,'\n 'qnet will be saved once target net update'\n)\nparser.add_argument('--seed', help='random seed', type=int, default=100)\nparser.add_argument('--env_id', default='CartPole-v0', help='CartPole-v0 or PongNoFrameskip-v4')\nparser.add_argument('--noisy_scale', type=float, default=0)\nparser.add_argument('--disable_double', action='store_true', default=False)\nparser.add_argument('--disable_dueling', action='store_true', default=False)\nparser.add_argument('--prioritized_replay', action='store_true', default=False)\nargs = parser.parse_args()\n\nrandom.seed(args.seed)\nnp.random.seed(args.seed)\ntf.random.set_seed(args.seed) # reproducible\nfileHandle = FileHandle()\n\n# env = gym.make(env_id)\ninstances = fileHandle.readInstance('ft_instances', 'train')\n# for instance in instances:\ninstance = instances[0]\nenv_id = instance.instanceUid\nenv = InstanceEnvCNN(instance)\n\nenv.seed(args.seed)\nnoise_scale = args.noisy_scale\ndouble = not args.disable_double\ndueling = not args.disable_dueling\nprioritized_replay = not args.prioritized_replay\nstep = 0\nalg_name = 'DQN'\nif dueling: alg_name = 'Dueling_' + alg_name\nif double: alg_name = 'Double_' + alg_name\nif noise_scale != 0: alg_name = 'Noisy_' + alg_name\nif prioritized_replay: alg_name = alg_name + '_with_prioritized_replay'\nprint(alg_name)\n\n\n# ############################## Network ####################################\nclass MLP(tl.models.Model):\n\n def __init__(self, name):\n super(MLP, self).__init__(name=name)\n self.h1 = tl.layers.Dense(300, tf.nn.tanh, in_channels=in_dim[0], name=\"h1\")\n self.h2 = tl.layers.Dense(400, tf.nn.tanh, in_channels=300, name=\"h2\")\n self.h3 = tl.layers.Dense(100, tf.nn.tanh, in_channels=400, name=\"h3\")\n\n self.qvalue = tl.layers.Dense(out_dim, in_channels=100, name='mlpq', W_init=tf.initializers.GlorotUniform())\n self.svalue = tl.layers.Dense(1, in_channels=100, name='mlps', W_init=tf.initializers.GlorotUniform())\n self.noise_scale = 0\n\n def forward(self, ni):\n feature = self.h1(ni)\n feature = self.h2(feature)\n feature = self.h3(feature)\n # apply noise to all linear layer\n if self.noise_scale != 0:\n noises = []\n for layer in [self.qvalue, self.svalue]:\n for var in layer.trainable_weights:\n noise = tf.random.normal(tf.shape(var), 0, self.noise_scale)\n noises.append(noise)\n var.assign_add(noise)\n\n qvalue = self.qvalue(feature)\n svalue = self.svalue(feature)\n\n if self.noise_scale != 0:\n idx = 0\n for layer in [self.qvalue, self.svalue]:\n for var in layer.trainable_weights:\n var.assign_sub(noises[idx])\n idx += 1\n\n if dueling:\n # dueling network\n return svalue + qvalue - tf.reduce_mean(qvalue, 1, keepdims=True)\n else:\n return qvalue\n\n\nclass CNN(tl.models.Model):\n def __init__(self, name):\n super(CNN, self).__init__(name=name)\n h, w, in_channels = in_dim\n dense_in_channels = 16 * (h - 0) * (w - 0)\n self.conv1 = tl.layers.Conv2d(\n 16, (1, 1), (1, 1), tf.nn.relu, 'SAME', in_channels=in_channels, name='conv2d_1',\n W_init=tf.initializers.GlorotUniform()\n )\n self.pool1 = tl.layers.MeanPool2d((1, 1), (1, 1), 'VALID', name='pool2d_1')\n self.flatten = tl.layers.Flatten(name='flatten123')\n '''\n self.conv2 = tl.layers.Conv2d(\n 16, (2, 2), (1, 1), tf.nn.relu, 'VALID', in_channels=32, name='conv2d_2',\n W_init=tf.initializers.GlorotUniform()\n )\n \n \n self.conv3 = tl.layers.Conv2d(\n 64, (3, 3), (1, 1), tf.nn.relu, 'VALID', in_channels=64, name='conv2d_3',\n W_init=tf.initializers.GlorotUniform()\n )'''\n\n self.preq = tl.layers.Dense(\n 512, tf.nn.relu, in_channels=dense_in_channels, name='pre_q', W_init=tf.initializers.GlorotUniform()\n )\n self.qvalue = tl.layers.Dense(out_dim, in_channels=512, name='cnnq',\n W_init=tf.initializers.GlorotUniform())\n self.pres = tl.layers.Dense(\n 512, tf.nn.relu, in_channels=dense_in_channels, name='pre_s', W_init=tf.initializers.GlorotUniform()\n )\n self.svalue = tl.layers.Dense(1, in_channels=512, name='cnns', W_init=tf.initializers.GlorotUniform())\n self.noise_scale = 0\n\n def forward(self, ni):\n # feature = self.flatten(self.conv3(self.conv2(self.conv1(ni))))\n # feature = self.flatten(self.conv2(self.conv1(ni)))\n feature = self.flatten(self.conv1(ni))\n # apply noise to all linear layer\n if self.noise_scale != 0:\n noises = []\n for layer in [self.preq, self.qvalue, self.pres, self.svalue]:\n for var in layer.trainable_weights:\n noise = tf.random.normal(tf.shape(var), 0, self.noise_scale)\n noises.append(noise)\n var.assign_add(noise)\n\n qvalue = self.qvalue(self.preq(feature))\n # qvalue = self.qvalue(feature)\n svalue = self.svalue(self.pres(feature))\n\n if self.noise_scale != 0:\n idx = 0\n for layer in [self.preq, self.qvalue, self.pres, self.svalue]:\n for var in layer.trainable_weights:\n var.assign_sub(noises[idx])\n idx += 1\n\n if dueling:\n # dueling network\n return svalue + qvalue - tf.reduce_mean(qvalue, 1, keepdims=True)\n else:\n return qvalue\n\n\nclass SegmentTree(object):\n\n def __init__(self, capacity, operation, neutral_element):\n \"\"\"Build a Segment Tree data structure.\n https://en.wikipedia.org/wiki/Segment_tree\n Can be used as regular array, but with two\n important differences:\n a) setting item's value is slightly slower.\n It is O(lg capacity) instead of O(1).\n b) user has access to an efficient ( O(log segment size) )\n `reduce` operation which reduces `operation` over\n a contiguous subsequence of items in the array.\n Paramters\n ---------\n capacity: int\n Total size of the array - must be a power of two.\n operation: lambda obj, obj -> obj\n and operation for combining elements (eg. sum, max)\n must form a mathematical group together with the set of\n possible values for array elements (i.e. be associative)\n neutral_element: obj\n neutral element for the operation above. eg. float('-inf')\n for max and 0 for sum.\n \"\"\"\n assert capacity > 0 and capacity & (capacity - 1) == 0, \\\n \"capacity must be positive and a power of 2.\"\n self._capacity = capacity\n self._value = [neutral_element for _ in range(2 * capacity)]\n self._operation = operation\n\n def _reduce_helper(self, start, end, node, node_start, node_end):\n if start == node_start and end == node_end:\n return self._value[node]\n mid = (node_start + node_end) // 2\n if end <= mid:\n return self._reduce_helper(start, end, 2 * node, node_start, mid)\n else:\n if mid + 1 <= start:\n return self._reduce_helper(start, end, 2 * node + 1, mid + 1, node_end)\n else:\n return self._operation(\n self._reduce_helper(start, mid, 2 * node, node_start, mid),\n self._reduce_helper(mid + 1, end, 2 * node + 1, mid + 1, node_end)\n )\n\n def reduce(self, start=0, end=None):\n \"\"\"Returns result of applying `self.operation`\n to a contiguous subsequence of the array.\n Parameters\n ----------\n start: int\n beginning of the subsequence\n end: int\n end of the subsequences\n Returns\n -------\n reduced: obj\n result of reducing self.operation over the specified range of array.\n \"\"\"\n if end is None:\n end = self._capacity\n if end < 0:\n end += self._capacity\n end -= 1\n return self._reduce_helper(start, end, 1, 0, self._capacity - 1)\n\n def __setitem__(self, idx, val):\n # index of the leaf\n idx += self._capacity\n self._value[idx] = val\n idx //= 2\n while idx >= 1:\n self._value[idx] = self._operation(self._value[2 * idx], self._value[2 * idx + 1])\n idx //= 2\n\n def __getitem__(self, idx):\n assert 0 <= idx < self._capacity\n return self._value[self._capacity + idx]\n\n\nclass SumSegmentTree(SegmentTree):\n\n def __init__(self, capacity):\n super(SumSegmentTree, self).__init__(capacity=capacity, operation=operator.add, neutral_element=0.0)\n\n def sum(self, start=0, end=None):\n \"\"\"Returns arr[start] + ... + arr[end]\"\"\"\n return super(SumSegmentTree, self).reduce(start, end)\n\n def find_prefixsum_idx(self, prefixsum):\n \"\"\"Find the highest index `i` in the array such that\n sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum\n if array values are probabilities, this function\n allows to sample indexes according to the discrete\n probability efficiently.\n Parameters\n ----------\n perfixsum: float\n upperbound on the sum of array prefix\n Returns\n -------\n idx: int\n highest index satisfying the prefixsum constraint\n \"\"\"\n assert 0 <= prefixsum <= self.sum() + 1e-5\n idx = 1\n while idx < self._capacity: # while non-leaf\n if self._value[2 * idx] > prefixsum:\n idx = 2 * idx\n else:\n prefixsum -= self._value[2 * idx]\n idx = 2 * idx + 1\n return idx - self._capacity\n\n\nclass MinSegmentTree(SegmentTree):\n\n def __init__(self, capacity):\n super(MinSegmentTree, self).__init__(capacity=capacity, operation=min, neutral_element=float('inf'))\n\n def min(self, start=0, end=None):\n \"\"\"Returns min(arr[start], ..., arr[end])\"\"\"\n\n return super(MinSegmentTree, self).reduce(start, end)\n\n\n# ############################## Replay ####################################\nclass ReplayBuffer(object):\n\n def __init__(self, size):\n self._storage = []\n self._maxsize = size\n self._next_idx = 0\n\n def __len__(self):\n return len(self._storage)\n\n def add(self, *args):\n if self._next_idx >= len(self._storage):\n self._storage.append(args)\n else:\n self._storage[self._next_idx] = args\n self._next_idx = (self._next_idx + 1) % self._maxsize\n\n def _encode_sample(self, idxes):\n b_o, b_a, b_r, b_o_, b_d = [], [], [], [], []\n for i in idxes:\n o, a, r, o_, d = self._storage[i]\n b_o.append(o)\n b_a.append(a)\n b_r.append(r)\n b_o_.append(o_)\n b_d.append(d)\n return (\n np.stack(b_o).astype('float32') * ob_scale,\n np.stack(b_a).astype('int32'),\n np.stack(b_r).astype('float32'),\n np.stack(b_o_).astype('float32') * ob_scale,\n np.stack(b_d).astype('float32'),\n )\n\n def sample(self, batch_size):\n indexes = range(len(self._storage))\n idxes = [random.choice(indexes) for _ in range(batch_size)]\n return self._encode_sample(idxes)\n\n\nclass PrioritizedReplayBuffer(ReplayBuffer):\n\n def __init__(self, size, alpha, beta):\n \"\"\"Create Prioritized Replay buffer.\n Parameters\n ----------\n size: int\n Max number of transitions to store in the buffer. When the buffer\n overflows the old memories are dropped.\n alpha: float\n how much prioritization is used\n (0 - no prioritization, 1 - full prioritization)\n See Also\n --------\n ReplayBuffer.__init__\n \"\"\"\n super(PrioritizedReplayBuffer, self).__init__(size)\n assert alpha >= 0\n self._alpha = alpha\n\n it_capacity = 1\n while it_capacity < size:\n it_capacity *= 2\n\n self._it_sum = SumSegmentTree(it_capacity)\n self._it_min = MinSegmentTree(it_capacity)\n self._max_priority = 1.0\n self.beta = beta\n\n def add(self, *args):\n \"\"\"See ReplayBuffer.store_effect\"\"\"\n idx = self._next_idx\n super().add(*args)\n self._it_sum[idx] = self._max_priority ** self._alpha\n self._it_min[idx] = self._max_priority ** self._alpha\n\n def _sample_proportional(self, batch_size):\n res = []\n p_total = self._it_sum.sum(0, len(self._storage) - 1)\n every_range_len = p_total / batch_size\n for i in range(batch_size):\n mass = random.random() * every_range_len + i * every_range_len\n idx = self._it_sum.find_prefixsum_idx(mass)\n res.append(idx)\n return res\n\n def sample(self, batch_size):\n \"\"\"Sample a batch of experiences\"\"\"\n idxes = self._sample_proportional(batch_size)\n\n it_sum = self._it_sum.sum()\n p_min = self._it_min.min() / it_sum\n max_weight = (p_min * len(self._storage)) ** (-self.beta)\n\n p_samples = np.asarray([self._it_sum[idx] for idx in idxes]) / it_sum\n weights = (p_samples * len(self._storage)) ** (-self.beta) / max_weight\n encoded_sample = self._encode_sample(idxes)\n return encoded_sample + (weights.astype('float32'), idxes)\n\n def update_priorities(self, idxes, priorities):\n \"\"\"Update priorities of sampled transitions\"\"\"\n assert len(idxes) == len(priorities)\n for idx, priority in zip(idxes, priorities):\n assert priority > 0\n assert 0 <= idx < len(self._storage)\n self._it_sum[idx] = priority ** self._alpha\n self._it_min[idx] = priority ** self._alpha\n\n self._max_priority = max(self._max_priority, priority)\n\n\n# ############################# Functions ###################################\ndef huber_loss(x):\n \"\"\"Loss function for value\"\"\"\n return tf.where(tf.abs(x) < 1, tf.square(x) * 0.5, tf.abs(x) - 0.5)\n\n\ndef sync(net, net_tar):\n \"\"\"Copy q network to target q network\"\"\"\n for var, var_tar in zip(net.trainable_weights, net_tar.trainable_weights):\n var_tar.assign(var)\n\n\ndef log_softmax(x, dim):\n temp = x - np.max(x, dim, keepdims=True)\n return temp - np.log(np.exp(temp).sum(dim, keepdims=True))\n\n\ndef softmax(x, dim):\n temp = np.exp(x - np.max(x, dim, keepdims=True))\n return temp / temp.sum(dim, keepdims=True)\n\n\n# ############################### DQN #####################################\nclass DQN(object):\n def __init__(self):\n model = MLP if qnet_type == 'MLP' else CNN\n self.qnet = model('q')\n self.lastAction = None\n if args.train:\n self.qnet.train()\n self.targetqnet = model('targetq')\n self.targetqnet.infer()\n sync(self.qnet, self.targetqnet)\n else:\n self.qnet.infer()\n self.load(args.save_path)\n self.niter = 0\n if clipnorm is not None:\n self.optimizer = tf.optimizers.Adam(learning_rate=lr, clipnorm=clipnorm)\n else:\n self.optimizer = tf.optimizers.Adam(learning_rate=lr)\n self.noise_scale = noise_scale\n\n def get_action(self, obv):\n eps = epsilon(self.niter)\n obv = np.expand_dims(obv, 0).astype('float32') * ob_scale\n if args.train:\n if random.random() < eps:\n if len(instance.bestActions) == instance.totalOpNum and random.random() <= 0.5:\n return instance.bestActions[step], self._qvalues_func(obv).numpy()[0][\n instance.bestActions[step] - 1]\n else:\n return int(random.random() * out_dim), np.max(self._qvalues_func(obv).numpy())\n if self.niter < explore_timesteps:\n self.qnet.noise_scale = self.noise_scale\n q_ptb = self._qvalues_func(obv).numpy()\n self.qnet.noise_scale = 0\n if i % noise_update_freq == 0:\n q = self._qvalues_func(obv).numpy()\n kl_ptb = (log_softmax(q, 1) - log_softmax(q_ptb, 1))\n kl_ptb = np.sum(kl_ptb * softmax(q, 1), 1).mean()\n kl_explore = -np.log(1 - eps + eps / out_dim)\n if kl_ptb < kl_explore:\n self.noise_scale *= 1.01\n else:\n self.noise_scale /= 1.01\n return q_ptb.argmax(1)[0], np.max(q_ptb)\n else:\n if random.random() < 1.0:\n return self._qvalues_func(obv).numpy().argmax(1)[0], np.max(\n self._qvalues_func(obv).numpy())\n else:\n return instance.bestActions[step], self._qvalues_func(obv).numpy()[0][\n instance.bestActions[step] - 1]\n\n else:\n # obv = np.expand_dims(obv, 0).astype('float32') * ob_scale\n return self._qvalues_func(obv).numpy().argmax(1)[0], np.max(self._qvalues_func(obv).numpy())\n\n def get_max_action(self, obv):\n obv = np.expand_dims(obv, 0).astype('float32') * ob_scale\n return self._qvalues_func(obv).numpy().argmax(1)[0], np.max(self._qvalues_func(obv).numpy())\n\n @tf.function\n def _qvalues_func(self, obv):\n return self.qnet(obv)\n\n # def train(self, b_o, b_a, b_r, b_o_, b_d):\n # self._train_func(b_o, b_a, b_r, b_o_, b_d)\n #\n # self.niter += 1\n # if self.niter % target_q_update_freq == 0:\n # sync(self.qnet, self.targetqnet)\n # self.save(args.save_path)\n def train(self, b_o, b_a, b_r, b_o_, b_d, weights=None):\n if weights is None:\n weights = np.ones_like(b_r)\n td_errors, loss = self._train_func(b_o, b_a, b_r, b_o_, b_d, weights)\n # tf.summary.scalar('loss', tf.reduce_sum(td_errors), step=self.niter)\n self.niter += 1\n if self.niter % target_q_update_freq == 0:\n sync(self.qnet, self.targetqnet)\n self.save(args.save_path)\n return td_errors.numpy(), loss\n\n @tf.function\n def _train_func(self, b_o, b_a, b_r, b_o_, b_d, weights):\n with tf.GradientTape() as tape:\n td_errors = self._tderror_func(b_o, b_a, b_r, b_o_, b_d)\n loss = tf.reduce_mean(huber_loss(td_errors))\n\n grad = tape.gradient(loss, self.qnet.trainable_weights)\n self.optimizer.apply_gradients(zip(grad, self.qnet.trainable_weights))\n\n return td_errors, loss\n\n @tf.function\n def _tderror_func(self, b_o, b_a, b_r, b_o_, b_d):\n if double:\n b_a_ = tf.one_hot(tf.argmax(self.qnet(b_o_), 1), out_dim)\n b_q_ = (1 - b_d) * tf.reduce_sum(self.targetqnet(b_o_) * b_a_, 1)\n else:\n b_q_ = (1 - b_d) * tf.reduce_max(self.targetqnet(b_o_), 1)\n\n b_q = tf.reduce_sum(self.qnet(b_o) * tf.one_hot(b_a, out_dim), 1)\n return b_q - (b_r + reward_gamma * b_q_)\n\n def save(self, path):\n if path is None:\n path = os.path.join('model', '_'.join([alg_name, env_id]))\n if not os.path.exists(path):\n os.makedirs(path)\n tl.files.save_weights_to_hdf5(os.path.join(path, 'q_net.hdf5'), self.qnet)\n\n def load(self, path):\n if path is None:\n path = os.path.join('model', '_'.join([alg_name, env_id]))\n tl.files.load_hdf5_to_weights_in_order(os.path.join(path, 'q_net.hdf5'), self.qnet)\n\n\n# #################### hyper parameters ####################\nin_dim = env.observation_space.shape\nout_dim = env.action_space.n\nreward_gamma = 1 # reward discount\nbatch_size = 64 # batch size for sampling from replay buffer\nwarm_start = 50 # sample times befor learning\nnoise_update_freq = 500 # how frequency param noise net update\nprioritized_replay_alpha = 0.6 # alpha in PER\nprioritized_replay_beta0 = 0.4 # initial beta in PER\nskipFrame = 4 # skiped frames\n\nif env_id == 'CartPole-v0':\n qnet_type = 'MLP'\n number_timesteps = 3000 # total number of time steps to train on\n explore_timesteps = number_timesteps * env.instance.totalOpNum / skipFrame * 0.4\n # epsilon-greedy schedule, final exploit prob is 0.99\n epsilon = lambda i_iter: 1 - 0.9 * min(1, i_iter / explore_timesteps)\n lr = 1e-6 # learning rate\n buffer_size = 1000000 # replay buffer size\n target_q_update_freq = 500 # how frequency target q net update\n ob_scale = 1.0 # scale observations\n clipnorm = None\nelse:\n # reward will increase obviously after 1e5 time steps\n qnet_type = 'CNN'\n number_timesteps = 8000 # total number of time steps to train on\n explore_timesteps = number_timesteps * env.instance.totalOpNum / skipFrame * 0.5\n # epsilon-greedy schedule, final exploit prob is 0.99\n epsilon = lambda i_iter: 1 - 0.9 * min(1, i_iter / explore_timesteps)\n lr = 1e-6 # learning rate\n buffer_size = 1000000 # replay buffer size\n target_q_update_freq = 200 # how frequency target q net update\n ob_scale = 1.0 # scale observations\n clipnorm = 1\n\n# ############################# Trainer ###################################\nif __name__ == '__main__':\n instanceScheduler = HeuristicScheduleManager()\n for rule in instance.rules:\n instance.reset()\n InstanceUtil.setInstanceComparator(instance, 0, rule=rule)\n instance.algorithm = \"ruleName-\" + rule.ruleName\n instanceScheduler.schedule(instance, 0)\n instanceSolution = InstanceSolution()\n instanceSolution.algorithmName = \"rule:\" + rule.ruleName\n instanceSolution.instanceName = instance.name\n instanceSolution.objectives.append(instance.objective.objectiveValue)\n fileHandle.writeInstanceSolution(\"train\", instance.getInstanceCategory(), str(instance.index) + \"\",\n instanceSolution)\n dqn = DQN()\n instance.dqn = dqn\n # 注意这里路径定义要使用\\\\，不能使用/\n # tensorboard --logdir=\"\\home\\hba\\scheduling\\logs\" tensorboard --logdir=\"/home/hba/scheduling/logs\"\n log_dir = \"\\\\home\\\\hba\\\\scheduling\\\\logs\\\\\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n writer = tf.summary.create_file_writer(log_dir)\n tf.summary.trace_on()\n t0 = time.time()\n if args.train:\n\n if prioritized_replay:\n buffer = PrioritizedReplayBuffer(buffer_size, prioritized_replay_alpha, prioritized_replay_beta0)\n else:\n buffer = ReplayBuffer(buffer_size)\n nepisode = 0\n all_episode_reward = []\n all_episode_objective = []\n all_episode_epsilon = []\n all_episode_startQ = []\n all_episode_Cmax = []\n all_episode_error = []\n with writer.as_default():\n for i in range(1, number_timesteps + 1):\n o = env.reset()\n episode_reward = 0\n episode_errors = []\n episode_error = 0\n startQ = None\n accuReward = 0\n with writer.as_default():\n tf.summary.scalar(\"epsilon\", epsilon(dqn.niter), step=i)\n step = 0\n while True:\n actionIndex = 0\n if prioritized_replay:\n buffer.beta += (1 - prioritized_replay_beta0) / number_timesteps\n a, maxQ = dqn.get_action(o)\n\n if startQ is None:\n startQ = maxQ\n with writer.as_default():\n tf.summary.scalar(\"startQ\", startQ, step=i)\n # execute action and feed to replay buffer\n # note that `_` tail in var name means next\n while actionIndex < skipFrame:\n o_, r, done, info = env.step(a)\n episode_reward += r\n accuReward += r\n actionIndex = actionIndex + 1\n step = step + 1\n if done:\n break\n buffer.add(o, a, accuReward, o_, done)\n accuReward = 0\n if i >= warm_start:\n if i == warm_start and step == 0:\n tf.summary.trace_on(graph=True, profiler=True)\n if i == warm_start and step == 1:\n tf.summary.trace_export(name=\"model_trace\", step=0, profiler_outdir=log_dir)\n if prioritized_replay:\n *transitions, w, idxs = buffer.sample(batch_size)\n priorities, loss = dqn.train(*transitions)\n priorities = np.clip(np.abs(priorities), 1e-6, None)\n buffer.update_priorities(idxs, priorities)\n else:\n transitions = buffer.sample(batch_size)\n priorities, loss = dqn.train(*transitions)\n episode_errors.append(loss)\n if done:\n if len(episode_errors) > 0:\n episode_error = tf.reduce_mean(episode_errors).numpy()\n break\n else:\n o = o_\n with writer.as_default():\n tf.summary.scalar(\"error\", episode_error, step=i)\n tf.summary.scalar(\"bestObj\", instance.bestCmax, step=i)\n if nepisode == 0:\n all_episode_reward.append(episode_reward)\n with writer.as_default():\n tf.summary.scalar(\"episode reward\", episode_reward, step=i)\n else:\n all_episode_reward.append(all_episode_reward[-1] * 0.9 + episode_reward * 0.1)\n with writer.as_default():\n tf.summary.scalar(\"episode reward\", all_episode_reward[-1] * 0.9 + episode_reward * 0.1, step=i)\n with writer.as_default():\n tf.summary.scalar(\"objective\", env.instance.objective.objectiveValue, step=i)\n nepisode += 1\n print(\n 'Training | Episode: {}/{} | Episode Reward: {:.4f} | Objective:{} | epsilon:{:.4f} | startQ:{'\n ':.4f} | bestObj:{} | error:{} | Running Time: {:.4f}'.format(\n nepisode, number_timesteps, episode_reward, env.instance.objective.objectiveValue,\n epsilon(dqn.niter), startQ, instance.bestCmax, episode_error, time.time() - t0\n )\n ) # episode num starts from 1 in print\n dqn.save(args.save_path)\n\n nepisode = 0\n episode_reward = 0\n learned_actions = []\n env.instance.writeDynamic = True\n env.instance.algorithm = alg_name\n o = env.reset()\n step = 0\n while True:\n actionIndex = 0\n a, qVal = dqn.get_max_action(o)\n while actionIndex < skipFrame:\n o_, r, done, info = env.step(a)\n episode_reward += r\n print(str(a) + \"(\" + str(r) + \")\" + \":\" + str(qVal))\n learned_actions.append(a)\n step = step + 1\n actionIndex = actionIndex + 1\n if done:\n break\n if done:\n break\n else:\n o = o_\n nepisode += 1\n print(\n 'Training-Testing | Episode: {} | Episode Reward: {:.4f} | Objective:{} |Running Time: {:.4f}'.format(\n nepisode, episode_reward, env.instance.objective.objectiveValue,\n time.time() - t0\n )\n )\n print(\"训练动作序列：{}\".format(learned_actions))\n print(\"最优动作序列：{}\".format(instance.bestActions))\n InstanceUtil.outputInstanceSolution(env.instance, alg_name)\n if args.test:\n nepisode = 0\n for i in range(1, 2):\n o = env.reset()\n episode_reward = 0\n while True:\n actionIndex = 0\n env.instance.writeDynamic = True\n a, maxQ = dqn.get_action(o)\n while actionIndex < skipFrame:\n o_, r, done, info = env.step(a)\n print(str(a) + \"(\" + str(r) + \")\" + \":\" + str(maxQ))\n episode_reward += r\n actionIndex = actionIndex + 1\n if done:\n break\n else:\n o = o_\n nepisode += 1\n print(\n 'Testing | Episode: {} | Episode Reward: {:.4f} | Objective:{} |Running Time: {:.4f}'.format(\n nepisode, episode_reward, env.instance.objective.objectiveValue,\n time.time() - t0\n )\n )\n InstanceUtil.outputInstanceSolution(env.instance, alg_name)\n","sub_path":"algorithm/rl/core/tutorial_DQN_variants_CNN_TB_Skip.py","file_name":"tutorial_DQN_variants_CNN_TB_Skip.py","file_ext":"py","file_size_in_byte":31663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"579228028","text":"import pandas as pd\nimport numpy as np\nimport json\nfrom SMD_Package.FCtoDataFrame import event_fc_to_df\nfrom SMD_Package.load_config import SMDConfigs\nfrom arcpy import env\nimport os\n\n\nclass Kemantapan(object):\n def __init__(self, df_event, grading_col, route_col, from_m_col, to_m_col, lane_code, kemantapan_type='ROUGHNESS',\n lane_based=False, rni_mfactor=1, to_km_factor=0.01):\n \"\"\"\n Initialize the Kemantapan class for grading kemantapan value\n :param df_event: The DataFrame for the input table.\n :param grading_col: The value used for grading\n :param route_col: The RouteID column of the input DataFrame.\n :param from_m_col: The From Measure column of the input DataFrame.\n :param to_m_col: The To Measure column of the input DataFrame.\n :param kemantapan_type: The type of kemantapan will be calculated. ROUGHNESS or PCI only. The selection will\n effect the amount of grading level.\n :param lane_based: Determine whether the Kemantapan will be calculated as lane based or calculated based on the\n segment interval.\n \"\"\"\n # Convert the measurement value of the event dataframe to DM\n df_event[from_m_col] = df_event[from_m_col].astype(float)*to_km_factor*100\n df_event[to_m_col] = df_event[to_m_col].astype(float)*to_km_factor*100\n df_event[[from_m_col, to_m_col]] = df_event[[from_m_col, to_m_col]].round(1).astype(int)\n\n # make sure the kemantapan_type is between 'ROUGHNESS' and 'PCI'\n if kemantapan_type not in ['ROUGHNESS', 'PCI']:\n raise Exception('{0} is not a valid kemantapan type.'.format(kemantapan_type)) # Raise an exception\n else:\n self.type = kemantapan_type\n\n if len(df_event) == 0:\n raise Exception('Input Event DataFrame is Empty')\n\n # Get the RNI table details.\n rni_table = SMDConfigs().table_names['rni']\n rni_route_col = SMDConfigs().table_fields['rni']['route_id']\n rni_from_col = SMDConfigs().table_fields['rni']['from_measure']\n rni_to_col = SMDConfigs().table_fields['rni']['to_measure']\n rni_lane_code = SMDConfigs().table_fields['rni']['lane_code']\n surftype_col = SMDConfigs().table_fields['rni']['surface_type']\n rni_request_cols = [rni_route_col, rni_from_col, rni_to_col, rni_lane_code, surftype_col]\n input_routes = df_event[route_col].unique().tolist()\n\n df_rni = event_fc_to_df(rni_table, rni_request_cols, input_routes, rni_route_col, env.workspace, True)\n df_rni[rni_from_col] = pd.Series(df_rni[rni_from_col]*rni_mfactor).round(1).astype(int) # Convert the RNI measurement\n df_rni[rni_to_col] = pd.Series(df_rni[rni_to_col]*rni_mfactor).round(1).astype(int)\n\n self.df_rni = df_rni\n self.rni_route_col = rni_route_col\n self.rni_from_col = rni_from_col\n self.rni_to_col = rni_to_col\n self.surftype_col = surftype_col\n self.grading_col = grading_col\n self.route_col = route_col\n\n self.group_details = self.group_details()\n self.lane_based = lane_based\n self.lane_code = lane_code\n\n # The input and RNI DataFrame merge result\n merge_df = self.rni_table_join(df_rni, df_event, route_col, from_m_col, to_m_col, grading_col,\n rni_route_col, rni_from_col, rni_to_col, surftype_col, lane_based,\n match_only=False, lane_code=lane_code, rni_lane_code=rni_lane_code)\n self.merged_df = merge_df\n self.match_only = merge_df.loc[merge_df['_merge'] == 'both']\n self.graded_df = self.grading(self.match_only, surftype_col, grading_col, self.group_details, kemantapan_type)\n self.mantap_percent = self.kemantapan_percentage(self.graded_df, route_col, from_m_col, to_m_col, 0.01)\n self.no_match_event = len(merge_df.loc[merge_df['_merge'] == 'left_only'])\n\n if self.no_match_event != 0:\n self.all_match = False\n else:\n self.all_match = True\n\n def summary(self, flatten=True, lane_km=True):\n \"\"\"\n Create a summary DataFrame which contain the length for every road grade and the percentage for every road grade\n in a single route. The column with '_p' suffix contain the length percentage.\n :param flatten: If true then the returned DataFrame does not use any multi index column.\n :return:\n \"\"\"\n if not self.lane_based:\n # Create the pivot table\n pivot_grade = self.create_pivot(columns=['_surf_group', '_grade'])\n pivot_mantap = self.create_pivot(columns=['_surf_group', '_kemantapan'])\n pivot_mantap_all = self.create_pivot(columns=['_kemantapan'])\n pivot_grade_all = self.create_pivot(columns=['_grade'])\n elif self.lane_based:\n # Create the pivot table\n pivot_grade = self.create_pivot(columns=['_surf_group', '_grade'], lane_code=self.lane_code, lane_km=lane_km)\n pivot_mantap = self.create_pivot(columns=['_surf_group', '_kemantapan'], lane_code=self.lane_code, lane_km=lane_km)\n pivot_mantap_all = self.create_pivot(columns=['_kemantapan'], lane_code=self.lane_code, lane_km=lane_km)\n pivot_grade_all = self.create_pivot(columns=['_grade'], lane_code=self.lane_code, lane_km=lane_km)\n\n # All the required grades and surfaces\n if self.type == 'ROUGHNESS':\n required_grades = np.array(['good', 'fair', 'poor', 'bad'])\n elif self.type == 'PCI':\n required_grades = np.array(['good', 'satisfactory', 'fair', 'poor', 'very poor', 'serious', 'failed'])\n required_mantap = np.array(['mantap', 'tdk_mantap'])\n required_surftype = ['p', 'up']\n\n # Complete all the surface type and surface grades in every pivot table.\n pivot_grade = self._complete_surftype_grade(pivot_grade, required_grades, required_surftype)\n pivot_mantap = self._complete_surftype_grade(pivot_mantap, required_mantap, required_surftype)\n pivot_mantap_all = self._complete_surftype_grade(pivot_mantap_all, required_mantap, None)\n pivot_grade_all = self._complete_surftype_grade(pivot_grade_all, required_grades, None)\n\n # Add suffix for non-percentage table.\n pivot_grade_s = self._add_suffix(pivot_grade, '_km', levels=1)\n pivot_mantap_s = self._add_suffix(pivot_mantap, '_km', levels=1)\n pivot_grade_all_s = self._add_suffix(pivot_grade_all, '_km', levels=0)\n pivot_mantap_all_s = self._add_suffix(pivot_mantap_all, '_km', levels=0)\n\n # Create all the percentage DataFrame\n pivot_grade_p = self._percentage(pivot_grade, required_surftype, modify_input=False)\n pivot_mantap_p = self._percentage(pivot_mantap, required_surftype, modify_input=False)\n pivot_grade_all_p = self._percentage_singlecol(pivot_grade_all, modify_input=False)\n pivot_mantap_all_p = self._percentage_singlecol(pivot_mantap_all, modify_input=False)\n\n # Join the multilevel column DataFrame first\n pivot_join = pivot_grade_s.join(pivot_mantap_s)\n pivot_join = pivot_join.join(pivot_grade_p)\n pivot_join = pivot_join.join(pivot_mantap_p)\n\n if flatten:\n # Flatten the Multi Level Columns\n new_column = pd.Index([str(x[0]+'_'+x[1].replace(' ', '')) for x in pivot_join.columns.values])\n pivot_join.columns = new_column\n\n # Join all the single level column DataFrame.\n pivot_join = pivot_join.join(pivot_grade_all_s)\n pivot_join = pivot_join.join(pivot_mantap_all_s)\n pivot_join = pivot_join.join(pivot_grade_all_p) # Summary of all surface group\n pivot_join = pivot_join.join(pivot_mantap_all_p) # Summary of all surface group\n\n # The grade average for all route\n avg_grade = self.graded_df.groupby(by=[self.route_col])[self.grading_col].mean()\n\n pivot_join = pivot_join.join(avg_grade) # Join the average grade DataFrame.\n\n return pivot_join\n\n @staticmethod\n def _add_suffix(pivot_table, suffix, levels=1):\n \"\"\"\n This static method will add suffix to a pivot table.\n :param pivot_table: The input pivot table.\n :param suffix: The suffix that will be added to the column.\n :param levels: The number of column level in the input pivot table.\n :return: Modified pivot table.\n \"\"\"\n\n if levels == 0: # For single level pivot table\n cols = np.array(pivot_table.columns.get_level_values(levels))\n result = pivot_table.rename(columns={x: (x + suffix) for x in cols})\n return result\n else: # For multilevel pivot table\n compiled = None # For compiling the result\n # Iterate over all upper column\n for upper_col in pivot_table.columns.get_level_values(levels-1).unique():\n lower = pivot_table[upper_col] # The lower DataFrame\n cols = np.array(lower.columns.values) # The columns in the lower DataFrame\n cols_w_suffix = pd.Index([(x + suffix) for x in cols]) # Create the column with the suffix\n lower.columns = cols_w_suffix # Assign the columns with the suffix\n\n upper = dict()\n upper[upper_col] = lower\n lower = pd.concat(upper, axis=1)\n\n if compiled is None:\n compiled = lower\n else:\n compiled = compiled.join(lower)\n\n return compiled\n\n @staticmethod\n def _complete_surftype_grade(pivot_table, required_grades, required_surftype):\n \"\"\"\n This static method is used to complete the required surface type and grade columns in every surface type upper\n index column. Example:\n\n input output\n\n paved || paved | unpaved\n baik sedang rusak ringan || baik sedang rusak ringan | baik sedang rusak ringan rusak berat\n\n :param pivot_table: The input pivot table.\n :param required_grades: The required grades. Example ['baik', 'sedang', ...] or ['mantap', 'tidak mantap'].\n :param required_surftype: The requierd surface type. Example ['paved', 'unpaved'].\n :return: Modified pivot table\n \"\"\"\n if required_surftype is not None: # If the surface type is specified (multilevel column)\n\n # Create the Column for Missing Grade in Every Surface Type.\n surftype_set = set(x for x in pivot_table.columns.get_level_values(0)) # All the list of surface type\n missing_surftype = np.setdiff1d(required_surftype, list(surftype_set)) # Check for missing surface type\n\n # Iterate over all available surftype:\n for surface in surftype_set:\n surface_grades = np.array(pivot_table[surface].columns.tolist())\n missing_grades = np.setdiff1d(required_grades, surface_grades)\n\n # Check for missing grade in available surface type\n for grade in missing_grades:\n # Add the missing grade\n pivot_table[surface, grade] = pd.Series(0, index=pivot_table.index)\n\n # If there is a missing surface type in the pivot table, then add the missing surface type to pivot table\n if len(missing_surftype) != 0:\n for surface in missing_surftype:\n for grade in required_grades:\n pivot_table[(surface, grade)] = pd.Series(0, index=pivot_table.index) # Contain 0 value\n\n else: # if the surface type is not specified (single level column)\n pivot_grade = pivot_table.columns.values # The available grade in the pivot table\n missing_grade = np.setdiff1d(required_grades, pivot_grade) # Check for missing grade\n for grade in missing_grade:\n # Add the missing grade\n pivot_table[grade] = pd.Series(0, index=pivot_table.index) # Add the missing grade column\n\n return pivot_table\n\n @staticmethod\n def _percentage(pivot_table, required_surftype, suffix='_psn', modify_input=False):\n \"\"\"\n This static method will add a percentage column for every required grades in the pivot table. The newly added\n column will have an suffix determined by a parameter.\n If the pivot table have a missing grade, then a new column will be added which contain 0 value.\n :param pivot_table: The input pivot table.\n :param required_surftype: The required surface type.\n :param suffix: The percentage column name suffix.\n :return: Modified pivot table\n \"\"\"\n # Iterate over all required surface type\n result = None # Variable for compiling the result\n for surface in required_surftype:\n\n surface_df = pivot_table.loc[:, [surface]] # Create the DataFrame for a single surface\n grade_percent = surface_df.div(surface_df.sum(axis=1), axis=0) * 100\n surface_grades = np.array(grade_percent[surface].columns.values)\n percent_col = pd.Index([(x + suffix) for x in surface_grades]) # Create the percentage column. suffix '_p'\n grade_percent.columns = percent_col\n grade_percent.fillna(0, inplace=True) # Fill the NA value with zero\n\n upper_col = dict()\n upper_col[surface] = grade_percent\n grade_percent = pd.concat(upper_col, axis=1)\n\n if modify_input:\n # Join the pivot table with the percent table\n result = pivot_table.join(grade_percent, how='inner')\n else:\n if result is None:\n result = grade_percent # Initalize the result variable\n else:\n result = result.join(grade_percent, how='inner') # Join the result\n\n return result # Return the percent DataFrame without modifying the input pivot table.\n\n @staticmethod\n def _percentage_singlecol(pivot_table, suffix='_psn', modify_input=False):\n \"\"\"\n This static method will add a percentage column for every required grades in the pivot table. The newly added\n column will have an suffix determined by a parameter.\n If the pivot table have a missing grade, then a new column will be added which contain 0 value.\n :param pivot_table: The input pivot table.\n :param required_grades: The required grades.\n :param suffix: The percentage column name suffix.\n :return: Modified pivot table.\n \"\"\"\n\n grade_percent = pivot_table.div(pivot_table.sum(axis=1), axis=0) * 100\n grades = np.array(pivot_table.columns.values)\n percent_col = pd.Index([(x + suffix) for x in grades]) # Create the percentage column. suffix '_p'\n grade_percent.columns = percent_col\n grade_percent.fillna(0, inplace=True) # Fill the NA value with zero\n\n if modify_input:\n # Join the pivot table with the percent table\n pivot_table = pivot_table.join(grade_percent, how='inner')\n return pivot_table\n else:\n return grade_percent\n\n def comparison(self, compare_table, grading_col, route_col, from_m_col, to_m_col, route, sde_connection):\n \"\"\"\n Compare the Kemantapan percentage from the event table and the compare table.\n :param compare_table: The Feature Class used for comparing the kemantapan status.\n :param grading_col: The column in the compare_table used for grading.\n :param route_col: The RouteID column in the compare_table\n :param from_m_col: The from measure column in the compare_table\n :param to_m_col: The to measure column in the compare_table\n :param route: Route selection for compare_table\n :param sde_connection: The SDE connection for accessing compare_table\n :return:\n \"\"\"\n # Create the compare_table DataFrame\n comp_df = event_fc_to_df(compare_table, [route_col, from_m_col, to_m_col, grading_col], route, route_col,\n sde_connection, orderby=None)\n\n if len(comp_df) == 0: # If the comparison table is empty\n return None\n\n comp_df[from_m_col] = pd.Series(comp_df[from_m_col]*100)\n comp_df[to_m_col] = pd.Series(comp_df[to_m_col]*100)\n\n merge_comp = self.rni_table_join(self.df_rni, comp_df, route_col, from_m_col, to_m_col, grading_col,\n self.rni_route_col, self.rni_from_col, self.rni_to_col, self.surftype_col)\n graded_comp = self.grading(merge_comp, self.surftype_col, grading_col, self.group_details)\n mantap_comp = self.kemantapan_percentage(graded_comp, route_col, from_m_col, to_m_col)\n\n return mantap_comp\n\n def create_pivot(self, columns, lane_code=None, lane_km=False):\n \"\"\"\n Create a pivot DataFrame from the DataFrame which already being graded\n :param columns: The column used to create pivot table.\n :param lane_code: The lane code column.\n :param lane_km: If True then the calculation will sum all the segment length from all available lane.\n :return:\n \"\"\"\n if lane_code is None: # If no lane code column is specified\n pivot = self.graded_df.pivot_table('_len', index=self.route_col, columns=columns, aggfunc=np.sum,\n fill_value=0)\n elif lane_code is not None:\n pivot = self.graded_df.pivot_table('_len', index=[self.route_col, lane_code], columns=columns, aggfunc=np.sum,\n fill_value=0)\n\n if lane_km:\n pivot_reset = pivot.reset_index()\n grouped = pivot_reset.groupby(self.route_col).sum()\n return grouped\n\n return pivot\n\n @staticmethod\n def rni_table_join(df_rni, df_event, route_col, from_m_col, to_m_col, grading_col, rni_route_col, rni_from_col,\n rni_to_col, surftype_col, lane_based, match_only=True, lane_code=None, rni_lane_code=None):\n \"\"\"\n This static method used for joining the input event table and the RNI table\n :param df_rni: The RNI DataFrame.\n :param df_event: The input event DataFrame.\n :param route_col: The column which stores the RouteID for event table.\n :param from_m_col: The From Measure column for event table.\n :param to_m_col: The To Measure column for event table.\n :param grading_col: The value used for calculating Kemantapan.\n :param rni_route_col: The column in RNI Table which stores the RouteID\n :param rni_from_col: The column in RNI Table which stores the From Measure.\n :param rni_to_col: The column in RNI Table which stores the To Measure.\n :param surftype_col: The column in RNI Table which stores the surface type data.\n :param match_only: If True then this method only returns the 'both' merge result.\n :param lane_code: The Input DataFrame lane code column.\n :param rni_lane_code: The RNI Table lane code column.\n :return: A DataFrame from the merge result between the RNI and input event table.\n \"\"\"\n if not lane_based: # Do the table join with linkid, from, and to as join key.\n input_group_col = [route_col, from_m_col, to_m_col] # The column used for input groupby\n rni_group_col = [rni_route_col, rni_from_col, rni_to_col] # The column used for the RNI groupby\n df_rni[surftype_col] = pd.Series(df_rni[surftype_col].astype(int)) # Convert the surftype to integer type\n\n # GroupBy the input event DataFrame to make summarize the value used for grading from all lane.\n input_groupped = df_event.groupby(by=input_group_col)[grading_col].mean().reset_index()\n\n # GroupBy the RNI Table to get the summary of surface type from all lane in a segment.\n # Get the first surface type in single RNI segment group\n rni_groupped = df_rni.groupby(by=rni_group_col)[surftype_col].\\\n agg(lambda x: x.value_counts().index[0]).reset_index()\n\n # Merge the RNI DataFrame and the event DataFrame\n df_merge = pd.merge(input_groupped, rni_groupped, how='outer', left_on=input_group_col, right_on=rni_group_col,\n indicator=True, suffixes=['_INPUT', '_RNI'])\n\n df_match = df_merge.loc[df_merge['_merge'] == 'both'] # DataFrame for only match segment interval\n return df_match if match_only else df_merge # If 'match_only' is true then only return the 'both'\n\n elif lane_based: # Do the table join with linkid, from, to and lane code as join key\n input_key = list([route_col, from_m_col, to_m_col, lane_code])\n rni_key = list([rni_route_col, rni_from_col, rni_to_col, rni_lane_code])\n rni_col = list(rni_key) # RNI table column used for merge\n rni_col.append(surftype_col)\n\n df_merge = pd.merge(df_event, df_rni[rni_col], how='outer', left_on=input_key, right_on=rni_key, indicator=True,\n suffixes=['_INPUT', '_RNI'])\n\n df_match = df_merge.loc[df_merge['_merge'] == 'both'] # DataFrame for only match segment interval\n return df_match if match_only else df_merge\n\n @staticmethod\n def grading(df_merge, surftype_col, grading_col, group_details, kemantapan_type, grading_result='_grade',\n grading_level='_grade_level', surftype_group='_surf_type', surftype_cat='_surf_group'):\n \"\"\"\n This static method will grade every segment in the df_merge to (\"baik\", \"sedang\", \"rusak_ringan\", \"rusak_berat\")\n based on the segment surface type group and value in the grading column.\n :param df_merge: The merge result of event DataFrame and the RNI DataFrame.\n :param surftype_col: The surface type column in the merge result.\n :param grading_col: The value used in the grading process.\n :param group_details: The surface type group details (grading value and surface type (paved or unpaved)).\n :param kemantapan_type: The kemantapan type tha will be calculated\n :param grading_result: The new column used to store the grading result.\n :param grading_level: The new column used to store the grade leve in integer.\n :param surftype_group: The new column used to store the surface group (asphalt, penmac, rigid)\n :param surftype_cat: The new column used to store the surface category (paved or unpaved)\n :return: The df_merge with new column which store the grading result.\n \"\"\"\n # Iterate over all row in the df_merge\n for index, row in df_merge.iterrows():\n group_not_found = True\n\n while group_not_found: # Iterate until a group is found\n\n for group in group_details:\n if row[surftype_col] in group_details[group]['group']: # If the group was found\n group_not_found = False # group not found is False\n surface_group = str(group) # surface group\n paved_group = group_details[group]['category']\n range = np.array(group_details[group]['range']) # group's range in np.array\n\n lower_bound = np.amin(range) # The lower bound\n upper_bound = np.amax(range) # The upper bound\n mid = range[1] # The mid value\n\n df_merge.loc[index, surftype_group] = surface_group # Write the surface group name in '_surf_group'\n df_merge.loc[index, surftype_cat] = paved_group\n\n if kemantapan_type == 'ROUGHNESS': # If the kemantapan type is ROUGHNESS\n\n # Start the grading process\n if row[grading_col] <= lower_bound:\n grade = 'good'\n level = 1\n if (row[grading_col] > lower_bound) & (row[grading_col] <= mid):\n grade = 'fair'\n level = 2\n if (row[grading_col] > mid) & (row[grading_col] <= upper_bound):\n grade = 'poor'\n level = 3\n if row[grading_col] > upper_bound:\n grade = 'bad'\n level = 4\n\n df_merge.loc[index, grading_result] = grade\n df_merge.loc[index, grading_level] = level\n\n elif kemantapan_type == 'PCI': # If the kemantapan type is PCI\n continue\n\n else:\n continue\n\n return df_merge\n\n @staticmethod\n def kemantapan_percentage(df_graded, route_col, from_m_col, to_m_col, to_km_factor, grade_result_col='_grade',\n kemantapan_col='_kemantapan'):\n \"\"\"\n This function will find the length percentage of every route with 'Mantap' dan 'Tidak Mantap' status.\n :param df_graded: The event DataFrame which already being graded.\n :param grade_result_col: The column which store the grade statue for every segment.\n :param kemantapan_col: The newly added column which store the kemantapan status.\n :return: DataFrame with '_kemantapan' column.\n \"\"\"\n df_graded.loc[:, '_len'] = pd.Series(df_graded[to_m_col]-df_graded[from_m_col])\n df_graded['_len'] = df_graded['_len']*to_km_factor\n df_graded.loc[df_graded[grade_result_col].isin(['good', 'fair']), kemantapan_col] = 'mantap'\n df_graded.loc[df_graded[grade_result_col].isin(['poor', 'bad']), kemantapan_col] = 'tdk_mantap'\n\n kemantapan_len = df_graded.groupby(by=[route_col, kemantapan_col]).agg({'_len': 'sum'})\n kemantapan_prcnt = kemantapan_len.groupby(level=0).apply(lambda x: 100*x/float(x.sum())).reset_index()\n kemantapan_prcnt.set_index(kemantapan_col, inplace=True)\n\n return kemantapan_prcnt\n\n @staticmethod\n def group_details():\n module_folder = os.path.dirname(__file__)\n surftype_json_file = os.path.join(module_folder, 'surftype_group.json')\n with open(surftype_json_file) as group_json:\n group_details = json.load(group_json) # Load the surface type group JSON\n\n return group_details\n\n\n\n\n\n","sub_path":"SMD_Package/event_table/kemantapan.py","file_name":"kemantapan.py","file_ext":"py","file_size_in_byte":26469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"209268238","text":"\"\"\"\n\n@file : inference.py\n\n@author: xiaolu\n\n@time : 2020-05-25\n\n\"\"\"\nimport torch\nfrom transformers import BertTokenizer\nfrom model import Model\nfrom config import Config\n\n\nif __name__ == '__main__':\n # 1. 准备数据\n input_sentence = '中华人民共和国是世界最屌的民族, 中国银行, 哈哈西安电子科技大学'\n tokenizer = BertTokenizer.from_pretrained(Config.model_vocab_path)\n tokens = tokenizer.tokenize(input_sentence)\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\n input_ids = torch.LongTensor([input_ids])\n batch_masks = input_ids.gt(0)\n\n # 2. 加载标签\n id2tag = {}\n with open('./data/msra/tags.txt', 'r') as f:\n lines = f.readlines()\n for i, line in enumerate(lines):\n line = line.strip()\n id2tag[i] = line\n\n # 2. 加载模型\n model = Model().to(Config.device)\n model.load_state_dict(torch.load('./save_model/' + 'best_model.bin', map_location='cpu'))\n print(\"模型加载成功...\")\n model.eval()\n\n with torch.no_grad():\n bert_encode = model(input_ids, attention_mask=batch_masks)\n predicts = model.predict(bert_encode, batch_masks)\n predicts = predicts.numpy().tolist()\n result = [id2tag[i] for i in predicts]\n print(' '.join(result))\n\n\n \n\n # labels = torch.max(logits.data, 1)[1].cpu().numpy()\n # tags = [id2tag[i] for i in labels]\n # print(' '.join(tags))\n","sub_path":"NER/Bert_CRF_Ner/inference.py","file_name":"inference.py","file_ext":"py","file_size_in_byte":1436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"388889855","text":"from fb_post_clean_arch.adapters.service_adapter import get_service_adapter\nfrom fb_post_clean_arch.exceptions.custom_exceptions import InvalidPostId\nfrom fb_post_clean_arch.interactors.presenters.presenter_interface import \\\n PresenterInterface\nfrom fb_post_clean_arch.interactors.storages.storage_interface import \\\n StorageInterface, PostReactionCompleteDetailsDto\n\n\nclass GetPostReactionsInteractor:\n\n def __init__(self, storage: StorageInterface):\n self.storage = storage\n\n def get_post_reactions_wrapper(self,\n post_id: int,\n presenter: PresenterInterface):\n try:\n post_reaction_complete_details_dtos = self.get_post_reactions(\n post_id=post_id)\n except InvalidPostId:\n return presenter.raise_exception_for_invalid_post()\n\n return presenter.get_response_for_get_post_reactions(\n post_reaction_dtos=post_reaction_complete_details_dtos)\n\n def get_post_reactions(self, post_id: int):\n self.storage.validate_post_id(post_id=post_id)\n post_reaction_dtos = self.storage.get_post_reaction_dtos(\n post_id=post_id)\n user_ids = post_reaction_dtos.user_ids\n service_adapter = get_service_adapter()\n user_dtos = service_adapter.auth_service.get_user_dtos(\n user_ids=user_ids\n )\n\n post_reaction_complete_details_dtos = PostReactionCompleteDetailsDto(\n reaction_dtos=post_reaction_dtos.reaction_dtos,\n user_dtos=user_dtos)\n return post_reaction_complete_details_dtos\n","sub_path":"fb_post_clean_arch/interactors/get_post_reactions_interactor.py","file_name":"get_post_reactions_interactor.py","file_ext":"py","file_size_in_byte":1618,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"134860522","text":"from selenium import webdriver\n\"\"\"Video Tutorial Link:\nhttps://www.youtube.com/watch?v=rajXpxvHiV0\n\"\"\"\n\nweb = webdriver.Chrome('support files/chromedriver.exe')\n\nmyupc = '65s421'\n\n# Home Depot Data\nurl_homedepot = 'https://homedepot.com/s/%2522'+myupc+'%2522?NCNI-5'\nweb.get(url_homedepot)\ntitle_homedepot = web.find_element_by_class_name('product-details__title').text\nprice_homedepot = web.find_element_by_class_name('price').text[:-2]+'.'+web.find_element_by_class_name('price').text[-2:]\n\nprint('UPC: ' + myupc)\nprint('Home Depot:')\nprint(title_homedepot)\nprint(price_homedepot)\n\n# Walmart data\nurl_walmart = 'https://www.walmart.com/search/?cat_id=0&query=%22'+myupc+'%22'\nweb.get(url_walmart)\ntitle_walmart = web.find_element_by_class_name('product-title-link').text\nprice_walmart = web.find_element_by_class_name('price').text\n\nprint('Walmart:')\nprint(title_walmart)\nprint(price_walmart)\n\n# Ebay data\n","sub_path":"priceComp.py","file_name":"priceComp.py","file_ext":"py","file_size_in_byte":908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"144011329","text":"\n\nfrom xai.brain.wordbase.nouns._monkey import _MONKEY\n\n#calss header\nclass _MONKEYS(_MONKEY, ):\n\tdef __init__(self,): \n\t\t_MONKEY.__init__(self)\n\t\tself.name = \"MONKEYS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"monkey\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_monkeys.py","file_name":"_monkeys.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"252538458","text":"import cv2 as cv\r\nimport numpy as np\r\nW = 700\r\ndef my_ellipse(img, angle):\r\n thickness = 2\r\n line_type = 8\r\n cv.ellipse(img,\r\n (W // 2, W // 2),\r\n (W // 4, W // 16),\r\n angle,\r\n 0,\r\n 360,\r\n (255, 0, 0),\r\n thickness,\r\n line_type)\r\ndef my_filled_circle(img, center):\r\n thickness = -1\r\n line_type = 8\r\n cv.circle(img,\r\n center,\r\n W // 32,\r\n (0, 0, 255),\r\n thickness,\r\n line_type)\r\n\r\n\r\natom_window = \"Drawing 1: Atom\"\r\n\r\n# Create black empty images\r\nsize = W, W, 3\r\natom_image = np.zeros(size, dtype=np.uint8)\r\n\r\n# 1.a. Creating ellipses\r\nmy_ellipse(atom_image, 90)\r\nmy_ellipse(atom_image, 0)\r\nmy_ellipse(atom_image, 45)\r\nmy_ellipse(atom_image, -45)\r\n# 1.b. Creating circles\r\nmy_filled_circle(atom_image, (W // 2, W // 2))\r\n\r\n\r\ncv.imshow(atom_window, atom_image)\r\ncv.moveWindow(atom_window, 0, 200)\r\n\r\ncv.waitKey(0)\r\ncv.destroyAllWindows()","sub_path":"open.py","file_name":"open.py","file_ext":"py","file_size_in_byte":1042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"584080107","text":"# -*- coding: UTF-8 -*-\n'''\nCreated on 03.06.2010\n\n@author: Daniel Beßler \n'''\n\nfrom shader_utils import FragShaderFunc, VertShaderFunc\nfrom texture_shader import TextureShader\n\nclass RadialBlurFrag(FragShaderFunc):\n \"\"\"\n blurs by distance to center.\n \"\"\"\n \n # distance between samples\n BLUR_DIST = \"radialBlurDist\"\n # influence of texel distance to center\n BLUR_STRENGTH = \"radialBlurStrength\"\n \n def __init__(self,\n textureIndex=0):\n FragShaderFunc.__init__(self, \"radialBlur\")\n self.textureIndex = textureIndex\n self.addUniform(type=\"sampler2D\", name=\"Texture%d\" % textureIndex)\n self.addConstant(type=\"float\", name=self.BLUR_STRENGTH, val=7.0 )\n self.addConstant(type=\"float\", name=self.BLUR_DIST, val=0.1 )\n # 10 samples next to the current texel\n self.offsets = [-0.08,-0.05,-0.03,-0.02,-0.01,0.01,0.02,0.03,0.05,0.08]\n\n def code(self):\n code = \"\"\"\n void %(NAME)s(out vec4 outcol)\n {\n // a vector pointing to the middle of the screen\n vec2 dir = 0.5 - gl_TexCoord[0]; \n // distance to the center of the screen\n float dist = sqrt(dir.x*dir.x + dir.y*dir.y); \n dir = dir/dist;\n // original color\n vec4 texel = texture2D( Texture%(TEX)d, gl_TexCoord[0] ); \n vec4 sum = texel;\n\"\"\" % { 'NAME': self.name, 'TEX': self.textureIndex }\n \n for offset in self.offsets:\n code += \" sum += texture2D( Texture%d, \" % self.textureIndex+\\\n \"gl_TexCoord[0] + dir * %g * %s );\\n\" % (offset, self.BLUR_DIST)\n return code + \"\"\"\n sum *= 1.0/%(NSAMPLES)f;\n outcol = mix( texel, sum, clamp( dist * %(BULRS)s , 0.0, 1.0 ));\n }\n\"\"\" % { 'NSAMPLES': float(len(self.offsets) + 1),\n 'BULRS': self.BLUR_STRENGTH }\n\n\nRADIAL_BLUR_TEXCO = \"radialBlurTexco\"\nclass RadialBlurFastVert(VertShaderFunc):\n \"\"\"\n radial blur based on nvidia example.\n \"\"\"\n \n # distance between samples\n BLUR_DIST = \"radialBlurFastDist\"\n # blur origin, (0.5, 0.5) is centered blur with untranslated texture\n BLUR_CENTER = \"radialBlurFastCenter\"\n \n def __init__(self,\n numSamples=8,\n texelSize=1.0/256.0):\n VertShaderFunc.__init__(self, 'radialBlurFast')\n self.numSamples = numSamples\n self.texelSize = texelSize\n \n self.addVarying(\"vec2[%d]\" % numSamples, RADIAL_BLUR_TEXCO)\n \n self.addConstant( \"vec2\", self.BLUR_CENTER, (0.5, 0.5))\n self.addConstant( \"float\", self.BLUR_DIST, 0.14)\n \n def code(self):\n return \"\"\"\n void %(NAME)s()\n {\n vec2 s = gl_MultiTexCoord0 + %(TEXELS)f * 0.5 - %(BLURC)s;\n for(int i=0; i < %(NSAMPLES_INT)d ; i++) {\n %(TEXCO)s[i].xy = s * ( 1.0 - %(BLURD)s *\n ( float(i) / %(NSAMPLES)f ) ) + %(BLURC)s;\n }\n }\n\"\"\" % { 'NAME': self.name,\n 'NSAMPLES_INT': self.numSamples,\n 'NSAMPLES': float(self.numSamples - 1),\n 'TEXELS': self.texelSize,\n 'BLURD': self.BLUR_DIST,\n 'BLURC': self.BLUR_CENTER,\n 'TEXCO': RADIAL_BLUR_TEXCO }\nclass RadialBlurFastFrag(TextureShader):\n \"\"\"\n radial blur based on nvidia example.\n \"\"\"\n \n def __init__(self,\n textureIndex=0,\n numSamples=8):\n TextureShader.__init__(self, \"radialBlurFast\", textureIndex)\n self.numSamples = numSamples\n self.addVarying(\"vec2[%d]\" % numSamples, RADIAL_BLUR_TEXCO)\n\n def code(self):\n return \"\"\"\n void %(NAME)s(out vec4 col)\n {\n vec4 c = vec4(0.0);\n for(int i = 0; i < %(NSAMPLES_INT)d ; i++) {\n c += texture2D( Texture%(TEX)d, %(TEXCO)s[i].xy );\n }\n col = c / %(NSAMPLES_FLOAT)d;\n }\n\"\"\" % { 'NAME': self.name,\n 'TEX': self.textureIndex,\n 'NSAMPLES_INT': self.numSamples,\n 'NSAMPLES_FLOAT': float(self.numSamples),\n 'TEXCO': RADIAL_BLUR_TEXCO }\n","sub_path":"src/shader/blur_shader.py","file_name":"blur_shader.py","file_ext":"py","file_size_in_byte":4035,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"407463522","text":"import tensorflow as tf\nfrom tensorflow import keras\nimport copy\nimport numpy as np\nimport sys\n\nfrom scipy.stats import truncnorm \nfrom numpy.random import choice \n\n\nclass sw_pathnet:\n # コンストラクタ\n def __init__(self, pre_model, n_comp, n_classes, transfer_all_layer, is_reuse_initweight=False):\n print(pre_model.summary())\n self.n_classes = n_classes\n self.transfer_all_layer = transfer_all_layer\n \n # 学習用のテンプレとして，pre_modelを複製\n # VRAM上のモデル単位のメモリ解放ができない臭い\n self.tmp_model = self.gen_tmp_model(pre_model)\n \n # 重みのある層の取得\n # batchnormのせいで4も省かないと<-BN層も入れないとまずいんじゃないか\n #self.li_is_weighted = self.gen_li_weighted(self.tmp_model)\n self.li_is_weighted = [self.is_weighted(l) for l in self.tmp_model.layers]\n print(self.li_is_weighted)\n \n # パラメータの保存\n # 学習済みモデルに合わせて作成，重みがある箇所は初期値を置く\n self.source_weights = []\n self.target_weights = []\n self.len_geopath = 0\n self.initializer_k = truncnorm(-1, 1, loc=0, scale=0.25)\n self.val_b = 0.0\n for i_layer in range(len(self.tmp_model.layers)):\n # 重みのある層のみの処理\n if self.li_is_weighted[i_layer]:\n # len_geopathの更新\n self.len_geopath += 1\n \n # ソースの重みの格納\n self.source_weights.append(self.tmp_model.layers[i_layer].get_weights())\n \n # ターゲットの重みの初期値の格納\n # 学習済みの重みを使うかで分岐\n # 最終層のみは学習済みの重みを使うかに依らずに生成\n # kernel, biasの順（変わらないでくれ頼む）\n if is_reuse_initweight and i_layer != len(self.tmp_model.layers)-1:\n # 学習済みの重みを引っ張るだけ\n self.target_weights.append(self.tmp_model.layers[i_layer].get_weights())\n \n else:\n print('layer: ', i_layer)\n \n tmp_target_weight = []\n for weight in self.tmp_model.layers[i_layer].weights:\n print('given', weight.shape)\n # shapeのlenでbiasかkernelかを判定\n tmp_target_weight.append(self.get_init_weight(weight.shape))\n #if len(weight.shape) == 1:\n # b_len = weight.shape[0]\n # tmp_target_weight.append(\n # np.array([val_b for i in range(b_len)]))\n # \n #else:\n # tmp_target_weight.append(self.get\n # initializer_k.rvs(weight.shape))\n print('generated', tmp_target_weight[-1].shape)\n \n self.target_weights.append(tmp_target_weight)\n else:\n # 重みなしレイヤーは空リストを追加\n self.source_weights.append([])\n self.target_weights.append([])\n \n \n # 重みの初期値の生成\n # 1次元ならバイアス，2次元なら畳み込みor全結合層　\n def get_init_weight(self, weight_shape):\n if len(weight_shape) == 1:\n init_weight = np.array([self.val_b for i in range(weight_shape[0])])\n else:\n init_weight = self.initializer_k.rvs(weight_shape)\n return init_weight\n\n\n # 重みのあるレイヤーがTrueになるリストを返す\n def is_weighted(self, layer):\n if self.transfer_all_layer:\n not_weighted = len(layer.weights) == 0\n else:\n not_weighted = len(layer.weights) == 0 or len(layer.weights) == 4 or len(layer.weights) == 3\n \n return not(not_weighted)\n\n\n def gen_li_weighted(self, model):\n li_is_weighted = []\n for i_layer in range(len(model.layers)):\n if self.is_weighted(model.layers[i_layer]):\n li_is_weighted.append(False)\n else:\n li_is_weighted.append(True)\n \n return li_is_weighted\n \n \n # top-layerだけn_classesに合わせたモデルを生成\n # https://stackoverflow.com/questions/49492255/how-to-replace-or-insert-intermediate-layer-in-keras-model より　笹川実装を参考　\n def gen_tmp_model(self, pre_model):\n tmp_li_is_weighted = [self.is_weighted(l) for l in pre_model.layers]\n i_last_weighted = -1 - tmp_li_is_weighted[::-1].index(True)\n print(i_last_weighted, 'will be replaced to dense with', self.n_classes)\n \n tmp_li_is_weighted = tmp_li_is_weighted[:i_last_weighted]\n i_replaced = -1 - tmp_li_is_weighted[::-1].index(True)\n \n x = pre_model.layers[i_last_weighted-1].output\n predictions = keras.layers.Dense(self.n_classes, activation='softmax', name='dense_top')(x)\n tmp_model = keras.models.Model(inputs=pre_model.input, outputs=predictions)\n \n print(tmp_model.summary())\n \n return tmp_model\n \n \n # 遺伝子型の生成\n # デフォはソースタスクレイヤーと新規学習レイヤーが当確率で選ばれる\n def gen_geopath(self, bias_pretrained=0.5, is_top_unfixed=True):\n geopath = choice(\n [0, 1], self.len_geopath, \n p=[1-bias_pretrained, bias_pretrained])\n \n if is_top_unfixed:\n geopath[-1] = 1\n \n return geopath\n \n \n # 遺伝子型からmodelへの変換\n # 0: 学習済みモデルのモジュール\n # 1: 新しく学習するモジュール\n def gene2model(self, gene):\n # copy pretrained model\n model = self.tmp_model\n model.reset_states()\n \n i_gene = 0\n for i_layer in range(len(model.layers)):\n # 重みがあればgeneを参照してtrainableを変更\n # 新規学習レイヤーの場合は重みをロード\n if self.li_is_weighted[i_layer]:\n if gene[i_gene] == 0:\n model.layers[i_layer].trainable=False\n model.layers[i_layer].set_weights(self.source_weights[i_layer])\n elif gene[i_gene] == 1:\n model.layers[i_layer].trainable=True\n model.layers[i_layer].set_weights(self.target_weights[i_layer])\n else:\n sys.exit('invalid gene value %s' % gene)\n \n # geneのイテレータを増やす\n i_gene += 1\n \n return model\n \n \n # 突然変異\n # 最終識別層以外を1/(層の数+1)の確率で反転\n def mutate_geopath(self, geopath):\n # 突然変異させる箇所を乱数で生成\n i_rand = int(np.random.rand() * (self.len_geopath))\n \n # i_randがout-of-rangeでなければ突然変異\n if i_rand < self.len_geopath - 1:\n is_mutated = True\n if geopath[i_rand] == 1:\n geopath[i_rand] = 0\n elif geopath[i_rand] == 0:\n geopath[i_rand] = 1\n else:\n sys.exit('invalid geopath value %s' % geopath)\n \n return geopath\n \n \n # 新規学習レイヤーの重みを保存\n def store_weights(self, gene, weights):\n i_gene = 0\n for i_layer in range(len(self.li_is_weighted)):\n if self.li_is_weighted[i_layer]:\n if gene[i_gene] == 1 and i_gene != 0:\n self.target_weights[i_layer] = weights[i_layer]\n \n i_gene += 1\n \n \n # kerasなmodelからの重みの抽出\n def extract_weights(self, model):\n tmp_weights = []\n for i_layer, is_weighted in enumerate(self.li_is_weighted):\n if is_weighted:\n tmp_weights.append(model.layers[i_layer].get_weights())\n else:\n tmp_weights.append([])\n return tmp_weights\n","sub_path":"swpathnet_func.py","file_name":"swpathnet_func.py","file_ext":"py","file_size_in_byte":8430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"186867919","text":"#!/usr/bin/python\n#\n# This file is part of Ansible\n#\n# Ansible 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# Ansible 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 Ansible. If not, see .\n#\n\nANSIBLE_METADATA = {\n \"metadata_version\": \"1.1\",\n \"status\": [\"preview\"],\n \"supported_by\": \"community\"\n}\n\nDOCUMENTATION = '''\n---\nmodule: fortimgr_lock\nversion_added: \"2.3\"\nshort_description: Manages ADOM locking and unlocking\ndescription:\n - Manages FortiManager ADOM locking and unlocking using jsonrpc API\nauthor: Jacob McGill (@jmcgill298), Don Yao (@fortinetps)\noptions:\n host:\n description:\n - The FortiManager's Address.\n required: true\n type: str\n port:\n description:\n - The TCP port used to connect to the FortiManager if other than the default used by the transport\n method(http=80, https=443).\n required: false\n type: int\n use_ssl:\n description:\n - Determines whether to use HTTPS(True) or HTTP(False).\n required: false\n default: True\n type: bool\n validate_certs:\n description:\n - Determines whether to validate certs against a trusted certificate file (True), or accept all certs (False).\n required: false\n default: False\n type: bool\n session_id:\n description:\n - The session_id of an established and active session\n required: false\n type: str\n username:\n description:\n - The username used to authenticate with the FortiManager.\n required: false\n type: str\n password:\n description:\n - The password associated with the username account.\n required: false\n type: str\n adom:\n description:\n - The ADOM that should have device being added to or removed from.\n required: false\n default: root\n type: str\n lock:\n description:\n - Locks the ADOM in the FortiManager.\n - True ensures the ADOM is locked.\n required: false\n type: bool\n default: False\n save:\n description:\n - Saves the config before unlocking a session.\n - True saves the configuration.\n - False does not save the configuration and all changes in the session will be lost if unlocked.\n required: false\n default: False\n type: bool\n unlock:\n description:\n - Unlocks the ADOM in the FortiManager.\n - True ensures the ADOM is unlocked and closes the current session with the FortiManager.\n required: false\n type: bool\n default: False\n'''\n\nEXAMPLES = '''\n- name: Lock the lab ADOM\n fortimgr_lock:\n host: \"{{ inventory_hostname }}\"\n username: \"{{ username }}\"\n password: \"{{ password }}\"\n adom: \"lab\"\n lock: True\n register: session\n- name: Set Session ID\n set_fact:\n session_id: \"{{ session.session_id }}\"\n- name: Make Change\n fortimgr_address:\n host: \"{{ inventory_hostname }}\"\n session_id: \"{{ session_id }}\"\n adom: \"lab\"\n address_name: \"Server01\"\n type: \"ipmask\"\n subnet: \"10.1.1.1/32\"\n- name: Save and Unlock the ADOM\n fortimgr_lock:\n host: \"{{ inventory_hostname }}\"\n session_id: \"{{ session_id }}\"\n adom: \"lab\"\n save: True\n unlock: True\n'''\n\nRETURN = '''\nlocked:\n description: States whether the ADOM was successfully locked during module execution. This does not report the\n current lock status.\n returned: always\n type: bool\n sample: True\nsaved:\n description: States whether the ADOM was successfully saved during module execution.\n returned: always\n type: bool\n sample: True\nunlocked:\n description: States whether the ADOM was successfully unlocked during module execution. This does not report the\n current lock status.\n returned: always\n type: bool\n sample: False\nsession_id:\n description: The session ID created by the FortiManager upon login.\n returned: when locked is True\n type: str\n sample: \"By6M1iHhyGnFY9cLRdRaaaXCgelNkjEKIlS7fQEilwH0XmH99nsaepk9EE3pWvySssspRzMCmr/ltYavQnuIjA==\"\n'''\n\nfrom ansible.module_utils.fortimgr_utils import *\n\n\ndef main():\n argument_spec = dict(\n adom=dict(required=False, type=\"str\"),\n host=dict(required=False, type=\"str\"),\n port=dict(required=False, type=\"int\"),\n username=dict(fallback=(env_fallback, [\"ANSIBLE_NET_USERNAME\"])),\n password=dict(fallback=(env_fallback, [\"ANSIBLE_NET_PASSWORD\"]), no_log=True),\n use_ssl=dict(required=False, type=\"bool\"),\n validate_certs=dict(required=False, type=\"bool\"),\n provider=dict(required=False, type=\"dict\"),\n save=dict(required=False, type=\"bool\"),\n session_id=dict(required=False, type=\"str\"),\n lock=dict(required=False, type=\"bool\"),\n unlock=dict(required=False, type=\"bool\")\n )\n\n module = AnsibleModule(argument_spec)\n\n # handle params and insure they are represented as the data type expected by fortimanager\n host = module.params[\"host\"]\n port = module.params[\"port\"]\n use_ssl = module.params[\"use_ssl\"]\n if use_ssl is None:\n use_ssl = True\n validate_certs = module.params[\"validate_certs\"]\n if validate_certs is None:\n validate_certs = False\n session_id = module.params[\"session_id\"]\n username = module.params[\"username\"]\n password = module.params[\"password\"]\n adom = module.params[\"adom\"]\n lock = module.params[\"lock\"]\n save = module.params[\"save\"]\n unlock = module.params[\"unlock\"]\n\n argument_check = dict(adom=adom, host=host)\n for key, val in argument_check.items():\n if not val:\n module.fail_json(msg=\"{} is required\".format(key))\n\n kwargs = dict()\n if port:\n kwargs[\"port\"] = port\n\n # use established session id or validate successful login\n session = FortiManager(host, username, password, use_ssl, validate_certs, adom, **kwargs)\n if session_id:\n session.session = session_id\n else:\n session_login = session.login()\n if not session_login.json()[\"result\"][0][\"status\"][\"code\"] == 0:\n module.fail_json(msg=\"Unable to login\", fortimgr_response=session_login.json())\n\n results = {\"locked\": False, \"saved\": False, \"unlocked\": False, \"changed\": True}\n \n if lock:\n session.config_lock(module)\n results.update(dict(locked=True, session_id=session.session))\n \n if save:\n save_status = session.save()\n if save_status[\"result\"][0][\"status\"][\"code\"] != 0:\n module.fail_json(msg=\"Unable to Save Session Config\", session_id=session.session, fortimgr_response=save_status)\n\n results[\"saved\"] = True\n \n if unlock:\n unlock_status = session.unlock()\n if unlock_status[\"result\"][0][\"status\"][\"code\"] != 0:\n module.fail_json(msg=\"Unable to Unlock Session\", session_id=session.session, fortimgr_response=unlock_status)\n\n results[\"unlocked\"] = True\n\n # logout, build in check for future logging capabilities\n session_logout = session.logout()\n # if not session_logout.json()[\"result\"][0][\"status\"][\"code\"] == 0:\n # results[\"msg\"] = \"Completed tasks, but unable to logout of FortiManager\"\n # module.fail_json(**results)\n\n return module.exit_json(**results)\n\n\nif __name__ == \"__main__\":\n main()","sub_path":"library/fortimgr_lock.py","file_name":"fortimgr_lock.py","file_ext":"py","file_size_in_byte":7639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"540742665","text":"import random\nfrom tkinter import *\n\nimport numpy as np\nfrom PIL import Image, ImageTk\n\n\ndef show_imgs(ndarray_path, disp_img_dim=(64, 64)):\n img_arr = np.load(ndarray_path)\n\n def read_image():\n random_img_id = random.randint(0, img_arr.shape[0]-1)\n img = ImageTk.PhotoImage(image=Image.fromarray(img_arr[random_img_id, :, :]).resize(disp_img_dim))\n canvas.itemconfig(p, image=img)\n canvas.mainloop()\n\n root = Tk()\n canvas = Canvas(root, width=disp_img_dim[0]+20, height=disp_img_dim[1]+20)\n canvas.pack()\n next_image = Button(root, command=read_image, text=\"Next image\", width=17, default=ACTIVE)\n next_image.pack()\n\n first_img = ImageTk.PhotoImage(image=Image.fromarray(img_arr[0, :, :]).resize(disp_img_dim))\n p = canvas.create_image(20, 20, anchor=NW, image=first_img)\n\n mainloop()\n\n\nif __name__ == '__main__':\n npy_file_path = r\"../../sample/output/full_simplified_ambulance.npy\"\n img_size = (200, 200)\n show_imgs(npy_file_path, img_size)\n","sub_path":"src/data_generator/data_viewer.py","file_name":"data_viewer.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"512063097","text":"import os\nfrom setuptools import setup, find_packages\n\nhere = os.path.abspath(os.path.dirname(__file__))\n\nwith open(os.path.join(here, 'README.rst')) as f:\n README = f.read()\n\nwith open(os.path.join(here, 'CHANGES.txt')) as f:\n CHANGES = f.read()\n\nversion = '0.2'\nrequire = ['pyramid', 'soapbox']\n\nsetup(name='pyramid_soap',\n version=version,\n description=\"Soap for pyramid.\",\n long_description=\"\"\"\\\n \"\"\",\n classifiers=[\n \"Programming Language :: Python\",\n \"Framework :: Pyramid\",\n \"License :: OSI Approved :: MIT License\",\n \"Topic :: Internet :: WWW/HTTP\",\n \"Topic :: Internet :: WWW/HTTP :: WSGI :: Application\",\n ],\n keywords='web pyramid pylons soap soapbox',\n author='Gines Martinez Sanchez',\n author_email='ginsmar@artgins.com',\n url='https://bitbucket.org/artgins/pyramid_soap',\n license='MIT',\n packages=find_packages(),\n include_package_data=True,\n zip_safe=False,\n install_requires=require,\n tests_require=require,\n test_suite=\"pyramid_soap\",\n entry_points=\"\"\"\n # -*- Entry points: -*-\n \"\"\",\n paster_plugins=['pyramid'],\n)\n","sub_path":"pypi_install_script/pyramid_soap-0.2.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"116689749","text":"\"\"\"\nConstants defined for Mayhem.\n\"\"\"\nfrom Vector2D import Vector2D\nfrom pygame import Rect\nfrom pygame.locals import *\nimport os\n\nos.environ['SDL_VIDEO_CENTERED'] = '1'\n\nFPS = 30\nSCREEN_W = 1920\nSCREEN_H = 1080\nSCREEN_SIZE = (SCREEN_W, SCREEN_H)\nWORLD_W = SCREEN_W * 4\nWORLD_H = SCREEN_H * 3\nWORLD_SIZE = (WORLD_W, WORLD_H)\nWORLD = Rect(0, 0, WORLD_W, WORLD_H)\nGRAVITY = Vector2D(0, 0.81)\nSPEED_LIMIT = 10\n\n# Player defaults and constants\nP1START = Vector2D(WORLD_W*0.45, WORLD_H*0.9)\nP2START = Vector2D(WORLD_W*0.55, WORLD_H*0.9)\nINITIAL_FUEL = 100\nINITIAL_AMMO = 25\nINITIAL_HULL = 3\nINITIAL_LIFE = 3\nINITIAL_DIRECTION = Vector2D(0, -1)\nINITIAL_VELOCITY = Vector2D(0, 0)\nTURN_RATE = 5\nSHIP_ACCELERATION = 1.2\n\nLASER_SPEED = 25\n# Times are in milliseconds\nLASER_COOLDOWN = 500\nLASER_DURATION = 2000\nINVULN_TIME = 500\nRESTART_TIME = 15000\n\nNUM_ASTEROIDS = (WORLD_W*WORLD_H)//100000\n\n\nLAYERS = {'far_background': 0,\n 'mid_background': 1,\n 'near_background':2,\n 'obstacles': 5,\n 'lasers': 5,\n 'players': 6,\n 'fuelpads': 7,\n 'foreground': 8}\n\nNEWSPRITE = USEREVENT + 1\nPLAYERDEATH = NEWSPRITE + 1\nGAMEOVER = PLAYERDEATH + 1\n\nCONTROLS = {1: {'up': K_i, 'left': K_j, 'right': K_l, 'fire': K_u},\n 0: {'up': K_w, 'left': K_a, 'right': K_d, 'fire': K_e}}\nNUM_PLAYERS = len(CONTROLS)\n\n# Uniform distribution of fuelpads.\nFUELPAD_LOCATIONS = {(WORLD_W/4*x, WORLD_H/3*y) for x in range(1, 4) for y in range(1, 3)}\n\nIMAGES = {'ship': ['images/ship1_1.png', 'images/ship2_1.png'],\n 'damage': ['images/damage3.png', 'images/damage2.png', 'images/damage1.png'],\n 'bullet': ['images/bullet1.png', 'images/bullet2.png'],\n 'arrow' : ['images/ship1_arrow.png', 'images/ship2_arrow.png'],\n 'bulleticon': ['images/bulleticon1.png', 'images/bulleticon2.png'],\n 'lifeicon': ['images/lifeicon1.png', 'images/lifeicon2.png'],\n 'obstacle': ['images/meteors/med1.png', 'images/meteors/med3.png',\n 'images/meteors/big1.png', 'images/meteors/big2.png',\n 'images/meteors/small1.png', 'images/meteors/small2.png'],\n 'fuel': 'images/fuel.png',\n 'star': 'images/star.png'}\n\nSOUNDS = {'laser': 'sounds/laser.ogg',\n 'fuel': 'sounds/ding.ogg'}\n\nFONTS = {'hud': 'fonts/emulogic.ttf',\n 'ship': 'fonts/ship.ttf'}\n\n# Colors\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\nDARK_GREY = (80, 80, 80)\nRED = (255, 0, 0)\nGREEN = (0, 255, 0)\nBLUE = (0, 0, 255)\n","sub_path":"CompletedSemesters/V2016/INF-1400/Oblig3/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":2518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"474038255","text":"# events-example0.py\r\n# Barebones timer, mouse, and keyboard events\r\n\r\nfrom tkinter import *\r\nimport time\r\nimport argparse\r\nimport stimuli\r\nimport numpy as np\r\nimport pandas as pd\r\nimport myConfig as mc\r\n\r\n\r\n# q1 q2\r\n# q3 q4\r\n\r\n\r\n####################################\r\n# Draw Functions\r\n####################################\r\n\r\ndef drawTaskCanvas(canvas, data):\r\n if data.state == 'copy task canvas':\r\n canvas.create_text(data.width/2, data.height-2.5*data.margin, text=\"Go to Reference\", font=\"Arial 16\")\r\n canvas.create_rectangle(data.width/2-100, data.height-3*data.margin, data.width/2+100, data.height-2*data.margin)\r\n time_remaining = \"Time Remaining: \" + (\"%d\" % (data.copy_time_remaining / 1000))\r\n if data.state == 'recall task canvas':\r\n time_remaining = \"Time Remaining: \" + ((\"%d\" % (data.recall_time_remaining / 1000)))\r\n canvas.create_text(data.width/2, data.margin, text=\"Canvas\", font=\"Arial 24 bold\")\r\n canvas.create_text(2.25*data.margin,data.height-2.25*data.margin, text=time_remaining, font=\"Arial 16\")\r\n canvas.create_text(data.width-2.25*data.margin, data.height-2.5*data.margin, text=\"Done!\", font=\"Arial 16\")\r\n canvas.create_rectangle(data.width-data.margin-150, data.height-3*data.margin, data.width-data.margin, data.height-2*data.margin)\r\n offset = data.width/2 - data.grid_width # for centering grid\r\n for r in range(len(data.exp0)):\r\n for c in range(len(data.exp0[0])):\r\n color = 'black' if data.exp0[r][c] != 0 else 'white'\r\n canvas.create_rectangle(data.rect_width*c+offset,\r\n data.rect_height*r+2*data.margin,\r\n data.rect_width*(c+1)+offset,\r\n data.rect_height*(r+1)+2*data.margin,\r\n fill=color)\r\n for r in range(len(data.exp1)):\r\n for c in range(len(data.exp1[0])):\r\n color = 'black' if data.exp1[r][c] != 0 else 'white'\r\n canvas.create_rectangle(data.rect_width*c+offset+data.grid_width,\r\n data.rect_height*r+2*data.margin,\r\n data.rect_width*(c+1)+offset+data.grid_width,\r\n data.rect_height*(r+1)+2*data.margin,\r\n fill=color)\r\n for r in range(len(data.exp2)):\r\n for c in range(len(data.exp2[0])):\r\n color = 'black' if data.exp2[r][c] != 0 else 'white'\r\n canvas.create_rectangle(data.rect_width*c+offset,\r\n data.rect_height*r+2*data.margin+data.grid_width,\r\n data.rect_width*(c+1)+offset,\r\n data.rect_height*(r+1)+2*data.margin+data.grid_width,\r\n fill=color)\r\n for r in range(len(data.exp3)):\r\n for c in range(len(data.exp3[0])):\r\n color = 'black' if data.exp3[r][c] != 0 else 'white'\r\n canvas.create_rectangle(data.rect_width*c+offset+data.grid_width,\r\n data.rect_height*r+2*data.margin+data.grid_width,\r\n data.rect_width*(c+1)+offset+data.grid_width,\r\n data.rect_height*(r+1)+2*data.margin+data.grid_width,\r\n fill=color)\r\n\r\ndef drawTaskRef(canvas, data):\r\n if data.state == 'copy task ref':\r\n canvas.create_text(data.width/2, data.height-2.5*data.margin, text=\"Go to Canvas\", font=\"Arial 16\")\r\n canvas.create_rectangle(data.width/2-100, data.height-3*data.margin, data.width/2+100, data.height-2*data.margin)\r\n time_remaining = \"Time Remaining: \" + (\"%d\" % (data.copy_time_remaining / 1000))\r\n if data.state == 'recall task ref':\r\n time_remaining = \"Time Remaining: \" + ((\"%d\" % (data.recall_presentation_time_remaining / 1000)))\r\n canvas.create_text(data.width/2, data.margin, text=\"Reference\", font=\"Arial 24 bold\")\r\n canvas.create_text(2.25*data.margin,data.height-2.25*data.margin, text=time_remaining, font=\"Arial 16\")\r\n if data.state != 'recall task ref':\r\n canvas.create_text(data.width-2.25*data.margin, data.height-2.5*data.margin, text=\"Done!\", font=\"Arial 16\")\r\n canvas.create_rectangle(data.width-data.margin-150, data.height-3*data.margin, data.width-data.margin, data.height-2*data.margin)\r\n offset = data.width/2 - data.grid_width # for centering grid\r\n for r in range(len(data.stim0)):\r\n for c in range(len(data.stim0[0])):\r\n color = 'black' if data.stim0[r][c] != 0 else 'white'\r\n canvas.create_rectangle(data.rect_width*c+offset,\r\n data.rect_height*r+2*data.margin,\r\n data.rect_width*(c+1)+offset,\r\n data.rect_height*(r+1)+2*data.margin,\r\n fill=color)\r\n for r in range(len(data.stim1)):\r\n for c in range(len(data.stim1[0])):\r\n color = 'black' if data.stim1[r][c] != 0 else 'white'\r\n canvas.create_rectangle(data.rect_width*c+offset+data.grid_width,\r\n data.rect_height*r+2*data.margin,\r\n data.rect_width*(c+1)+offset+data.grid_width,\r\n data.rect_height*(r+1)+2*data.margin,\r\n fill=color)\r\n for r in range(len(data.stim2)):\r\n for c in range(len(data.stim2[0])):\r\n color = 'black' if data.stim2[r][c] != 0 else 'white'\r\n canvas.create_rectangle(data.rect_width*c+offset,\r\n data.rect_height*r+2*data.margin+data.grid_width,\r\n data.rect_width*(c+1)+offset,\r\n data.rect_height*(r+1)+2*data.margin+data.grid_width,\r\n fill=color)\r\n for r in range(len(data.stim3)):\r\n for c in range(len(data.stim3[0])):\r\n color = 'black' if data.stim3[r][c] != 0 else 'white'\r\n canvas.create_rectangle(data.rect_width*c+offset+data.grid_width,\r\n data.rect_height*r+2*data.margin+data.grid_width,\r\n data.rect_width*(c+1)+offset+data.grid_width,\r\n data.rect_height*(r+1)+2*data.margin+data.grid_width,\r\n fill=color)\r\n\r\ndef drawInstructions(canvas, data):\r\n canvas.create_text(data.width/2, data.height/2,\r\n text=\"You will begin with 2 practice trials.\\nClick the mouse on top of the box to fill in color.\\nUse the “To Canvas” and “To Reference” button to switch between the reference and canvas boards\\nPress [Space] to begin copy task\",\r\n font=\"Arial 18\")\r\n\r\ndef drawReadyCopyTask(canvas, data):\r\n canvas.create_text(data.width/2, data.height/2,\r\n text=\"The time limit is 15 seconds\\nYou can refer to reference board at anytime\\nClick ‘done’ button after filling out the board\\nPress [Space] to begin copy task\",\r\n font=\"Arial 18\")\r\n\r\ndef drawReadyRecallTask(canvas, data):\r\n canvas.create_text(data.width/2, data.height/2,\r\n text=\"You will be given 5 seconds to memorize the board\\nYou cannot refer back to the reference board after the 5 seconds\\n10 seconds will be the time limit to fill in the board\\nPress [Space] to begin recall task\",\r\n font=\"Arial 18\")\r\n\r\ndef drawCorrect(canvas, data):\r\n canvas.create_text(data.width/2, data.height/2, text=\"CORRECT!\", font=\"Arial 24 bold\", fill=\"green\")\r\n\r\ndef drawWrong(canvas, data):\r\n txt = str(int(data.correctness / 64 * 100)) + \"% Correct\"\r\n canvas.create_text(data.width/2, data.height/2, text=txt, font=\"Arial 24 bold\", fill=\"red\")\r\n\r\ndef drawCopySwitchCanvas(canvas, data):\r\n canvas.create_rectangle(0, 0, data.width, data.height, fill='white', width=0)\r\n\r\ndef drawCopySwitchRef(canvas, data):\r\n canvas.create_rectangle(0, 0, data.width, data.height, fill='white', width=0)\r\n\r\ndef drawEnd(canvas, data):\r\n canvas.create_text(data.width/2, data.height/2, text=\"END!\", font=\"Arial 24 bold\")\r\n\r\ndef drawPaused(canvas, data):\r\n canvas.create_text(data.width/2, data.height/2, text=\"PAUSED\", font=\"Arial 24 bold\")\r\n\r\n\r\n####################################\r\n# List Functions\r\n####################################\r\n\r\ndef create2dlist(width=4, height=4):\r\n ret = []\r\n for h in range(height):\r\n L = [0] * width\r\n ret.append(L)\r\n return ret\r\n\r\ndef reset_exp(data):\r\n data.exp0 = create2dlist()\r\n data.exp1 = create2dlist()\r\n data.exp2 = create2dlist()\r\n data.exp3 = create2dlist()\r\n\r\n\r\n####################################\r\n# Canvas Interaction Functions\r\n####################################\r\n\r\ndef fill_rect(event, data):\r\n offset = data.width/2 - data.grid_width # for centering grid\r\n for r in range(len(data.exp0)):\r\n for c in range(len(data.exp0[0])):\r\n if (event.x > data.rect_width*c+offset and\r\n event.y > data.rect_height*r+2*data.margin and\r\n event.x < data.rect_width*(c+1)+offset and\r\n event.y < data.rect_height*(r+1)+2*data.margin):\r\n data.exp0[r][c] = 1 if data.exp0[r][c] == 0 else 0\r\n for r in range(len(data.exp1)):\r\n for c in range(len(data.exp1[0])):\r\n if (event.x > data.rect_width*c+offset+data.grid_width and\r\n event.y > data.rect_height*r+2*data.margin and\r\n event.x < data.rect_width*(c+1)+offset+data.grid_width and\r\n event.y < data.rect_height*(r+1)+2*data.margin):\r\n data.exp1[r][c] = 1 if data.exp1[r][c] == 0 else 0\r\n for r in range(len(data.exp2)):\r\n for c in range(len(data.exp2[0])):\r\n if (event.x > data.rect_width*c+offset and\r\n event.y > data.rect_height*r+2*data.margin+data.grid_height and\r\n event.x < data.rect_width*(c+1)+offset and\r\n event.y < data.rect_height*(r+1)+2*data.margin+data.grid_height):\r\n data.exp2[r][c] = 1 if data.exp2[r][c] == 0 else 0\r\n for r in range(len(data.exp3)):\r\n for c in range(len(data.exp3[0])):\r\n if (event.x > data.rect_width*c+offset+data.grid_width and\r\n event.y > data.rect_height*r+2*data.margin+data.grid_height and\r\n event.x < data.rect_width*(c+1)+offset+data.grid_width and\r\n event.y < data.rect_height*(r+1)+2*data.margin+data.grid_height):\r\n data.exp3[r][c] = 1 if data.exp3[r][c] == 0 else 0\r\n\r\ndef switch_button_pressed(event, data):\r\n assert(data.state == 'copy task ref' or data.state == 'copy task canvas')\r\n if (event.x > data.width/2-100 and event.x < data.width/2+100 and\r\n event.y > data.height-3*data.margin and event.y < data.height-2*data.margin):\r\n return True\r\n return False\r\n\r\ndef done_button_pressed(event, data):\r\n assert(data.state == 'copy task ref' or data.state == 'copy task canvas' or\r\n data.state == 'recall task canvas')\r\n if (event.x > data.width-data.margin-150 and event.x < data.width-data.margin and\r\n event.y > data.height-3*data.margin and event.y < data.height-2*data.margin):\r\n return True\r\n return False\r\n\r\ndef mousePressedCopyRef(event, data):\r\n if switch_button_pressed(event,data):\r\n data.state = 'copy switch canvas'\r\n elif done_button_pressed(event,data):\r\n data.correctness = None\r\n check_correctness(data)\r\n if data.correctness == 8*8:\r\n data.state = 'copy correct'\r\n else:\r\n data.state = 'copy wrong'\r\n\r\ndef mousePressedCopyCanvas(event, data):\r\n fill_rect(event,data)\r\n if switch_button_pressed(event,data):\r\n data.state = 'copy switch ref'\r\n elif done_button_pressed(event,data):\r\n data.correctness = None\r\n check_correctness(data)\r\n if data.correctness == 8*8:\r\n data.state = 'copy correct'\r\n else:\r\n data.state = 'copy wrong'\r\n\r\ndef mousePressedRecallCanvas(event, data):\r\n fill_rect(event,data)\r\n if done_button_pressed(event,data):\r\n data.correctness = None\r\n check_correctness(data)\r\n if data.correctness == 8*8:\r\n data.state = 'recall correct'\r\n else:\r\n data.state = 'recall wrong'\r\n\r\n\r\n####################################\r\n# Utility Functions\r\n####################################\r\n\r\ndef collect_statistics(data):\r\n if data.recall:\r\n q1 = stimuli.getPatternID(data.trial[0][0])\r\n q2 = stimuli.getPatternID(data.trial[1][0])\r\n q3 = stimuli.getPatternID(data.trial[2][0])\r\n q4 = stimuli.getPatternID(data.trial[3][0])\r\n name = data.id\r\n size = data.condition\r\n freq = data.trial[4]\r\n rt = 10 * 1000 - data.recall_time_remaining\r\n correct = data.correctness\r\n df = pd.DataFrame([[name,size,freq,q1,q2,q3,q4,rt,correct]], columns=['id','size','freq','q1','q2','q3','q4','RT','correct'])\r\n data.statistics = data.statistics.append(df)\r\n\r\ndef check_correctness(data):\r\n assert(data.correctness == None) # otherwise some shit is wrong\r\n exp_list = [data.exp0, data.exp1, data.exp2, data.exp3]\r\n stim_list = [data.stim0, data.stim1, data.stim2, data.stim3]\r\n correct = 8*8\r\n incorrect = 0\r\n for q in range(4): # 4 quadrants\r\n exp = exp_list[q]\r\n stim = stim_list[q]\r\n for r in range(len(exp)):\r\n for c in range(len(exp[r])):\r\n if exp[r][c] != stim[r][c]:\r\n incorrect += 1\r\n correct = correct - incorrect\r\n data.correctness = correct\r\n collect_statistics(data)\r\n reset_exp(data)\r\n data.copy_time_remaining = 15 * 1000 # reset to 15 seconds\r\n data.recall_time_remaining = 10 * 1000 # reset to 10 seconds\r\n if data.recall:\r\n data.trial = data.trials[data.recall_trial]\r\n elif data.practice:\r\n data.trial = data.trials[data.practice_trial]\r\n elif data.copy:\r\n data.trial = data.trials[data.copy_trial]\r\n for p in [data.trial[0], data.trial[1], data.trial[2], data.trial[3]]:\r\n if p[1] == 1:\r\n data.stim0 = p[0]\r\n elif p[1] == 2:\r\n data.stim1 = p[0]\r\n elif p[1] == 3:\r\n data.stim2 = p[0]\r\n elif p[1] == 4:\r\n data.stim3 = p[0]\r\n return correct\r\n\r\n\r\n####################################\r\n# Kosbie's Functions\r\n####################################\r\n\r\ndef init(data):\r\n # load data.xyz as appropriate\r\n\r\n # INITIAL STATE\r\n cond = \"LS\" if data.condition == 0 else \"HS\"\r\n data.copy_trials = stimuli.generateTrialListCopy(cond)\r\n data.practice_trials = stimuli.generateTrialListPractice(cond)\r\n data.recall_trials = stimuli.generateTrialListRecall(cond)\r\n data.trials = data.practice_trials\r\n data.practice = True\r\n data.copy = False\r\n data.recall = False\r\n\r\n data.practice_trial = 0\r\n data.total_practice_trials = len(data.practice_trials)\r\n\r\n data.copy_trial = 0\r\n data.total_copy_trials = len(data.copy_trials)\r\n\r\n data.recall_trial = 0\r\n data.total_recall_trials = len(data.recall_trials)\r\n\r\n data.trial = data.trials[data.practice_trial]\r\n data.stim0 = create2dlist()\r\n data.stim1 = create2dlist()\r\n data.stim2 = create2dlist()\r\n data.stim3 = create2dlist()\r\n for p in [data.trial[0], data.trial[1], data.trial[2], data.trial[3]]:\r\n if p[1] == 1:\r\n data.stim0 = p[0]\r\n elif p[1] == 2:\r\n data.stim1 = p[0]\r\n elif p[1] == 3:\r\n data.stim2 = p[0]\r\n elif p[1] == 4:\r\n data.stim3 = p[0]\r\n data.exp0 = create2dlist()\r\n data.exp1 = create2dlist()\r\n data.exp2 = create2dlist()\r\n data.exp3 = create2dlist()\r\n data.prev_state = None\r\n data.margin = mc.margin\r\n data.rect_width = mc.rectWidth\r\n data.rect_height = mc.rectHeight\r\n data.grid_width = data.rect_width * 4\r\n data.grid_height = data.rect_height * 4\r\n data.copy_time_remaining = 15 * 1000 # 15 seconds\r\n data.switch_time = 0.5 * 1000 # half a second\r\n data.correctness = None\r\n data.feedback_time = 2 * 1000 # 2 seconds\r\n data.recall_presentation_time_remaining = 5 * 1000 # 5 seconds\r\n data.recall_time_remaining = 10 * 1000 # 10 seconds\r\n data.statistics = pd.DataFrame([[-1,-1,-1,-1,-1,-1,-1,-1,-1]],\r\n columns=['id','size','freq','q1','q2','q3','q4','RT','correct']) # first row will be dummy row\r\n\r\ndef mousePressed(event, data):\r\n if data.state == 'copy task ref':\r\n mousePressedCopyRef(event,data)\r\n elif data.state == 'copy task canvas':\r\n mousePressedCopyCanvas(event,data)\r\n elif data.state == 'recall task canvas':\r\n mousePressedRecallCanvas(event,data)\r\n\r\ndef keyPressed(event, data):\r\n if data.state != 'paused':\r\n print(event.char == 'p')\r\n if event.char == 'p':\r\n data.prev_state = data.state\r\n data.state = 'paused'\r\n if data.state == 'instructions':\r\n if event.char == ' ':\r\n data.practice = True\r\n data.state = 'copy task ref'\r\n elif data.state == 'ready copy task':\r\n if event.char == ' ':\r\n data.practice = False\r\n data.copy = True\r\n data.state = 'copy task ref'\r\n data.trials = data.copy_trials\r\n data.trial = data.trials[data.copy_trial]\r\n for p in [data.trial[0], data.trial[1], data.trial[2], data.trial[3]]:\r\n if p[1] == 1:\r\n data.stim0 = p[0]\r\n elif p[1] == 2:\r\n data.stim1 = p[0]\r\n elif p[1] == 3:\r\n data.stim2 = p[0]\r\n elif p[1] == 4:\r\n data.stim3 = p[0]\r\n elif data.state == 'ready recall task':\r\n if event.char == ' ':\r\n data.copy = False\r\n data.recall = True\r\n data.state = 'recall task ref'\r\n data.trials = data.recall_trials\r\n data.trial = data.trials[data.recall_trial]\r\n for p in [data.trial[0], data.trial[1], data.trial[2], data.trial[3]]:\r\n if p[1] == 1:\r\n data.stim0 = p[0]\r\n elif p[1] == 2:\r\n data.stim1 = p[0]\r\n elif p[1] == 3:\r\n data.stim2 = p[0]\r\n elif p[1] == 4:\r\n data.stim3 = p[0]\r\n else:\r\n if event.char == 'p':\r\n data.state = data.prev_state\r\n\r\ndef timerFired(data):\r\n if data.state == 'copy task ref' or data.state == 'copy task canvas':\r\n data.copy_time_remaining -= data.timerDelay * mc.timeFactor\r\n if data.copy_time_remaining < 0:\r\n data.correctness = None\r\n check_correctness(data)\r\n data.state = 'copy wrong'\r\n data.copy_time_remaining = 15 * 1000\r\n return\r\n elif data.state == 'copy correct' or data.state == 'copy wrong':\r\n data.feedback_time -= data.timerDelay * mc.timeFactor\r\n if data.feedback_time < 0:\r\n data.state = 'copy task ref'\r\n data.feedback_time = 2 * 1000\r\n if data.practice:\r\n data.practice_trial += 1\r\n if data.practice_trial == data.total_practice_trials:\r\n data.practice = False\r\n data.practice_trial = -1\r\n data.copy = True\r\n data.state = 'ready copy task'\r\n return\r\n data.trial = data.trials[data.practice_trial]\r\n for p in [data.trial[0], data.trial[1], data.trial[2], data.trial[3]]:\r\n if p[1] == 1:\r\n data.stim0 = p[0]\r\n elif p[1] == 2:\r\n data.stim1 = p[0]\r\n elif p[1] == 3:\r\n data.stim2 = p[0]\r\n elif p[1] == 4:\r\n data.stim3 = p[0]\r\n if data.copy:\r\n data.copy_trial += 1\r\n if data.copy_trial == data.total_copy_trials:\r\n data.copy = False\r\n data.recall = True\r\n data.copy_trial = -1\r\n data.state = 'ready recall task'\r\n return\r\n elif data.state == 'recall correct' or data.state == 'recall wrong':\r\n data.feedback_time -= data.timerDelay * mc.timeFactor\r\n if data.feedback_time < 0:\r\n data.state = 'recall task ref'\r\n data.feedback_time = 2 * 1000\r\n data.recall_trial += 1\r\n if data.recall_trial == data.total_recall_trials:\r\n data.recall = False\r\n data.recall_trial = -1\r\n data.state = 'end'\r\n elif data.state == 'copy switch canvas':\r\n data.switch_time -= data.timerDelay * mc.timeFactor\r\n if data.switch_time < 0:\r\n data.state = 'copy task canvas'\r\n data.switch_time = 0.5 * 1000\r\n elif data.state == 'copy switch ref':\r\n data.switch_time -= data.timerDelay * mc.timeFactor\r\n if data.switch_time < 0:\r\n data.state = 'copy task ref'\r\n data.switch_time = 0.5 * 1000\r\n elif data.state == 'recall task ref':\r\n data.recall_presentation_time_remaining -= data.timerDelay * mc.timeFactor\r\n if data.recall_presentation_time_remaining < 0:\r\n data.state = 'recall task canvas'\r\n data.recall_presentation_time_remaining = 5 * 1000\r\n return\r\n elif data.state == 'recall task canvas':\r\n data.recall_time_remaining -= data.timerDelay * mc.timeFactor\r\n if data.recall_time_remaining < 0:\r\n data.correctness = None\r\n check_correctness(data)\r\n data.state = 'recall wrong'\r\n return\r\n\r\ndef redrawAll(canvas, data):\r\n if data.state == 'paused':\r\n drawPaused(canvas,data)\r\n elif data.state == 'instructions':\r\n drawInstructions(canvas,data)\r\n elif data.state == 'ready copy task':\r\n drawReadyCopyTask(canvas,data)\r\n elif data.state == 'copy task ref':\r\n drawTaskRef(canvas,data)\r\n elif data.state == 'copy task canvas':\r\n drawTaskCanvas(canvas,data)\r\n elif data.state == 'recall task ref':\r\n drawTaskRef(canvas,data)\r\n elif data.state == 'recall task canvas':\r\n drawTaskCanvas(canvas,data)\r\n elif data.state == 'copy switch canvas':\r\n drawCopySwitchCanvas(canvas,data)\r\n elif data.state == 'copy switch ref':\r\n drawCopySwitchRef(canvas,data)\r\n elif data.state == 'ready recall task':\r\n drawReadyRecallTask(canvas,data)\r\n elif data.state == 'copy correct':\r\n drawCorrect(canvas,data)\r\n elif data.state == 'copy wrong':\r\n drawWrong(canvas,data)\r\n elif data.state == 'recall correct':\r\n drawCorrect(canvas,data)\r\n elif data.state == 'recall wrong':\r\n drawWrong(canvas,data)\r\n elif data.state == 'end':\r\n drawEnd(canvas,data)\r\n else:\r\n return # we should never reach this state\r\n\r\n####################################\r\n# use the run function as-is\r\n####################################\r\n\r\ndef run(name, condition, width=300, height=300): # thanks koz\r\n def redrawAllWrapper(canvas, data):\r\n canvas.delete(ALL)\r\n canvas.create_rectangle(0, 0, data.width, data.height,\r\n fill='white', width=0)\r\n redrawAll(canvas, data)\r\n canvas.update()\r\n\r\n def mousePressedWrapper(event, canvas, data):\r\n mousePressed(event, data)\r\n redrawAllWrapper(canvas, data)\r\n\r\n def keyPressedWrapper(event, canvas, data):\r\n keyPressed(event, data)\r\n redrawAllWrapper(canvas, data)\r\n\r\n def timerFiredWrapper(canvas, data):\r\n timerFired(data)\r\n redrawAllWrapper(canvas, data)\r\n # pause, then call timerFired again\r\n canvas.after(data.timerDelay, timerFiredWrapper, canvas, data)\r\n # Set up data and call init\r\n class Struct(object): pass\r\n data = Struct()\r\n data.width = width\r\n data.height = height\r\n data.timerDelay = 5 # milliseconds\r\n data.state = 'instructions'\r\n data.id = name\r\n data.condition = condition\r\n init(data)\r\n # create the root and the canvas\r\n root = Tk()\r\n canvas = Canvas(root, width=data.width, height=data.height)\r\n canvas.pack()\r\n # set up events\r\n root.bind(\"\", lambda event:\r\n mousePressedWrapper(event, canvas, data))\r\n root.bind(\"\", lambda event:\r\n keyPressedWrapper(event, canvas, data))\r\n timerFiredWrapper(canvas, data)\r\n # and launch the app\r\n root.mainloop() # blocks until window is closed\r\n print(data.statistics)\r\n csv_name = \"data/\" + data.id + \".csv\"\r\n data.statistics.to_csv(csv_name)\r\n print(\"bye!\")\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument(\"--id\", type=str, help=\"Participant ID\")\r\n parser.add_argument('--cond', type=int, help=\"Experiment Condition\", default=0)\r\n args = parser.parse_args()\r\n run(args.id, args.cond, mc.screenWidth, mc.screenHeight)\r\nmain()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":25597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"476797723","text":"import numpy as np\nimport pandas as pa\nimport csv\nfrom statsmodels.tsa.arima_model import ARIMA\nfrom pythonpdevs.src.Utils import Timer\nfrom sklearn.metrics import mean_squared_error\n\n# get data\ndef GetData(fileName):\n return pa.read_csv(fileName, header=0, parse_dates=[0], index_col=0)\n\n\ndef trainingSet(serie):\n trainPercent = 0.90\n X = serie.values\n size = int(len(X) * trainPercent)\n train = X[0:size]\n test = X[size:len(X)]\n return train, test\n\n\ndef startTraining(filename):\n series = GetData(filename)\n predictions = list()\n train, test = trainingSet(series)\n history = [x for x in train]\n for t in range(len(test)):\n model = ARIMA(history, order=(7, 1, 0))\n model_fit = model.fit(disp=0)\n output = model_fit.forecast()\n yhat = output[0]\n predictions.append(yhat)\n obs = test[t]\n history.append(obs)\n #print('predicted=%f, expected=%f' % (int(yhat), obs))\n error = mean_squared_error(test, predictions)\n print('Test MSE: %.3f' % error)\n\n X = difference(series.values)\n\n # save coefficients\n coef = model_fit.params\n window_size = 7\n np.save('./data/model/temp_model.npy', coef)\n\n # save lag\n lag = X[-window_size:]\n np.save('./data/temp_data.npy', lag)\n\n # save the last ob\n np.save('./data/temp_obs.npy', [series.values[-1]])\n\n # Start temperature prediction\n #predictions = startPredict()\n\n #return predictions\n\n\n# create a difference transform of the dataset\ndef difference(dataset):\n diff = list()\n for i in range(1, len(dataset)):\n value = dataset[i] - dataset[i - 1]\n diff.append(value)\n return np.array(diff)\n\n\ndef predict(coef, history):\n yhat = coef[0]\n for i in range(1, len(coef)):\n yhat += coef[i] * history[-i]\n return yhat\n\n\ndef startPredict():\n timer = Timer()\n timer.start()\n # load model\n coef = np.load('./data/model/temp_model.npy')\n lag = np.load('./data/temp_data.npy')\n last_ob = np.load('./data/temp_obs.npy')\n\n # make prediction\n prediction = predict(coef, lag)\n # transform prediction\n yhat = prediction + last_ob[0]\n timer.stop()\n print('Prediction: %f' % yhat)\n\n return yhat\n","sub_path":"Predict_ARIMA_class.py","file_name":"Predict_ARIMA_class.py","file_ext":"py","file_size_in_byte":2224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"629790230","text":"from django.conf.urls import url\nfrom django.conf import settings\nfrom . import views\n\n\nurlpatterns = [\n url(r'^$', views.index, name='index'),\n url(r'^register$', views.register_view, name='register_view'),\n url(r'^login$', views.login_view, name='login_view'),\n url(r'^logout$', views.logout_view, name='logout_view'),\n url(r'^info$', views.info_view, name='info_view'),\n url(r'^backword$', views.backword_view, name='backword_view'),\n url(r'^addnote$', views.addnote, name='addnote'),\n url(r'^static/(?P.*)$', 'django.views.static.serve',{'document_root': settings.STATIC_URL}),\n]\n","sub_path":"backwordweb/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"561065916","text":"from django.contrib import admin\nfrom models import Area, Department, Drawing, Product, Change, Masslog, UserProfile\n\n# Register your models here.\n\nclass AreaAdmin(admin.ModelAdmin):\n list_display = ('name', 'id')\n list_display_links = ('name',)\n \n# class DepartmentInline(admin.TabularInline):\n# fields = ('name',)\n \nclass DepartmentAdmin(admin.ModelAdmin):\n list_display = ('name', 'id')\n list_display_links = ('name',)\n \nclass DrawingAdmin(admin.ModelAdmin):\n list_display = ('code', 'name', 'id', 'product')\n list_display_links = ('code',)\n \nclass MasslogAdmin(admin.ModelAdmin):\n list_display = ('drawing', 'change', 'current_mass')\n list_display_links = ('drawing',)\n\nadmin.site.register(Area, AreaAdmin)\n\nadmin.site.register(Department, DepartmentAdmin)\nadmin.site.register(Drawing, DrawingAdmin)\nadmin.site.register(Product)\nadmin.site.register(Change)\nadmin.site.register(Masslog, MasslogAdmin)\nadmin.site.register(UserProfile)","sub_path":"app/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"602671666","text":"import pandas as pd\nimport time\nfrom jikanscrap import get_data\nimport numpy as np\n\ngenres = ['Historical',\n 'Drama',\n 'Shoujo',\n 'Adventure',\n 'Seinen',\n 'Fantasy',\n 'Josei',\n 'Slice of Life',\n 'Mystery',\n 'Dementia',\n 'Thriller',\n 'Psychological',\n 'Space',\n 'Sci-Fi',\n 'Police',\n 'Samurai',\n 'Supernatural',\n 'Music',\n 'Military',\n 'Vampire',\n 'Sports',\n 'Martial Arts',\n 'Shoujo Ai',\n 'Cars',\n 'Kids',\n 'Parody',\n 'Romance',\n 'Super Power',\n 'Demons',\n 'Horror',\n 'Magic',\n 'Shounen',\n 'Game',\n 'Mecha',\n 'Comedy',\n 'Ecchi',\n 'Harem',\n 'Action',\n 'School',\n 'Yaoi',\n 'Yuri',\n 'Shounen Ai',\n 'Hentai']\n\nstudio = ['8bit',\n 'A-1 Pictures',\n 'AIC',\n 'APPP',\n 'Artland',\n 'Asahi Production',\n 'Bee Train',\n 'Bones',\n \"Brain's Base\",\n 'CloverWorks',\n 'CoMix Wave Films',\n 'Daume',\n 'David Production',\n 'Diomedea',\n 'Doga Kobo',\n 'Fuji TV',\n 'Gainax',\n 'GoHands',\n 'Gonzo',\n 'Graphinica',\n 'Hal Film Maker',\n 'Imagin',\n 'J.C.Staff',\n 'Khara',\n 'Kinema Citrus',\n 'Kyoto Animation',\n 'LIDENFILMS',\n 'Lerche',\n 'MAPPA',\n 'Madhouse',\n 'Manglobe',\n 'Mushi Production',\n 'Nippon Animation',\n 'OLM',\n 'Oh! Production',\n 'Orange',\n 'P.A. Works',\n 'Palm Studio',\n 'Passione',\n 'Polygon Pictures',\n 'Production I.G',\n 'Production IMS',\n 'Radix',\n 'Satelight',\n 'Science SARU',\n 'Seven',\n 'Shaft',\n 'Shuka',\n 'Silver Link.',\n 'Studio 3Hz',\n 'Studio 4°C',\n 'Studio Deen',\n 'Studio Gallop',\n 'Studio Ghibli',\n 'Studio Gokumi',\n 'Studio Hibari',\n 'Studio Pierrot',\n 'Studio Rikka',\n 'Sunrise',\n 'SynergySP',\n 'TMS Entertainment',\n 'TYO Animations',\n 'Tatsunoko Production',\n 'Telecom Animation Film',\n 'Tezuka Productions',\n 'Toei Animation',\n 'Tokyo Movie Shinsha',\n 'Trigger',\n 'White Fox',\n 'Wit Studio',\n 'Xebec',\n 'Zexcs',\n 'feel.',\n 'ufotable',\n 'Other']\n\n\ntype = ['TV', 'Movie', 'OVA', 'Special', 'ONA', 'Music_type']\n\nsource = ['Manga', 'Novel', 'Original', 'Lightnovel', 'Visualnovel', 'Game', 'Other_source']\n\ndata_cols = ['name', 'id_ref', 'score', 'mean', 'rank', 'popularity', 'members', 'fav/members', 'rank/pop', 'year', 'fav', 'ep', 'notDone']\n\ndata_cols = data_cols + type + source + genres + studio\n\ndef create_df():\n df = pd.DataFrame()\n\n for c in data_cols:\n df[c] = 0\n for c in genres:\n df[c] = 0\n\n df.to_csv('data/data.csv')\n\n\ncreate_df()\n","sub_path":"anime_prediction_app_ISO_encode/df_creation.py","file_name":"df_creation.py","file_ext":"py","file_size_in_byte":2861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"408725811","text":"#This file is part of Tryton. The COPYRIGHT file at the top level of\n#this repository contains the full copyright notices and license terms.\nfrom trytond.model import ModelView, ModelSQL, fields\nfrom trytond.pyson import Eval\nfrom trytond.transaction import Transaction\nfrom trytond.pool import Pool, PoolMeta\n\n__all__ = ['InvoiceLine', 'Account']\n__metaclass__ = PoolMeta\n\n\nclass InvoiceLine:\n __name__ = 'account.invoice.line'\n analytic_accounts = fields.Many2One('analytic_account.account.selection',\n 'Analytic Accounts',\n states={\n 'invisible': Eval('type') != 'line',\n },\n depends=['type'])\n\n @classmethod\n def _view_look_dom_arch(cls, tree, type, field_children=None):\n AnalyticAccount = Pool().get('analytic_account.account')\n AnalyticAccount.convert_view(tree)\n return super(InvoiceLine, cls)._view_look_dom_arch(tree, type,\n field_children=field_children)\n\n @classmethod\n def fields_get(cls, fields_names=None):\n AnalyticAccount = Pool().get('analytic_account.account')\n\n fields = super(InvoiceLine, cls).fields_get(fields_names)\n\n analytic_accounts_field = super(InvoiceLine, cls).fields_get(\n ['analytic_accounts'])['analytic_accounts']\n\n fields.update(AnalyticAccount.analytic_accounts_fields_get(\n analytic_accounts_field, fields_names,\n states=cls.analytic_accounts.states,\n required_states=Eval('type') == 'line'))\n return fields\n\n @classmethod\n def default_get(cls, fields, with_rec_name=True):\n fields = [x for x in fields if not x.startswith('analytic_account_')]\n return super(InvoiceLine, cls).default_get(fields,\n with_rec_name=with_rec_name)\n\n @classmethod\n def read(cls, ids, fields_names=None):\n if fields_names:\n fields_names2 = [x for x in fields_names\n if not x.startswith('analytic_account_')]\n else:\n fields_names2 = fields_names\n\n res = super(InvoiceLine, cls).read(ids, fields_names=fields_names2)\n\n if not fields_names:\n fields_names = cls._fields.keys()\n\n root_ids = []\n for field in fields_names:\n if field.startswith('analytic_account_') and '.' not in field:\n root_ids.append(int(field[len('analytic_account_'):]))\n if root_ids:\n id2record = {}\n for record in res:\n id2record[record['id']] = record\n lines = cls.browse(ids)\n for line in lines:\n for root_id in root_ids:\n id2record[line.id]['analytic_account_'\n + str(root_id)] = None\n if line.type != 'line':\n continue\n if not line.analytic_accounts:\n continue\n for account in line.analytic_accounts.accounts:\n if account.root.id in root_ids:\n id2record[line.id]['analytic_account_'\n + str(account.root.id)] = account.id\n for field in fields_names:\n if field.startswith('analytic_account_'\n + str(account.root.id) + '.'):\n ham, field2 = field.split('.', 1)\n id2record[line.id][field] = account[field2]\n return res\n\n @classmethod\n def create(cls, vlist):\n Selection = Pool().get('analytic_account.account.selection')\n vlist = [x.copy() for x in vlist]\n for vals in vlist:\n selection_vals = {}\n for field in vals.keys():\n if field.startswith('analytic_account_'):\n if vals[field]:\n selection_vals.setdefault('accounts', [])\n selection_vals['accounts'].append(('add',\n [vals[field]]))\n del vals[field]\n if vals.get('analytic_accounts'):\n Selection.write([Selection(vals['analytic_accounts'])],\n selection_vals)\n elif vals.get('type', 'line') == 'line':\n selection, = Selection.create([selection_vals])\n vals['analytic_accounts'] = selection.id\n return super(InvoiceLine, cls).create(vlist)\n\n @classmethod\n def write(cls, *args):\n Selection = Pool().get('analytic_account.account.selection')\n actions = iter(args)\n args = []\n for lines, values in zip(actions, actions):\n values = values.copy()\n selection_vals = {}\n for field, value in values.items():\n if field.startswith('analytic_account_'):\n root_id = int(field[len('analytic_account_'):])\n selection_vals[root_id] = value\n del values[field]\n if selection_vals:\n for line in lines:\n if line.type != 'line':\n continue\n accounts = []\n if not line.analytic_accounts:\n # Create missing selection\n selection, = Selection.create([{}])\n cls.write([line], {\n 'analytic_accounts': selection.id,\n })\n for account in line.analytic_accounts.accounts:\n if account.root.id in selection_vals:\n value = selection_vals[account.root.id]\n if value:\n accounts.append(value)\n else:\n accounts.append(account.id)\n for account_id in selection_vals.values():\n if account_id \\\n and account_id not in accounts:\n accounts.append(account_id)\n to_remove = list(\n set((a.id for a in line.analytic_accounts.accounts))\n - set(accounts))\n Selection.write([line.analytic_accounts], {\n 'accounts': [\n ('remove', to_remove),\n ('add', accounts),\n ],\n })\n args.extend((lines, values))\n super(InvoiceLine, cls).write(*args)\n\n @classmethod\n def delete(cls, lines):\n Selection = Pool().get('analytic_account.account.selection')\n\n selection_ids = []\n for line in lines:\n if line.analytic_accounts:\n selection_ids.append(line.analytic_accounts.id)\n\n super(InvoiceLine, cls).delete(lines)\n Selection.delete(Selection.browse(selection_ids))\n\n @classmethod\n def copy(cls, lines, default=None):\n Selection = Pool().get('analytic_account.account.selection')\n\n new_lines = super(InvoiceLine, cls).copy(lines, default=default)\n\n for line in new_lines:\n if line.analytic_accounts:\n selection, = Selection.copy([line.analytic_accounts])\n cls.write([line], {\n 'analytic_accounts': selection.id,\n })\n return new_lines\n\n def _credit(self):\n Selection = Pool().get('analytic_account.account.selection')\n\n result = super(InvoiceLine, self)._credit()\n\n if self.analytic_accounts:\n selection, = Selection.copy([self.analytic_accounts])\n result['analytic_accounts'] = selection.id\n return result\n\n def get_move_line(self):\n values = super(InvoiceLine, self).get_move_line()\n if self.analytic_accounts and self.analytic_accounts.accounts:\n for value in values:\n value['analytic_lines'] = []\n to_create = []\n for account in self.analytic_accounts.accounts:\n vals = {}\n vals['name'] = self.description\n vals['debit'] = value['debit']\n vals['credit'] = value['credit']\n vals['account'] = account.id\n vals['journal'] = self.invoice.journal.id\n vals['date'] = (self.invoice.accounting_date\n or self.invoice.invoice_date)\n vals['reference'] = self.invoice.reference\n vals['party'] = self.invoice.party.id\n to_create.append(vals)\n if to_create:\n value['analytic_lines'] = [('create', to_create)]\n return values\n\n\nclass Account(ModelSQL, ModelView):\n __name__ = 'analytic_account.account'\n\n @classmethod\n def delete(cls, accounts):\n InvoiceLine = Pool().get('account.invoice.line')\n super(Account, cls).delete(accounts)\n # Restart the cache on the fields_view_get method of\n # account.invoice.line\n InvoiceLine._fields_view_get_cache.clear()\n\n @classmethod\n def create(cls, vlist):\n InvoiceLine = Pool().get('account.invoice.line')\n accounts = super(Account, cls).create(vlist)\n # Restart the cache on the fields_view_get method of\n # account.invoice.line\n InvoiceLine._fields_view_get_cache.clear()\n return accounts\n\n @classmethod\n def write(cls, accounts, values, *args):\n InvoiceLine = Pool().get('account.invoice.line')\n super(Account, cls).write(accounts, values, *args)\n # Restart the cache on the fields_view_get method of\n # account.invoice.line\n InvoiceLine._fields_view_get_cache.clear()\n","sub_path":"analytic_invoice/invoice.py","file_name":"invoice.py","file_ext":"py","file_size_in_byte":9815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"509769541","text":"from django.db import models\nimport json\nfrom django.utils import timezone\n# Create your models here.\nclass Quote(models.Model):\n unique_id = models.CharField(max_length=100, null=True)\n text = models.TextField()\n author = models.CharField(max_length=512)\n @property\n def to_dict(self):\n data={\n 'text': json.loads(self.text),\n 'author': json.loads(self.author),\n 'unique_id': json.loads(self.unique_id)\n }\n return data\n \n def __str__(self):\n return self.unique_id\n\nclass ScrapyItem(models.Model):\n unique_id = models.CharField(max_length=100, null=True)\n data = models.TextField()\n date = models.DateTimeField(default=timezone.now)\n\n @property\n def to_dict(self):\n data={\n 'data': json.loads(self.data),\n 'date': self.date\n }\n return data\n \n def __str__(self):\n return self.unique_id\n \n\n","sub_path":"main/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"266049234","text":"# import libraries\nimport os\n\nimport numpy as np\nimport tensorflow as tf\nfrom skimage.color import lab2rgb, rgb2lab\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.layers import Conv2D, InputLayer, UpSampling2D\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.preprocessing.image import (array_to_img, img_to_array,load_img)\nfrom tqdm import tqdm\n\n\ndef get_lab(img):\n l = rgb2lab(img/255)[:,:,0]\n return l\n\ndef get_color(img):\n x = rgb2lab(img/255)[:,:,1:] # this is the A and B values; a-magenta-green; b-yellow-blue\n x/=128\n return x\n\ndef get_images(path, color=\"lab\"):\n images = list()\n for filename in os.listdir(path):\n if filename[0] != '.':\n if color == \"lab\":\n img = get_lab(np.array(img_to_array(load_img(path + filename)), dtype=float))\n images.append(img.reshape(1,img.shape[0],img.shape[1],1))\n else:\n img = get_color(np.array(img_to_array(load_img(path + filename)), dtype=float))\n images.append(img.reshape(1,img.shape[0],img.shape[1],2))\n return images\n\n'''\nConvert all training images from the RGB color space to the Lab color space.\nUse the L channel as the input to the network and train the network to predict the ab channels.\nCombine the input L channel with the predicted ab channels.\nConvert the Lab image back to RGB.\n'''\nx = get_images(\"./OurTrainingImages/\") #l value only\nprint(len(x))\ny = get_images(\"./OurTrainingImages/\", color=\"yes\") #a and b values\n\n# Recreate the exact same model, including its weights and the optimizer\n# model = tf.keras.models.load_model('./img_predictions/model.h5')\n\n# create model\nmodel = Sequential()\nmodel.add(InputLayer(input_shape=(None, None, 1))) # input shape is only needed for first layer? input_shape=(256, 256, 3)\n# 3x3 kernel used and 8 filters?\nmodel.add(Conv2D(8, (3, 3), activation='relu', padding='same', strides=2))\nmodel.add(Conv2D(16, (3, 3), activation='relu', padding='same'))\nmodel.add(Conv2D(16, (3, 3), activation='relu', padding='same', strides=2))\nmodel.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\nmodel.add(Conv2D(32, (3, 3), activation='relu', padding='same', strides=2))\n# figure out what this does\n# model.add(layers.MaxPooling2D((2, 2)))\nmodel.add(UpSampling2D((2, 2)))\nmodel.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\nmodel.add(UpSampling2D((2, 2)))\nmodel.add(Conv2D(16, (3, 3), activation='relu', padding='same'))\nmodel.add(UpSampling2D((2, 2)))\nmodel.add(Conv2D(2, (3,3), activation='tanh', padding='same'))\n# get working after we get NN working better\n'''\n# supposed to soften image\nmodel.add(layers.Dense(64, activation='relu'))\nmodel.add(layers.Dense(10, activation='softmax'))\n'''\n# get summary of layers and compile\nmodel.summary()\nmodel.compile(optimizer='adam',loss='mse') # loss='sparse_categorical_crossentropy', optomizer='rmsprop'\n\n\n# there is an issue fitting the data\nfor e in tqdm(range(10000)):\n for i,j in enumerate(x):\n model.fit(x=x[i],y=y[i], batch_size=50,verbose=0, epochs=1)\n\n# evaluate model\n# model.evaluate(x, y, batch_size=1)\n\n# save model\nmodel.save('./img_predictions/model.h5') \n\n\n#Load test images\ntest_images = get_images(\"./OurTrainingImages/\")\n# print(len(test_images))\n\nfor i,z in enumerate(test_images):\n # make predictions\n output = model.predict(z)\n output*=128\n cur = np.zeros((256,256,3))\n cur[:,:,0] = z[:,:,0] # L layer?\n cur[:,:,1:] = output[0] # A B layers?\n rgb_image = lab2rgb(cur)\n\n img = array_to_img(rgb_image)\n img.save(\"./img_predictions/{}.jpg\".format(i))\n img.show() \n","sub_path":"Final_Model.py","file_name":"Final_Model.py","file_ext":"py","file_size_in_byte":3624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"646902219","text":"'''\nPDSCH stats\n'''\n\nimport pandas as pd\n\npcell = 'PCELL'\nscell = 'SCC1'\n\npcell_RBs = []\nscell_RBs = []\n\npcell_crc = []\nscell_crc = []\n\npcell_discard = []\nscell_discard = []\n\npcell_recombine = []\nscell_recombine = []\n\npcell_tb = []\nscell_tb = []\n\npcell_mcs = []\nscell_mcs = []\n\npcell_mod = []\nscell_mod = []\n\npcell_ack_nak = []\nscell_ack_nak = []\n\nfh = open('pdsch.txt')\nfor line in fh:\n if pcell in line:\n pcell_stats=line\n RBs = pcell_stats[23:26]\n crc = pcell_stats[66:70]\n disc = pcell_stats[89:96]\n recomb = pcell_stats[141:144]\n tb_size = pcell_stats[147:152]\n mcs = pcell_stats[154:156]\n mod = pcell_stats[165:171]\n ack_nak = pcell_stats[196:199]\n \n pcell_RBs.append(RBs)\n pcell_crc.append(crc)\n pcell_discard.append(disc)\n pcell_recombine.append(recomb)\n pcell_tb.append(tb_size)\n pcell_mcs.append(mcs)\n pcell_mod.append(mod)\n pcell_ack_nak.append(ack_nak)\n\n elif scell in line:\n scell_stats=line\n RBs = scell_stats[23:26]\n crc = scell_stats[66:70]\n disc = scell_stats[89:96]\n recomb = scell_stats[141:144]\n tb_size = scell_stats[147:152]\n mcs = scell_stats[154:156]\n mod = scell_stats[165:171]\n ack_nak = pcell_stats[196:199]\n \n scell_RBs.append(RBs)\n scell_crc.append(crc)\n scell_discard.append(disc)\n scell_recombine.append(recomb)\n scell_tb.append(tb_size)\n scell_mcs.append(mcs)\n scell_mod.append(mod)\n scell_ack_nak.append(ack_nak)\n\n\ndf_pcell = pd.DataFrame({'RBs':pcell_RBs, 'CRC':pcell_crc, 'Discarded reTX':pcell_discard, 'Did recombine':pcell_recombine,\n 'TB Size':pcell_tb, 'MCS':pcell_mcs, 'Modulation':pcell_mod, 'ACK/NACK':pcell_ack_nak})\n\n\ndf_scell = pd.DataFrame({'RBs':scell_RBs, 'CRC':scell_crc, 'Discarded reTX':scell_discard, 'Did recombine':scell_recombine,\n 'TB Size':scell_tb, 'MCS':scell_mcs, 'Modulation':scell_mod, 'ACK/NACK':scell_ack_nak})\n\ndf_pcell.to_csv('pcell.csv')\ndf_scell.to_csv('scell.csv')\n\nprint(df_pcell)\nprint(df_scell)\n\n'''\nprint(pcell_RBs)\nprint(scell_RBs)\n\nprint(pcell_crc)\nprint(scell_crc)\n\nprint(pcell_discard)\nprint(pcell_discard)\n\nprint(pcell_recombine)\nprint(scell_recombine)\n\nprint(pcell_tb)\nprint(scell_tb)\n\nprint(pcell_mcs)\nprint(scell_mcs)\n\nprint(pcell_mod)\nprint(scell_mod)\n\nprint(pcell_ack_nak)\nprint(scell_ack_nak)\n\n'''\n\n'''\n#print(list(map(lambda x:x.strip(), pcell_scell_stats)))\n#print(list(map(str.strip,pcell_scell_stats)))\n\n#for i in pcell_scell_stats:\n# pcell_scell_stats.append(i.strip())\n\n#print(pcell_scell_stats)\n'''\n\n'''\ndf = pd.DataFrame(all_stats)\nprint(df)\ntry:\n df.to_csv('stats.csv')\nexcept:\n print(\"\\n\")\n print(\"stats.csv is already open.. cannot generate file\")\n'''\n\n\n\n\n","sub_path":"pdsch_stats.py","file_name":"pdsch_stats.py","file_ext":"py","file_size_in_byte":2866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"296432275","text":"# set flask server to debug mode\nDEBUG = True\n\n# site specific settings\nSITE = {\n\t# Required settings\n\n\t# Site title\n\t'title': 'Zerodha Techblog',\n\n\n\t# Theme specific settings\n\n\t# Site description\n\t'description': 'Official zerodha techblog',\n\n\t# Summary length, used by specific themes\n\t'summary_offset': 180,\n\n\n\t# Optional settings\n\n\t# default theme, defaults to 'basic' or theme provided via commandline utility\n\t# try \"olaf utils -t\" to get list of inbuilt and custom themes installed\n\t'theme': '',\n\n\t# Default pagination limit, defaults to 10 if not set\n\t'limit': 10,\n\n\t# Site author(s) name\n\t# used in atom feeds and by themes\n\t'author': ['Your name'],\n\n\t# Set default home page using page/post slug\n\t# defaults to recent posts list\n\t'custom_home_page': '',\n\n\t# Feeds page limit, defaults to 10 if not set\n\t'feed_limit': 15,\n\n\t# Google analytics tracking id\n\t# if not set analytics scripts not included (depends on themes)\n\t'analytics': 'UA-XXX-VVV',\n\n\t# Enable or disable disqus comments\n\t# If enabled add disqus script to disqus.html page in site root\n\t'disqus': True,\n\n\t# Base domain to be used in xm sitemaps and feed urls\n\t# Please add full url with http or https\n\t# defaults to host name of the app(\"localhost\")\n\t'domain_url': 'http://zerodha.github.io',\n\n\t# Domain name to be used for Github pages\n\t# If setting changed run \"olaf cname\" from site root directory to update CNAME file\n\t'github_domain': 'zerodha.github.io',\n\n\t# Sites git url - required if you are using git uploads commandline tool\n\t# Try \"olaf upload --help\" from site root directory for more help\n\t'github_repo': 'https://github.com/Zerodha/zerodha.github.io.git',\n\n\t# Set syntax highlighting style (pygments styles)\n\t# Try \"olaf utils -p\" to get list of inbuilt styles\n\t'pygments_style': ''\n\n}\n","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"442500858","text":"import pandas as pd\r\nimport numpy as np\r\nimport sklearn\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.pipeline import Pipeline\r\ndataread=pd.read_csv(\"wine_data.txt\",header=None, names=[\"class\",\"Alcohol\",\",Malic acid\",\"Ash\",\"Alcalinity of ash\",\"Magnesium\",\"Total phenols\",\"Flavanoids\",\"Nonflavanoid phenols\",\"Proanthocyanins\",\"Color intensity\",\"Hue\",\"OD280/OD315 of diluted wines\",\"Proline\"])\r\ndataread.head()\r\nprint (dataread)\r\nclass MultiColumnLabelEncoder:\r\n\tdef __init__(self,columns = None):\r\n\t\tself.columns = columns\r\n\tdef fit(self,x,y=None):\r\n\t\treturn self\r\n\tdef transform(self,x):\r\n\t\toutput = x.copy()\r\n\t\tif self.columns is not None:\r\n\t\t\tfor col in self.columns:\r\n\t\t\t\toutput [col] =LabelEncoder().fit_transform(output[col])\r\n\t\telse:\r\n\t\t\tfor colname,col in output.iteritems():\r\n\t\t\t\toutput[colname] =LabelEncoder().fit_transform(col)\r\n\t\treturn output\r\n\tdef fit_transform(self,x,y=None):\r\n\t\treturn self.fit(x,y).transform(x)\r\nencoding_pipeline = Pipeline([\r\n\t(\"encoding\",MultiColumnLabelEncoder(columns=['class','Alcohol',',Malic acid','Ash','Alcalinity of ash','Magnesium','Total phenols','Flavanoids','Nonflavanoid phenols','Proanthocyanins','Color intensity','Hue','OD280/OD315 of diluted wines','Proline']))\r\n])\r\ndataread = encoding_pipeline.fit_transform(dataread)\r\nout = dataread.ix[:,0:1]\r\ninp = dataread.ix[:,1:14]\r\ninp.columns.tolist()\r\n#print(inp)\r\n#print (out)\r\nfrom sklearn.cross_validation import train_test_split\r\ntrain_inp1,test_inp1,train_out1,test_out1=train_test_split(inp,out,train_size=0.75,test_size=0.25)\r\ntrain_inp2,test_inp2,train_out2,test_out2=train_test_split(inp,out,train_size=0.75,test_size=0.25)\r\nprint(np.shape(train_inp1))\r\nprint(np.shape(train_inp2))\r\nprint (dataread.head())\r\n\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nmodel=DecisionTreeClassifier()\r\nmodel.fit(train_inp1, train_out1)\r\nprint(model)\r\ntrain_dt_pred=model.predict(train_inp1)\r\ntest_dt_pred=model.predict(test_inp1)\r\nprint (\"Train Prediction of Decisiontree = \" ,train_dt_pred)\r\nprint (np.shape(train_dt_pred))\r\nprint(\"Test Pediction of Decisiontree= \" ,test_dt_pred)\r\nprint(np.shape(test_dt_pred))\r\nfrom sklearn.metrics import accuracy_score\r\ntrain_dt_acc = accuracy_score(train_out1,train_dt_pred)\r\nprint(\"Train Accuracy of DecisionTree = \",train_dt_acc)\r\ntest_dt_acc=accuracy_score(test_out1, test_dt_pred)\r\nprint(\"Test Accuracy of DecisionTree = \",test_dt_acc)\r\n\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nmodel=RandomForestClassifier(n_estimators=10, criterion='gini', max_depth=7, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False, class_weight=None)\r\nmodel.fit(train_inp2, train_out2)\r\nprint(model)\r\ntrain_rf_pred=model.predict(train_inp2)\r\ntest_rf_pred=model.predict(test_inp2)\r\nprint(\"Train Prediction of Randomforest = \",train_rf_pred)\r\nprint(np.shape(test_rf_pred))\r\nprint(\"Test Prediction of Randomforest= \", test_rf_pred)\r\nprint(np.shape(test_rf_pred))\r\nfrom sklearn.metrics import accuracy_score\r\ntrain_rf_acc=accuracy_score(train_out2,train_rf_pred)\r\nprint(\"Train Accuracy of Randomforest = \",train_rf_acc)\r\ntest_rf_acc = accuracy_score(test_out2,test_rf_pred)\r\nprint(\"Test Accuracy of Random forest =\",test_rf_acc)\r\n","sub_path":"Classification/Decission tree/wine.py","file_name":"wine.py","file_ext":"py","file_size_in_byte":3374,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"189171451","text":"from django.conf import settings\nfrom django.core.exceptions import ImproperlyConfigured\nfrom django.utils.translation import ugettext as _\nfrom rest_framework import exceptions, serializers\n\nfrom . import engines, models\n\n\nclass CurrentGroupDefault:\n def set_context(self, serializer_field):\n self.user = serializer_field.context[\"request\"].user\n\n def __call__(self):\n return self.user.group\n\n def __str__(self):\n return self.user.group\n\n\nclass TemplateSerializer(serializers.ModelSerializer):\n group = serializers.CharField(allow_null=True, default=CurrentGroupDefault())\n\n def validate_group(self, group):\n request = self.context[\"request\"]\n if group and group not in request.user.groups:\n raise exceptions.ValidationError(_(f\"User is not member of group {group}\"))\n\n return group\n\n def validate(self, data):\n engine = data.get(\"engine\", self.instance and self.instance.engine)\n template = data.get(\"template\", self.instance and self.instance.template)\n\n engine = engines.get_engine(engine, template)\n engine.validate()\n\n return data\n\n class Meta:\n model = models.Template\n fields = (\"slug\", \"description\", \"template\", \"engine\", \"group\")\n\n\nclass TemplateMergeSerializer(serializers.Serializer):\n data = serializers.JSONField(\n required=True, help_text=\"Data as json used for merging\"\n )\n convert = serializers.ChoiceField(\n allow_null=True,\n required=False,\n choices=settings.UNOCONV_ALLOWED_TYPES,\n help_text=\"Optionally convert result document to this type.\",\n )\n\n def validate_convert(self, value):\n if not settings.UNOCONV_URL and not settings.UNOCONV_LOCAL:\n raise ImproperlyConfigured(\n \"To use conversion you need to configure `UNOCONV_URL`\"\n )\n\n return value\n","sub_path":"document_merge_service/api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"516006145","text":"\"\"\"Test the media accessors.\n\nIf run directly from the command line, also tests the accessors and the names\nof all the media by getting just about everything in a naive brute-force way.\nThis, of course, takes a lot of time to run.\n\"\"\"\n\nimport pytest\n\nimport os\nimport re\n\nfrom pokedex.db import tables, connect\nfrom pokedex.util import media\n\ndef pytest_addoption(parser):\n group = parser.getgroup(\"pokedex\")\n group.addoption(\"--media-root\", dest=\"media_root\", action=\"store\", default=None,\n help=\"path to pokedex-media\")\n\ndef pytest_funcarg__root(request):\n root = request.config.option.media_root\n if not root:\n root = os.path.join(os.path.dirname(__file__), *'../data/media'.split('/'))\n if not media.BaseMedia(root).available:\n raise pytest.skip(\"Media unavailable\")\n return root\n\nsession = connect()\n\npath_re = re.compile('^[-a-z0-9./]*$')\n\ndef test_totodile(root):\n \"\"\"Totodile's female sprite -- same as male\"\"\"\n totodile = session.query(tables.PokemonSpecies).filter_by(identifier='totodile').one()\n accessor = media.PokemonSpeciesMedia(root, totodile)\n assert accessor.sprite() == accessor.sprite(female=True)\n\ndef test_chimecho(root):\n \"\"\"Chimecho's Platinum female backsprite -- diffeent from male\"\"\"\n chimecho = session.query(tables.PokemonSpecies).filter_by(identifier='chimecho').one()\n accessor = media.PokemonSpeciesMedia(root, chimecho)\n male = accessor.sprite('platinum', back=True, frame=2)\n female = accessor.sprite('platinum', back=True, female=True, frame=2)\n assert male != female\n\ndef test_venonat(root):\n \"\"\"Venonat's shiny Yellow sprite -- same as non-shiny\"\"\"\n venonat = session.query(tables.PokemonSpecies).filter_by(identifier='venonat').one()\n accessor = media.PokemonSpeciesMedia(root, venonat)\n assert accessor.sprite('yellow') == accessor.sprite('yellow', shiny=True)\n\ndef test_arceus_icon(root):\n \"\"\"Arceus fire-form icon -- same as base icon\"\"\"\n arceus = session.query(tables.PokemonSpecies).filter_by(identifier='arceus').one()\n accessor = media.PokemonSpeciesMedia(root, arceus)\n fire_arceus = [f for f in arceus.forms if f.form_identifier == 'fire'][0]\n fire_accessor = media.PokemonFormMedia(root, fire_arceus)\n assert accessor.icon() == fire_accessor.icon()\n\ndef test_strict_castform(root):\n \"\"\"Castform rainy form overworld with strict -- unavailable\"\"\"\n with pytest.raises(ValueError):\n castform = session.query(tables.PokemonSpecies).filter_by(identifier='castform').first()\n rainy_castform = [f for f in castform.forms if f.form_identifier == 'rainy'][0]\n print(rainy_castform)\n rainy_castform = media.PokemonFormMedia(root, rainy_castform)\n rainy_castform.overworld('up', strict=True)\n\ndef test_strict_exeggcute(root):\n \"\"\"Exeggcutes's female backsprite, with strict -- unavailable\"\"\"\n with pytest.raises(ValueError):\n exeggcute = session.query(tables.PokemonSpecies).filter_by(identifier='exeggcute').one()\n accessor = media.PokemonSpeciesMedia(root, exeggcute)\n accessor.sprite(female=True, strict=True)\n\n\n\ndef get_all_filenames(root):\n all_filenames = set()\n\n for dirpath, dirnames, filenames in os.walk(root):\n dirnames[:] = [dirname for dirname in dirnames if dirname != '.git']\n for filename in filenames:\n path = os.path.join(dirpath, filename)\n assert path_re.match(path), path\n all_filenames.add(path)\n\n return all_filenames\n\ndef hit(filenames, method, *args, **kwargs):\n \"\"\"\n Run the given accessor method with args & kwargs; if found remove the\n result path from filenames and return True, else return False.\n \"\"\"\n try:\n medium = method(*args, **kwargs)\n #print 'Hit', medium.relative_path\n assert medium.exists\n except ValueError as e:\n #print 'DNF', e\n return False\n except:\n print('Error while processing', method, args, kwargs)\n raise\n try:\n filenames.remove(medium.path)\n except KeyError:\n pass\n return True\n\n@pytest.mark.skipif(\"not config.getvalue('all')\", reason='`--all` not specified')\ndef test_get_everything(root, pytestconfig):\n \"\"\"\n For every the accessor method, loop over the Cartesian products of all\n possible values for its arguments.\n Make sure we get every file in the repo, and that we get a file whenever\n we should.\n\n Well, there are exceptions of course.\n \"\"\"\n assert pytestconfig.getvalue('all')\n\n versions = list(session.query(tables.Version).all())\n versions.append('red-green')\n\n # We don't have any graphics for Colosseum or XD\n versions.remove(session.query(tables.Version).filter_by(identifier='colosseum').one())\n versions.remove(session.query(tables.Version).filter_by(identifier='xd').one())\n\n black = session.query(tables.Version).filter_by(identifier='black').one()\n\n filenames = get_all_filenames(root)\n\n # Some small stuff first\n\n for damage_class in session.query(tables.MoveDamageClass).all():\n assert hit(filenames, media.DamageClassMedia(root, damage_class).icon)\n\n for habitat in session.query(tables.PokemonHabitat).all():\n assert hit(filenames, media.HabitatMedia(root, habitat).icon)\n\n for shape in session.query(tables.PokemonShape).all():\n assert hit(filenames, media.ShapeMedia(root, shape).icon)\n\n for item_pocket in session.query(tables.ItemPocket).all():\n assert hit(filenames, media.ItemPocketMedia(root, item_pocket).icon)\n assert hit(filenames, media.ItemPocketMedia(root, item_pocket).icon, selected=True)\n\n for contest_type in session.query(tables.ContestType).all():\n assert hit(filenames, media.ContestTypeMedia(root, contest_type).icon)\n\n for elemental_type in session.query(tables.Type).all():\n assert hit(filenames, media.TypeMedia(root, elemental_type).icon)\n\n # Items\n versions_for_items = [\n None,\n session.query(tables.Version).filter_by(identifier='emerald').one(),\n ]\n\n for item in session.query(tables.Item).all():\n accessor = media.ItemMedia(root, item)\n assert hit(filenames, accessor.berry_image) or not item.berry\n for rotation in (0, 90, 180, 270):\n assert hit(filenames, accessor.underground, rotation=rotation) or (\n not item.appears_underground or rotation)\n for version in versions_for_items:\n success = hit(filenames, accessor.sprite, version=version)\n if version is None:\n assert success\n\n for color in 'red green blue pale prism'.split():\n for big in (True, False):\n accessor = media.UndergroundSphereMedia(root, color=color, big=big)\n assert hit(filenames, accessor.underground)\n\n for rock_type in 'i ii o o-big s t z'.split():\n accessor = media.UndergroundRockMedia(root, rock_type)\n for rotation in (0, 90, 180, 270):\n success = hit(filenames, accessor.underground, rotation=rotation)\n assert success or rotation\n\n # Pokemon!\n accessors = []\n\n accessors.append(media.UnknownPokemonMedia(root))\n accessors.append(media.EggMedia(root))\n manaphy = session.query(tables.PokemonSpecies).filter_by(identifier='manaphy').one()\n accessors.append(media.EggMedia(root, manaphy))\n accessors.append(media.SubstituteMedia(root))\n\n for form in session.query(tables.PokemonForm).all():\n accessors.append(media.PokemonFormMedia(root, form))\n\n for pokemon in session.query(tables.PokemonSpecies).all():\n accessors.append(media.PokemonSpeciesMedia(root, pokemon))\n\n for accessor in accessors:\n assert hit(filenames, accessor.footprint) or not accessor.is_proper\n assert hit(filenames, accessor.trozei) or not accessor.is_proper or (\n accessor.introduced_in > 3)\n assert hit(filenames, accessor.cry) or not accessor.is_proper\n assert hit(filenames, accessor.cropped_sprite) or not accessor.is_proper\n for female in (True, False):\n assert hit(filenames, accessor.icon, female=female) or not accessor.is_proper\n assert hit(filenames, accessor.sugimori, female=female) or (\n not accessor.is_proper or int(accessor.species_id) >= 647)\n for shiny in (True, False):\n for frame in (1, 2):\n for direction in 'up down left right'.split():\n assert hit(filenames, accessor.overworld,\n direction=direction,\n shiny=shiny,\n female=female,\n frame=frame,\n ) or not accessor.is_proper or (\n accessor.introduced_in > 4)\n for version in versions:\n for animated in (True, False):\n for back in (True, False):\n for color in (None, 'gray', 'gbc'):\n success = hit(filenames,\n accessor.sprite,\n version,\n animated=animated,\n back=back,\n color=color,\n shiny=shiny,\n female=female,\n frame=frame,\n )\n if (version == black and not animated\n and not back and not color and not\n shiny and not female and\n frame == 1):\n # All pokemon are in Black\n assert success or not accessor.is_proper\n if (str(accessor.species_id) == '1'\n and not animated and not color and\n frame == 1):\n # Bulbasaur is in all versions\n assert success\n\n # Remove exceptions\n exceptions = [os.path.join(root, dirname) for dirname in\n 'chrome fonts ribbons'.split()]\n exceptions.append(os.path.join(root, 'items', 'hm-'))\n exceptions = tuple(exceptions)\n\n unaccessed_filenames = set(filenames)\n for filename in filenames:\n if filename.startswith(exceptions):\n unaccessed_filenames.remove(filename)\n if filename.endswith('-beta.png'):\n unaccessed_filenames.remove(filename)\n\n if unaccessed_filenames:\n print('Unaccessed files:')\n for filename in unaccessed_filenames:\n print(filename)\n\n assert unaccessed_filenames == set()\n\n return (not filenames)\n","sub_path":"pokedex/tests/test_media.py","file_name":"test_media.py","file_ext":"py","file_size_in_byte":11078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"92554373","text":"# invite link:\n# https://discord.com/oauth2/authorize?client_id=721521983518670959&permissions=8&scope=bot\n\nimport discord\nfrom discord.ext import commands\nfrom discord.utils import get\n\nimport json\nimport ChessClient # for all of the Chess.com API data scrapping\n\nimport cairosvg\nimport ChessGame as chessgame # for the Discord-based Chess game\n\nimport os\n\nintents = discord.Intents.all()\nclient = commands.Bot(command_prefix = '$', intents=intents)\nclient.remove_command(\"$help\")\n\nmatch_requests = [ ]\nmatches = [ ]\n\n@client.event\nasync def on_ready():\n await client.change_presence(status=discord.Status.online, activity=discord.Game(name=\"Chess :D\"))\n print(\"My body is ready\")\n print('We have logged in as {0.user}'.format(client))\n\n@client.event\nasync def on_message(message):\n await client.process_commands(message)\n\n@client.command()\nasync def challenge(ctx: discord.ext.commands.Context):\n \"\"\"Challenges user to a match\"\"\"\n global match_requests\n message = ctx.message\n\n challenger = message.author\n member = message.mentions[0]\n\n match_requests.append(chessgame.ChessGame(challenger, member))\n await ctx.send('User {0.display_name}#{0.discriminator} has been challenged!'.format(message.mentions[0]))\n\n@client.command()\nasync def accept(ctx: discord.ext.commands.Context):\n \"\"\"Accepts a user's request\"\"\"\n global match_requests\n global matches\n message = ctx.message\n\n found = False\n for request in match_requests:\n # we have found the request\n if request.players[1].id == message.author.id:\n svg = request.board_to_svg()\n with open('board.svg', 'w') as f:\n f.write(svg)\n cairosvg.svg2png(url='board.svg', write_to='board.png')\n fi = discord.File('board.png')\n await ctx.send('Challenge from <@{0.id}> has been accepted!'.format(request.players[0]))\n await ctx.send('It is <@{0.id}>\\'s turn!'.format(request.player), file=fi)\n matches.append(request)\n match_requests.remove(request)\n found = True\n if not found:\n await ctx.send('No pending challenges!')\n\n@client.command()\nasync def move(ctx: discord.ext.commands.Context):\n \"\"\"Makes move\"\"\"\n global matches\n\n message = ctx.message\n move = message.content.split(' ')[1]\n\n found = False\n for match in matches:\n # we have found the match\n if match.player.id == message.author.id:\n found = True\n valid, result = match.make_move(move)\n winner = None\n draw = False\n if result is not None:\n if result == '1-0':\n winner = match.player\n elif result == '0-1':\n winner = match.players[match.moves % 2]\n elif result == '1/2-1/2':\n draw = True\n if not valid:\n await ctx.send('Invalid move, \\'{0}\\''.format(move))\n else:\n svg = match.board_to_svg()\n with open('board.svg', 'w') as f:\n f.write(svg)\n cairosvg.svg2png(url='board.svg', write_to='board.png')\n fi = discord.File('board.png')\n m = 'It is now <@{0.id}>\\'s turn!'.format(match.player)\n if winner is not None:\n m = '<@{0.id}> wins!'.format(winner)\n elif draw is True:\n m = 'The match was a draw!'\n await ctx.send(m, file=fi)\n if result is not None:\n matches.remove(match)\n if not found:\n await ctx.send('No match currently.')\n\n@client.command()\nasync def end(ctx: discord.ext.commands.Context):\n \"\"\"Ends match, what a loser\"\"\"\n global matches\n\n message = ctx.message\n\n found = False\n for match in matches:\n # we have found the match\n if match.player.id == message.author.id:\n found = True\n matches.remove(match)\n await ctx.send('Match forfeited.')\n if not found:\n await ctx.send('No match currently.')\n\n@client.command()\nasync def server(ctx):\n \"\"\"Shows server info\"\"\"\n channel = ctx.message.channel\n\n embed = discord.Embed(title = \"Kaweees's Player Stats\", url = \"https://google.com\")\n embed.title = server.name\n embed.description = 'Server Info'\n embed.color = 0x7fa650\n embed.title = server.name\n await ctx.send(embed=embed)\n await ctx.send(\":flag_us: :flag_US: \")\n\n@client.command()\nasync def playerstats(ctx, username):\n try:\n playerGeneralData = ChessClientInstance.getPlayerInformation(username)\n print(\"Sucessfully fetched Data :D\")\n except:\n channel = ctx.message.channel\n await channel.send('Invalid Chess.com username, please try again')\n \n embed = makeEmbed(playerGeneralData)\n await ctx.send(embed=embed) \n\ndef getToken():\n # code to open and read token\n return os.environ.get('TOKEN')\n\ndef makeEmbed(playerStats):\n Chessplayer = ChessClient.Chessplayer(playerStats)\n playerStatsData = ChessClientInstance.getPlayerStats(Chessplayer.username)\n Chessplayer.updatePlayerStats(playerStatsData)\n embed = discord.Embed()\n embed.title = Chessplayer.embedname\n embed.url = Chessplayer.url\n embed.set_thumbnail(url=Chessplayer.avatarurl)\n embed.description = 'Player Info and Stats'\n embed.color = Chessplayer.color\n embed.add_field(name = \"Country:\", value = Chessplayer.getCountry(Chessplayer.country), inline = True)\n embed.add_field(name = \"Date Joined:\", value = f\"{Chessplayer.dateJoined}\", inline = True)\n embed.add_field(name = \"Last Online:\", value = f\"{Chessplayer.lastOnline}\", inline = True)\n embed.add_field(name = \"<:bullet:816514940327297035> Bullet [x games played]\", value = f\"Rating: **x** - Highest Rating **{str(Chessplayer.chessBulletBest)}**\", inline = False)\n embed.add_field(name = \"<:blitz:816501491266355221> Blitz: [x games played]\", value = f\"Rating: **x** - Highest Rating **{str(Chessplayer.chessBlitzBest)}**\", inline = False)\n embed.add_field(name = \"<:rapid:816501511101087802> Rapid: [x games played]\", value = f\"Rating: **x** - Highest Rating **{str(Chessplayer.chessRapidBest)}**\", inline = False)\n embed.add_field(name = \"<:daily:816501455401648169> Daily: [x games played]\", value = f\"Rating: **x** - Highest Rating **{str(Chessplayer.chessDailyBest)}**\", inline = False)\n return embed\n\nChessClientInstance = ChessClient.ChessClient()\nclient.run(getToken())","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":6580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"512748272","text":"class Solution:\n def myAtoi(self, str):\n \"\"\"\n :type str: str\n :rtype: int\n \"\"\"\n t = str\n newT = \"\"\n for i in range(len(t)):\n if t[i] != ' ':\n newT = newT + t[i]\n if i == len(t) - 1 or t[i + 1] == ' ':\n break\n newDigit = \"\"\n for i in range(len(newT)):\n if newT[i] == '-' or newT[i] == '+' or (newT[i] >= '0' and newT[i] <= '9'):\n newDigit = newDigit + newT[i]\n else:\n break\n if i == len(newT) - 1 or (newT[i + 1] < '0' or newT[i + 1] > '9'):\n break\n if newDigit == \"\":\n return 0\n \n positive = True\n if newDigit[0] == '-':\n if len(newDigit) == 1:\n return 0\n positive = False\n newDigit = newDigit[1:]\n if newDigit[0] == '+':\n if len(newDigit) == 1:\n return 0\n newDigit = newDigit[1:]\n res = 0\n for i in range(len(newDigit)):\n res = res * 10 + int(newDigit[i])\n if i == len(newDigit) - 1:\n break\n if positive == True and (res > 214748364 or (res == 214748364 and int(newDigit[i + 1]) > 7)):\n return 2147483647\n if positive == False and (res > 214748364 or (res == 214748364 and int(newDigit[i + 1]) > 7)):\n return -2147483648\n if positive:\n return res\n else:\n return -res\n \n","sub_path":"leetcode/0001-0100/0008.py","file_name":"0008.py","file_ext":"py","file_size_in_byte":1551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"216870172","text":"# encoding:utf-8\nfrom bs4 import BeautifulSoup\nimport requests,json,time\n\nclass vs_5eplay():\n\n def get_html(self, server_port):\n headers = {\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36\"\n }\n url = \"http://vs.5eplay.com/details/\" + server_port\n res = requests.get(url,headers=headers)\n res.encoding = 'utf-8'\n soup = BeautifulSoup(res.text,'html.parser')\n return soup\n \n def get_server_info(self, server_port):\n if self.check_connect(server_port) == 1:\n soup = self.get_html(server_port)\n server_info = []\n for li in soup.select('.l')[1].contents[1]:\n if len(str(li).strip()) != 0:\n server_info.append(str(li).replace('',':').rstrip('').lstrip('

  • '))\n result = \"\\n\".join(server_info[1:])\n else:\n result = '找不到服务器'\n return result\n \n def get_server_param(self, server_port):\n if self.check_connect(server_port) == 1:\n soup = self.get_html(server_port)\n server_param, server_param_name, server_param_value, result2, result3 = [], [], [], [], []\n for li2 in soup.select('.r li'):\n server_param.append(str(li2).replace('

    ',' ').rstrip('
    • ').lstrip('
  • '))\n for i in server_param[1:]:\n server_param_name.append(i.split(' ')[0])\n server_param_value.append(i.split(' ')[1])\n for i2 in range(len(server_param_name)):\n result2.append(server_param_name[i2]+':'+server_param_value[i2])\n result = \"\\n\".join(result2)\n else:\n result = '找不到服务器'\n return result\n\n def get_player_info(self, server_port):\n if self.check_connect(server_port) == 1:\n soup = self.get_html(server_port)\n player_info, player_info_name, player_info_time, player_info_score, result2, result3 = [], [], [], [], [], []\n for ul in soup.select('.mt20 li'):\n player_info.append(str(ul).replace('

    ','').replace('','').rstrip('

    • ').lstrip('

  • '))\n for i in player_info[2:]:\n player_info_time.append(i.split('

    ')[0])\n player_info_score.append(i.split('')[1])\n player_info_name.append(i.split('')[2])\n num = 1\n for i2 in range(len(player_info_name)):\n num2 = num + i2\n result2.append(str(num2)+'; '+player_info_name[i2]+'; '+player_info_score[i2]+'; '+player_info_time[i2])\n if len(result2) != 0:\n result = \"\\n\".join(result2)\n else:\n result = '服务器暂时没有玩家'\n else:\n result = '找不到服务器'\n return result\n \n def check_connect(self, server_port):\n soup = self.get_html(server_port)\n err = soup.select('.text')\n if len(err) == 1:\n result = 0\n else:\n result = 1\n return result\n \n def organize_team(self, server_port):\n headers = {\n \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8\",\n # \"Accept-Encoding\": \"gzip, deflate\",\n # \"Host\": \"vs.5eplay.com\",\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36\"\n }\n cookie = {'__cfduid':'d11887dce9f79962e4e3669824f79f26e1481563385',\n 'Hm_lvt_12861524735e59efe36180e8485a6c92':'1482138711,1482139066,1482139976,1482140921',\n 'Hm_lpvt_12861524735e59efe36180e8485a6c92':'1482142905',\n '5e_firstvisit':'0',\n 'sf_auth':'9f5488HXEgfGK0lSAQf0sqZxyxiciRWkpVuZ%2FxNdT1XSRbv4WHikUqaBCIAUZ4tLVNEsLRov6t4c1tvBfgVmEbPppWc'\n }\n \n if self.check_connect(server_port) == 1:\n # print(headers)\n\n ip = server_port.split(':')[0]\n port = server_port.split(':')[1]\n # 毫秒级时间戳\n millis = int(round(time.time() * 1000))\n # = :\n url = 'http://vs.5eplay.com/?mod=vs&action=ajax&op=vspost&ip='+ ip +'%3A'+ port +'&password=&about=0&_' + str(millis)\n # print(url)\n \n res = requests.Session()\n result_unicode = res.get(url,cookies=cookie,headers=headers)\n result_unicode.encoding = 'utf-8'\n result_str = json.loads(result_unicode.text)\n result = result_str['alert'].encode('utf-8')\n else:\n result = '找不到服务器'\n return result\n","sub_path":"WeChatHandle/vs_5eplay.py","file_name":"vs_5eplay.py","file_ext":"py","file_size_in_byte":4770,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"246801807","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm\nfrom matplotlib.ticker import LinearLocator, FormatStrFormatter\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom Analysis_Helpers import *\nfrom dateutil import parser\nimport os\nfrom scipy.interpolate import griddata\n\n\nfilename = r\"\\\\jena-optronik.de\\daten\\home\\praktikanten\\tom.baumann\\IFM_Tests\\FinePointing\\piezo_control_log_20200601-002904_0.csv\"\nfilename = r\"\\\\jena-optronik.de\\daten\\home\\praktikanten\\tom.baumann\\IFM_Tests\\FinePointing\\piezo_control_log_20200601-222157_0.csv\"\n\n\n\n# Set to None if you want all data\nstart = None\nend = None # start + 10000 # Remove last two measurements, since they are not part of actual measurement cycle\n\n\n#Read data\ndf = pd.read_csv(filename, sep=',')\n\nvoltages = np.array(df[\"alphaVoltage\"].tolist()[start:end])\ndelta_angles = np.array(df['alpha_angle_ifm'].tolist()[start:end])\ndelta_angles_beta = np.array(df['beta_angle_ifm'].tolist()[start:end])\nalpha_angles_cmd = np.array(df['alpha_angle_cmd'].tolist()[start:end])\nbeta_angles_cmd = np.array(df['beta_angle_cmd'].tolist()[start:end])\n\ntimes = np.array(df[\"Timestamp\"].tolist()[start:end])\npressure = df[\"ifm_pressure\"].to_numpy()[start:end]/100.0\ntemp = df[\"ifm_temp\"].to_numpy()[start:end]\nparsed_times = [parser.parse(time) for time in times]\n\n#clean up data\nmask = np.absolute(delta_angles) > 500\ndelta_angles[mask] = 0.0\n\n# Take mean\n# total_length = len(voltages)\n# number_to_mean = 20\n# #Check, if we can reshape\n# diff = total_length % number_to_mean\n# voltages = np.mean(voltages[:-diff].reshape(-1, number_to_mean), axis=1)\n# delta_angles = np.mean(delta_angles[:-diff].reshape(-1, number_to_mean), axis=1)\n# pressure = np.mean(pressure[:-diff].reshape(-1, number_to_mean), axis=1)\n# temp = np.mean(temp[:-diff].reshape(-1, number_to_mean), axis=1)\n# parsed_times = np.mean(parsed_times[:-diff].reshape(-1, number_to_mean), axis=1)\nmeasurements_per_angle = 20\nskip = 3\nmean_alpha_angles = np.mean(delta_angles.reshape(-1, measurements_per_angle)[:,skip:], axis=1)\nmean_beta_angles = np.mean(delta_angles_beta.reshape(-1, measurements_per_angle)[:,skip:], axis=1)\nmean_alpha_angles_cmd = np.mean(alpha_angles_cmd.reshape(-1, measurements_per_angle)[:,skip:], axis=1)\nmean_beta_angles_cmd = np.mean(beta_angles_cmd.reshape(-1, measurements_per_angle)[:,skip:], axis=1)\n\nstd_alpha_angles = np.std(delta_angles.reshape(-1, measurements_per_angle)[:,skip:], axis=1)\nstd_beta_angles = np.std(delta_angles_beta.reshape(-1, measurements_per_angle)[:,skip:], axis=1)\nstd_alpha_angles_cmd = np.std(alpha_angles_cmd.reshape(-1, measurements_per_angle)[:,skip:], axis=1)\nstd_beta_angles_cmd = np.std(beta_angles_cmd.reshape(-1, measurements_per_angle)[:,skip:], axis=1)\n\ndifference_alpha = mean_alpha_angles - mean_alpha_angles_cmd\ndiffernece_beta = mean_beta_angles - mean_beta_angles_cmd\n\n# Grid is 20*20 and 2*4 measurements\ngrid_number = 20\nfine_positions = 4\ntotal_per_grid = grid_number*fine_positions*2\ndifference_alpha_grid = difference_alpha.reshape(grid_number, total_per_grid)\ndifference_beta_grid = differnece_beta.reshape(grid_number, total_per_grid)\n\n#each grid consists of 4 times:\n# One measurement set of center position\n# fine positioning delta\n\n# Fit\nfit_alpha = polyfit2d(mean_alpha_angles, mean_beta_angles, mean_alpha_angles_cmd)\nfit_beta = polyfit2d(mean_alpha_angles, mean_beta_angles, mean_beta_angles_cmd)\n\nfitted_alpha = polyval2d(delta_angles, delta_angles_beta, fit_alpha)\nfitted_beta = polyval2d(delta_angles, delta_angles_beta, fit_beta)\n\nmean_fitted_alpha_angles = np.mean(fitted_alpha.reshape(-1, measurements_per_angle)[:,skip:], axis=1)\nmean_fitted_beta_angles = np.mean(fitted_beta.reshape(-1, measurements_per_angle)[:,skip:], axis=1)\ndifference_fitted_alpha = mean_fitted_alpha_angles - mean_alpha_angles_cmd\ndiffernece_fitted_beta = mean_fitted_beta_angles - mean_beta_angles_cmd\n\n# calculate nrsme\nalpha_rmse = rmse(mean_alpha_angles_cmd, mean_alpha_angles)\nbeta_rmse = rmse(mean_beta_angles_cmd, mean_beta_angles)\nalpha_nrmse = nrmse(mean_alpha_angles_cmd, mean_alpha_angles)\nbeta_nrmse = nrmse(mean_beta_angles_cmd, mean_beta_angles)\nalpha_max_error = np.max(difference_alpha)\nbeta_max_error = np.max(differnece_beta)\n\nalpha_rmse_fitted = rmse(mean_alpha_angles_cmd, mean_fitted_alpha_angles)\nbeta_rmse_fitted = rmse(mean_beta_angles_cmd, mean_fitted_beta_angles)\nalpha_nrmse_fitted = nrmse(mean_alpha_angles_cmd, mean_fitted_alpha_angles)\nbeta_nrmse_fitted = nrmse(mean_beta_angles_cmd, mean_fitted_beta_angles)\nalpha_max_error_fitted = np.max(difference_fitted_alpha)\nbeta_max_error_fitted = np.max(differnece_fitted_beta)\n\nprint(\"Alpha RMSE: {} arcsec\".format(alpha_rmse))\nprint(\"Beta RMSE: {} arcsec\".format(beta_rmse))\nprint(\"Alpha NRMSE: {} %\".format(alpha_nrmse*100))\nprint(\"Beta NRMSE: {} %\".format(beta_nrmse*100))\nprint(\"Alpha Max: {} arcsec\".format(alpha_max_error))\nprint(\"Beta Max: {} arcsec\".format(beta_max_error))\n\nprint(\"Fitted Alpha RMSE: {} arcsec\".format(alpha_rmse_fitted))\nprint(\"Fitted Beta RMSE: {} arcsec\".format(beta_rmse_fitted))\nprint(\"Fitted Alpha NRMSE: {} %\".format(alpha_nrmse_fitted*100))\nprint(\"Fitted Beta NRMSE: {} %\".format(beta_nrmse_fitted*100))\nprint(\"Fitted Alpha Max: {} arcsec\".format(alpha_max_error_fitted))\nprint(\"Fitted Beta Max: {} arcsec\".format(beta_max_error_fitted))\n\n\n# PLOTTING \n\nfig_3D = plt.figure()\nax_3d = fig_3D.gca(projection='3d')\n\n# Make data.\nX = mean_alpha_angles.reshape((grid_number, -1))\nY = mean_beta_angles.reshape((grid_number, -1))\nZ = np.linalg.norm(np.column_stack((difference_alpha, differnece_beta)), axis=1).reshape((grid_number, -1))\n\nvmin = 0\nvmax = 0.08\nsurf = ax_3d.plot_surface(X, Y, Z, cmap=cm.coolwarm,\n linewidth=0, antialiased=False, vmin=vmin, vmax=vmax)\n\n# Customize the z ax_3dis.\nax_3d.set_zlim(0, vmax)\n# Add a color bar which maps values to colors.\ncbar1 = fig_3D.colorbar(surf, shrink=0.5, aspect=5)\ncbar1.set_label('Error [arcsec]')\nax_3d.set_title('Raw Data\\n{}'.format(os.path.basename(filename)))\nax_3d.set_xlabel('Alpha Angle [arcsec]')\nax_3d.set_ylabel('Beta Angle [arcsec]')\nax_3d.set_zlabel(\"Error [arcsec]\")\n\nfig_3D2 = plt.figure()\nax_ed2 = fig_3D2.gca(projection='3d')\n\n# Make data.\nX = mean_fitted_alpha_angles.reshape((grid_number, -1))\nY = mean_fitted_beta_angles.reshape((grid_number, -1))\nZ = np.linalg.norm(np.column_stack((difference_fitted_alpha, differnece_fitted_beta)), axis=1).reshape((grid_number, -1))\n\n\nsurf = ax_ed2.plot_surface(X, Y, Z, cmap=cm.coolwarm,\n linewidth=0, antialiased=False, vmin=vmin, vmax=vmax)\n\n# Customize the z ax_ed2is.\nax_ed2.set_zlim(0, vmax)\n# Add a color bar which maps values to colors.\ncbar2 = fig_3D2.colorbar(surf, shrink=0.5, aspect=5)\ncbar2.set_label('Error [arcsec]')\nax_ed2.set_title('Calibrated Data\\n{}'.format(os.path.basename(filename)))\nax_ed2.set_xlabel('Alpha Angle [arcsec]')\nax_ed2.set_ylabel('Beta Angle [arcsec]')\nax_ed2.set_zlabel(\"Error [arcsec]\")\n\nfig, ax = plt.subplots(constrained_layout=True)\nfig.suptitle(\"Raw Plot\\n{}\".format(os.path.basename(filename)))\nfig.set_size_inches(13, 9)\n\nax.scatter(delta_angles, delta_angles_beta, label=\"Raw Data\")\nax.scatter(fitted_alpha, fitted_beta, label=\"Fitted Data\")\nax.scatter(alpha_angles_cmd, beta_angles_cmd, label=\"CMD\")\nax.grid(True)\nax.legend(loc='lower right')\nax.set_title('Raw Data')\nax.set_xlabel('Alpha Angle [arcsec]')\nax.set_ylabel('Beta Angle [arcsec]')\n\nfig3, ax3 = plt.subplots(constrained_layout=True)\nfig3.suptitle(\"Raw Plot\\n{}\".format(os.path.basename(filename)))\nfig3.set_size_inches(13, 9)\n\nax3.plot(difference_alpha, label=\"Error Alpha\")\nax3.plot(differnece_beta, label=\"Error Beta\")\nax3.grid(True)\nax3.legend(loc='right')\nax3.set_title('Error')\nax3.set_xlabel('Measurement')\nax3.set_ylabel('Error Angle [arcsec]')\nax3.set_ylim(-0.1, 0.1)\n\nfig6, ax6 = plt.subplots(constrained_layout=True)\nfig6.suptitle(\"Grid Error Plot\\n{}\".format(os.path.basename(filename)))\nfig6.set_size_inches(13, 9)\n\nax6.plot(difference_alpha, label=\"Error Alpha Raw\")\nax6.plot(differnece_beta, label=\"Error Beta Raw\")\nax6.plot(difference_fitted_alpha, label=\"Error Alpha Clibrated\")\nax6.plot(differnece_fitted_beta, label=\"Error Beta Calibrated\")\nax6.grid(True)\nax6.legend(loc='lower right')\nax6.set_title('Error')\nax6.set_xlabel('Measurement')\nax6.set_ylabel('Error Angle [arcsec]')\nax6.set_ylim(-0.1, 0.1)\n\nfig4, ax4 = plt.subplots(constrained_layout=True)\nfig4.suptitle(\"Raw Plot\\n{}\".format(os.path.basename(filename)))\nfig4.set_size_inches(13, 9)\n\nax4.plot(std_alpha_angles, label=\"Std Alpha\")\nax4.plot(std_beta_angles, label=\"Std Beta\")\nax4.grid(True)\nax4.legend(loc='right')\nax4.set_title('Std')\nax4.set_xlabel('Measurement')\nax4.set_ylabel('Std Angle [arcsec]')\nax4.set_ylim(0, 0.025)\n\n#Plot of Environment Data\nfig2, ax2 = plt.subplots(nrows=2, ncols=1, sharex=True)\nfig2.suptitle(\"Environmental Data\")\n\nax2[0].grid(True)\nax2[1].grid(True)\n\nax2[0].set_ylabel('Pressure [hPa]')\nax2[1].set_ylabel('Temp [C]')\nax2[0].set_xlabel('Time')\n\n#Plot Environment data\nax2[0].plot(pressure, label=\"Pressure\", linewidth=1)\nax2[1].plot(temp, label=\"Temp\", linewidth=1, linestyle=\"dashed\")\n\n#check environment parameters\ntemp_avg = np.average(temp)\ntemp_maxdev = np.max(temp) - np.min(temp)\ntemp_std = np.std(temp)\n\npressure_avg = np.average(pressure)\npressure_maxdev = np.max(pressure) - np.min(pressure)\npressure_std = np.std(pressure)\n\n#display on plot\ntemp_text = \"Avg: {:2.4f} - StdDev: {:2.4f} - MaxDev: {:2.4f}\".format(temp_avg, temp_std, temp_maxdev)\npressure_text = \"Avg: {:3.4f} - StdDev: {:2.4f} - MaxDev: {:2.4f}\".format(pressure_avg, pressure_std, pressure_maxdev)\nleft = 0.5\nbottom = 0.1\nax2[0].text(left, bottom, pressure_text,\n horizontalalignment='center',\n verticalalignment='bottom',\n transform=ax2[0].transAxes, bbox=dict(facecolor='red', alpha=0.5))\nax2[1].text(left, bottom, temp_text,\n horizontalalignment='center',\n verticalalignment='bottom',\n transform=ax2[1].transAxes, bbox=dict(facecolor='red', alpha=0.5))\n\n#print this in console\nprint(\"{}: Temp: {}\".format(os.path.basename(filename), temp_text))\nprint(\"{}: Pressure: {}\".format(os.path.basename(filename), pressure_text))\n\nplt.show()\n","sub_path":"Digitale_Abgabe/3-Messungen/Auswertung/Display_Raw_Data_Grid_Fine.py","file_name":"Display_Raw_Data_Grid_Fine.py","file_ext":"py","file_size_in_byte":10232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"162429042","text":"# learning agent by Monte Carlo method\n\nimport gym\nimport requests\nimport numpy as np\nimport csv\nimport matplotlib.pyplot as pl\nfrom collections import deque\nimport time\nimport threading\n#import # 敵のデータ\n\ndef getAction(env, q_table, observation, episode,choice): # get action (array) # フィールド外は再計算\n #epsilon = 0.5 * (1 / (episode + 1))\n epsilon = 0.5\n a = []\n b = False\n n = 2\n if choice == 0:\n n = 2\n m = 6\n elif choice == 1:\n n = 0\n m = 5\n\n for i in range(2):\n if np.random.uniform(0, 1) > epsilon: # e-greedy low is off\n x = np.argsort(q_table[observation[i]])[::-1]\n b = False\n c = 0\n while b!=True:\n b, d, ms, next_pos = env.judAc(i+1+n, x[c])\n lv = env.show()\n try:\n if lv[next_pos[0],next_pos[1]] == m:\n c += 1\n b = False\n else:\n c += 1\n except:\n c += 1\n a.append([d, ms, next_pos])\n\n else: # e-greedy low is on\n b = False\n while b!=True:\n pa = np.random.choice([0, 1, 2, 3, 4, 5, 6, 7, 8])\n b, d, ms, next_pos = env.judAc(i+1+n, pa)\n a.append([d, ms, next_pos])\n\n return a # [[int(direction), str(movement), list(next position)],[]]\n\"\"\"\ndef getAction(env, q_table, observation, episode,choice): # get action (array) # フィールド外は罰金\n #epsilon = 0.5 * (1 / (episode + 1))\n epsilon = 0.5\n a = []\n b = False\n n = 2\n if choice == 0:\n n = 2\n elif choice == 1:\n n = 0\n\n for i in range(2):\n if np.random.uniform(0, 1) > epsilon: # e-greedy low is off\n x = np.argsort(q_table[observation[i]])[::-1]\n b, d, ms, next_pos = env.judAc(i+1+n, x[0])\n a.append([d, ms, next_pos])\n\n else: # e-greedy low is on\n pa = np.random.choice([0, 1, 2, 3, 4, 5, 6, 7, 8])\n b, d, ms, next_pos = env.judAc(i+1+n, pa)\n a.append([d, ms, next_pos])\n return a # [[int(direction), str(movement), list(next position)],[]]\n\"\"\"\n\n# [] update Qtables\ndef updateQtable(q_table, memory):#observation, action, reward, next_observation):\n gamma = 0.99 # time discount rate\n alpha = 0.1 # learning rate\n total_reward_t = 0\n while (memory.len() > 0):\n (state, action, reward) = memory.sample()\n total_reward_t = gamma * total_reward_t # 時間割引率をかける\n # Q関数を更新\n q_table[state, action[0]] = q_table[state, action[0]] + alpha*(reward+total_reward_t-q_table[state, action[0]])\n total_reward_t = total_reward_t + reward # ステップtより先でもらえた報酬の合計を更新\n\n return q_table\n\n\nclass Memory:\n def __init__(self, max_size):\n self.buffer = deque(maxlen=max_size)\n\n def add(self, experience):\n self.buffer.append(experience)\n\n def sample(self):\n return self.buffer.pop()\n\n def len(self):\n return len(self.buffer)\n\n\"\"\"\n# [] main processing\nif __name__ == '__main__':\n # [] make environment\n env = gym.make('procon18env-v0')\n #ene = .make('') # make enemy\n num_episode = 50 # 1050\n\n is_learned = 0 #学習終了フラグ\n is_render = 0 #描画フラグ\n\n win1 = 0\n win2 = 0\n\n # [] make Qtable (state,action)\n #q_table = np.zeros((144, 9))\n\n # read q tables from csv file\n with open('q_table_MCM.csv', 'r') as file:\n lst = list(csv.reader(file))\n a = []\n for i in range(144):\n a.append(list(map(float,lst[i])))\n q_table = np.array(a)\n\n for episode in range(num_episode):\n observation = env.reset(episode+1) #array\n terns = env.num_terns\n row = env.Row\n column = env.Column\n total_reward = 0\n memory1 = Memory(terns)\n memory2 = Memory(terns)\n\n for i in range(terns):\n env.steps = i+1\n # choose action (num)\n ob = env.getStatus(observation)\n action = getAction(env, q_table, observation, episode) # array\n\n\n enemy_action = ene.get_enemy_Action() # array [int, int]\n for i in range(2):\n if action[i][3] == enemy_action[i][1]: # 移動先が被ったら停留\n action[i][0] == 4\n enemy_action[i][0] == 4\n\n\n # step\n next_observation, reward, done, _ = env.step(action, \"M\")\n #ene.step(enemy_action)\n\n memory1.add((ob[0], action[0], reward))\n memory2.add((ob[1], action[1], reward))\n\n total_reward += reward\n observation = next_observation\n\n if done:\n # update q_table\n q_table = updateQtable(q_table, memory1)\n q_table = updateQtable(q_table, memory2)\n break\n a = np.array([episode + 1, total_reward])\n print(env.judVoL())\n if env.judVoL() == \"Win_1\":\n win1 += 1\n else:\n win2 += 1\n print(episode)\n\n # save q table\n with open('q_table_MCM.csv', 'w') as file:\n writer = csv.writer(file, lineterminator='\\n')\n writer.writerows(q_table)\n\n print(win1)\n print(win2)\n\"\"\"\n","sub_path":"org/mcl0917.py","file_name":"mcl0917.py","file_ext":"py","file_size_in_byte":5370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"586134831","text":"#!/usr/bin/python\n\n\nimport math\nimport shapely.geometry as geo\nimport xml.etree.ElementTree as xml\nimport rosbag\n\n\nclass Bag:\n '''\n a class for storing and analyzing bag file contents\n '''\n\n def __init__(self, folder, file, launch_file):\n '''\n initialize class\n :param string folder: absolute path of the bag file directory\n :param string file: name of the bag file\n :param string launch_file: launch file that was launched to generate the bag file\n '''\n # absolute path of the bag file directory\n self.folder = folder\n\n # name of the bag file\n self.file = file\n\n # id of bag file\n self.id = file.split('_')[3]\n\n # coverage starting time\n self.tstart = 0.0\n\n # coverage ending time\n self.tstop = 0.0\n\n # first target found time\n self.found = 0.0\n\n # first target rescued time\n self.rescued = 0.0\n\n # list of goal coordinates\n self.goal = []\n\n # list pose coordinates\n self.pose = []\n\n # number of messages in bag file\n self.bag_msg_count = 0\n\n # topics in bag file\n self.bag_topics = {}\n\n # the fov of the UAV in meters on ground right and left\n self.fov = 0\n\n # the UAV trajectory as line object\n self.traj = None\n\n # get the global pose origin from launch file\n self.origin = [0,0]\n launch = xml.parse(launch_file).getroot()\n for include in launch.iter('include'):\n args = include.findall('arg')\n found = False\n for arg in args:\n if arg.get('name') == 'id' and arg.get('value') == self.id:\n found = True\n break\n if found:\n for arg in args:\n if arg.get('name') == 'x':\n self.origin[0] = float(arg.attrib['value'])\n if arg.get('name') == 'y':\n self.origin[1] = float(arg.attrib['value'])\n break\n\n def info(self, ns, verbose=False):\n '''\n get infos about a bag file\n and store the results in class variables\n :param string ns: name space of topics\n :param bool verbose: whether to be verbose (default False)\n '''\n # open bag to read details\n bag = rosbag.Bag(self.folder + '/' + self.file)\n\n # read statistics\n self.bag_msg_count = bag.get_message_count()\n info = bag.get_type_and_topic_info()[1]\n self.bag_topics = dict([(topic, (info[topic].msg_type, info[topic].message_count)) for topic in info])\n\n # read begin and end times of coverage\n # assuming at most one goal and result message\n for topic,msg,t in bag.read_messages(ns + 'uav_coverage/goal'):\n self.tstart = t.secs + t.nsecs / 1000000000.0\n break\n for topic,msg,t in bag.read_messages(ns + 'uav_coverage/result'):\n self.tstop = t.secs + t.nsecs / 1000000000.0\n break\n\n # read target found and rescued times\n # considering only the first target\n for topic,msg,t in bag.read_messages(ns + 'target_found'):\n self.found = t.secs + t.nsecs / 1000000000.0 - self.tstart\n break\n for topic,msg,t in bag.read_messages(ns + 'bridge/events/mission_abort'): # target_rescued event not existent\n self.rescued = t.secs + t.nsecs / 1000000000.0 - self.tstart\n break\n\n # close bag\n bag.close()\n\n # print statistics about the current bag file contents\n if verbose:\n print\n print(\"\\x1b[1;34m\" + \"{0}\".format(self.file) + \"\\x1b[0m\")\n print(\"No. Messages\\tTopic (Message Type)\")\n print(\"------------\\t--------------------\")\n for topic, info in self.bag_topics.items():\n print(\"{0:6d} msgs\\t{1} ({2})\".format(info[1], topic, info[0]))\n print(\"------------\\n{0:6d} total\".format(self.bag_msg_count))\n\n def parse(self, ns, res_space, verbose=False):\n '''\n parse the messages of a bag file\n and store the results in class variables\n :param string ns: name space of topics\n :param float res_space: spatial resolution in meter to filter the input data by (minimum distance between two consecutive coordinates)\n :param bool verbose: whether be verbose (default False)\n '''\n # open bag to read messages\n bag = rosbag.Bag(self.folder + '/' + self.file)\n\n # read goal coordinates\n goal_coords = [msg[1].pose.position for msg in bag.read_messages(ns + 'pos_controller/goal_position')]\n self.goal = [[coord.x + self.origin[0] for coord in goal_coords], [coord.y + self.origin[1] for coord in goal_coords]]\n\n # read actual pose coordinates\n for topic,msg,t in bag.read_messages(ns + 'mavros/local_position/pose'):\n if len(self.pose) == 0:\n self.pose.append((t.secs + t.nsecs / 1000000000.0, msg.pose.position.x + self.origin[0], msg.pose.position.y + self.origin[1], msg.pose.position.z))\n elif math.hypot(msg.pose.position.x + self.origin[0] - self.pose[-1][1], msg.pose.position.y + self.origin[1] - self.pose[-1][2]) > res_space:\n self.pose.append((t.secs + t.nsecs / 1000000000.0, msg.pose.position.x + self.origin[0], msg.pose.position.y + self.origin[1], msg.pose.position.z))\n\n # validate end time of coverage\n if self.tstop == 0.0 and len(self.pose) > 0:\n self.tstop = self.pose[-1][0]\n\n # trim poses according to begin and end times\n self.pose = [p for p in self.pose if self.tstart <= p[0] and p[0] <= self.tstop]\n\n if verbose:\n print(\"Obtained {0} goal coordinates\".format(len(self.goal[0])))\n print(\"Obtained {0} pose coordinates\".format(len(self.pose)))\n\n # close bag\n bag.close()\n\n def process(self, fov, verbose=False):\n '''\n process bag file contents to get data\n :param float fov: field of view of the UAVs in radian\n :param bool verbose: whether to be verbose (default False)\n '''\n # compute altitude\n alt = 0\n if len(self.pose) > 0:\n alts = [p[3] for p in self.pose]\n alt = sum(alts) / len(alts)\n\n # area visible to UAVs in each direction (inflation of line)\n self.fov = alt * math.tan(fov/2)\n\n if verbose:\n print(\"Tracking camera field of view:\\n fov = {0:.2f} m * tan({1:.2f}/2) = {2:.2f} m\".format(alt, fov, self.fov))\n\n # create a line string that represents the trajectory of the UAV\n if len(self.pose) > 0:\n self.traj = geo.LineString(self.path(self.tstop))\n else:\n self.traj = geo.LineString()\n\n if verbose:\n #print(\"Average velocity:\\n v = {0:.2f}/{1:.2f} = {2:.2f}\".format(self.traj.length, self.time[-1]-self.time[0], self.traj.length / (self.time[-1]-self.time[0])))\n if self.tstop != self.tstart:\n print(\"Average velocity:\\n v = {0:.2f} m / {1:.2f} s = {2:.2f} m/s\".format(self.traj.length, self.tstop-self.tstart, self.traj.length / (self.tstop-self.tstart)))\n\n\n def path(self, tmax):\n '''\n generate a path of the UAV as list of poses\n :param float tmax: the maximum time up to which poses are included\n '''\n i = 0\n while i < len(self.pose) and self.pose[i][0] <= tmax:\n yield self.pose[i][1], self.pose[i][2]\n i += 1\n\n # make sure a path has at least length of two\n if len(self.pose) > 0:\n if i == 0:\n yield self.pose[0][1], self.pose[0][2]\n yield self.pose[0][1], self.pose[0][2]\n\n if i == 1:\n yield self.pose[0][1], self.pose[0][2]\n","sub_path":"cpswarm_sar/scripts/modules/bag.py","file_name":"bag.py","file_ext":"py","file_size_in_byte":7896,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"516439563","text":"#!/usr/bin/python3\n\n# use a tkinter label as a panel/frame with a background image\n# note that tkinter only reads gif and ppm images\n# use the Python Image Library (PIL) for other image formats\n# free from [url]http://www.pythonware.com/products/pil/index.htm[/url]\n# give tkinter a namespace to avoid conflicts with PIL\n# (they both have a class named Image)\n\nimport tkinter as tk\nfrom PIL import Image, ImageTk\nfrom tkinter.ttk import Frame, Button, Style\nimport time\nimport glob\nimport os\nimport sys\n\nclass ShowLatestImage():\n\n def __init__(self):\n self.root = tk.Tk()\n\n # pick an image file you have .bmp .jpg .gif. .png\n # load the file and covert it to a Tkinter image object\n imageFile = self.getLatestImage()\n self.latestImage = self.readImage(imageFile)\n self.currentImage = imageFile\n self.root.title(imageFile)\n print(\"Will display %s\" % self.currentImage)\n\n # get the image size\n w = self.latestImage.width()\n h = self.latestImage.height()\n\n # position coordinates of root 'upper left corner'\n x = 0\n y = 0\n\n # make the root window the size of the image\n self.root.geometry(\"%dx%d+%d+%d\" % (w, h, x, y))\n\n # root has no image argument, so use a label as a panel\n self.panel1 = tk.Label(self.root, image=self.latestImage)\n # Keep a reference to the image to prevent it from being\n # garbage cleaned\n self.panel1.image = self.latestImage\n self.panel1.pack(side=tk.TOP, fill=tk.BOTH, expand=tk.YES)\n\n print(\"Starting to display %s\" % imageFile)\n self.root.after(30, self.updateImage)\n self.root.mainloop()\n\n def readImage(self, imageFile):\n readOk = False\n readCount = 0\n while not readOk:\n try:\n image = ImageTk.PhotoImage(Image.open(imageFile))\n readOk = True\n except:\n readCount = readCount + 1\n if readCount >5:\n print(\"Too many file reading failures on %s\" % imageFile)\n sys.exit()\n readOk = False\n time.sleep(0.02)\n pass\n return image\n\n\n def getLatestImage(self):\n while 1:\n listOfFiles = glob.glob('*.bmp')\n if listOfFiles:\n break\n time.sleep(0.05)\n latestFile = max(listOfFiles, key=os.path.getctime)\n while os.path.exists(latestFile + '.lock'):\n time.sleep(0.01)\n return latestFile\n\n def updateImage(self):\n imageFile = self.getLatestImage()\n if imageFile != self.currentImage:\n self.currentImage = imageFile\n self.latestImage = self.readImage(imageFile)\n print(\"Display %s\" % imageFile)\n self.root.title(imageFile)\n self.panel1.configure(image=self.latestImage)\n # Keep a reference to the image to prevent it from being\n # garbage cleaned\n self.panel1.image = self.latestImage\n self.root.after(30, self.updateImage) # Set to call itself again\n\ndef main():\n app = ShowLatestImage()\n\nif __name__ == '__main__':\n main()\n","sub_path":"showimages.py","file_name":"showimages.py","file_ext":"py","file_size_in_byte":3207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"502228018","text":"\"\"\" config \"\"\"\nimport os\nfrom logging import INFO as LOG_LEVEL_INFO\nfrom pathlib import Path\n\nCOMMAND_NAME = \"sf\"\nEMAIL_HOST = \"sciencefeedback.co\"\nFLASK_ROOT_PATH = Path(os.path.dirname(os.path.realpath(__file__))) / '..'\nBROWSER_URL = os.environ.get('BROWSER_URL', 'http://localhost:3000')\nENV = os.environ.get('ENV', 'development')\nIS_DEV = ENV == 'development'\nIS_STAGING = ENV == 'staging'\nIS_PROD = ENV == 'production'\nLOG_LEVEL = int(os.environ.get('LOG_LEVEL', LOG_LEVEL_INFO))\n\nif IS_DEV:\n API_URL = 'localhost'\nelif IS_PROD:\n API_URL = 'https://backend.sciencefeedback.co'\nelse:\n API_URL = 'https://backend-%s.sciencefeedback.co' % ENV\n","sub_path":"utils/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"626254260","text":"import cv2\nimport numpy as np\n\nimport time\nimport sys\nfrom pydarknet import Detector, Image\n\nfrom PyQt5.QtWidgets import QApplication, QWidget, QLineEdit, QLabel, QPushButton, QProgressBar, \\\n QRadioButton, QMessageBox\nfrom PyQt5.QtCore import QThread, pyqtSignal\n\nCONFIDENCE = 0.5\nSCORE_THRESHOLD = 0.5\nIOU_THRESHOLD = 0.5\nfont_scale = 1\nthickness = 1\nlabels = open(\"data/coco.names\").read().strip().split(\"\\n\")\ncolors = np.random.randint(0, 255, size=(len(labels), 3), dtype=\"uint8\")\n\nclass MyWidget(QWidget):\n def __init__ (self):\n QWidget. __init__ (self)\n myclose = True\n\n def closeEvent(self,event):\n if self.myclose:\n print(self.myclose)\n try:\n cap.release()\n cv2.destroyAllWindows()\n except:\n print(\"\")\n else:\n event.ignore()\n\nif __name__ == \"__main__\":\n app = QApplication(sys.argv)\n w = MyWidget()\n w.resize(310,210)\n w.setWindowTitle('Cars')\n\n pbar = QProgressBar(w)\n pbar.setGeometry(10, 165, 290, 30)\n\n def progBarUpdate(percent):\n pbar.setValue(percent)\n\n radiobuttonYolo = QRadioButton(\"yolov3\", w)\n radiobuttonYoloTiny = QRadioButton(\"yolov3-tiny\", w)\n radiobuttonYolo.move(10,70)\n radiobuttonYoloTiny.move(10,100)\n radiobuttonYoloTiny.setChecked(True)\n radiobuttonYolo.show()\n radiobuttonYoloTiny.show()\n\n font_scale = 1\n thickness = 1\n\n def startRec():\n global cap\n if radiobuttonYoloTiny.isChecked():\n config_path = \"cfg/yolov3-tiny.cfg\"\n weights_path = \"weights/yolov3-tiny.weights\"\n else:\n config_path = \"cfg/yolov3.cfg\"\n weights_path = \"weights/yolov3.weights\"\n net = cv2.dnn.readNetFromDarknet(config_path, weights_path)\n ln = net.getLayerNames()\n ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]\n #now = datetime.now()\n\n starting_time = time.time()\n frame_id = 0\n\n video_file = nameEdit.text()\n try:\n cap = cv2.VideoCapture(video_file)\n nb_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n net = Detector(bytes(\"cfg/yolov3-tiny.cfg\", encoding=\"utf-8\"), bytes(\"weights/yolov3-tiny.weights\", encoding=\"utf-8\"), 0, bytes(\"cfg/coco.data\", encoding=\"utf-8\"))\n _, image = cap.read()\n h, w = image.shape[:2]\n #fourcc = cv2.VideoWriter_fourcc(*\"XVID\")\n fourcc = cv2.VideoWriter_fourcc(*'DIVX')\n out = cv2.VideoWriter(\"output.avi\", fourcc, 20.0, (w, h))\n k = 0\n while True:\n _, image = cap.read()\n try:\n h, w = image.shape[:2]\n except:\n progBarUpdate(100)\n break\n frame_id += 1\n start = time.perf_counter()\n dark_frame = Image(image)\n results = net.detect(dark_frame)\n del dark_frame\n time_took = time.perf_counter() - start\n \n progBarUpdate(100 * (k / nb_frames))\n k += 1\n print(\"Time took:\", time_took, k, \"/\", nb_frames)\n \n for cat, score, bounds in results:\n catDec = str(cat.decode(\"utf-8\"))\n text = f\"{catDec}: {score:.2f}\"\n print(text)\n if (catDec == \"car\" or catDec == \"bus\" or catDec == \"truck\" or catDec == \"motorbike\"):\n x, y, w, h = bounds\n cv2.rectangle(image, (int(x-w/2),int(y-h/2)),(int(x+w/2),int(y+h/2)),(0,118,255))\n cv2.putText(image, text, (int(x), int(y)), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 128))\n\n out.write(image)\n cv2.imshow(\"image\", image)\n\n if ord(\"q\") == cv2.waitKey(1):\n break\n\n cap.release()\n cv2.destroyAllWindows()\n #now1 = datetime.now()\n #print(now1-now)\n except:\n msg = QMessageBox()\n msg.setIcon(QMessageBox.Critical)\n msg.setText(\"Файл недоступен\")\n msg.setInformativeText(\"Проверьте корректность вводимого пути\")\n msg.setWindowTitle(\"Ошибка чтения\")\n msg.exec_()\n\n dirLabel = QLabel(w)\n dirLabel.setText(\"Расположение видеофайла:\")\n dirLabel.move(10,10)\n dirLabel.show()\n\n nameEdit = QLineEdit(w)\n nameEdit.move(10,40)\n nameEdit.show()\n\n button = QPushButton(w)\n button.setText('Обработать')\n button.move(10,130)\n button.show()\n button.clicked.connect(startRec)\n\n w.show()\n sys.exit(app.exec_())\n","sub_path":"carsQt.py","file_name":"carsQt.py","file_ext":"py","file_size_in_byte":4816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"279409466","text":"import unittest\nfrom Connectors.ConnectionToInfluxDB import ConnectionToInfluxDB\nfrom CreateData.CreateTestData import CreateTestData\n\nclass BaseTestSLogStorage(unittest.TestCase):\n\n def setUp(self):\n self.influx=ConnectionToInfluxDB()\n self.influx.connect()\n self.data=None\n self.newData=None\n\n def tearDown(self):\n self.influx.disconnect()\n self.influx=None\n self.data=None\n self.newData=None","sub_path":"Test_LMS/Tests/SLog/BaseTestSLogStorage.py","file_name":"BaseTestSLogStorage.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"71453530","text":"## let's play with cluster_std and try to find number of clusters\n\nnp.random.seed(110119)\n\nX, y = make_blobs(n_samples = 700, n_features = 2, centers = np.array([[-10, -20],\n [-5, -15],\n [-2, -9],\n [5, 12],\n [7, 17]\n ]), cluster_std=1.4 )\n\nimport pickle\nobject_blobs_1 = [X,y]\nfile_blobs_1 = open('blobs_1.obj', 'wb')\npickle.dump(object_blobs_1, file_blobs_1)\n\n\n## let's instantiate kmeans algorithm\n## don't forget to check its parameters\nk_means = KMeans(n_clusters= 5)\n\n# dont forget to fit the model!\nk_means.fit(X)\n\n## we make a prediction for each point\ny_hat = k_means.predict(X)\n\n## we can access the coordinates of the cluster centers by cluster_centers_ method\ncl_centers = k_means.cluster_centers_\n\nprint(cl_centers)\n## note that the colors are different - Is this a problem?\nplt.scatter(X[:,0], X[:,1], c = y_hat, s = 25)\n\n\n## also let's mark the cluster centers too.\nplt.scatter(cl_centers[:, 0], cl_centers[:, 1], c='black', s=100);\n","sub_path":"lectures/week-8/kmeans_part1/support.py","file_name":"support.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"250277455","text":"'''\r\nCreated on 2014年12月7日\r\n\r\n@author: Leo\r\n'''\r\nimport tkinter as tk\r\nimport random, time\r\nfrom tkinter import filedialog \r\nfrom tkinter import messagebox \r\nfrom pubsub import pub\r\nfrom threading import Thread\r\nimport logging.handlers\r\nimport os, sys\r\nimport zipfile, shutil\r\nfrom bs4 import BeautifulSoup, SoupStrainer\r\nimport configparser, ast\r\nimport ntpath\r\nfrom operator import itemgetter\r\nimport filecmp\r\n\r\nbitmaps = [\"error\", \"gray75\", \"gray50\", \"gray25\", \"gray12\", \"hourglass\", \"info\", \"questhead\", \"question\", \"warning\"]\r\n\r\n# 字符长度数组\r\nwidths = [\r\n (126, 1), (159, 0), (687, 1), (710, 0), (711, 1),\r\n (727, 0), (733, 1), (879, 0), (1154, 1), (1161, 0),\r\n (4347, 1), (4447, 2), (7467, 1), (7521, 0), (8369, 1),\r\n (8426, 0), (9000, 1), (9002, 2), (11021, 1), (12350, 2),\r\n (12351, 1), (12438, 2), (12442, 0), (19893, 2), (19967, 1),\r\n (55203, 2), (63743, 1), (64106, 2), (65039, 1), (65059, 0),\r\n (65131, 2), (65279, 1), (65376, 2), (65500, 1), (65510, 2),\r\n (120831, 1), (262141, 2), (1114109, 1)]\r\n \r\n\r\n\r\n#---------------------------------------------------------------------- \r\n# 居中window \r\ndef center_window(self, w=400, h=400, title=\"Comee\"):\r\n self.title(title)\r\n self.resizable(False, False)\r\n\r\n # get screen width and height\r\n ws = self.winfo_screenwidth()\r\n hs = self.winfo_screenheight()\r\n # calculate position x, y\r\n x = (ws / 2) - (w / 2)\r\n y = (hs / 2) - (h / 2)\r\n \r\n # width x height + x_offset + y_offset:\r\n self.geometry('%dx%d+%d+%d' % (w, h, x, y))\r\n \r\n \r\n#----------------------------------------------------------------------\r\n# 创建Bitmap \r\ndef createBitmap(root):\r\n canvas_width = 300\r\n canvas_height = 80\r\n\r\n canvas = tk.Canvas(root, width=canvas_width, height=canvas_height)\r\n canvas.pack()\r\n\r\n \r\n nsteps = len(bitmaps)\r\n step_x = int(canvas_width / nsteps)\r\n\r\n for i in range(0, nsteps):\r\n canvas.create_bitmap((i + 1) * step_x - step_x / 2, 20, bitmap=bitmaps[i])\r\n \r\n \r\n#---------------------------------------------------------------------- \r\n'''\r\ndescription:获得字符串长度，一个中文字符算2\r\n''' \r\ndef get_str_width(s):\r\n slen = 0\r\n for word in s:\r\n slen += get_width(ord(word))\r\n return slen\r\n\r\n\r\n#----------------------------------------------------------------------\r\n'''\r\ndescription:从字符串尾部获取指定宽度的字符串，一个中文字符串算2\r\nreturn: 返回截取后的字符串\r\n'''\r\ndef get_sub_str(spath, width):\r\n s = spath\r\n s0 = s[::-1]\r\n slen = 0\r\n for i in range(len(s0)):\r\n slen += get_width(ord(s0[i]))\r\n if slen > width:\r\n return s[:3] + 3 * \".\" + s[len(s) - i:]\r\n return s\r\n\r\n#---------------------------------------------------------------------- \r\ndef get_width(o):\r\n \"\"\"Return the screen column width for unicode ordinal o.\"\"\"\r\n if o == 0xe or o == 0xf:\r\n return 0\r\n for num, wid in widths:\r\n if o <= num:\r\n return wid\r\n return 1 \r\n\r\n#---------------------------------------------------------------------- \r\n'''\r\ndescription: 判断是否是空目录，若目录不存在，则返回True\r\n'''\r\ndef is_empty_dir(directory):\r\n return sum([len(files) + len(dirs) for root, dirs, files in os.walk(directory)]) == 0 \r\n \r\n#---------------------------------------------------------------------- \r\n'''\r\ndescription:遍历目录，包含文件和空目录\r\nreturn list [[path,isfile,isleft,isright,issamefile],...]\r\n'''\r\ndef walk_list(leftdir, rightdir):\r\n alllist = []\r\n for root, dirs, files in os.walk(leftdir):\r\n for file in files: \r\n filelist = [os.path.join(root, file).replace(leftdir, \"\"), True, True, False, False] \r\n alllist.append(filelist)\r\n for directory in dirs:\r\n if is_empty_dir(os.path.join(root, directory)):\r\n directorylist = [os.path.join(root, directory).replace(leftdir, \"\"), False, True, False, False] \r\n alllist.append(directorylist)\r\n \r\n \r\n cmplist = [itemgetter(0, 1)(i) for i in alllist]\r\n for root, dirs, files in os.walk(rightdir):\r\n for file in files: \r\n # asbpath,isFile,isOld,isNew \r\n filelist = [os.path.join(root, file).replace(rightdir, \"\"), True, False, True, False]\r\n cmpfilelist = filelist[:2] \r\n if cmplist.count(tuple(cmpfilelist)) > 0:\r\n i = cmplist.index(tuple(cmpfilelist))\r\n alllist[i][3] = True\r\n if filecmp.cmp(leftdir + alllist[i][0], rightdir + alllist[i][0]):\r\n alllist[i][4] = True\r\n else:\r\n alllist.append(filelist) \r\n \r\n for directory in dirs:\r\n if is_empty_dir(os.path.join(root, directory)):\r\n directorylist = [os.path.join(root, directory).replace(rightdir, \"\"), False, False, True, False] \r\n cmpdirectorylist = directorylist[0:2] \r\n if cmplist.count(tuple(cmpdirectorylist)) > 0:\r\n i = cmplist.index(tuple(cmpdirectorylist))\r\n alllist[i][3] = True\r\n else:\r\n alllist.append(directorylist)\r\n return alllist\r\n \r\n#----------------------------------------------------------------------\r\nclass LogFrame(tk.Toplevel):\r\n \"\"\"显示日志\"\"\"\r\n \r\n #----------------------------------------------------------------------\r\n def __init__(self, index=None, oldpath=\"\", newpath=\"\", logger=None):\r\n \"\"\"Constructor\"\"\"\r\n \r\n # 格式化日期 yyyy-mm-dd hh24:mi:ss\r\n self.ISOTIMEFORMAT = '%Y-%m-%d %X'\r\n \r\n # nis path\r\n self.oldpath = oldpath\r\n self.newpath = newpath\r\n \r\n # 直接完全替换\r\n self.replace_all = False\r\n \r\n # 排除操作目录 针对所有操作\r\n self.exclusion_dirs = [\"WEB-INF\\orcus\\grab\", \"WEB-INF\\dlls\", \"WEB-INF\\rep-files\"]\r\n self.exclusion_files = [\"WEB-INF\\orcus\\diagnosis.xml\"]\r\n \r\n # 排除的待合并的文件\r\n self.merge_xml_files = [\"WEB-INF\\orcus_web.xml\", \"WEB-INF\\orcus\\diagnosis.xml\"]\r\n \r\n # 必须要进行先删除，后拷贝操作的目录\r\n self.force_delete_update_directory = [\"pages\", \"WEB-INF\\classes\"]\r\n \r\n # 排除的key 值不是true或false的都属于被排除的\r\n self.exclusion_element_keys = [\"localhost\", \"web.context.url\", \"server.path\", \"Course.Parse.loader\", \"Course.Parse.loader.ftp.url\", \"Course.Parse.loader.dsName\", \"Course.Parse.loader.ws.address\"] # 不覆盖的element元素\r\n # 针对上一项目的补充，如值不是true或false 但是需要进行更新， 例如App.Version\r\n self.must_update_element_keys = [\"App.Version\"]\r\n # 与上2项结合使用：开关选项 orcusweb.xml中element元素值是true或false的元素是需要更新的，其他的element元素是不需要更新的；若为true，则其他的element元素也更新\r\n self.VALUE_TRUE_FALSE_PROTECTED = False\r\n \r\n self.BO_DELETE_ELEMENT = True\r\n self.OC_UPDATE_ELEMENT = True\r\n self.BO_DELETE_TASK = True\r\n self.OC_UPDATE_TASK = True\r\n self.BO_DELETE_FILE = True\r\n self.BO_DELETE_EMPTY_DIR = True\r\n \r\n \r\n self.logger = logger\r\n self.initconfig()\r\n \r\n \r\n \r\n # 过滤exclusion_files\r\n self.exclusion_merge() # 必须在initconfig之后\r\n \r\n \r\n tk.Toplevel.__init__(self)\r\n center_window(self, w=750, h=500, title=\"日 志\")\r\n \r\n # 处理x 关闭窗口事件，用于覆盖原事件\r\n self.protocol(\"WM_DELETE_WINDOW\", self.onClose)\r\n \r\n S = tk.Scrollbar(self)\r\n T = tk.Text(self, height=500, width=500)\r\n S.pack(side=tk.RIGHT, fill=tk.Y)\r\n T.pack(side=tk.LEFT, fill=tk.Y)\r\n S.config(command=T.yview)\r\n T.config(yscrollcommand=S.set)\r\n \r\n T.tag_configure('big', foreground='#476042', font=('黑体', 20, 'bold'))\r\n T.tag_configure(\"center\", justify='center')\r\n T.tag_configure('big_center', justify='center', foreground='#476042', font=('黑体', 20, 'bold'))\r\n T.tag_configure('color_info', foreground='#476042', font=('宋体', 12))\r\n T.tag_configure('color_error', foreground='red', font=('宋体', 12, 'bold'))\r\n T.tag_configure('color_important', foreground='green', font=('宋体', 12, 'bold'))\r\n \r\n # 防止编辑Text\r\n T.bind(\"\", lambda e : \"break\")\r\n \r\n self.T = T\r\n \r\n # 根据index调用事件\r\n self.onIndex(index)\r\n \r\n \r\n #----------------------------------------------------------------------\r\n '''\r\n description:根据index来处里事件\r\n '''\r\n def onIndex(self, index):\r\n if index == 2:\r\n self.T.insert(tk.END, \"\\n更新记录\\n\\n\", \"big_center\")\r\n self.info(\"原始nis目录为：%s\" % self.oldpath)\r\n self.info(\"新nis目录为：%s\" % self.newpath)\r\n \r\n t1 = Thread(target=self.update_nis) # 指定目标函数，传入参数，这里参数也是元组 \r\n t1.start() # 启动线程 \r\n \r\n elif index == 3:\r\n print(\"Check OOMMonitor\")\r\n \r\n elif index == 4:\r\n print(\"Don't touch me!\")\r\n \r\n \r\n #----------------------------------------------------------------------\r\n '''\r\n description:更新nis程序\r\n '''\r\n def update_nis(self):\r\n \r\n webapps_dir = ntpath.split(self.oldpath)[0]\r\n TOMCAT_HOME = ntpath.split(webapps_dir)[0]\r\n \r\n self.info(\"开始备份nis系统...\")\r\n self.zip_dir(self.oldpath, r\"%s/nis-backup %s.zip\" % (TOMCAT_HOME, time.strftime('%Y-%m-%d %H-%M-%S', time.localtime(time.time()))))\r\n self.info(\"nis系统备份完成\")\r\n try:\r\n if self.replace_all == True:\r\n self.info(\"更新模式为直接替换整个文件夹...\")\r\n \r\n self.info(\"清空目录%s\" % self.oldpath)\r\n shutil.rmtree(self.oldpath, True, None)\r\n self.info(\"目录%s清空完成\" % self.oldpath)\r\n \r\n self.info(\"开始将新文件%s拷贝到%s下\" % (self.newpath, self.oldpath))\r\n shutil.copytree(self.newpath, self.oldpath, True)\r\n self.info(\"文件拷贝完成！\")\r\n \r\n self.info(\"开始清空work目录...\")\r\n shutil.rmtree(r\"%s/work\" % TOMCAT_HOME, True, None)\r\n self.info(\"work目录清空成功，NIS更新结束!\")\r\n \r\n return \r\n else:\r\n self.info(\"更新模式为单文件分析...\")\r\n self.info(\"开始分析文件目录结构...\")\r\n alllist = walk_list(self.oldpath, self.newpath)\r\n \r\n self.info(\"开始更新...\")\r\n self.procee_walklist(alllist)\r\n self.info(\"常规更新结束\")\r\n \r\n self.process_force_delete_update_direcory()\r\n \r\n self.info(\"开始分析orcus_web.xml...\")\r\n self.merge_orcusweb_xml()\r\n# self.info(\"orcus_web.xml更新结束\")\r\n self.info(\"orcus_web.xml更新提醒结束\")\r\n \r\n self.info(\"开始分析diagnosis.xml...\")\r\n self.merge_diagnosis_xml()\r\n self.info(\"diagnosis.xml更新提醒结束\")\r\n \r\n self.info(\"开始清空work目录...\")\r\n shutil.rmtree(r\"%s/work\" % TOMCAT_HOME, True, None)\r\n self.info(\"work目录清空成功！\")\r\n \r\n self.info(\"NIS系统更新结束\")\r\n return\r\n \r\n except Exception as ex:\r\n self.error(\"系统更新出错：%s\" % ex)\r\n finally:\r\n self.line_finished()\r\n \r\n \r\n #----------------------------------------------------------------------\r\n '''\r\n description:处理force_delete_update_direcory目录\r\n '''\r\n def process_force_delete_update_direcory(self):\r\n for dir0 in self.force_delete_update_directory:\r\n old_path = self.oldpath.replace(\"/\", \"\\\\\");\r\n new_path = self.newpath.replace(\"/\", \"\\\\\");\r\n \r\n self.info(\"清空目录%s\" % (old_path + \"\\\\\" + dir0))\r\n shutil.rmtree(old_path + \"\\\\\" + dir0, True, None)\r\n self.info(\"目录%s清空完成\" % (old_path + \"\\\\\" + dir0))\r\n \r\n self.info(\"开始将新文件%s拷贝到%s下\" % (new_path + \"\\\\\" + dir0, old_path + \"\\\\\" + dir0))\r\n shutil.copytree(new_path + \"\\\\\" + dir0, old_path + \"\\\\\" + dir0, True)\r\n self.info(\"文件拷贝完成！\")\r\n return\r\n \r\n \r\n \r\n \r\n \r\n #----------------------------------------------------------------------\r\n '''\r\n description:获得配置文件\r\n '''\r\n def initconfig(self):\r\n config = configparser.ConfigParser()\r\n \r\n # self.logger.info(\"配置文件为：%s\" % os.path.realpath(__file__))\r\n configfile = os.path.join(os.path.split(sys.path[0])[0], \"nis_update.ini\")\r\n \r\n config.read(configfile)\r\n if len(config.sections()) == 0:\r\n self.logger.info(\"LogFrame不使用配置文件，系统将自动初始化配置信息\")\r\n elif len(config.sections()) > 1:\r\n self.logger.error(\"LogFrame配置文件配置错误！不允许多个配置项！系统将使用默认项 CODE:%s\" % len(config.sections()))\r\n else:\r\n self.logger.info(\"LogFrame配置文件为：%s\" % configfile)\r\n section = config.sections().pop()\r\n # 通过配置文件赋值\r\n for key in config[section]:\r\n if key in self.__dict__:\r\n self.setattr(key, ast.literal_eval(config[section][key])) \r\n \r\n #----------------------------------------------------------------------\r\n '''\r\n description:压缩目录\r\n parameter：dirname 待压缩的目录\r\n paramete：zipfilename 压缩后文件名（包含路径）\r\n ''' \r\n def zip_dir(self, dirname, zipfilename):\r\n self.info(\"待压缩目录为：%s 压缩后的文件名为：%s\" % (dirname, zipfilename))\r\n if os.path.exists(dirname):\r\n filelist = []\r\n if os.path.isfile(dirname):\r\n filelist.append(dirname)\r\n else :\r\n for root, dirs, files in os.walk(dirname):\r\n for name in files:\r\n filelist.append(os.path.join(root, name))\r\n \r\n zf = zipfile.ZipFile(zipfilename, \"w\", zipfile.zlib.DEFLATED)\r\n for tar in filelist:\r\n arcname = tar[len(dirname):]\r\n zf.write(tar, arcname)\r\n zf.close()\r\n self.info(\"目录%s压缩完成\" % dirname)\r\n else:\r\n self.info(\"目录%s不存在\" % dirname)\r\n \r\n #---------------------------------------------------------------------- \r\n '''\r\n description:清空目录；删除文件：os.remove(filename)\r\n '''\r\n def delete_dir(self, dirname):\r\n self.info(\"开始清空目录%s\" % dirname)\r\n shutil.rmtree(dirname, True, None)\r\n self.info(\"目录%s清空完成\" % dirname)\r\n #----------------------------------------------------------------------\r\n \r\n '''\r\n description:diagnosis.xml\r\n '''\r\n def merge_diagnosis_xml(self):\r\n lfile = os.path.join(self.oldpath, self.merge_xml_files[1]).replace(\"\\\\\", \"/\")\r\n self.info(\"原始diagnosis.xml文件路径为：%s\" % lfile)\r\n \r\n rfile = os.path.join(self.newpath, self.merge_xml_files[1]).replace(\"\\\\\", \"/\")\r\n self.info(\"新diagnosis.xml文件路径为：%s\" % rfile)\r\n self.important(\"请用Beyond Compare工具更新WEB-INF\\orcus\\diagnosis.xml 和 WEB-INF\\web.xml\")\r\n \r\n \r\n '''\r\n description:合并orcus_web.xml文件\r\n ''' \r\n def merge_orcusweb_xml(self):\r\n lfile = os.path.join(self.oldpath, self.merge_xml_files[0]).replace(\"\\\\\", \"/\")\r\n self.info(\"原始orcus_web.xml文件路径为：%s\" % lfile)\r\n \r\n rfile = os.path.join(self.newpath, self.merge_xml_files[0]).replace(\"\\\\\", \"/\")\r\n self.info(\"新orcus_web.xml文件路径为：%s\" % rfile)\r\n \r\n self.update_orcusweb_xml(lfile, rfile)\r\n \r\n def contain_elementortask(self, tag, taglist):\r\n if tag.name == \"element\":\r\n for t in taglist:\r\n if t.name == \"element\" and t[\"key\"].strip() == tag[\"key\"].strip():\r\n if tag[\"value\"].strip() == t[\"value\"].strip():\r\n return True\r\n else:\r\n return \"CNQ\" # contain but not equal\r\n elif tag.name == \"task\":\r\n for t in taglist:\r\n if t.name == \"task\" and tag.string.strip() == t.string.strip():\r\n return True\r\n return False\r\n \r\n def process_element(self, all_element_tags):\r\n if len(all_element_tags) == 0:\r\n return\r\n \r\n element_parent = all_element_tags[0][0].parent\r\n for tag in all_element_tags:\r\n if tag[1] and tag[2] and tag[3]:\r\n pass\r\n elif tag[1] and tag[2] and not tag[3]:\r\n if tag[0][\"key\"].strip() in self.must_update_element_keys:\r\n self.important(\"key=%s的element元素为必须更新的元素，请更新\" % tag[0][\"key\"].strip())\r\n# self.info(\"key=%s的element元素为必须更新的元素，所以更新\" % tag[0][\"key\"].strip())\r\n# tag[0][\"value\"] = tag[4][\"value\"]\r\n # 新的(right)值与旧的(left)值不一致，新的替换旧的\r\n else:\r\n if self.is_exclusion_element(tag[0][\"value\"].strip()) or tag[0][\"key\"].strip() in self.exclusion_element_keys:\r\n self.important(\"key=%s的element元素基本不需要更新，请慎重更改\" % tag[0][\"key\"].strip())\r\n# self.info(\"key=%s的element元素为受保护的元素，不进行更新\" % tag[0][\"key\"].strip())\r\n else:\r\n self.important(\"key=%s的element元素的value属性不同，原始的为：%s，新的为：%s，请更新\" % (tag[0][\"key\"].strip(), tag[0][\"value\"], tag[4][\"value\"]))\r\n# self.info(\"key=%s的element元素的value属性不同，原始的为：%s，新的为：%s，将进行更新\" % (tag[0][\"key\"].strip(), tag[0][\"value\"], tag[4][\"value\"]))\r\n# tag[0][\"value\"] = tag[4][\"value\"]\r\n elif tag[1] and not tag[2]:\r\n # 旧的有，新的没有\r\n self.important(\"key=%s的element元素的在新版本中已被删除，请更新\" % (tag[0][\"key\"].strip()))\r\n \r\n# if self.BO_DELETE_ELEMENT:\r\n# self.info(\"BO_DELETE_ELEMENT的配置为%s，所以删除新nis中没有的element元素%s\" % (self.BO_DELETE_ELEMENT, tag[0]))\r\n# tag[0].decompose()\r\n# else:\r\n# self.info(\"BO_DELETE_ELEMENT的配置为%s，所以保留新nis中没有的element元素%s\" % (self.BO_DELETE_ELEMENT, tag[0]))\r\n elif not tag[1] and tag[2]:\r\n # 旧的没有，新的有\r\n self.important(\"key=%s的element元素的为新增加的元素，请更新\" % (tag[0][\"key\"].strip()))\r\n# if self.OC_UPDATE_ELEMENT:\r\n# self.info(\"OC_UPDATE_ELEMENT的配置为%s，所以添加新nis中存在而旧nis中不存在的element元素%s\" % (self.OC_UPDATE_ELEMENT, tag[0]))\r\n# element_parent.append(tag[0])\r\n# else:\r\n# self.info(\"OC_UPDATE_ELEMENT的配置为%s，所以不添加新nis中存在而旧nis中不存在的element元素%s\" % (self.OC_UPDATE_ELEMENT, tag[0]))\r\n \r\n def is_exclusion_element(self, tag_value):\r\n if self.VALUE_TRUE_FALSE_PROTECTED:\r\n return True\r\n elif tag_value == \"true\" or tag_value == \"false\":\r\n return False\r\n return True\r\n \r\n def process_task(self, all_task_tags):\r\n if len(all_task_tags) == 0:\r\n return\r\n task_parent = all_task_tags[0][0].parent\r\n \r\n for tag in all_task_tags:\r\n if tag[1] and tag[2]:\r\n pass\r\n if not tag[1] and tag[2]:\r\n self.important(\"task元素%s为新版本中增加的元素，请更新\" % tag[0])\r\n# if self.OC_UPDATE_TASK:\r\n# self.info(\"OC_UPDATE_TASK的配置为%s，所以添加新nis中存在而旧nis中不存在的task元素%s\" % (self.BO_DELETE_ELEMENT, tag[0]))\r\n# task_parent.append(tag[0])\r\n# else:\r\n# self.info(\"OC_UPDATE_TASK的配置为%s，所以不添加新nis中存在而旧nis中不存在的task元素%s\" % (self.BO_DELETE_ELEMENT, tag[0]))\r\n if tag[1] and not tag[2]:\r\n self.important(\"task元素%s为新版本中删除的元素，请更新\" % tag[0])\r\n# if self.BO_DELETE_TASK:\r\n# self.info(\"BO_DELETE_TASK的配置为%s，所以删除新nis中不存在而旧nis中存在的task元素%s\" % (self.BO_DELETE_ELEMENT, tag[0]))\r\n# tag[0].decompose()\r\n# else:\r\n# self.info(\"BO_DELETE_TASK的配置为%s，所以保留新nis中不存在而旧nis中存在的task元素%s\" % (self.BO_DELETE_ELEMENT, tag[0]))\r\n \r\n def filter_element(self, element_key):\r\n if element_key.strip() in self.exclusion_element_keys:\r\n return True\r\n else:\r\n return False \r\n \r\n def update_orcusweb_xml(self, oldfile, newfile):\r\n \r\n lfile = oldfile\r\n rfile = newfile\r\n \r\n oldfile = open(lfile, 'r', encoding=\"UTF-8\")\r\n soup = BeautifulSoup(oldfile, \"xml\")\r\n \r\n \r\n element_and_task_tags = SoupStrainer([\"element\", \"task\"])\r\n rsoup = BeautifulSoup(open(rfile, 'r', encoding=\"UTF-8\"), \"xml\", parse_only=element_and_task_tags)\r\n \r\n all_element_tags = []\r\n allkeys = []\r\n for tag in soup(\"element\"):\r\n d = [ tag, True, False, False, None ] \r\n if tag[\"key\"] in allkeys:\r\n self.important(\"原始文件%s存在多个key值为%s的element元素，请修改（只能保留一个）\" % (lfile, tag[\"key\"]))\r\n# self.info(\"原始文件%s存在多个key值为%s的element元素，程序将保留第一个\" % (lfile, tag[\"key\"]))\r\n tag.next_sibling.next_sibling.replace_with(\"\") # 注释\r\n tag.next_sibling.replace_with(\"\") # 换行符\r\n tag.decompose()\r\n else:\r\n all_element_tags.append(d)\r\n allkeys.append(tag[\"key\"])\r\n \r\n for rtag in rsoup(\"element\"):\r\n rd = [rtag, False, True, False, None]\r\n cmplist = [itemgetter(0)(i) for i in all_element_tags]\r\n if self.contain_elementortask(rd[0], cmplist) == \"CNQ\":\r\n index = allkeys.index(rd[0][\"key\"])\r\n all_element_tags[index][2] = True \r\n all_element_tags[index][4] = rtag \r\n elif self.contain_elementortask(rd[0], cmplist):\r\n index = allkeys.index(rd[0][\"key\"])\r\n all_element_tags[index][2] = True \r\n all_element_tags[index][3] = True\r\n else:\r\n all_element_tags.append(rd)\r\n \r\n #--------------------------------------------------------------\r\n all_task_tags = []\r\n all_task_string = []\r\n for tag in soup(\"task\"):\r\n d = [ tag, True, False]\r\n if tag.string.strip() in all_task_string:\r\n self.important(\"原始文件%s存在多个值为%s的task元素，请修改（只能保留一个）\" % (lfile, tag.string.strip()))\r\n# self.info(\"原始文件%s存在多个值为%s的task元素，程序将保留第一个\" % (lfile, tag.string.strip()))\r\n# tag.decompose()\r\n else:\r\n all_task_tags.append(d)\r\n all_task_string.append(tag.string.strip())\r\n \r\n for rtag in rsoup(\"task\"):\r\n rd = [rtag, False, True]\r\n cmp_list = [itemgetter(0)(i) for i in all_task_tags]\r\n if self.contain_elementortask(rd[0], cmp_list):\r\n index = all_task_string.index(rd[0].string.strip())\r\n all_task_tags[index][2] = True \r\n else:\r\n all_task_tags.append(rd)\r\n #----------------------------------------------------------------\r\n \r\n self.process_element(all_element_tags)\r\n self.process_task(all_task_tags)\r\n \r\n \r\n oldfile.close()\r\n \r\n# self.info(\"开始将更��写入%s...\" % lfile)\r\n# orcusweb_xml = soup.prettify(\"utf-8\")\r\n# orcusweb_xml = soup.prettify(\"utf-8\", formatter=None)\r\n# \r\n# with open(lfile, \"wb\") as file:\r\n# file.write(orcusweb_xml)\r\n# self.info(\"写入成功\")\r\n# \r\n# return orcusweb_xml\r\n \r\n \r\n #----------------------------------------------------------------------\r\n '''\r\n description:往Text元素里面写内容，并生成日志文件\r\n '''\r\n def info(self, msg):\r\n self.logger.info(msg)\r\n self.T.insert(tk.END, time.strftime(self.ISOTIMEFORMAT, time.localtime()) + \"\\n\" + str(msg) + \"\\n\\n\" , \"color_info\")\r\n self.T.update()\r\n self.T.see(tk.END)\r\n \r\n def error(self, msg):\r\n self.logger.error(msg)\r\n self.T.insert(tk.END, time.strftime(self.ISOTIMEFORMAT, time.localtime()) + \"\\n\" + str(msg) + \"\\n\\n\" , \"color_error\")\r\n self.T.update()\r\n self.T.see(tk.END)\r\n \r\n def line(self):\r\n self.T.insert(tk.END, \"\\n\" + 52 * \"-\" + \"\\n\\n\", \"center\")\r\n self.T.update()\r\n self.T.see(tk.END)\r\n \r\n def important(self, msg):\r\n self.logger.info(msg)\r\n self.T.insert(tk.END, time.strftime(self.ISOTIMEFORMAT, time.localtime()) + \"\\n\" + str(msg) + \"\\n\\n\" , \"color_important\")\r\n self.T.update()\r\n self.T.see(tk.END)\r\n \r\n \r\n def line_finished(self):\r\n self.logger.info(\"=======================================================\\n\")\r\n self.T.insert(tk.END, \"\\n\" + 23 * \"-\" + \"finished\" + 23 * \"-\" + \"\\n\\n\", \"center\")\r\n self.T.update()\r\n self.T.see(tk.END)\r\n \r\n #---------------------------------------------------------------------- \r\n \r\n '''\r\n description:alllist来自于 walk_list\r\n '''\r\n def procee_walklist(self, alllist):\r\n olddir = self.oldpath\r\n newdir = self.newpath\r\n for flist in alllist:\r\n if self.filter_special(flist[0]):\r\n self.info(\"需要特殊处理(受保护或待合并或待处理)的文件%s，已跳过\" % flist[0])\r\n continue;\r\n if flist[1]:\r\n # 文件\r\n if flist[2] and flist[3] and flist[4]:\r\n self.info(\"相同的文件%s，已跳过\" % flist[0])\r\n elif flist[2] and flist[3] and not flist[4]:\r\n # 都存在但不同， 用right替换left\r\n self.info(\"文件都存在，但不同，用新文件替换旧文件%s\" % flist[0])\r\n shutil.copyfile(newdir + flist[0], olddir + flist[0])\r\n elif not flist[2] and flist[3]:\r\n # left不存在，right复制到left\r\n oldnewpath = ntpath.split(olddir + flist[0])[0]\r\n os.makedirs(oldnewpath, mode=0o777, exist_ok=True)\r\n shutil.copy(newdir + flist[0], oldnewpath)\r\n self.info(\"新文件：%s，已复制\" % flist[0])\r\n elif flist[2] and not flist[3]:\r\n # left 存在，right不存在，保留left\r\n if self.BO_DELETE_FILE:\r\n self.info(\"BO_DELETE_FILE的值为%s，所以删除旧nis存在而新nis不存在的文件%s\" % (self.BO_DELETE_FILE, flist[0]))\r\n os.remove(olddir + flist[0])\r\n else:\r\n self.info(\"BO_DELETE_FILE的值为%s，所以保留旧nis存在而新nis不存在的文件%s\" % (self.BO_DELETE_FILE, flist[0]))\r\n else:\r\n # 空目录\r\n if self.filter_special_dir(flist[0]):\r\n self.info(\"需要特殊处理(受保护或待合并或待处理)的目录%s，已跳过\" % flist[0])\r\n continue;\r\n if flist[2] and flist[3]:\r\n # 都存在，啥都不做\r\n pass\r\n elif flist[2] and not flist[3]:\r\n # left存在，right不存在，则left维持原样\r\n if self.BO_DELETE_EMPTY_DIR:\r\n self.info(\"BO_DELETE_EMPTY_DIR的值为%s，所以删除旧nis存在而新nis不存在的空文件夹%s\" % (self.BO_DELETE_EMPTY_DIR, flist[0]))\r\n os.rmdir(olddir + flist[0])\r\n else:\r\n self.info(\"BO_DELETE_EMPTY_DIR的值为%s，所以保留旧nis存在而新nis不存在的空文件夹%s\" % (self.BO_DELETE_EMPTY_DIR, flist[0]))\r\n elif not flist[2] and flist[3]:\r\n # left不存在，right存在，则在left也创建新目录\r\n os.makedirs(olddir + flist[0], mode=0o777, exist_ok=True)\r\n self.info(\"空目录：%s，已创建\" % flist[0])\r\n \r\n #---------------------------------------------------------------------- \r\n '''\r\n description:是否在目录中\r\n '''\r\n def is_in_dir(self, directory, file):\r\n if not len(directory) > 1:\r\n return False\r\n if file[0:1] == \"\\\\\":\r\n file = file[1:]\r\n if file[:len(directory) + 1] == directory + \"\\\\\":\r\n return True\r\n return False\r\n \r\n '''\r\n description:是否在目录集合中\r\n '''\r\n def is_in_dirs(self, directorys, file):\r\n if not len(directorys) > 0:\r\n return False\r\n if file[0:1] == \"\\\\\":\r\n file = file[1:]\r\n for directory in directorys:\r\n if file[:len(directory) + 1] == directory + \"\\\\\":\r\n return True\r\n return False\r\n \r\n \r\n '''\r\n description:是否是排除的文件(merge_xml_files也算是排除的文件)\r\n '''\r\n def is_exclusion_file(self, file):\r\n if file[0:1] == \"\\\\\":\r\n file = file[1:]\r\n if file in self.exclusion_files:\r\n return True\r\n return False\r\n \r\n \r\n '''\r\n return 返回被exclusion_dirs过滤过的exclusion_files列表\r\n '''\r\n def exclusion_merge(self): \r\n for must_delete_update_dir in self.force_delete_update_directory:\r\n if must_delete_update_dir not in self.exclusion_dirs:\r\n self.exclusion_dirs.append(must_delete_update_dir)\r\n temp = []\r\n for file in self.exclusion_files:\r\n for directory in self.exclusion_dirs:\r\n if not self.is_in_dir(directory, file):\r\n temp.append(file)\r\n \r\n for f in self.merge_xml_files:\r\n if f not in temp:\r\n temp.append(f)\r\n \r\n self.exclusion_files = temp\r\n \r\n for k in self.must_update_element_keys:\r\n if k in self.exclusion_element_keys:\r\n self.exclusion_element_keys.remove(k)\r\n \r\n '''\r\n description:是否是procee_walklist不需要处理的文件\r\n '''\r\n def filter_special(self, oldfile):\r\n if self.is_in_dirs(self.exclusion_dirs, oldfile):\r\n return True\r\n elif self.is_exclusion_file(oldfile):\r\n return True\r\n else:\r\n return False \r\n \r\n def filter_special_dir(self, olddir):\r\n if olddir[0:1] == \"\\\\\":\r\n olddir = olddir[1:]\r\n if olddir in self.exclusion_dirs:\r\n return True\r\n return False\r\n\r\n \r\n #---------------------------------------------------------------------- \r\n '''\r\n description:设置类的属性\r\n ''' \r\n def setattr(self, name, value):\r\n self.__dict__[name] = value \r\n \r\n #----------------------------------------------------------------------\r\n def onClose(self):\r\n \"\"\"\r\n closes the frame and sends a message to the main frame\r\n \"\"\"\r\n self.destroy()\r\n pub.sendMessage(\"logFrameClosed\", arg1=\"data\")\r\n \r\n#----------------------------------------------------------------------\r\n\r\n\r\nclass Application(tk.Frame):\r\n \"\"\"\"\"\"\r\n \r\n# labelbuttons = [' 单击选择当前nis文件夹...', ' 单击选择新nis文件夹...', '更 新', 'OOMMonitor', '']\r\n# normal bold italic\r\n# fonts = [('楷体', 18, 'normal'), ('楷体', 18, 'normal'), ('宋体', 24, 'italic'), ('times', 24, 'italic'), ('times', 24, 'italic')]\r\n\r\n labelbuttons = ['Open current nis directory...', 'Open new nis directory...', 'Update', 'OOMMonitor', '']\r\n# normal bold italic\r\n fonts = [('times', 12, 'normal'), ('times', 12, 'normal'), ('times', 24, 'italic'), ('times', 24, 'italic'), ('times', 24, 'italic')]\r\n labelbitmaps = ['', '', 'hourglass', '', 'questhead']\r\n textalign = [tk.CENTER, tk.CENTER, tk.CENTER, tk.CENTER, tk.CENTER ]\r\n bitmapalign = [tk.LEFT, tk.LEFT, tk.LEFT, tk.LEFT, tk.LEFT]\r\n \r\n #----------------------------------------------------------------------\r\n def __init__(self, master=None):\r\n \"\"\"Constructor\"\"\"\r\n tk.Frame.__init__(self, master)\r\n self.pack()\r\n self.root = master\r\n \r\n self.oldpath = \"\"\r\n self.newpath = \"\"\r\n # 日志配置 若为0 则只产生一个日志文件，且backupCount失效\r\n self.maxMegabytes = 10\r\n self.backupCount = 5\r\n \r\n self.logger = self._getLogger()\r\n self.initconfig() # 由于initconfig里面使用日志记录，所以日志没法配置，如果要使日志可配置，请取消initconfig()中的日志记录。\r\n \r\n \r\n createBitmap(master)\r\n self.pack()\r\n \r\n self.mainview()\r\n self.pack()\r\n \r\n \r\n pub.subscribe(self.listener, \"logFrameClosed\")\r\n \r\n #----------------------------------------------------------------------\r\n def listener(self, arg1, arg2=None):\r\n \"\"\"\r\n pubsub listener - opens main frame when logFrame closes\r\n \"\"\"\r\n self.show()\r\n \r\n #----------------------------------------------------------------------\r\n def hide(self):\r\n \"\"\"\r\n hides main frame\r\n \"\"\"\r\n self.root.withdraw()\r\n \r\n #----------------------------------------------------------------------\r\n def openFrame(self, index=None):\r\n \"\"\"\r\n opens other frame and hides main frame\r\n \"\"\"\r\n self.hide()\r\n subFrame = LogFrame(index, self.oldpath, self.newpath, self.logger)\r\n \r\n #----------------------------------------------------------------------\r\n def show(self):\r\n \"\"\"\r\n shows main frame\r\n \"\"\"\r\n self.root.update()\r\n self.root.deiconify()\r\n \r\n #----------------------------------------------------------------------\r\n '''\r\n description:获得配置文件\r\n '''\r\n def initconfig(self):\r\n config = configparser.ConfigParser()\r\n \r\n # self.logger.info(\"配置文件为：%s\" % os.path.realpath(__file__))\r\n configfile = os.path.join(os.path.split(sys.path[0])[0], \"nis_update.ini\")\r\n \r\n config.read(configfile)\r\n if len(config.sections()) == 0:\r\n self.logger.info(\"不使用配置文件，系统将自动初始化配置信息\")\r\n elif len(config.sections()) > 1:\r\n self.logger.error(\"配置文件配置错误！不允许多个配置项！系统将使用默认项 CODE:%s\\n==================================\" % len(config.sections()))\r\n else: \r\n self.logger.info(\"配置文件为：%s\" % configfile)\r\n section = config.sections().pop()\r\n # 通过配置文件赋值\r\n for key in config[section]:\r\n if key in self.__dict__:\r\n self.setattr(key, ast.literal_eval(config[section][key])) \r\n \r\n #----------------------------------------------------------------------\r\n '''\r\n description:日志初始化\r\n '''\r\n def _getLogger(self):\r\n \r\n logger = logging.getLogger('[NIS-UPDATE]') \r\n \r\n handler = logging.handlers.RotatingFileHandler(os.path.join(os.path.split(sys.path[0])[0], \"NIS_UPDATE.LOG\"), maxBytes=int(self.maxMegabytes) * 1024 * 1024, backupCount=int(self.backupCount)) \r\n formatter = logging.Formatter('%(asctime)s %(name)-12s %(levelname)-8s %(message)s', '%Y-%m-%d %H:%M:%S') \r\n handler.setFormatter(formatter) \r\n \r\n logger.addHandler(handler) \r\n logger.setLevel(logging.INFO) \r\n \r\n return logger \r\n \r\n #----------------------------------------------------------------------\r\n # 主界面\r\n def mainview(self):\r\n for i in range(5):\r\n ct = [random.randrange(256) for x in range(3)]\r\n brightness = int(round(0.299 * ct[0] + 0.587 * ct[1] + 0.114 * ct[2]))\r\n ct_hex = \"%02x%02x%02x\" % tuple(ct)\r\n bg_colour = '#' + \"\".join(ct_hex)\r\n \r\n l = tk.Label(self.root, text=self.labelbuttons[i], fg='White' if brightness < 120 else 'Black', anchor=self.textalign[i], bg=bg_colour, compound=self.bitmapalign[i], bitmap=self.labelbitmaps[i])\r\n \r\n if i >= 2:\r\n l.config(font=self.fonts[i])\r\n \r\n space = lambda x: x > 2 and 20 or 0\r\n l.place(x=50, y=80 + i * 50 + space(i), width=300, height=40)\r\n l.bind('', lambda event, arg=l, index=i: self.click(event, arg, index))\r\n \r\n #----------------------------------------------------------------------\r\n '''\r\n description:处理单击事件\r\n '''\r\n def click(self, event, this, index):\r\n \r\n def getPathName(pathname, title):\r\n name = filedialog.askdirectory(initialdir=\"D:\\\\runtime\\\\tomcat-6.0.29\\\\webapps\\\\nis\", title=title)\r\n if len(name) != 0:\r\n self.setattr(pathname, name)\r\n this[\"text\"] = get_sub_str(name, 42)\r\n \r\n \r\n if index == 0:\r\n getPathName(\"oldpath\", \"选择旧nis文件夹\\n例如：D:\\\\runtime\\\\tomcat-6.0.29\\\\webapps\\\\nis\")\r\n elif index == 1:\r\n getPathName(\"newpath\", \"选择新nis文件夹\\n例如：D:\\\\runtime\\\\nis\")\r\n elif index == 2:\r\n if len(self.oldpath) > 0 and len(self.newpath) > 0:\r\n self.openFrame(2)\r\n else:\r\n messagebox.showerror(\"nis文件名不能为空\", \"nis文件名不允许为空\\n请选择nis文件路径\")\r\n \r\n else:\r\n self.openFrame(index)\r\n \r\n #---------------------------------------------------------------------- \r\n '''\r\n description:设置类的属性\r\n ''' \r\n def setattr(self, name, value):\r\n self.__dict__[name] = value \r\n#----------------------------------------------------------------------\r\n\r\nroot = tk.Tk()\r\ncenter_window(root, w=400, h=400, title=\"自动更新nis系统 v1.0\")\r\napp = Application(master=root)\r\napp.mainloop()\r\n","sub_path":"NisUpdate.py","file_name":"NisUpdate.py","file_ext":"py","file_size_in_byte":40532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"394414529","text":"class Solution:\n def countBinarySubstrings(self, s):\n \"\"\"\n :type s: str\n :rtype: int\n \"\"\"\n ret = 0\n for length in range(2,len(s)+1,2):\n for start in range(0,len(s)-length+1):\n print(s[start:start+length])\n ones = '1'*int(length/2)\n zeros = '0'*int(length/2)\n firstHalf = s[start:start+int(length/2)]\n secondHalf = s[start+int(length/2):start+length]\n if (firstHalf == ones and secondHalf == zeros) or (firstHalf == zeros and secondHalf == ones):\n ret += 1\n print(s[start:start+length],length,int(length/2)*'1')\n return ret\n\n def countBinarySubstrings2(self, s):\n count, countList = 1, []\n for i in range(1,len(s)):\n if s[i] == s[i-1]:\n count += 1\n else:\n countList.append(count)\n count = 1\n countList.append(count)\n return sum(min(a,b) for a,b in zip(countList, countList[1:]))\n\n def countBinarySubstrings3(self, s):\n countList = []\n for group in s.replace('01', '0 1').replace('10','1 0').split():\n countList.append(len(group))\n return sum(min(a,b) for a,b in zip(countList,countList[1:]))\n\n\nprint(Solution().countBinarySubstrings3('001100111'))","sub_path":"countBinarySubstrings.py","file_name":"countBinarySubstrings.py","file_ext":"py","file_size_in_byte":1370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"324378644","text":"## SI 364\n## Winter 2018\n## HW 2 - Part 1\n\n## This homework has 3 parts, all of which should be completed inside this file (and a little bit inside the /templates directory).\n\n## Add view functions and any other necessary code to this Flask application code below so that the routes described in the README exist and render the templates they are supposed to (all templates provided are inside the templates/ directory, where they should stay).\n\n## As part of the homework, you may also need to add templates (new .html files) to the templates directory.\n\n##Resources##\n# https://www.programsinformationpeople.org/runestone/static/publicpy3/NestedData/ListswithComplexItems.html\n# Lecture4Example1\n\n\n#############################\n##### IMPORT STATEMENTS #####\n#############################\nfrom flask import Flask, request, render_template, url_for, flash, redirect\nfrom flask_wtf import FlaskForm\nfrom wtforms import StringField, SubmitField, RadioField, ValidationError\nfrom wtforms.validators import Required\nimport requests, json\n#####################\n##### APP SETUP #####\n#####################\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'hardtoguessstring'\n\n####################\n###### FORMS #######\n####################\n\nclass AlbumEntryForm(FlaskForm):\n album = StringField('Enter the name of an album:',validators=[Required()])\n rbutton = RadioField('How much do you like this album? (1 low, 3 high)', choices=[('1', '1'), ('2','2'), ('3','3')], validators=[Required()])\n submit = SubmitField('Submit')\n\n\n\n####################\n###### ROUTES ######\n####################\n\n@app.route('/')\ndef hello_world():\n return 'Hello World!'\n\n\n@app.route('/user/')\ndef hello_user(name):\n return '

    Hello {0}

    '.format(name)\n\n@app.route('/artistinfo', methods=['GET','POST'])\ndef art_inf():\n for k in request.args:\n artist = \"{}\".format(request.args.get(k,\"\"))\n baseurl = \"https://itunes.apple.com/search\"\n params = {}\n params[\"term\"] = artist\n params[\"media\"] = \"music\"\n params[\"entity\"] = \"musicTrack\"\n o = requests.get(baseurl, params = params)\n d = json.loads(o.text)\n allinf= d['results']\n\n return render_template('artist_info.html', objects=allinf)\n\n@app.route('/artistlinks')\ndef art_link():\n return render_template('artist_links.html')\n\n@app.route('/artistform', methods=['GET', 'POST'])\ndef art_form():\n return render_template('artistform.html')\n\n@app.route('/specific/song/')\ndef spec_art(art_name):\n baseurl = \"https://itunes.apple.com/search\"\n params = {}\n params[\"term\"] = art_name\n params[\"media\"] = \"music\"\n params[\"entity\"] = \"musicTrack\"\n o = requests.get(baseurl, params = params)\n d = json.loads(o.text)\n\n ##the following grey code was used for debugging purposes##\n #print(type(d))\n #print(d.keys())\n allinf= d['results']\n #print(type(allinf))\n\n return render_template('specific_artist.html', results=allinf)\n\n@app.route('/album_entry')\ndef albument():\n form = AlbumEntryForm()\n return render_template('album_entry.html', form=form)\n\n@app.route('/album_result', methods=[\"GET\", \"POST\"])\ndef albumres():\n form = AlbumEntryForm()\n if form.validate_on_submit():\n album = form.album.data\n rbutton = form.rbutton.data\n return render_template('album_data.html', album=album, score=rbutton)\n\nif __name__ == '__main__':\n app.run(use_reloader=True,debug=True)\n","sub_path":"SI364W18_HW2.py","file_name":"SI364W18_HW2.py","file_ext":"py","file_size_in_byte":3454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"57702702","text":"#!/usr/bin/env python\n\"\"\"This script manages all tasks for the TRAVIS build server.\"\"\"\nimport glob\nimport os\nimport subprocess as sp\n\nif __name__ == \"__main__\":\n sp.check_call(\n \"pytest scrypy/tests --cov=scrypy/tests --cov-report term --cov-report xml:coverage.xml\",\n shell=True,\n )\n # Commented out to save travis around 5 minutes.\n # sp.check_call(\"python python/jac_estimation_chol.py\", shell=True)\n sp.check_call(\"python scrypy/script_uncertainty_propagation.py\", shell=True)\n os.chdir(\"notebooks\")\n for notebook in glob.glob(\"*.ipynb\"):\n cmd = \" jupyter nbconvert --execute {} --ExecutePreprocessor.timeout=-1\".format(\n notebook\n )\n sp.check_call(cmd, shell=True)\n","sub_path":"utils/travis_runner.py","file_name":"travis_runner.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"232666703","text":"# # Recursion\n# def fib(n):\n# if n == 0 or n == 1:\n# return n\n# else:\n# return fib(n - 1) + fib(n - 2)\n\n# # Dynamically\n# cache = {}\n# def fib(n):\n# # Base Case\n# if n == 0 or n == 1:\n# return n\n# else:\n# # Check Cache\n# if not n in cache:\n# # Set Cache\n# cache[n] = fib(n - 1) + fib(n - 2)\n# return cache[n]\n\n# Iteratively\ndef fib(n):\n a,b = 0,1\n\n for i in range(n):\n (a,b) = (b,a+b)\n return a\n\nprint(fib(10))","sub_path":"InterviewQuestions/Recursion/Fibonacci.py","file_name":"Fibonacci.py","file_ext":"py","file_size_in_byte":460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"248313799","text":"def Bubble_sort(arr):\n for i in range(len(arr)-1,0,-1):\n for j in range(0,i):\n if arr[j] > arr[j+1]:\n arr[j], arr[j+1] = arr[j+1], arr[j]\n\n\n#전체 테스트 케이스 수\nT = int(input())\n\nfor tc in range(1, T+1):\n N = int(input())\n\n number = list(map(int,input().split()))\n\n Bubble_sort(number)\n\n print(\"#{} {}\".format(tc, number[-1]-number[0]))\n\n max_value = 0\n min_value = 991231238\n\n for i in range(N):\n # 최대값 갱신\n if max_value < number[i]:\n max_value = number[i]\n\n # 최소값_갱신\n if min_value > numebr[i]:\n min_value = numebr[i]\n\n \n","sub_path":"수업시간/1주차/02.10 수업/bubble_sort.py","file_name":"bubble_sort.py","file_ext":"py","file_size_in_byte":666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"395763799","text":"import json\nfrom django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.http import JsonResponse\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User\nfrom MySQLdb import _mysql\nfrom django.core import serializers\nfrom vislib.models import Chart, Dashboard, ChartBoardMap, BoardOrder\nfrom django.utils import timezone\nimport uuid\n# Create your views here.\n\ndef default_datetime():\n now = timezone.now()\n return now\n\ndef index(request):\n return HttpResponse('hello python and django')\n\n@csrf_exempt\ndef user(request):\n if request.user.is_authenticated:\n username = request.user.get_username()\n return JsonResponse({'code': 20000, 'data': {'username': username}})\n else:\n return JsonResponse({'code': 40000, 'message': 'Please login'})\n\n@csrf_exempt\ndef userSignup(request):\n body = json.loads(request.body)\n if User.objects.filter(username=body['userName']).exists():\n return JsonResponse({'code': 10000, 'message': 'User Name ' + body['userName'] + ' is Already Tabken.'})\n if User.objects.filter(email=body['email']).exists():\n return JsonResponse({'code': 10000, 'message': 'Email ' + body['emaul'] + ' is Registered.'})\n user = User.objects.create_user(body['userName'], body['email'], body['password'])\n user.first_name=body['userName']\n user.save()\n return JsonResponse({'code': 20000, 'message': 'success'})\n\n@csrf_exempt\ndef userLogin(request):\n body = json.loads(request.body)\n user = authenticate(request, username=body['userName'], password=body['password'])\n if user is not None:\n login(request, user)\n return JsonResponse({'code': 20000, 'message': 'success'})\n else:\n return JsonResponse({'code': 10000, 'message': 'Name or Password Not Correct, Please Try Again.'})\n\n@csrf_exempt\ndef userLogout(request):\n logout(request)\n return JsonResponse({'code': 20000, 'message': 'success'})\n\ndef execSql(request):\n db=_mysql.connect( \"127.0.0.1\", \"root\", \"123456xxf\", \"sql12298540\", charset='utf8')\n db.query(request.GET['sql'])\n data = db.store_result().fetch_row(maxrows=0, how=2)\n db.close()\n json_data = []\n for index in range(len(data)):\n row = data[index]\n json_data.append({})\n for key in row:\n if(key.find('.')>0):\n column = (key.split('.'))[1]\n else:\n column = key\n if isinstance(row[key], bytes):\n json_data[index][column] = row[key].decode('UTF-8')\n else:\n json_data[index][column] = row[key]\n response = {\n 'code': 20000,\n 'message': 'success',\n 'data': json_data\n }\n return JsonResponse(response)\n\n@csrf_exempt\ndef chartList(request):\n charts = Chart.objects.filter(creator=request.user)\n charts = serializers.serialize('json', charts)\n charts = json.loads(charts)\n chartArr = []\n for chart in charts:\n chart['fields']['chart_id'] = chart['pk']\n chartArr.append(chart['fields'])\n return JsonResponse({'code': 20000, 'data': chartArr})\n\n@csrf_exempt\ndef createChart(request):\n body_unicode = request.body.decode('utf-8')\n body = json.loads(body_unicode)\n chart_name = body['chart_name']\n desc = body.get('desc', None)\n content = body['content']\n creator = request.user\n chart_id = uuid.uuid4()\n Chart.objects.create(\n chart_id=chart_id,\n chart_name=chart_name,\n desc=desc,\n content=json.dumps(content),\n creator=creator,\n is_private=True,\n status=1,\n updated_at=default_datetime()\n )\n return JsonResponse({'code': 20000, 'message': 'success', 'data': {'id': chart_id}})\n\n@csrf_exempt\ndef updateChart(request):\n body_unicode = request.body.decode('utf-8')\n body = json.loads(body_unicode)\n chart = Chart.objects.get(chart_id=body['id'])\n chart.chart_name = body['chart_name']\n chart.desc = body['desc']\n chart.content = json.dumps(body['content'])\n chart.updated_at = default_datetime()\n chart.save()\n return JsonResponse({'code': 20000, 'message': 'success', 'data': {'id': body['id']}})\n\n@csrf_exempt\ndef deleteChart(request):\n body_unicode = request.body.decode('utf-8')\n body = json.loads(body_unicode)\n chart = Chart.objects.get(chart_id=body['chart_id'])\n chart.delete()\n return JsonResponse({'code': 20000, 'message': 'success'})\n\n@csrf_exempt\ndef chartDetail(request, chartId):\n chartDetail = Chart.objects.get(chart_id=chartId)\n chartDetail = serializers.serialize('json', [chartDetail])\n chartDetail = json.loads(chartDetail)[0]\n\n return JsonResponse({'code': 20000, 'message': 'success', 'data':chartDetail['fields'] })\n\n@csrf_exempt\ndef createDashboard(request):\n body_unicode = request.body.decode('utf-8')\n body = json.loads(body_unicode)\n name = body['name']\n desc = body.get('desc', '')\n content = body.get('content', '')\n creator = request.user\n dashboard_id = uuid.uuid4()\n\n Dashboard.objects.create(\n dashboard_id=dashboard_id,\n name=name,\n desc=desc,\n content=json.dumps(body.get('content', {})),\n creator=creator,\n is_private=True,\n status=1,\n updated_at=default_datetime()\n )\n return JsonResponse({'code': 20000, 'message': 'success', 'data': {'id': dashboard_id}})\n\n@csrf_exempt\ndef updateDashboard(request):\n body_unicode = request.body.decode('utf-8')\n body = json.loads(body_unicode)\n dashboard_id = body.get('dashboard_id')\n board = Dashboard.objects.get(dashboard_id=dashboard_id)\n board.name = body['name']\n board.desc = body.get('desc', '')\n board.content = json.dumps(body.get('content', {}))\n board.updated_at = default_datetime()\n board.save()\n return JsonResponse({'code': 20000, 'message': 'success', 'data': {'id': dashboard_id}})\n\n@csrf_exempt\ndef dashboardDetail(request, dashboardId):\n dashboard = Dashboard.objects.get(dashboard_id= dashboardId)\n dashboard = serializers.serialize('json',[dashboard])\n dashboard = json.loads(dashboard)[0]\n dashboard['fields']['dashboard_id'] = dashboardId\n return JsonResponse({'code': 20000, 'message': 'success', 'data': dashboard['fields']})\n\n@csrf_exempt\ndef deleteDashboard(request):\n body_unicode = request.body.decode('utf-8')\n body = json.loads(body_unicode)\n dashboard = Dashboard.objects.get(dashboard_id=body['dashboard_id'])\n dashboard.delete()\n return JsonResponse({'code': 20000, 'message': 'success'})\n\n@csrf_exempt\ndef dashboardList(request):\n dashboards = Dashboard.objects.filter(creator=request.user)\n dashboards = serializers.serialize('json', dashboards)\n dashboards = json.loads(dashboards)\n dbArr = []\n for db in dashboards:\n db['fields']['dashboard_id'] = db['pk']\n db['fields']['content'] = json.loads(db['fields']['content'])\n dbArr.append(db['fields'])\n order = BoardOrder.objects.filter(creator=request.user)\n order = json.loads(serializers.serialize('json', order))\n if len(order)!=0:\n order = order[0]\n order = order['fields']['order']\n order = order.split('|')\n else:\n order = []\n return JsonResponse({'code': 20000, 'message': 'success', 'data':{'dashboards': dbArr, 'order': order} })\n\n@csrf_exempt\ndef chartBoardMap(request):\n body = request.body.decode('utf-8')\n body = json.loads(body)\n dashboard = Dashboard.objects.get(dashboard_id=body['dashboard_id'])\n chart = Chart.objects.get(chart_id=body['chart_id'])\n\n ChartBoardMap.objects.create(\n id=uuid.uuid4(),\n chart=chart,\n dashboard=dashboard,\n updated_at=default_datetime()\n )\n return JsonResponse({'code': 20000, 'message': 'success'})\n\n@csrf_exempt\ndef chartBoardUnmap(request):\n body = request.body.decode('utf-8')\n body = json.loads(body)\n chart_id = body['chart_id']\n dashboard_id = body['dashboard_id']\n map = ChartBoardMap.objects.get(chart=chart_id, dashboard=dashboard_id)\n map.delete()\n return JsonResponse({'code': 20000, 'message': 'success'})\n\n@csrf_exempt\ndef chartByBoard(request):\n map = ChartBoardMap.objects.filter(dashboard=request.GET['dashboard_id'])\n charts = []\n for item in map:\n chart = serializers.serialize('json', [item.chart])\n chart = json.loads(chart)[0]\n chart['fields']['chart_id'] = chart['pk']\n charts.append(chart['fields'])\n\n return JsonResponse({'code': 20000, 'message': 'success', 'data': charts})\n\n@csrf_exempt\ndef boardByChart(request):\n map = ChartBoardMap.objects.filter(chart=request.GET['chart_id'])\n boards = []\n for item in map:\n board = serializers.serialize('json', [item.dashboard])\n board = json.loads(board)[0]\n board['fields']['dashboard_id'] = board['pk']\n boards.append(board['fields'])\n return JsonResponse({'code': 20000, 'message': 'success', 'data': boards})\n\n@csrf_exempt\ndef dashboardOrder(request):\n body = json.loads(request.body)\n split = '|'\n orderStr = split.join(body['order'])\n order = BoardOrder.objects.filter(creator=request.user)\n if order:\n order[0].order = orderStr\n order[0].save()\n else:\n BoardOrder.objects.create(\n order=orderStr,\n id=uuid.uuid4(),\n creator=request.user,\n updated_at=default_datetime()\n )\n return JsonResponse({'code': 20000, 'message': 'success'})\n","sub_path":"vislib/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9079,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"299382029","text":"#!/usr/bin/env python\n\"\"\"\nThis module listens on a TCP socket and runs a worker pool.\n\nThe socket adress is specified by ``taskqueue.LISTEN_ADDRESS``.\n\nTasks (a python callable and arguments) can be enqueued into the pool\nwhich returns an alphanumeric id to calling connection. That id can\nthen be used to request the current status or final result of the\ntask.\n\n\"\"\"\nimport logging\nimport multiprocessing\nimport os.path\nimport collections\nimport sys\n\nfrom path import path\n\nimport taskqueue\nfrom taskqueue import NoSuchTaskError\nfrom taskqueue.task import Task, async_wrapper\n\n\nlog = logging.getLogger(__name__)\n\n\nclass Server(object):\n pool = None\n work_dir = None\n manager = None\n\n # TODO: this dict needs to become a weakref.WeakValueDict + deque\n # so we don't hang on to every task ever.\n tasks = None\n\n def __init__(self, **kwargs):\n self.__dict__.update(kwargs)\n if not self.work_dir:\n # storage for tasks in progress/log info.\n self.work_dir = os.path.join(taskqueue.BASE_DIR, 'queue')\n if not os.path.exists(self.work_dir):\n log.debug(\"Making work_dir: %r\", self.work_dir)\n os.makedirs(self.work_dir)\n self.tasks = dict()\n if self.pool is None:\n # can use pool=False to disable\n self.pool = multiprocessing.Pool()\n log.info(\"Finished initializing server, work_dir: %s\", self.work_dir)\n\n def get_task(self, tid):\n if tid not in self.tasks:\n log.debug(\"Missing task %s, checking filesystem.\", tid)\n task = Task.unpickle(tid, self.work_dir)\n self._enqueue(task)\n return self.tasks[tid]\n\n def wait(self, tid, timeout=None):\n return self.get_task(tid).get(timeout)\n\n def task_count(self):\n return len(self.tasks)\n\n def ready(self, tid):\n log.debug(\"Checking on status of %s\", tid)\n promise = self.get_task(tid)\n return promise.ready()\n\n def result(self, tid, timeout=0.01):\n log.debug(\"Checking on result of %s\", tid)\n promise = self.get_task(tid)\n return promise.get(timeout)\n\n ## TODO: these need to use sanitize_id or a stored log path for the task\n # def log_output(self, tid, position=0):\n # log.debug(\"Retrieving log output for %s from pos:%s\", tid, position)\n # pth = os.path.join(self.work_dir, tid + '.log')\n # with open(pth, 'rb') as f:\n # f.seek(position)\n # buf = f.read()\n # log.debug(\"Read %d bytes from log.\", len(buf))\n # return buf\n\n # def log_tail(self, tid, line_count=1):\n # log.debug(\"Retrieving %d log lines for %s\", line_count, tid)\n\n # # make sure we have the running task (raises exception if missing)\n # self.get_task(tid)\n\n # pth = os.path.join(self.work_dir, tid + '.stderr')\n # if not os.path.exists(pth):\n # return [\"Queued\\n\"]\n # try:\n # with open(pth, 'rb') as f:\n # try:\n # # 2 is from end\n # f.seek(- (line_count + 1) * 120, 2)\n # except:\n # f.seek(0)\n # lines = collections.deque(maxlen=line_count)\n # while True:\n # line = f.readline()\n # if line == '':\n # return list(lines)\n # elif line != '\\n':\n # lines.append(line)\n # except:\n # log.exception(\"Error reading lines from %r\", pth)\n # raise\n\n def _enqueue(self, task):\n log.info('Enqueuing %r', task)\n assert task.tid\n promise = self.pool.apply_async(async_wrapper, [task, self.work_dir])\n self.tasks[task.tid] = promise\n return promise\n\n def enqueue(self, fn, tid=None, after=None):\n if tid:\n if tid in self.tasks:\n return tid\n if after:\n after = filter(None, after)\n # check that all tasks specified exist or raise the\n # exception now if one of them doesn't. an effect this\n # enforces is that everything must be queued into the pool\n # before this task is to prevent deadlock\n map(self.get_task, after)\n task = Task(fn, tid=tid, after=after)\n task.pickle(self.work_dir)\n self._enqueue(task)\n return task.tid\n","sub_path":"taskqueue/taskqueue/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":4405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"75655511","text":"\"\"\"\nTake a pipeline.config from a pre-trained model and configure it to train on a set of mosaic.\nThis involves (possibly) changing the number of classes and pointing to configuration to the correct paths for the\ntraining and testing TFRecords and the label map.\n\"\"\"\n\n__author__ = 'Ian Randman'\n\nimport logging\nimport os\nimport sys\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # suppress TensorFlow logging\n\nimport tensorflow as tf\nfrom google.protobuf import text_format\nfrom object_detection.protos import pipeline_pb2\n\nfrom scripts.util.file_utils import full_path\n\nlogger = logging.getLogger(__name__)\n\n\ndef load_config(pipeline_config_path):\n \"\"\"\n Load a pipeline.config.\n\n :param pipeline_config_path: the path to the pipeline.config\n :return: the config object\n \"\"\"\n\n pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()\n with tf.io.gfile.GFile(pipeline_config_path, 'r') as f:\n proto_str = f.read()\n text_format.Merge(proto_str, pipeline_config)\n\n return pipeline_config\n\n\ndef edit_pipeline_config(pretrained_model_dir, output_dir, num_classes, annotations_dir, num_steps):\n \"\"\"\n Edit a pipeline configuration model to work with training on a specific set of training data.\n This changes the number of classes, the batch size, the path to the pre-trained model checkpoint, the type of\n training, the label map path for the train and evaluation input readers, and the TFRecord paths for the train and\n evaluation input readers.\n\n :param pretrained_model_dir: the directory containing the pre-trained model from the model zoo\n :param output_dir: the directory for the model that the configuration is for\n :param num_classes: the number of labels that training will occur on\n :param annotations_dir: the directory containing the train/test data and the label map\n :return: the path to the new pipeline.config\n \"\"\"\n\n # determine the path to the pipeline.config of the pre-trained model\n pipeline_config_path = os.path.join(pretrained_model_dir, 'pipeline.config')\n\n # load the configuration\n pipeline_config = load_config(pipeline_config_path)\n\n # The configuration object has fields for each of the possible models. We need to edit the model that is in the\n # pre-trained model pipeline.config.\n model_names = list(pipeline_config.model.DESCRIPTOR.fields_by_name)\n found_model = False\n for model_name in model_names:\n model_obj = getattr(pipeline_config.model, model_name)\n if model_obj.ByteSize() > 0: # found the actual model in the config\n model_obj.num_classes = num_classes\n found_model = True\n break\n\n # exit if the model from the pre-trained model pipeline.config cannot be found in the config object\n if not found_model:\n logger.error('Cannot find specific model in pipeline.config')\n sys.exit(1)\n\n # set the batch size\n pipeline_config.train_config.batch_size = 8 # TODO change? Affects memory usage\n logger.info(f\"Batch size: {pipeline_config.train_config.batch_size}\")\n\n # set the number of training steps\n pipeline_config.train_config.num_steps = num_steps\n\n # Set the path to the checkpoint from the pre-trained model. This does not have to be changed for continuing\n # training from a checkpoint. TensorFlow will figure out the most recent checkpoint automatically.\n pipeline_config.train_config.fine_tune_checkpoint = pretrained_model_dir + '/checkpoint/ckpt-0'\n\n # set this to \"detection\" since we want to be training the full detection model\n pipeline_config.train_config.fine_tune_checkpoint_type = 'detection' # TODO what do other options mean\n\n # set the path to the label map for the train input reader\n pipeline_config.train_input_reader.label_map_path = annotations_dir + '/label_map.pbtxt'\n # set the path to the train TFRecord for the train input reader\n pipeline_config.train_input_reader.tf_record_input_reader.input_path[:] = [annotations_dir + '/train.record']\n\n # set the path to the label map for the evaluation input reader\n pipeline_config.eval_input_reader[0].label_map_path = annotations_dir + '/label_map.pbtxt'\n # set the path to the train TFRecord for the evaluation input reader\n pipeline_config.eval_input_reader[0].tf_record_input_reader.input_path[:] = [annotations_dir + '/test.record']\n\n # save the new pipeline.config\n config_text = text_format.MessageToString(pipeline_config)\n output_path = os.path.join(output_dir, 'pipeline.config')\n with tf.io.gfile.GFile(output_path, \"wb\") as f:\n f.write(config_text)\n\n logger.info(f'Created {full_path(output_path)}')\n\n # return the path to the new pipeline.config\n return output_path\n","sub_path":"AI/scripts/util/edit_pipeline_config.py","file_name":"edit_pipeline_config.py","file_ext":"py","file_size_in_byte":4728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"555047745","text":"'''\nPurpose: creat new cap/floor instrument and trades and defin ezero nominal of CF starting after 2y\nDepartment : Trading\nDesk : IRD Desk\nRequester : Parin Gokaldas\nDeveloper : Anil Parbhoo\nCR Number : 1047867\nJira Reference Number : ABITFA:1729\n\n'''\n\nimport acm, PS_Functions\n\n# main functions\n\ncut_off_date = acm.Time().DateAddDelta(acm.Time().DateToday(), 2, 0, 0)\n\n\ndef all_new_ins_names():\n\n s = \"expiryDate > %s\" % acm.Time().DateToday()\n\n cap_ins = acm.FCap.Select(s)\n floor_ins = acm.FFloor.Select(s)\n\n new_cap_floor_ins = []\n\n\n for c in cap_ins:\n if '_2y' in c.Name():\n new_cap_floor_ins.append(c.Name())\n \n for f in floor_ins:\n if '_2y' in f.Name():\n new_cap_floor_ins.append(f.Name())\n \n return new_cap_floor_ins\n\ndef newInstrument(trade):\n i = trade.Instrument()\n new_ins_name = i.Name() + '_2y'\n if acm.FInstrument[new_ins_name]:\n acm.Log('the NEW instrument %s already exists for existing trade %s ' % (new_ins_name, trade.Oid()))\n else:\n ins = i.Clone()\n ins.Name(new_ins_name) \n ins.ExternalId1('')\n ins.ExternalId2('')\n ins.Isin('')\n\n \n ins.Commit()\n acm.Log('Created ins = %s' %(ins.Name()))\n \n \n legs = ins.Legs()\n for l in legs:\n cfs = l.CashFlows()\n for cf in cfs:\n \n PS_Functions.SetAdditionalInfo(cf, 'Org_nominal_factor', cf.NominalFactor())\n \n \n \n \n\ndef nominal_factor_adjustment(cf):\n \n ccf = cf.Clone()\n if cf.StartDate()<=cut_off_date:\n ccf.NominalFactor(cf.add_info('Org_nominal_factor'))\n else:\n ccf.NominalFactor(0.00) \n cf.Apply(ccf)\n cf.Commit()\n\n\ndef ReversePayments(trade):\n\n for payment in trade.Payments():\n payment.Amount(payment.Amount()*-1)\n payment.Commit()\n \n\n\ndef offsetting_corresponding_Trade(trade):\n\n i = trade.Instrument()\n new_ins_name = i.Name() + '_2y' # based on an existing instrument name of a trade + _2y\n\n \n existing_ins = acm.FInstrument[new_ins_name]\n \n \n #Create offsetting trade ot - that is equal and opposite to existing trade\n \n ot = trade.Clone() \n ot.MirrorTrade = None\n ot.Instrument(existing_ins) \n ot.Quantity(trade.Quantity()*-1) \n ot.Premium(trade.Premium()*-1)\n ot.Counterparty('NLD DESK')\n ot.OptionalKey('')\n ot.Status('FO Confirmed')\n ot.Text1('Offsetting Trade')\n ot.Text2(trade.Oid())\n ot.ContractTrdnbr(trade.Oid())\n ot.TrxTrade(trade.Oid())\n \n ot.Commit()\n acm.Log('for existing trade %s the trade number of Offsetting Trade = %s' %(trade.Oid(), ot.Oid()))\n ReversePayments(ot)\n \n #Create corresponding trade ct - that is same as existing trade\n\n pd={'NLDO':'NLDO_SE', 'Swap Risk':'Swap_Risk_SE'}\n\n ct = ot.Clone()\n #ct.MirrorTrade = None\n \n changePort = acm.FPhysicalPortfolio[pd[ot.Portfolio().Name()]]\n ct.Instrument(existing_ins)\n ct.Quantity(ot.Quantity()*-1) \n ct.Premium(ot.Premium()*-1)\n ct.Counterparty('NLD DESK')\n ct.Text1('Corresponding Trade')\n ct.Text2(trade.Oid())\n ct.Portfolio = changePort #define a portfolio for the new offsetting trade\n ct.OptionalKey('')\n ct.Status('FO Confirmed')\n ct.ContractTrdnbr(ot.Oid())\n ct.TrxTrade(ot.Oid())\n \n ct.Commit()\n acm.Log('for existing trade %s the trade number of Corresponding Trade = %s' %(trade.Oid(), ct.Oid()))\n ReversePayments(ct)\n\n# set up task \n\nael_variables = [\n['newIns_Trades', 'Create New Ins Trades and set Nominal Factor_General', 'string', ['No', 'Yes'], 'Yes', 0, 0, 'Create New Instruments, Offsetting and Corresponding Trades', None, 1],\n['selectedSimulatedStatus', 'Rollback - Set ALL New trades to Simulate Status_RollBack', 'string', ['No', 'Yes'], 'No', 0, 0, 'newly created trades set to simulate by system user', None, 1]\n]\n\n\n\n\ndef ael_main(dict):\n if dict['newIns_Trades'] == 'Yes':\n \n \n \n new_ins_set = tuple(all_new_ins_names()) #create a tuple of new ins names\n \n tf = acm.FTradeSelection['Used to book new caps floors']\n\n existing_trades = tuple(tf.Trades())# create a tuple of existing trades \n\n # loop through the existing trades \n for et in existing_trades:\n \n new_ins_name = et.Instrument().Name()+'_2y'\n \n if new_ins_name in new_ins_set:\n ins = acm.FInstrument[new_ins_name]\n new_trds = ins.Trades()\n \n if len(new_trds) == 0:\n offsetting_corresponding_Trade(et)\n else:\n count_new_trdnbrs=[]\n \n \n for nt in new_trds:\n \n if nt.Text2()==str(et.Oid()) and nt.Text1() == 'Offsetting Trade' and nt.Status()!='Simulated' :\n #print 'Offsetting trade %s does exists for existing trade %s' % (nt.Oid(),et.Oid())\n count_new_trdnbrs.append(nt.Oid())\n \n elif nt.Text2()==str(et.Oid()) and nt.Text1() == 'Corresponding Trade' and nt.Status()!='Simulated':\n #print 'Corresponding trade %s does exists for existing trade %s' % (nt.Oid(),et.Oid())\n count_new_trdnbrs.append(nt.Oid())\n \n \n if len(count_new_trdnbrs) == 0:\n offsetting_corresponding_Trade(et)\n elif len(count_new_trdnbrs) == 1:\n acm.Log('!!! Recon Problem - Existing trade %s only has ONE of an offsetting and a corresponsing trade' % et.Oid())\n #elif len(count_new_trdnbrs) == 2:\n #acm.Log('Existing trade %s has both offsetting and a corresponsing trade' % et.Oid())\n \n \n \n else:\n newInstrument(et)\n offsetting_corresponding_Trade(et)\n \n new_ins_set_after_creation = tuple(all_new_ins_names()) #create a tuple of new ins names after all the neccessary ins have been created \n for i in new_ins_set_after_creation:\n new_ins = acm.FInstrument[i]\n legs = new_ins.Legs()\n for l in legs:\n all_cfs = l.CashFlows()\n for scf in all_cfs:\n nominal_factor_adjustment(scf)\n\n acm.Log('Completed Successfully') \n if dict['selectedSimulatedStatus'] == 'Yes':\n\n #Rollback - Set all newly created trades set to simulate. Task to be run by user ATS as a once off after stopping further scheduling of the usual task \n new_ins_set_current = tuple(all_new_ins_names()) #create a tuple of new ins names the trades of which can be simulated\n \n for i in new_ins_set_current:\n ins = acm.FInstrument[i]\n new_trds = ins.Trades()\n for t in new_trds:\n clone_trade = t.Clone()\n clone_trade.Status('Simulated')\n t.Apply(clone_trade)\n t.Commit()\n \n \n acm.Log('all trades to the new instruments have been set to simulated status') \n \n \n\n","sub_path":"Python modules/caps and floors 2y split.py","file_name":"caps and floors 2y split.py","file_ext":"py","file_size_in_byte":7466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"67105565","text":"import json\nimport os\n\ndef createFolder(directory):\n try:\n if not os.path.exists(directory):\n os.makedirs(directory)\n except OSError:\n print ('Error: Creating directory. ' + directory)\n\nwith open('./values.json', 'r') as f:\n values = json.load(f)\n\n\nwith open('./index.html', 'r') as f:\n line = f.read()\n\nfor key, value in values.items():\n line = line.replace(\"${\" + key + \"}\", value)\n\ncreateFolder(\"./outHTML/\")\n\nwith open('./outHTML/index.html', 'w') as f:\n f.write(line)\n\nprint('작업완료!')","sub_path":"makeTest.py","file_name":"makeTest.py","file_ext":"py","file_size_in_byte":540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"278434012","text":"\nfrom flask import Flask\nimport requests\nimport json\nimport subprocess\n\napp = Flask(__name__)\n\n@app.route('/')\ndef downloader():\n\ttableget = \"https://t0ag1yvaye.execute-api.eu-west-1.amazonaws.com/prod\"\n\tresponse = requests.get(tableget)\n\tdatas = response.json()\n\t#print(datas)\n\turls = [url['url'] for url in datas['body']]\n\tprint(urls)\n\tscript = './script.sh'\n\tfor url in urls:\n\t\tprint(\"Processing video\", url)\n\t\tsubprocess.check_call([script, url])\n\t#subprocess.check_call(['./pushgs.sh'])\n\treturn 'Hello'\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"122537242","text":"from mxene_polymer.build_functions.aa_mxenes import build_alkylammonium_mxene\n\ncomposition = {'OH': 1}\nchain_length = 12\nperiods = [26, 26, 1]\n#displacement = 1.1\ndisplacement = 2.4\nn_compounds = 120\n\nbuild_alkylammonium_mxene(chain_length=chain_length,\n displacement=displacement,\n n_compounds=n_compounds,\n composition=composition,\n periods=periods)\n","sub_path":"mxene_polymer/simulations/aa/12/build_mxene.py","file_name":"build_mxene.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"197313233","text":"#from django_test.settings import *\n\nimport os\nDEBUG = True\nTEMPLATE_DEBUG = DEBUG\nBASEDIR = os.path.dirname(__file__)\n\n\npath='/web/development/eddy_flux/config'\nif path not in sys.path:\n sys.path.append(path)\n\n\nADMINS = (\n#('My Exampel', 'example@domain.com'),\n)\nMANAGERS = ADMINS\nDATABASES = {\n'default': {\n'ENGINE': 'django.db.backends.sqlite3',\n'NAME': '::memory::',\n'USER': '', # Not used with sqlite3.\n'PASSWORD': '', # Not used with sqlite3.\n'HOST': '', # Set to empty string for localhost. Not used with sqlite3.\n'PORT': '', # Set to empty string for default. Not used with sqlite3.\n}\n}\nTIME_ZONE = 'America/Chicago'\nLANGUAGE_CODE = 'en-us'\nSITE_ID = 1\nUSE_I18N = True\nUSE_L10N = True\nMEDIA_ROOT = os.path.join(BASEDIR, 'media')\nSTATIC_ROOT = os.path.join(BASEDIR, 'static')\nMEDIA_URL = '/media/'\nSTATIC_URL = '/static/'\nADMIN_MEDIA_PREFIX = '/admin/media/'\n#SECRET_KEY = '*)7&l7ri*t%kat%+sfujmtc9sw*o&114mx56&2nt&-l0xad*_w'\nTEMPLATE_LOADERS = (\n)\nMIDDLEWARE_CLASSES = (\n)\nROOT_URLCONF = 'example.urls'\nTEMPLATE_DIRS = (\nos.path.join(BASEDIR, 'template'),\n)\nINSTALLED_APPS = (\n'django.contrib.auth',\n'django.contrib.contenttypes',\n'django.contrib.sessions',\n'django.contrib.sites',\n'django.contrib.messages',\n'django.contrib.staticfiles',\n\n# 'django.contrib.auth',\n#'django.contrib.contenttypes',\n#'django.contrib.sessions',\n# 'django.contrib.sites',\n# 'django.contrib.messages',\n# 'django.contrib.admin',\n# 'django.contrib.admindocs',\n\n'download_stats',\n\n#'example.core',\n#'compress_storage',\n)\n\nSECRET_KEY = 'fake-key'\n\n","sub_path":"d_stats/test_settings.py","file_name":"test_settings.py","file_ext":"py","file_size_in_byte":1533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"326928537","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Sep 25 16:20:03 2016\n\n@author: ringoyen\n\"\"\"\n\n\nfrom time import sleep\nimport matplotlib.pyplot as plt\n\ndef main():\n \n pass\n\n\ndef display_metrics(instant_hr, ecg_time, one_min_avg, five_min_avg, bradycardia=60, tachycardia=100):\n \n \"\"\" \n This function should take the Heart Rate Data Array, 1 min average array, 5 min average array, Heart Rate Time Array\n and print out the data to the terminal. There will also be a condition to send an \"alarm\" if there\n are bradycardia or tachycardia conditions\n \n :param: HR_Data = heart rate array. oneMinAvg = array of oneMin HR values, averaged. fiveMinAvg = array of fiveMin HR values.\n\n HR_time = array of time that heart rate occurs at.\n\n\n\n \n \"\"\"\n\n if instant_hr < bradycardia:\n print(\"The Instantaneous Heart Rate is\", instant_hr, \"bpm\")\n sleep(1)\n\n print('Danger: Bradycardia Detected!!')\n sleep(2)\n\n print(\"The Signal Time is\", ecg_time, \"min\")\n sleep(2)\n\n print(\"The one minute average heart rate is\", one_min_avg, \"bpm\")\n sleep(2)\n\n print(\"The five average heart rate is\", five_min_avg, \"bpm\")\n\n return 0\n\n elif instant_hr > tachycardia:\n print(\"The Instantaneous Heart Rate is\", instant_hr, \"bpm\")\n sleep(1)\n\n print('Danger: Tachycardia Detected!!')\n sleep(2)\n\n print(\"The Signal Time is\", ecg_time, \"min\")\n sleep(2)\n\n print(\"The one minute average heart rate is\",one_min_avg, \"bpm\")\n sleep(2)\n\n print(\"The five average heart rate is\", five_min_avg, \"bpm\")\n\n return 0\n\n else:\n print(\"The Instantaneous Heart Rate is\", instant_hr, \"bpm\")\n sleep(1)\n\n print(\"The Signal Time is\", ecg_time, \"min\")\n sleep(2)\n\n print(\"The one minute average heart rate is\", one_min_avg, \"bpm\")\n sleep(2)\n\n print(\"The five minute average heart rate is\", five_min_avg, \"bpm\")\n\n return 0\n\n\ndef plot_heart_rate(hr_data, time_data):\n x = time_data\n y = hr_data\n plt.plot(x, y)\n plt.xlabel('Time in min')\n plt.ylabel('Heart Rate Trace (bpm)')\n plt.show()\n\n return 0\n\nif __name__ == '__main__':\n main()\n\n\n","sub_path":"outputMetrics_functions.py","file_name":"outputMetrics_functions.py","file_ext":"py","file_size_in_byte":2242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"284759369","text":"from rest_framework import serializers as ser\n\nfrom modularodm.exceptions import ValidationValueError\n\nfrom api.base.exceptions import InvalidModelValueError\nfrom api.base.serializers import AllowMissing\nfrom website.models import User\n\nfrom api.base.serializers import (\n JSONAPISerializer, LinksField, RelationshipField, DevOnly, IDField, TypeField\n)\nfrom api.base.utils import add_dev_only_items\n\n\nclass UserSerializer(JSONAPISerializer):\n filterable_fields = frozenset([\n 'full_name',\n 'given_name',\n 'middle_names',\n 'family_name',\n 'id'\n ])\n non_anonymized_fields = ['type']\n id = IDField(source='_id', read_only=True)\n type = TypeField()\n full_name = ser.CharField(source='fullname', required=True, label='Full name', help_text='Display name used in the general user interface')\n given_name = ser.CharField(required=False, allow_blank=True, help_text='For bibliographic citations')\n middle_names = ser.CharField(required=False, allow_blank=True, help_text='For bibliographic citations')\n family_name = ser.CharField(required=False, allow_blank=True, help_text='For bibliographic citations')\n suffix = ser.CharField(required=False, allow_blank=True, help_text='For bibliographic citations')\n date_registered = ser.DateTimeField(read_only=True)\n\n # Social Fields are broken out to get around DRF complex object bug and to make API updating more user friendly.\n gitHub = DevOnly(AllowMissing(ser.CharField(required=False, source='social.github',\n allow_blank=True, help_text='GitHub Handle'), required=False, source='social.github'))\n scholar = DevOnly(AllowMissing(ser.CharField(required=False, source='social.scholar',\n allow_blank=True, help_text='Google Scholar Account'), required=False, source='social.scholar'))\n personal_website = DevOnly(AllowMissing(ser.URLField(required=False, source='social.personal',\n allow_blank=True, help_text='Personal Website'), required=False, source='social.personal'))\n twitter = DevOnly(AllowMissing(ser.CharField(required=False, source='social.twitter',\n allow_blank=True, help_text='Twitter Handle'), required=False, source='social.twitter'))\n linkedIn = DevOnly(AllowMissing(ser.CharField(required=False, source='social.linkedIn',\n allow_blank=True, help_text='LinkedIn Account'), required=False, source='social.linkedIn'))\n impactStory = DevOnly(AllowMissing(ser.CharField(required=False, source='social.impactStory',\n allow_blank=True, help_text='ImpactStory Account'), required=False, source='social.impactStory'))\n orcid = DevOnly(AllowMissing(ser.CharField(required=False, source='social.orcid',\n allow_blank=True, help_text='ORCID'), required=False, source='social.orcid'))\n researcherId = DevOnly(AllowMissing(ser.CharField(required=False, source='social.researcherId',\n allow_blank=True, help_text='ResearcherId Account'), required=False, source='social.researcherId'))\n researchGate = DevOnly(AllowMissing(ser.CharField(required=False, source='social.researchGate',\n allow_blank=True, help_text='ResearchGate Account'), required=False, source='social.researchGate'))\n academiaInstitution = DevOnly(AllowMissing(ser.CharField(required=False, source='social.academiaInstitution',\n allow_blank=True, help_text='AcademiaInstitution Field'), required=False, source='social.academiaInstitution'))\n academiaProfileID = DevOnly(AllowMissing(ser.CharField(required=False, source='social.academiaProfileID',\n allow_blank=True, help_text='AcademiaProfileID Field'), required=False, source='social.academiaProfileID'))\n\n links = LinksField(\n add_dev_only_items({\n 'html': 'absolute_url',\n }, {\n 'profile_image': 'profile_image_url',\n })\n )\n\n nodes = RelationshipField(\n related_view='users:user-nodes',\n related_view_kwargs={'user_id': ''},\n )\n\n institutions = RelationshipField(\n related_view='users:user-institutions',\n related_view_kwargs={'user_id': ''},\n )\n\n class Meta:\n type_ = 'users'\n\n def absolute_url(self, obj):\n if obj is not None:\n return obj.absolute_url\n return None\n\n def profile_image_url(self, user):\n size = self.context['request'].query_params.get('profile_image_size')\n return user.profile_image_url(size=size)\n\n def update(self, instance, validated_data):\n assert isinstance(instance, User), 'instance must be a User'\n for attr, value in validated_data.items():\n if 'social' == attr:\n for key, val in value.items():\n instance.social[key] = val\n else:\n setattr(instance, attr, value)\n try:\n instance.save()\n except ValidationValueError as e:\n raise InvalidModelValueError(detail=e.message)\n return instance\n\n\nclass UserDetailSerializer(UserSerializer):\n \"\"\"\n Overrides UserSerializer to make id required.\n \"\"\"\n id = IDField(source='_id', required=True)\n","sub_path":"api/users/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":5649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"257327497","text":"#!/usr/bin/env python3\n\n# Name: James Houghton\n# Date: 9-10-15\n# Period: 3rd - Gabor\n\nimport urllib.request,sys,time,re,string\nfrom collections import deque\n\ndef getNeighbors(important,words,visited=set()):\n adj = set()\n alphabet = string.ascii_lowercase\n for i in range(len(important)):\n for l in alphabet:\n word = important[:i] + l + important[i+1:]\n if word in words and word not in visited and word != important:\n adj.add(word)\n return adj\ndef numberOfEdges(w_dict):\n visited = set()\n edges = 0\n for key in w_dict:\n visited.add(key)\n for word in w_dict[key]:\n if word not in visited:\n edges += 1\n return edges\ndef showProgress(current,total):\n sys.stdout.write (\"Generating graph: \"+str(float(current)/float(total)*100) +\"% \\r\")\n sys.stdout.flush()\ndef findComponents(w_dict):\n visited = set()\n components = {}\n highest_key = 0\n for word in w_dict:\n if word in visited: continue\n comp,visited = generateComponent(word,w_dict,visited)\n key = len(comp)\n highest_key = key if key > highest_key else highest_key\n if key in components:\n components[key].append(comp)\n else:\n components[key] = [comp]\n return components,highest_key\ndef generateComponent(key,w_dict,visited):\n component = [key]\n visited.add(key)\n for val in w_dict[key]:\n if val not in visited:\n to_add,visited = generateComponent(val,w_dict,visited)\n component += to_add\n return component,visited\ndef downloadWords(link):\n sys.stdout.write(\"Downloading words...\\r\")\n sys.stdout.flush()\n words = [word.decode() for word in urllib.request.urlopen(link).read().split()]\n sys.stdout.write(\" \\r\")\n sys.stdout.flush()\n return words\ndef mostNeighbors(w_dict,max_length,num):\n count = 0\n index = max_length\n words = []\n while count= mi:\n return None\n for val in w_dict[key]:\n # print(\"Max level: \" + str(mi) + \" :: Level: \" + str(level+1) + \" :: \" + str(val))\n if val not in visited:\n p = dfs_id(val,against,w_dict,visited.copy(),path+[val],mi,level+1)\n if p != None:\n return p\n return None\n\ndef bfs(word,words_dict,search=None):\n bfs_dict = {word:[word]}\n visited_set = set([word])\n current_list = [word]\n checked = 0\n while current_list:\n furthest_word = current_list[0]\n next_list = []\n for word in current_list:\n for n in words_dict[word]:\n if n in visited_set: continue\n checked+=1\n visited_set.add(n)\n bfs_dict[n] = bfs_dict[word] + [n]\n next_list.append(n)\n if search in bfs_dict:\n return search,bfs_dict[search],visited_set - set(furthest_word), checked\n current_list = next_list\n if search != None:\n return None,None,visited_set,checked\n return furthest_word,bfs_dict[furthest_word],visited_set - set(furthest_word), checked\n\nwords_dict = {}\nwords_dict_by_size = {}\n\nlink = \"https://academics.tjhsst.edu/compsci/ai/web2015/words.txt\"\nwords = downloadWords(link)\nword_set = set(words)\n\nold_time = time.time()\nhighest_length = 0\nfor index, word in enumerate(words):\n words_dict[word] = getNeighbors(word,word_set)\n length = len(words_dict[word])\n if length not in words_dict_by_size: words_dict_by_size[length] = {}\n highest_length = max(length,highest_length)\n words_dict_by_size[length][word] = words_dict[word]\n # if index%200 == 1: showProgress(index,len(words))\n\ncs = \"\\033[90mNeighbors:\\033[0m \"\n\n# print(cs + \"Generating graph: 100% in \"+str(time.time() - old_time)+\"s\")\nprint(cs + \"Words: \"+str(len(words)))\nprint(cs + \"Edges: \"+str(numberOfEdges(words_dict)))\nprint(cs + \"Words with most neighbors: \"+str(mostNeighbors(words_dict_by_size,highest_length,3)))\n# for i in range(highest_length+1):\n# if i in words_dict_by_size:\n# print(\"Nodes with \"+str(i)+\" neighbors: \"+str(len(words_dict_by_size[i])))\n# else:\n# print(\"Nodes with \"+str(i)+\" neighbors: 0\")\n\n######### COMPONENTS #########\n\ncomponents,largest = findComponents(words_dict)\ntotal_components = 0\nfor i in range(largest+1):\n if i in components:\n total_components += len(components[i])\n # print(\"Components with length \"+str(i)+\": \"+str(len(components[i])))\n\ncs = \"\\033[91mComponents:\\033[0m \"\nprint(\"-------------------------\")\nprint(cs + \"Largest component: \"+str(largest)+\" words long\")\nprint(cs + \"Total components: \"+str(total_components))\n\n######### BREADTH FIRST SEARCH #########\n\nstart_time = time.time()\ncs = \"\\033[93mBreadth First Search:\\033[0m \"\n\nif len(sys.argv) == 2:\n print(\"-------------------------\")\n for word in sys.argv[1:]:\n if word in words_dict:\n bfs_r,path,n,c = bfs(word,words_dict)\n print(cs +\"Furthest from \" + word + \": \"+str(bfs_r))\n print(cs + \"Path: \" + str(path))\n print(cs + \"Distance: \" + str(len(path)-1))\n print(cs + \"Vertices checked: \"+str(c))\n print(cs + \"Time: \"+str(time.time() - start_time)+\"s\")\n else: print(cs + \"\\033[91m\"+word+\" not found\\033[0m\")\nelif len(sys.argv) > 2:\n print(\"-------------------------\")\n word = sys.argv[1];\n if word in words_dict:\n against = sys.argv[2];\n bfs_r,path,n,c = bfs(word,words_dict,against)\n if bfs_r == None or path == None:\n print(\"\\033[93mBreadth First Search:\\033[0m \\033[91m No path from \"+word+\" to \" + against + \"\\033[0m\")\n else:\n print(\"\\033[93mBreadth First Search:\\033[0m Shortest path from \"+word + \" to \" + against + \": \"+str(path))\n print(\"\\033[93mBreadth First Search:\\033[0m Distance: \"+str(len(path)-1))\n print(\"\\033[93mBreadth First Search:\\033[0m Vertices checked: \"+str(c))\n print(\"\\033[93mBreadth First Search:\\033[0m Time: \"+str(time.time() - start_time)+\"s\")\n else: print(\"\\033[93mBreadth First Search:\\033[0m \\033[91m\"+word+\" not found\\033[0m\")\n\n######### DIAMETER CALCULATION #########\n\nprint(\"-------------------------\")\nindex = 0\ndiam = 0\nc = 0\np = []\nvisited = set()\nstart_time = time.time()\ncs = \"\\033[95mDiameter:\\033[0m \"\nfor w in words_dict:\n if w in visited: continue\n n,path,visited,checked = bfs(w,words_dict)\n if(len(path)-1 > diam):\n diam = len(path)-1\n p = path\n index += 1\n c += checked\n sys.stdout.write(cs + \"Calculating diameter: \" + str(100*index/len(words_dict)) + \"% \\r\")\n sys.stdout.flush()\nsys.stdout.write(\" \\r\");\nprint(cs + \"Diameter: \" + str(diam))\nprint(cs + \"Path: \" + str(p))\nprint(cs + \"Time: \" + str(time.time() - start_time) + \"s\")\nprint(cs + \"Vertices checked: \"+str(c))\n\n######### DEPTH FIRST SEARCH #########\n\nif len(sys.argv) > 2:\n start_time = time.time()\n cs = \"\\033[92mDepth First Search:\\033[0m \"\n print(\"-------------------------\")\n word = sys.argv[1]\n against = sys.argv[2]\n if word in words_dict and against in words_dict:\n p,b,c = dfs(word,against,words_dict,set(),[word],0)\n if b:\n print(cs + \"Path from \" +against+ \" to \"+word+\": \"+str(p))\n # print(cs + \"Path from \" + against+\" to \"+word+\": [Suppressed]\")\n print(cs + \"Vertices checked: \"+str(c))\n print(cs + \"Time: \" + str(time.time() - start_time)+\"s\")\n else:\n print(cs + \"\\033[91mNo path from \" +word+ \" to \"+against+\"\\033[0m\")\n else: print(cs + \"\\033[91m\"+word+\" or \"+against+\" not found in the dictionary\\033[0m\")\n\n print(\"-------------------------\")\n cs = \"\\033[94mIterative Deepening:\\033[0m \"\n word = sys.argv[1]\n against = sys.argv[2]\n length = len(words_dict)\n try:\n print(cs + \"\\033[1mPress ^C to skip this search.\\033[0m\")\n i = 0\n while i <= diam:\n if word in words_dict and against in words_dict:\n p = dfs_id(word,against,words_dict,set(word),[word],i)\n if p != None:\n print(cs + \"Path from \" +word+ \" to \"+against+\": \"+str(p))\n print(cs + \"Distance: \"+str(len(p)-1))\n break\n print(cs + \"Finished level \" + str(i))\n i += 1\n if i > diam:\n print(cs + \"\\033[91mNo path from \"+word+\" to \"+against+\"\\033[0m\")\n except KeyboardInterrupt:\n print(\"\\n\\033[94mIterative Deepening:\\033[0m Stopping search...\")\n\n######### DIJKSTRA #########\n\ndef genCombos(word):\n combos = []\n for i in range(len(word)):\n for j in range(i+1,len(word)):\n tmp = word[:i] + word[j] + word[i+1:j] + word[i] + word[j+1:]\n if tmp!=word: combos.append(tmp)\n return combos\n\ndef updateDict(w_dict):\n new_dict = {}\n for w in w_dict:\n combos = genCombos(w)\n new_dict[w] = {x:1 for x in w_dict[w]}\n new_dict[w].update({x:5 for x in combos if x in w_dict})\n return new_dict\n\ndef findLowest(q):\n index = 0\n min_cost = 9999999\n for i in range(len(q)):\n cost, v, path = q[i]\n if cost < min_cost:\n index = i\n return index\n\ndef dijk(w_dict, start, end):\n visited = set()\n q = [(0,start,[])]\n checked = 0\n max_q = 1\n while q:\n if len(q) > max_q:\n max_q = len(q)\n (cost, v, path) = q.pop(0)\n checked += 1\n if v not in visited:\n visited.add(v)\n if v == end:\n return path + [end],max_q,cost,checked\n path = path + [v]\n for n in w_dict[v]:\n if n not in visited:\n q.append((cost+w_dict[v][n],n,path))\n return [],max_q,0,checked\n\nif len(sys.argv) > 2:\n print(\"-------------------------\")\n cs = \"\\033[96mDijkstra:\\033[0m \"\n d_dict = updateDict(words_dict)\n start_time = time.time()\n p,ml,cost,c = dijk(d_dict,sys.argv[1],sys.argv[2])\n if len(p) == 0:\n print(cs + \"Path: \\033[91mNo path from \"+sys.argv[1]+\" to \"+sys.argv[2]+\"\\033[0m\")\n else:\n print(cs + \"Path: \"+str(p))\n print(cs + \"Cost: \"+str(cost))\n print(cs + \"Vertices checked: \"+str(c))\n print(cs + \"Max length of queue: \"+str(ml))\n print(cs + \"Time: \"+str(time.time()-start_time)+\"s\")\n \n","sub_path":"ai/graph/deprecated/graph.929.py","file_name":"graph.929.py","file_ext":"py","file_size_in_byte":11052,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"398336275","text":"\"\"\"\nSettings\n\nDo not import this file in code. Instead, import configs.xxx, which read\nenvironment variables as well. Related fields will be override by environment\nvariables.\n\"\"\"\n# Azure Custom Vision\nTRAINING_KEY = ''\nENDPOINT = ''\n\n# Azure IOT\nIOT_HUB_CONNECTION_STRING = ''\nDEVICE_ID = ''\nMODULE_ID = ''\n","sub_path":"factory-ai-vision/EdgeSolution/modules/WebModule/backend/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"206355574","text":"from myArray import arr\nimport time\nstart = time.time()\ndef selectionSort(arr):\n for fill in range(len(arr)-1,0,-1):\n positionMax = 0\n for locate in range(1,fill+1):\n if arr[locate] > arr[positionMax]:\n positionMax = locate\n \n temp = arr[fill]\n arr[fill] = arr[positionMax]\n arr[positionMax] = temp\n\nselectionSort(arr)\nprint(arr)\nend = time.time()\nprint(round(end-start,2))","sub_path":"selectionSort.py","file_name":"selectionSort.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"441235426","text":"from collections import defaultdict\nimport math\n\nprobs = defaultdict(float)\nfor line in open(\"model_bigram.txt\", \"r\"):\n temp = line.strip(\"\\n\").split(\"\\t\")\n probs[temp[0]] = float(temp[1])\n\nlambda_one = 0.95\nlambda_two = 0.05\nv = 1000000\nw = 0\nh = 0\n\nfor line in open(\"wiki-en-test.word\", \"r\"):\n words = line.split()\n words.append(\"\")\n words.insert(0, \"\")\n\n for i in range(1, len(words)):\n p1 = lambda_one * probs[words[i]] + (1 - lambda_one) / v\n p2 = lambda_two * probs[\"{} {}\".format(words[i - 1], words[i])] + (1 - lambda_two) * p1\n h -= math.log(p2, 2)\n w += 1\n\nprint (\"entropy = \" + str(h / w))\n","sub_path":"Yamagishi/tutorial02/test-bigram.py","file_name":"test-bigram.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"252350114","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Nov 10 17:38:19 2015\n\n@author: cliffk\n\"\"\"\n\nfrom pylab import *\n\ndef plotpeople(S):\n ppl = S['people']\n nstates = shape(ppl)[0]\n npops = shape(ppl)[1]\n count = 0\n figh = figure(figsize=(24,16), facecolor='w')\n figh.subplots_adjust(left=0.02, right=0.99, top=0.97, bottom=0.01, wspace=0.00, hspace=0.00) # Less space\n for s in range(nstates):\n for p in range(npops):\n count += 1\n h = subplot(nstates, npops, count)\n hold(True)\n plot(S['tvec'], ppl[s,p,:]/ppl[s,p,:].max()) # Plot values normalized across everything\n plot(S['tvec'], ppl[s,p,:]/ppl[1:,p,:].max()) # Plot values normalized across populations only\n h.set_xticks([])\n h.set_yticks([])\n h.set_ylim((0, 1.1))\n if s==0: title('Population %i' % p)\n if p==0: ylabel('%i' % s)","sub_path":"macedonia/plotpeople.py","file_name":"plotpeople.py","file_ext":"py","file_size_in_byte":912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"274629878","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*- \n\n#Author: Carlos Andres Delgado\n#Creation date 07th April 2018\n#Last edition date 07th April 2018\n#Description: This file contains some utilities for multifractality app\nimport numpy\nimport random as rnd\nimport os.path\nimport snap\nimport sys\n\n#Get distances matrix\ndef getDistancesMatrix(graph,numNodes,diameter):\n\t#Due to ID nodes are not contiguous, I use the array with relation beetween index of array and ID node (listaID)\n\tdistances = []\n\tfor NI in graph.Nodes():\n\t\tvaluePerNodes = numpy.zeros([diameter+1])\n\t\tvaluePerNodes[0] = 1\n\t\tfor radius in range(1, diameter+1):\n\t\t\tNodeVec = snap.TIntV()\n\t\t\tsnap.GetNodesAtHop(graph, NI.GetId(), radius, NodeVec, True)\n\t\t\tvaluePerNodes[radius]=int(len(NodeVec)+valuePerNodes[radius-1])\n\t\t\n\n\t\tdistances.append(valuePerNodes)\n\t\t\n\t\texit()\n\treturn distances\n\n\n#Reduce high computational cost operation\ndef getDistancesMatrixOLD(graph,numNodes,listID):\n\t#Due to ID nodes are not contiguous, I use the array with relation beetween index of array and ID node (listaID)\n\tdistances = numpy.zeros([numNodes,numNodes])\n\tfor i in range(0, numNodes):\n\t\tfor j in range(i, numNodes):\n\t\t\t#Same node\n\t\t\tif i == j:\t\t\t\t\n\t\t\t\tdistances[i][j] = 0\n\t\t\telse:\n\t\t\t\tdis = snap.GetShortPath(graph,listID[i],listID[j]);\n\t\t\t\tdistances[i][j] = dis\n\t\t\t\tdistances[j][i] = dis\n\t\n\treturn distances\n\t\n#Get adjacence pathMatrix\n#Reduce high computational cost operation\ndef getAdjacenceMatriz(distances, numNodes):\n\t#Due to ID nodes are not contiguous, I use the array with relation beetween index of array and ID node (listaID)\n\tadjMatrix = numpy.zeros([numNodes,numNodes])\n\tfor i in range(0, numNodes):\n\t\tfor j in range(0, numNodes):\n\t\t\t#Same node\n\t\t\tif distances[i][j]==1:\t\t\n\t\t\t\tadjMatrix[i][j] = 1\n\t\t\telse:\n\t\t\t\tadjMatrix[i][j] = 0\n\t\n\treturn distances\n\n\n\t\n#Lineal regression for calculte derivatives\ndef linealRegresssion(x, y):\n\n\tm = 0\n\tb = 0\n\tn = numpy.size(x)\n\tsumXplusY = numpy.sum(x*y)\n\tsumXplusX = numpy.sum(x*x)\n\tsumX = numpy.sum(x)\n\tsumY = numpy.sum(y)\n\tm = (n*sumXplusY - sumX*sumY)/(n*sumXplusX-sumX*sumX)\n\tb = (sumY - m*sumX)/n\n\t\n\treturn m,b\n\t\n#Get size giant component\ndef getSizeOfGiantComponent(graph):\n\tComponent = snap.GetMxScc(graph)\n\treturn Component.GetNodes()\n\n# Get average path lenght in the giant component\ndef getAveragePathLength(graph):\n\t\n\t#Test nodes are 40 percent of numNodes\n\t\n\tnumNodes = graph.GetNodes()\n\t\n\ttestNodes = int(float(numNodes)*0.4)\t\n\tif testNodes == 0: testNodes = 1\n\t\n\tmeanAverage = snap.GetBfsEffDiam(graph, testNodes, False)\t\t\t\t\t\n\treturn meanAverage\n#Copy a graph\n\ndef copyGraph(graph):\n\tg = snap.TUNGraph.New()\n\tfor NI in graph.Nodes():\n\t\tg.AddNode(NI.GetId())\n\t\n\tfor EI in graph.Edges():\n\t\tg.AddEdge(EI.GetSrcNId(),EI.GetDstNId())\n\t\t\n\treturn g\n\t\n#Get ordered closeness Centrality with node ID\n\ndef getOrderedClosenessCentrality(graph,N):\n\t#g = snap.GetMxScc(graph)\n\tClosenessCentrality = numpy.empty([N,2], dtype=float)\n\tindex=0\n\tfor NI in graph.Nodes():\n\t\tClosenessCentrality[index][0]=NI.GetId()\n\t\tcen = snap.GetClosenessCentr(graph,NI.GetId())\n\t\tClosenessCentrality[index][1]=snap.GetClosenessCentr(graph,NI.GetId())\n\t\tindex+=1\n\n\tClosenessCentrality = ClosenessCentrality[ClosenessCentrality[:,1].argsort()][::-1]\n\treturn ClosenessCentrality\n\t\n#Remove nodes\ndef removeNodes(graph,typeRemoval, p, numberNodesToRemove, ClosenessCentrality, listID, nodesToRemove = numpy.array([])):\n\tTotalRemoved = numberNodesToRemove\n\tmeasureGC = 0.\n\tmeasureAPL = 0.\n\tnumNodes = graph.GetNodes()\n\t\n\tif typeRemoval == 'Degree':\t\t\n\t\tfor i in range(0, TotalRemoved):\n\t\t\tnode = snap.GetMxDegNId(graph)\n\t\t\ttry:\n\t\t\t\tgraph.DelNode(node)\t\n\t\t\texcept:\n\t\t\t\tprint(\"error trying to delete node \",int(ClosenessCentrality[i][0]), node)\n\t\t\t\t\t\n\telif typeRemoval == 'Centrality':\n\t\tnodesToErase = snap.TIntV()\n\t\tfor i in range(0, TotalRemoved):\n\t\t\ttry:\n\t\t\t\tgraph.DelNode(int(ClosenessCentrality[i][0]))\t\t\t\n\t\t\texcept:\n\t\t\t\tprint(\"error trying to delete node \",ClosenessCentrality[i][0], typeRemoval\t)\n\t\t\t\t\n\telif typeRemoval == 'Random':\t\t\n\t\tnodesToRemoveRandom=numpy.array([listID[rnd.randint(0, numNodes-1)]],dtype=int)\n\t\twhile(numpy.size(nodesToRemoveRandom)

    \\s+

    http://[^\"]+)\"')\n \nre_ver2_dmg_link = re.compile(r'href=\"(?Phttp://.*? 2[.0-9]*.dmg)\"')\nre_ver3_dmg_link = re.compile(r'href=\"(?Phttp://.*? 3[.0-9]*.dmg)\"')\n\n\nclass Flip4MacURLProvider(Processor):\n \"\"\"Provides a download URL for the latest Flip4Mac release.\"\"\"\n input_variables = {\n \"base_url\": {\n \"required\": False,\n \"description\": \"Default is %s\" % BASE_URL,\n },\n \"major_version\": {\n \"required\": False,\n \"description\": \"Major version of Flip4Mac. Defaults to 3.\"\n }\n }\n output_variables = {\n \"url\": {\n \"description\": \"URL to the latest Flip4Mac release.\",\n },\n }\n description = __doc__\n\n def get_flip4mac_dmg_url(self, base_url, major_version):\n # Read HTML index.\n try:\n f = urllib2.urlopen(base_url)\n html = f.read()\n f.close()\n except BaseException as err:\n raise ProcessorError(\"Can't download %s: %s\" % (base_url, err))\n \n # Search for download page link.\n m = re_download_link.search(html)\n if not m:\n raise ProcessorError(\n \"Couldn't find Flip4Mac download URL in %s\" % base_url)\n \n # Get URL for download page.\n download_page_url = m.group(\"url\")\n try:\n f = urllib2.urlopen(download_page_url)\n html = f.read()\n f.close()\n except BaseException as err:\n raise ProcessorError(\"Can't download %s: %s\" % (base_url, err))\n \n if major_version == 3:\n m = re_ver3_dmg_link.search(html)\n elif major_version == 2:\n m = re_ver2_dmg_link.search(html)\n else:\n raise ProcessorError(\n \"Unsupported major_version number: %s\" % major_version)\n \n if not m:\n raise ProcessorError(\n \"Couldn't find Flip4Mac download URL in %s\" % download_page_url)\n \n return urllib2.quote(m.group(\"url\"), safe=\":/\")\n \n\n def main(self):\n \"\"\"Find and return a download URL\"\"\"\n major_version = int(self.env.get(\"major_version\", 3))\n base_url = self.env.get(\"base_url\", BASE_URL)\n self.env[\"url\"] = self.get_flip4mac_dmg_url(base_url, major_version)\n\n\nif __name__ == \"__main__\":\n processor = Flip4MacURLProvider()\n processor.execute_shell()\n","sub_path":"Recipes/Munki/Flip4Mac/Flip4MacURLProvider.py","file_name":"Flip4MacURLProvider.py","file_ext":"py","file_size_in_byte":3277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"288858629","text":"## @file Make new fasta from fasta + vcf.\n\nimport vcf\n\nDNA = {'A', 'C', 'G', 'T', 'N'}\nREF_READ_CHUNK_SIZE = 1000000\n\n\ndef make_new_ref_using_vcf(original_fasta, vcf_reader, out_fasta):\n \"\"\"\n Assumption: the vcf records are sorted in same order as the original_fasta\n :param original_fasta:\n :param vcf_reader:\n :param out_fasta:\n :return:\n \"\"\"\n in_parse = _refParser(original_fasta)\n out_parse = _FastaWriter(out_fasta)\n\n vcf_record = next(vcf_reader, None)\n if vcf_record is None:\n raise LookupError(\"vcf file {} does not contain any records\".format(vcf_reader.filename))\n else:\n start_pos = vcf_record.POS - 1\n cur_ref = vcf_record.REF\n cur_ref_pos = 0\n total_num_sites = 1\n\n inside_record = False\n start = True\n commit_ref = False\n\n num_discordances = 0\n chrom_sizes = []\n\n try:\n for fasta_char in in_parse:\n if fasta_char.header:\n if not start:\n out_parse.commit('\\n', is_used = fasta_char.use)\n chrom_sizes.append(reference_index + 1)\n else:\n start = False\n reference_index = -1\n\n elif fasta_char.use:\n reference_index += 1\n if reference_index == start_pos: # Commit the chosen ALT\n inside_record = True\n\n # Pick the allele based on GT column\n sample = vcf_record.samples[0]\n genotype = sample.gt_alleles\n\n # Case: no genotype. Let's pick REF allele.\n if set(genotype) == {None}:\n chosen_pos = 0\n\n #Case: haploid, or >1 calls. Pick the first (is most likely haploid from infer).\n else:\n chosen_pos = int(genotype[0])\n\n if chosen_pos == 0:\n commit_ref = True\n else: # Commit the full alt\n commit_ref = False\n chosen_allele = vcf_record.ALT[chosen_pos - 1]\n\n for alt_char in str(chosen_allele):\n if alt_char != '': # Full deletion means don't commit anything\n out_parse.commit(alt_char)\n\n\n if inside_record:\n # Make sure the record makes sense!\n expect = fasta_char.char.upper()\n got = cur_ref[cur_ref_pos].upper()\n try:\n assert expect == got, \"Expected in fasta: {} at pos {}, Got in vcf: {} from REF: {}\".format(expect, reference_index, got, vcf_record)\n except AssertionError:\n num_discordances += 1\n cur_ref_pos += 1\n\n # Case: get next record\n if cur_ref_pos == len(cur_ref):\n inside_record = False\n vcf_record = next(vcf_reader, None)\n if vcf_record is not None:\n start_pos = vcf_record.POS - 1\n cur_ref = vcf_record.REF\n cur_ref_pos = 0\n total_num_sites += 1\n\n if not commit_ref:\n continue\n\n out_parse.commit(fasta_char.char, is_used = fasta_char.use)\n out_parse.close()\n\n except ValueError as exc:\n out_parse.close()\n raise ValueError from exc\n\n chrom_sizes.append(reference_index + 1)\n\n return chrom_sizes, num_discordances, total_num_sites\n\n\n\n\n# Writes fasta characters in blocks\nclass _FastaWriter():\n max_load = REF_READ_CHUNK_SIZE\n line_length = 60\n\n def __init__(self, outfilename):\n self.load = []\n self.tally = 0\n self.fhandle = open(outfilename, \"w\")\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n self.close()\n\n def commit(self, char, is_used = True):\n if is_used:\n char = char.upper()\n self.load.append(char)\n if is_used:\n self.tally += 1\n else:\n self.tally = 0\n\n if self.tally == _FastaWriter.line_length:\n self.load.append('\\n')\n self.tally = 0\n if len(self.load) > _FastaWriter.max_load:\n self.fhandle.write(\"\".join(self.load))\n self.load = []\n\n def close(self):\n if self.tally > 0:\n self.load.append('\\n')\n if len(self.load) > 0:\n self.fhandle.write(\"\".join(self.load))\n self.fhandle.close()\n\nclass Fasta_Char():\n valid = {\"use\", \"header\", \"write\"}\n\n def __init__(self, char, mode):\n self.char = char\n\n if mode not in Fasta_Char.valid:\n raise ValueError(\"Must initialise with mode in {}\".format(str(Fasta_Char.valid)))\n\n if mode == \"use\":\n self.use = True\n self.header = False\n\n elif mode == \"header\":\n self.use = False\n self.header = True\n\n else:\n self.use = False\n self.header = False\n\n## Generator to parse fasta ref sequence in blocks.\ndef _refParser(ref_fpath, chunk_size=REF_READ_CHUNK_SIZE):\n header = False\n with open(ref_fpath) as fhandle:\n while True:\n chars = fhandle.read(chunk_size)\n\n if len(chars) == 0:\n return\n\n for char in chars:\n if char == \">\":\n header = True\n fasta_char = Fasta_Char(char, mode=\"header\")\n\n elif char == '\\n':\n if header:\n header = False\n fasta_char = Fasta_Char(char, mode=\"write\")\n else: # Skip newlines if they are not part of the header\n continue\n\n else:\n if header:\n fasta_char = Fasta_Char(char, mode=\"write\")\n else:\n if char.upper() not in DNA:\n raise ValueError(\"Found an invalid character: {}\".format(char))\n fasta_char = Fasta_Char(char, mode=\"use\")\n\n yield fasta_char\n\n","sub_path":"gramtools/fasta_from_vcf.py","file_name":"fasta_from_vcf.py","file_ext":"py","file_size_in_byte":6329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"405312855","text":"#!/usr/bin/env python\n#\n# Copyright 2007 Google 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#\nimport webapp2\nimport os\nimport jinja2\nimport urllib\nfrom xml.dom import minidom\nfrom string import letters\nfrom google.appengine.ext import db\nfrom operator import itemgetter\nimport re\n\ntemplate_dir = os.path.join(os.path.dirname(__file__), 'templates')\njinja_env = jinja2.Environment(loader = jinja2.FileSystemLoader(template_dir), autoescape=True)\n\nmainqueryindex={}\nimagequeryindex={}\ntitleq={}\n\nclass Handler(webapp2.RequestHandler):\n def write(self, *a, **kw):\n self.response.out.write(*a, **kw)\n def render_str(self, template, **params):\n t = jinja_env.get_template(template)\n return t.render(params)\n def render(self, template, **kw):\n self.write(self.render_str(template, **kw))\n\nclass SearchEngine(Handler):\n def html_to_plain(self,html):\n html=html.lower()\n cleaned = re.sub(r\"(?is)<(script|style).*?>.*?()\", \"\", html.strip())\n cleaned = re.sub(r\"(?s)[\\n]?\", \"\", cleaned)\n cleaned = re.sub(r\"(?s)<.*?>\", \" \", cleaned)\n cleaned = re.sub(r\" \", \" \", cleaned)\n cleaned = re.sub(r\" \", \" \", cleaned)\n cleaned = re.sub(r\" \", \" \", cleaned)\n cleaned = cleaned.strip()\n words=re.findall('\\w+',cleaned)\n return words\n def add_to_index(self,index,keyword,url):\n if keyword in index:\n listurl=index[keyword]\n if url not in listurl:\n index[keyword].append(url)\n else:\n index[keyword]=[url]\n\n def lookup(self,keyword,index):\n if keyword in index:\n return index[keyword]\n else:\n return []\n\n def lookup_multi(self,content,index):\n words=self.html_to_plain(content)\n listurl={}\n for keyword in words:\n url=self.lookup(keyword,index)\n for i in url:\n self.add_to_index(listurl,i,keyword)\n return listurl\n\n def add_page_to_index(self,index,url,content):\n global mainqueryindex\n words=self.html_to_plain(content)\n for keyword in words:\n self.add_to_index(index,keyword,url)\n self.add_to_index(mainqueryindex,keyword,url)\n\n def get_page(self,url):\n try:\n return urllib.urlopen(url).read()\n except:\n return \"\"\n\n def union(self,p, q):\n count=0\n for e in q:\n if e not in p:\n p.append(e)\n count+=1\n return count\n\n def get_next_target(self,page,tag):\n start_link = page.find(tag)\n if start_link == -1:\n return None, 0\n start_quote = page.find('\"', start_link)\n end_quote = page.find('\"', start_quote + 1)\n url = page[start_quote + 1:end_quote]\n start_http=url.find('http')\n if start_http==-1:\n return None,end_quote\n return url, end_quote\n\n def get_all_links(self,page,tag):\n links = []\n while True:\n url, endpos = self.get_next_target(page,tag)\n if url or endpos:\n links.append(url)\n page = page[endpos:]\n else:\n break\n return links\n\n def compute_ranks(self,graph):\n d=0.8\n numloops = 10\n ranks = {}\n npages = len(graph)\n for page in graph:\n ranks[page] = 1.0 / npages\n for i in range(0, numloops):\n newranks = {}\n for page in graph:\n newrank = (1 - d) / npages\n for node in graph:\n if page in graph[node]:\n newrank += ranks[node] * d / len(graph[node])\n newranks[page] = newrank\n ranks = newranks\n return ranks\n\n def sort_by_score(self,l):\n l=sorted(l,key=itemgetter(0),reverse=True)\n return l\n\n def lookup_best(self,index,keyword,ranks):\n result=[]\n if keyword in index:\n for url in index[keyword]:\n if url in ranks:\n result.append([ranks[url],url])\n if len(result)>0:\n result=self.sort_by_score(result)\n return result\n\n def get_news(self,link):\n news={}\n date={}\n newslink={}\n newstitle={}\n content=self.get_page(link)\n x=minidom.parseString(content)\n p=x.getElementsByTagName(\"item\")\n i=0\n for a in p:\n title=a.getElementsByTagName(\"title\")[0].childNodes[0].nodeValue\n description=a.getElementsByTagName(\"description\")[0].childNodes[0].nodeValue\n link=a.getElementsByTagName(\"link\")[0].childNodes[0].nodeValue\n pubdate=a.getElementsByTagName(\"pubDate\")[0].childNodes[0].nodeValue\n news[i]=description\n date[i]=pubdate\n newslink[i]=link\n newstitle[i]=title\n i+=1\n return news,date,newslink,newstitle\n\n # def get_video(self,link):\n # videolink={}\n # content=self.get_page(link)\n # x=minidom.parseString(content)\n # p=x.getElementsByTagName(\"entry\")\n\n def get_image_keyword(self,page):\n start_link = page.find('')\n if start_link == -1:\n return None\n start_content = page.find('>',start_link)\n end_content = page.find('', start_content)\n if end_content == -1:\n return None\n url = page[start_content + 1:end_content]\n return url\n\n def add_image(self,imglink,content):\n global imagequeryindex\n if not imglink:\n return\n words=self.get_image_keyword(content)\n if not words:\n return\n wordlist=self.html_to_plain(words)\n for i in imglink:\n for key in wordlist:\n self.add_to_index(imagequeryindex,key,i)\n\n\n def crawl_web(self,seed,max_pages=20,max_depth=1):\n tocrawl = [seed]\n crawled = []\n index={}\n depth=[0]\n graph={}\n global titleq\n global mainqueryindex\n global imagequeryindex\n if len(mainqueryindex)>=200000:\n mainqueryindex={}\n titleq={}\n imagequeryindex={}\n while tocrawl and len(crawled)= 3 and soma <= 4:\n print('Você está preso...\\n'\n 'VOCÊ É CÚMPLICE DESTE CRIME!')\nelif soma == 5:\n print('Conforme suas respostas já encontrei o culpado...\\n'\n 'VOCÊ É O ASSASSINO!')\n\n\n\n","sub_path":"exerc_25_perguntas_crime.py","file_name":"exerc_25_perguntas_crime.py","file_ext":"py","file_size_in_byte":1719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"175853573","text":"from os.path import join\nimport logging\nimport datetime\nimport numpy as np\n\nfrom yass import read_config\nfrom yass.util import file_loader, check_for_files, LoadFile\nfrom yass.cluster.subsample import random_subsample\nfrom yass.cluster.triage import triage\nfrom yass.cluster.coreset import coreset\nfrom yass.cluster.mask import getmask\nfrom yass.cluster.util import (run_cluster, run_cluster_location,\n calculate_sparse_rhat)\nfrom yass.mfm import get_core_data\n\n\n@check_for_files(filenames=[LoadFile(join('cluster',\n 'spike_train_cluster.npy')),\n LoadFile(join('cluster', 'tmp_loc.npy')),\n LoadFile(join('cluster', 'vbPar.pickle'))],\n mode='values', relative_to='output_directory',\n auto_save=True, prepend_root_folder=True)\ndef run(scores, spike_index, output_directory='tmp/',\n if_file_exists='skip', save_results=False):\n \"\"\"Spike clustering\n\n Parameters\n ----------\n scores: numpy.ndarray (n_spikes, n_features, n_channels), str or Path\n 3D array with the scores for the clear spikes, first simension is\n the number of spikes, second is the nymber of features and third the\n number of channels. Or path to a npy file\n\n spike_index: numpy.ndarray (n_clear_spikes, 2), str or Path\n 2D array with indexes for spikes, first column contains the\n spike location in the recording and the second the main channel\n (channel whose amplitude is maximum). Or path to an npy file\n\n output_directory: str, optional\n Location to store/look for the generate spike train, relative to\n CONFIG.data.root_folder\n\n if_file_exists: str, optional\n One of 'overwrite', 'abort', 'skip'. Control de behavior for the\n spike_train_cluster.npy. file If 'overwrite' it replaces the files if\n exists, if 'abort' it raises a ValueError exception if exists,\n if 'skip' it skips the operation if the file exists (and returns the\n stored file)\n\n save_results: bool, optional\n Whether to save spike train to disk\n (in CONFIG.data.root_folder/relative_to/spike_train_cluster.npy),\n defaults to False\n\n Returns\n -------\n spike_train: (TODO add documentation)\n\n Examples\n --------\n\n .. literalinclude:: ../../examples/pipeline/cluster.py\n\n \"\"\"\n # load files in case they are strings or Path objects\n scores = file_loader(scores)\n spike_index = file_loader(spike_index)\n\n CONFIG = read_config()\n\n startTime = datetime.datetime.now()\n\n Time = {'t': 0, 'c': 0, 'm': 0, 's': 0, 'e': 0}\n\n logger = logging.getLogger(__name__)\n\n scores_all = np.copy(scores)\n spike_index_all = np.copy(spike_index)\n\n ##########\n # Triage #\n ##########\n\n _b = datetime.datetime.now()\n logger.info(\"Randomly subsampling...\")\n scores, spike_index = random_subsample(scores, spike_index,\n CONFIG.cluster.max_n_spikes)\n logger.info(\"Triaging...\")\n scores, spike_index = triage(scores, spike_index,\n CONFIG.cluster.triage.nearest_neighbors,\n CONFIG.cluster.triage.percent,\n CONFIG.cluster.method == 'location')\n Time['t'] += (datetime.datetime.now()-_b).total_seconds()\n\n if CONFIG.cluster.method == 'location':\n ##############\n # Clustering #\n ##############\n _b = datetime.datetime.now()\n logger.info(\"Clustering...\")\n vbParam, tmp_loc, scores, spike_index = run_cluster_location(\n scores, spike_index, CONFIG.cluster.min_spikes, CONFIG)\n Time['s'] += (datetime.datetime.now()-_b).total_seconds()\n\n else:\n ###########\n # Coreset #\n ###########\n _b = datetime.datetime.now()\n logger.info(\"Coresetting...\")\n groups = coreset(scores,\n spike_index,\n CONFIG.cluster.coreset.clusters,\n CONFIG.cluster.coreset.threshold)\n Time['c'] += (datetime.datetime.now() - _b).total_seconds()\n\n ###########\n # Masking #\n ###########\n _b = datetime.datetime.now()\n logger.info(\"Masking...\")\n masks = getmask(scores, spike_index, groups,\n CONFIG.cluster.masking_threshold)\n Time['m'] += (datetime.datetime.now() - _b).total_seconds()\n\n ##############\n # Clustering #\n ##############\n _b = datetime.datetime.now()\n logger.info(\"Clustering...\")\n vbParam, tmp_loc, scores, spike_index = run_cluster(\n scores, masks, groups, spike_index,\n CONFIG.cluster.min_spikes, CONFIG)\n Time['s'] += (datetime.datetime.now()-_b).total_seconds()\n\n vbParam.rhat = calculate_sparse_rhat(vbParam, tmp_loc, scores_all,\n spike_index_all,\n CONFIG.neigh_channels)\n idx_keep = get_core_data(vbParam, scores_all, np.inf, 2)\n spike_train = vbParam.rhat[idx_keep]\n spike_train[:, 0] = spike_index_all[spike_train[:, 0].astype('int32'), 0]\n\n # report timing\n currentTime = datetime.datetime.now()\n logger.info(\"Mainprocess done in {0} seconds.\".format(\n (currentTime - startTime).seconds))\n logger.info(\"\\ttriage:\\t{0} seconds\".format(Time['t']))\n logger.info(\"\\tcoreset:\\t{0} seconds\".format(Time['c']))\n logger.info(\"\\tmasking:\\t{0} seconds\".format(Time['m']))\n logger.info(\"\\tclustering:\\t{0} seconds\".format(Time['s']))\n\n return spike_train, tmp_loc, vbParam\n","sub_path":"src/yass/cluster/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":5708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"206509542","text":"import webapp2\nimport jinja2\nimport os\n\nthe_jinja_env = jinja2.Environment(\n loader=jinja2.FileSystemLoader(os.path.dirname(__file__,)),\n extensions=['jinja2.ext.autoescape'],\n autoescape=True)\n\nclass MainPageHandler(webapp2.RequestHandler):\n def get(self):\n result_template = the_jinja_env.get_template(\"Templates/index.html\")\n self.response.write(result_template.render())\n\n\napp = webapp2.WSGIApplication([\n ('/', MainPageHandler),\n], debug=True)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"246242766","text":"\nimport torch\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport PIL\nfrom PIL import Image, ImageOps, ImageFilter\nimport math\nimport random\nfrom random import randint\n\nfrom torch.utils import data\nfrom torchvision import transforms\n\ndef read_labeled_image_list(data_dir, data_list):\n \"\"\"Reads txt file containing paths to images and ground truth masks.\n\n Args:\n data_dir: path to the directory with images and masks.\n data_list: path to the file with lines of the form '/path/to/image /path/to/mask'.\n\n Returns:\n Two lists with all file names for images and masks, respectively.\n \"\"\"\n f = open(data_list, 'r')\n images = []\n masks = []\n for line in f:\n image, mask = line.strip(\"\\n\").split(' ')\n images.append(data_dir + image)\n masks.append(data_dir + mask)\n\n return images, masks\n\nclass PascalDatasetRandomTripletAugmentedWeighted(data.Dataset):\n \"\"\"Data loader for the Pascal VOC semantic segmentation dataset.\n \"\"\"\n\n def __init__(\n self,\n *,\n pascal_root,\n split_file,\n n_triplets,\n ):\n self.split_file = split_file\n self.pascal_root = pascal_root\n self.n_triplets = n_triplets\n\n self.n_classes = 21\n \n self.crop_size = 513\n self.base_size = 513\n\n self.image_list, self.label_list = read_labeled_image_list(self.pascal_root, self.split_file)\n\n self.transforms = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225])])\n \n self.fill_image = (124, 116, 104)\n self.fill_label = 255\n\n\n def __len__(self):\n return len(self.label_list)\n\n def __getitem__(self, index):\n im_path = self.image_list[index]\n lbl_path = self.label_list[index]\n\n img = PIL.Image.open(im_path)\n lbl = PIL.Image.open(lbl_path)\n\n ## augmentation\n img, lbl = self._augment(img, lbl)\n\n img, lbl, minrange, maxrange = self._random_crop(img, lbl)\n \n img = np.array(img, dtype=np.float32) / 255.0\n lbl = np.array(lbl, dtype=np.long)\n # lbl[lbl==255] = 0\n\n img = self.transforms(img)\n \n minrange = [0, 0]\n maxrange = [513, 513]\n \n triplets = self._generate_triplet(lbl, minrange, maxrange)\n \n\n return img, triplets\n \n \n def _augment(self, img, lbl):\n \n if np.random.rand() > 0.5:\n img = img.transpose(PIL.Image.FLIP_LEFT_RIGHT)\n lbl = lbl.transpose(PIL.Image.FLIP_LEFT_RIGHT)\n \n if np.random.random() < 0.5:\n img = img.filter(PIL.ImageFilter.GaussianBlur(\n radius=random.random()))\n\n \n \n return img, lbl\n\n \n def _random_crop(self, img, mask):\n # random scale (short edge)\n short_size = random.randint(int(self.base_size * 0.5), int(self.base_size * 2.0))\n w, h = img.size\n if h > w:\n ow = short_size\n oh = int(1.0 * h * ow / w)\n else:\n oh = short_size\n ow = int(1.0 * w * oh / h)\n img = img.resize((ow, oh), Image.BILINEAR)\n mask = mask.resize((ow, oh), Image.NEAREST)\n # pad crop\n padh = self.crop_size - oh if oh < self.crop_size else 0\n padw = self.crop_size - ow if ow < self.crop_size else 0\n if short_size < self.crop_size:\n img = ImageOps.expand(img, border=(padw//2 + 1, padh//2 + 1, padw//2 + 1, padh//2 + 1), fill=self.fill_image)\n mask = ImageOps.expand(mask, border=(padw//2 + 1, padh//2 + 1, padw//2 + 1, padh//2 + 1), fill=self.fill_label)\n # random crop crop_size\n w, h = img.size\n x1 = random.randint(0, w - self.crop_size)\n y1 = random.randint(0, h - self.crop_size)\n img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size))\n mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size))\n\n ## TODO: unnecessary code\n minrange = [max(x1 - padw//2 - 1, 0), max(y1 - padh//2 - 1, 0)]\n maxrange = [min(x1 + 2 * self.crop_size - padw//2 - 1 - w, self.crop_size - 1), \n min(y1 + 2 * self.crop_size - padh//2 - 1 - h, self.crop_size - 1)]\n \n return img, mask, minrange, maxrange\n \n\n def _generate_triplet(self, lbl, minrange, maxrange):\n lbl_view = lbl[minrange[0]:maxrange[0], minrange[1]:maxrange[1]]\n \n classes, inv_map = np.unique(lbl_view, return_inverse=True)\n n_classes = len(classes)\n inv_map = inv_map.reshape(lbl_view.shape[0], lbl_view.shape[1])\n inv_map_flat = inv_map.reshape(-1)\n\n class_lookup = (np.arange(n_classes, dtype=np.int32).reshape((1, 1, n_classes)) !=\n inv_map.reshape(lbl_view.shape[0], lbl_view.shape[1], 1))\n class_lookup = np.transpose(class_lookup, axes=[2, 0, 1])\n \n lbl_view_flat = lbl_view.reshape(-1)\n \n ## generate the anchors\n \n if np.any(lbl_view.reshape(-1) != 255):\n n_objects = len(classes)\n if 255 in classes:\n n_objects -= 1\n counts = [self.n_triplets // n_objects] * n_objects\n counts[randint(0, len(counts)-1)] += self.n_triplets % n_objects\n \n ai = np.array([], dtype=np.int64)\n \n for i, c in enumerate(counts):\n options = np.nonzero(lbl_view.reshape(-1) == classes[i])[0]\n \n ai_o = np.random.randint(low=0, \n high=options.shape[0], \n size=(c,))\n ai_o = options[ai_o]\n \n ai = np.hstack((ai_o, ai))\n else:\n ai = np.array([0] * self.n_triplets, dtype=np.int64)\n \n\n \n \n lneg = []\n lpos = []\n for i in range(n_classes):\n lneg.append(np.transpose(np.logical_and(lbl_view != 255, class_lookup[i]).reshape(-1).nonzero()).reshape((-1)))\n lpos.append(np.transpose(\n np.logical_and(lbl_view != 255, \n np.logical_not(class_lookup[i])).reshape(-1).nonzero()).reshape((-1)))\n\n ni, pi = [], []\n for i in range(self.n_triplets):\n cni = lneg[inv_map_flat[ai[i]]]\n cpi = lpos[inv_map_flat[ai[i]]]\n if len(cni) == 0 or len(cpi) == 0:\n ni.append(ai[i])\n pi.append(ai[i])\n else:\n ni.append( np.random.choice(cni))\n pi.append( np.random.choice(cpi))\n \n aix, aiy = np.unravel_index(ai, dims=(lbl_view.shape[0], lbl_view.shape[1]))\n aix += minrange[0]\n aiy += minrange[1]\n ai = np.stack((aix, aiy))\n \n pix, piy = np.unravel_index(pi, dims=(lbl_view.shape[0], lbl_view.shape[1]))\n pix += minrange[0]\n piy += minrange[1]\n pi = np.stack((pix, piy))\n \n nix, niy = np.unravel_index(ni, dims=(lbl_view.shape[0], lbl_view.shape[1]))\n nix += minrange[0]\n niy += minrange[1]\n ni = np.stack((nix, niy))\n \n triplets = np.stack((ai, pi, ni), axis=0)\n triplets = torch.tensor(triplets, dtype=torch.long)\n \n return triplets\n \n\n\n \n\n def get_pascal_labels(self):\n \"\"\"Load the mapping that associates pascal classes with label colors\n\n Returns:\n np.ndarray with dimensions (21, 3)\n \"\"\"\n return np.asarray(\n [\n [0, 0, 0],\n [128, 0, 0],\n [0, 128, 0],\n [128, 128, 0],\n [0, 0, 128],\n [128, 0, 128],\n [0, 128, 128],\n [128, 128, 128],\n [64, 0, 0],\n [192, 0, 0],\n [64, 128, 0],\n [192, 128, 0],\n [64, 0, 128],\n [192, 0, 128],\n [64, 128, 128],\n [192, 128, 128],\n [0, 64, 0],\n [128, 64, 0],\n [0, 192, 0],\n [128, 192, 0],\n [0, 64, 128],\n ]\n )\n\n def encode_segmap(self, mask):\n \"\"\"Encode segmentation label images as pascal classes\n\n Args:\n mask (np.ndarray): raw segmentation label image of dimension\n (M, N, 3), in which the Pascal classes are encoded as colours.\n\n Returns:\n (np.ndarray): class map with dimensions (M,N), where the value at\n a given location is the integer denoting the class index.\n \"\"\"\n mask = mask.astype(int)\n label_mask = np.zeros((mask.shape[0], mask.shape[1]), dtype=np.int16)\n for ii, label in enumerate(self.get_pascal_labels()):\n label_mask[np.where(np.all(mask == label, axis=-1))[:2]] = ii\n label_mask = label_mask.astype(int)\n return label_mask\n\n def decode_segmap(self, label_mask, plot=False):\n \"\"\"Decode segmentation class labels into a color image\n\n Args:\n label_mask (np.ndarray): an (M,N) array of integer values denoting\n the class label at each spatial location.\n plot (bool, optional): whether to show the resulting color image\n in a figure.\n\n Returns:\n (np.ndarray, optional): the resulting decoded color image.\n \"\"\"\n label_colours = self.get_pascal_labels()\n r = label_mask.copy()\n g = label_mask.copy()\n b = label_mask.copy()\n for ll in range(0, self.n_classes):\n r[label_mask == ll] = label_colours[ll, 0]\n g[label_mask == ll] = label_colours[ll, 1]\n b[label_mask == ll] = label_colours[ll, 2]\n rgb = np.zeros((label_mask.shape[0], label_mask.shape[1], 3))\n rgb[:, :, 0] = r / 255.0\n rgb[:, :, 1] = g / 255.0\n rgb[:, :, 2] = b / 255.0\n if plot:\n plt.imshow(rgb)\n plt.show()\n else:\n return rgb\n","sub_path":"lib/deeptriplet/datasets/pascal_random_triplet_obj_weighted_aug.py","file_name":"pascal_random_triplet_obj_weighted_aug.py","file_ext":"py","file_size_in_byte":10290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"332946525","text":"from fabric.api import *\nfrom fabric.api import env, put, run, sudo, task, cd, settings, prefix, shell_env\nfrom fabric_ops import fabric_ops\nfrom salt import salt_master, salt_minion\nfrom hadoop_ops import hadoop\nfrom cloudFormationOps import *\nimport time\n\n\n\n@task\ndef keys(aws_access_key_id, aws_secret_access_key, aws_key_location, cloud_formation_stack, aws_security_token = None):\n c = cf(aws_access_key_id = aws_access_key_id, aws_secret_access_key = aws_secret_access_key, security_token = aws_security_token)\n instances = c.get_stack_resources(cloud_formation_stack)\n env.cluster = hadoop_cluster(instances = instances)\n env.h = hadoop(namenode = env.cluster.namenode, secondaryNamenode = env.cluster.secondarynamenode, dataNodes = env.cluster.datanodes)\n env.user = 'ubuntu'\n env.key_location = aws_key_location\n\n\n@task\ndef salt_install():\n master = salt_master(host_ip = env.cluster.saltmaster, host_user = env.user, host_key_file = env.key_location)\n master.install()\n time.sleep(5)\n for node in env.cluster.all_hadoop_nodes:\n minion = salt_minion(host_ip = node, host_user = env.user, host_key_file = env.key_location)\n minion.install(master = env.cluster.saltmaster, minion = node)\n time.sleep(2)\n\n master = salt_master(host_ip = env.cluster.saltmaster, host_user = env.user, host_key_file = env.key_location)\n master.keys_accept()\n master.ping()\n\n@task\ndef grant_access_hadoop_nodes():\n for node in env.cluster.all_hadoop_nodes:\n n = fabric_ops(host_ip = node, host_user = env.user, host_key_file = env.key_location)\n #Changing the host name of hadoop nodes to EC2 public dns name.\n cmd_change_hostname = 'hostname {0}'.format(node)\n sudo(cmd_change_hostname)\n #Changing /etc/hosts file to remove localhost and replacing it with the public dns name and 127.0.0.1 with localip.\n #sudo('sed -i -e \"s/localhost/{0}/\" /etc/hosts'.format(node))\n\n n = fabric_ops(host_ip = env.cluster.namenode, host_user = env.user, host_key_file = env.key_location)\n run('ssh-keygen -t rsa -f /home/ubuntu/.ssh/id_rsa -q -N \"\"')\n #adding StrictHostKeyChecking no in the .ssh/config file so that ssh login is not prompted.\n run('echo \"{0}\" > /home/ubuntu/.ssh/config'.format(\"Host *\"))\n run('echo \"{0}\" >> /home/ubuntu/.ssh/config'.format(\" StrictHostKeyChecking no\"))\n #Getting public key from hadoopnamenode\n public_key = sudo('cat /home/ubuntu/.ssh/id_rsa.pub')\n n = fabric_ops(host_ip = env.cluster.saltmaster, host_user = env.user, host_key_file = env.key_location)\n\n #Issuing a minion blast of public key to all hadoop nodes to enable passwordless login.\n minion_cmd = \"echo '{0}' >> /home/ubuntu/.ssh/authorized_keys\".format(public_key)\n sudo('salt \"*\" cmd.run \"{0}\"'.format(minion_cmd))\n\n@task\ndef install_java():\n n = fabric_ops(host_ip = env.cluster.saltmaster, host_user = env.user, host_key_file = env.key_location)\n with settings(warn_only = True):\n sudo('salt \"*\" cmd.run \"sudo apt-get update\"')\n sudo('salt \"*\" cmd.run \"sudo add-apt-repository ppa:webupd8team/java\"')\n sudo('salt \"*\" cmd.run \"echo oracle-java8-installer shared/accepted-oracle-license-v1-1 select true | sudo /usr/bin/debconf-set-selections\"')\n sudo('salt \"*\" cmd.run \"sudo apt-get update\"')\n sudo('salt \"*\" cmd.run \"sudo apt-get install -y oracle-java8-installer\"')\n sudo('salt \"*\" cmd.run \"sudo apt-get -f -y -q install\"')\n cmd = \"echo '{0}' >> /home/ubuntu/.bashrc\".format(\"export JAVA_HOME=/usr/lib/jvm/java-8-oracle\")\n sudo('salt \"*\" cmd.run \"{0}\"'.format(cmd))\n\n@task\ndef install_hadoop_packages():\n n = fabric_ops(host_ip = env.cluster.saltmaster, host_user = env.user, host_key_file = env.key_location)\n mirror_site = \"http://www-us.apache.org/dist/hadoop/common/hadoop-2.7.3/hadoop-2.7.3.tar.gz\"\n #Install hadoop binaries\n with settings(warn_only = True):\n sudo('salt \"*\" cmd.run \"wget {0} -P /home/ubuntu\"'.format(mirror_site))\n sudo('salt \"*\" cmd.run \"tar -xzvf /home/ubuntu/hadoop-2.7.3.tar.gz -C /home/ubuntu\"')\n sudo('salt \"*\" cmd.run \"mv /home/ubuntu/hadoop-2.7.3 /home/ubuntu/hadoop\"')\n sudo('salt \"*\" cmd.run \"rm -rf /home/ubuntu/hadoop-2.7.3.tar.gz\"')\n #changing the hadoop directory owner to ubuntu.\n sudo('salt \"*\" cmd.run \"sudo chown -R ubuntu /home/ubuntu/hadoop\"')\n\n #Sets environment variables and adds them to path.\n cmd = \"echo '{0}' >> /home/ubuntu/.bashrc\".format(\"export HADOOP_CONF=/home/ubuntu/hadoop/etc/hadoop\")\n sudo('salt \"*\" cmd.run \"{0}\"'.format(cmd))\n cmd = \"echo '{0}' >> /home/ubuntu/.bashrc\".format(\"export HADOOP_PREFIX=/home/ubuntu/hadoop\")\n sudo('salt \"*\" cmd.run \"{0}\"'.format(cmd))\n cmd = \"echo '{0}' >> /home/ubuntu/.bashrc\".format(\"export PATH='$'PATH:'$'HADOOP_PREFIX/bin\")\n sudo('salt \"*\" cmd.run \"{0}\"'.format(cmd))\n\n@task\ndef deploy_hadoop_config():\n n = fabric_ops(host_ip = env.cluster.namenode, host_user = env.user, host_key_file = env.key_location)\n\n hadoop_env_command = \"sed -i -e s/'\\\\\\${JAVA_HOME}'/'\\\\\\/usr\\\\\\/lib\\\\\\/jvm\\\\\\/java-8-oracle'/ /home/ubuntu/hadoop/etc/hadoop/hadoop-env.sh\"\n\n n = fabric_ops(host_ip = env.cluster.saltmaster, host_user = env.user, host_key_file = env.key_location)\n sudo('salt \"*\" cmd.run \"{0}\"'.format(hadoop_env_command))\n core_site_command = \"echo '{0}' > {1}\".format(env.h.core_site_text, env.h.config_coresite_path)\n sudo('salt \"*\" cmd.run \"{0}\"'.format(core_site_command))\n hdfs_site_command = \"echo '{0}' > {1}\".format(env.h.hdfs_site_text, env.h.config_hdfssite_path)\n sudo('salt \"*\" cmd.run \"{0}\"'.format(hdfs_site_command))\n mapred_site_command = \"echo '{0}' > {1}\".format(env.h.mapred_site_text, env.h.config_mapredsite_path)\n sudo('salt \"*\" cmd.run \"{0}\"'.format(mapred_site_command))\n\n@task\ndef setup_hadoop_master_slave():\n n = fabric_ops(host_ip = env.cluster.namenode, host_user = env.user, host_key_file = env.key_location)\n sudo(\"echo {0} > {1}\".format(env.cluster.namenode, env.h.config_master_path))\n sudo(\"echo {0} >> {1}\".format(env.cluster.secondarynamenode, env.h.config_master_path))\n sudo(\">{0}\".format(env.h.config_slave_path))\n for slave in env.cluster.datanodes:\n sudo(\"echo {0} >> {1}\".format(slave, env.h.config_slave_path))\n\n n = fabric_ops(host_ip = env.cluster.secondarynamenode, host_user = env.user, host_key_file = env.key_location)\n sudo(\"echo {0} > {1}\".format(env.cluster.namenode, env.h.config_master_path))\n sudo(\"echo {0} >> {1}\".format(env.cluster.secondarynamenode, env.h.config_master_path))\n sudo(\">{0}\".format(env.h.config_slave_path))\n for slave in env.cluster.datanodes:\n sudo(\"echo {0} >> {1}\".format(slave, env.h.config_slave_path))\n\n for slave in env.cluster.datanodes:\n n = fabric_ops(host_ip = slave, host_user = env.user, host_key_file = env.key_location)\n sudo(\"echo {0} > {1}\".format(slave, env.h.config_slave_path))\n\n@task\ndef start_services_hadoop_master():\n n = fabric_ops(host_ip = env.cluster.namenode, host_user = env.user, host_key_file = env.key_location)\n run(\"/home/ubuntu/hadoop/bin/hadoop namenode -format -force\")\n run(\"/home/ubuntu/hadoop/sbin/start-dfs.sh\")\n run(\"jps\")\n\n@task\ndef run_pi_test():\n n = fabric_ops(host_ip = env.cluster.namenode, host_user = env.user, host_key_file = env.key_location)\n with cd('/home/ubuntu/hadoop/share/hadoop/mapreduce'):\n run('/home/ubuntu/hadoop/bin/hadoop jar hadoop-mapreduce-examples-2.7.3.jar pi 10 1000000')\n\n\n@task\ndef provision_hadoop_cluster():\n execute(salt_install)\n execute(grant_access_hadoop_nodes)\n execute(install_java)\n execute(install_hadoop_packages)\n execute(deploy_hadoop_config)\n execute(setup_hadoop_master_slave)\n execute(start_services_hadoop_master)\n execute(run_pi_test)\n\n","sub_path":"aws-hadoop/fabfile/hadoop.py","file_name":"hadoop.py","file_ext":"py","file_size_in_byte":7889,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"648829349","text":"import random\nimport numpy as np\n\ndef sigmoid(x):\n return 1/(1 + np.exp(-x))\n\ndef sigmoid_guess(x,g=0.2):\n return g + (1-g)/(1 + np.exp(-x))\n\ndef loglik(theta,beta,eps0,alpha,len_beta,num_correct, g=0.2):\n \n \"\"\"\n takes in a theta and beta value and uses a sigmoid function to compute likelihood of answering correctly\n 1 / 1 + exp (-(theta-beta))\n also takes in a probability epsilon of making an incorrect inference\n \n \"\"\"\n eps = eps0 - alpha * (num_correct/len_beta)\n \n assert eps >= 0, \"Epsilon is a probability and must be between 0 and 1!\"\n assert eps <= 1, \"Epsilon is a probability and must be between 0 and 1!\"\n\n\n return np.log( (1-eps) * (g + ((1 - g) / (1 + np.exp(-(theta - beta))))) # 1-epsilon times prob right \n + eps * (1 - (g + ((1 - g) / (1 + np.exp(-(theta - beta)))))) )\n #+ eps * (g + ((1 - g) / (1 + np.exp(theta - beta)))) ) # plus epsilon times prob wrong\n\ndef loglikfail(theta,beta,eps0,alpha,len_beta,num_correct,g=0.2): # 1-loglik\n \n \"\"\"\n takes in a theta and beta value and uses a sigmoid function to compute likelihood of answering incorrectly\n 1 - (1 / 1 + exp (-(theta-beta)))\n \"\"\"\n \n eps = eps0 - alpha * (num_correct/len_beta)\n \n assert eps >= 0, \"Epsilon is a probability and must be between 0 and 1!\"\n assert eps <= 1, \"Epsilon is a probability and must be between 0 and 1!\"\n \n return np.log( eps * (g + ((1 - g) / (1 + np.exp(-(theta - beta))))) # epsilon times prob right \n + (1-eps) * (1 - (g + ((1 - g) / (1 + np.exp(-(theta - beta)))))) )\n #+ eps * (g + ((1 - g) / (1 + np.exp(theta - beta)))) ) # plus 1-epsilon times prob wrong\n\ndef logprior(mu, sigma, x): # just gaussian distributions\n \n \"\"\"\n gaussian distribution\n \n \"\"\"\n \n return np.log((1/(np.sqrt(2*np.pi)*sigma))*np.exp(-(x-mu)**2/(2*sigma**2)))\n\n# determine number of passes in advance via the num_correct variable\ndef post_large_choice(theta, betas, mut, sigt, mub, sigb, eps0, alpha, len_beta, num_correct,g=0.2):\n\n '''\n inputs:\n theta: a value corresponding to a person's belief about their ability\n betas: a vector of values corresponding to the difficulty of each of a set of problems\n (could this come from performance in an inital pilot study?)\n mut, sigt: mean and standard deviation of theta\n mub, sigb: mean and standard deviation of betas\n num_correct: how many we deem the person got correct\n \n outputs:\n post: posterior\n \n '''\n assert len(betas) == len_beta, \"Beta needs to have len_beta arguments in it!\"\n \n assert num_correct <= len_beta , \"Can't have more correct responses than total responses!\"\n \n post = logprior(mut,sigt,theta) # start with prior only based on theta\n \n for beta in betas[:num_correct]:\n post += loglik(theta,beta,eps0,alpha,len_beta,num_correct,g) + logprior(mub,sigb,beta)\n \n for beta in betas[num_correct:]:\n post += loglikfail(theta,beta,eps0,alpha,len_beta,num_correct,g) + logprior(mub,sigb,beta)\n \n return post\n \n \n# acceptance probability with choice\ndef acc_prob_choice(theta_old, betas_old, theta_new, betas_new, mut, sigt, mub, sigb, eps0, alpha, len_beta, num_correct,g=0.2):\n alpha = min(1, np.exp(post_large_choice(theta_new, betas_new, mut, sigt, mub, sigb, eps0, alpha, len_beta, num_correct,g)\n - post_large_choice(theta_old, betas_old, mut, sigt, mub, sigb, eps0, alpha, len_beta, num_correct,g)))\n \n return alpha\n\n# MCMC chain function\ndef MCMC(theta, betas, mut, sigt, mub, sigb, nsteps, len_beta, eps0, alpha, num_correct,g=0.2): \n \n \"\"\"\n input:\n theta: initial theta (should be random)\n betas: initial beta (vector)\n mut,sigt,mub,sigb: means and standard deviations\n nsteps: number of iterations of the chain\n len_beta: how long beta should be (meaning how many problems were solved)\n num_correct: out of the total number correct, how many were solved correctly\n \n Samples theta and then beta on each step: \n in one iteration, calculate alpha, sample a theta, update alpha, then sample a beta\n\n output:\n sampled posterior thetas \n sample posterior vector of beta values\n number of accepts of both thetas and betas\n \"\"\"\n \n assert len(betas) == len_beta, \"Beta needs to have len_beta arguments in it!\"\n\n assert num_correct <= len_beta , \"Can't have more correct responses than total responses!\"\n \n naccept_theta = 0 # number of acceptances\n naccept_beta = 0 # number of acceptances\n\n #storage\n thetas_all = [] # fill with thetas\n betas_all = [] #np.empty((nsteps,len_beta)) # fill with vectors of betas\n\n for i in range(nsteps):\n\n u1 = np.random.uniform() # generate random uniform number u on [0,1] to compare to acceptance probability \n theta_new = theta + np.random.normal(mut,sigt) # generate new values from the proposal distribution\n alpha1 = acc_prob_choice(theta, betas, theta_new, betas, mut, sigt, mub, sigb, eps0, alpha, len_beta, num_correct,g)\n\n # first sample a theta\n if u1 <= alpha1: # accept\n theta = theta_new\n naccept_theta += 1 # keep track of how many are accepted \n\n else: # u > alpha (reject)\n pass\n \n u2 = np.random.uniform() # generate random uniform number u on [0,1] to compare to acceptance probability\n betas_new = betas + np.random.normal(mub,sigb,len_beta) #np.random.normal(mub,sigb,(1,len_beta)) #np.random.randn(len_beta,) \n alpha2 = acc_prob_choice(theta, betas, theta, betas_new, mut, sigt, mub, sigb, eps0, alpha, len_beta, num_correct,g)\n \n # then sample a beta\n if u2 <= alpha2: # accept\n betas = betas_new\n naccept_beta += 1 # keep track of how many are accepted\n\n else: # u > alpha (reject)\n pass\n \n thetas_all.append(theta)\n betas_all.append(betas)\n\n \n return {'thetas': thetas_all, \n 'betas': betas_all,\n 'naccept_theta': naccept_theta,\n 'naccept_beta': naccept_beta,\n }\n","sub_path":"DunningKruger/Model/WithGuessing/mcmcs_variable_eps_guess.py","file_name":"mcmcs_variable_eps_guess.py","file_ext":"py","file_size_in_byte":6201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"485302103","text":"import numpy as np\nfrom noise import pnoise2\n\nfrom .colors import colorscale\nfrom .graphs import AStarGraph, AStarSearch\n\n# https://engineeredjoy.com/blog/perlin-noise/\n# TODO: support passing options for shape, scale and octaves\ndef perlin_array(\n shape=(200, 200),\n scale=100,\n octaves=100,\n persistence=0.5,\n lacunarity=2.0,\n seed=None,\n):\n\n if not seed:\n\n seed = np.random.randint(0, 100)\n\n arr = np.zeros(shape)\n for i in range(shape[0]):\n for j in range(shape[1]):\n arr[i][j] = pnoise2(\n i / scale,\n j / scale,\n octaves=octaves,\n persistence=persistence,\n lacunarity=lacunarity,\n repeatx=1024,\n repeaty=1024,\n base=seed,\n )\n\n max_arr = np.max(arr)\n min_arr = np.min(arr)\n norm_me = lambda x: (x - min_arr) / (max_arr - min_arr)\n norm_me = np.vectorize(norm_me)\n arr = norm_me(arr)\n return arr\n\n\nDEEP_WATER = \"#00439e\"\nNAVY = \"#0077ea\"\nWATER = \"#0063ea\"\nFOREST = \"#004e00\"\nBEACH = \"#ffd500\"\nJUNGLE = \"#007600\"\nSAVANNAH = \"#008900\"\nDESERT = \"#009d00\"\nSNOW = \"#009d00\"\n\n\ndef biome(e):\n if e < 0.1:\n return DEEP_WATER\n elif e < 0.2:\n return NAVY\n elif e < 0.3:\n return WATER\n elif e < 0.5:\n return FOREST\n elif e < 0.55:\n return FOREST\n elif e < 0.6:\n return FOREST\n elif e < 0.7:\n return FOREST\n elif e < 0.8:\n return JUNGLE\n elif e < 0.85:\n return SAVANNAH\n elif e < 0.9:\n return DESERT\n else:\n return SNOW\n\n\ndef generate_grid_data(dimensions=None):\n noise_grid = perlin_array(scale=100)\n grid_data = []\n for j in range(dimensions[1]):\n row = []\n for i in range(dimensions[0]):\n scale = 1 + np.random.rand() / 4\n square = {\n \"emoji\": \"deciduous_tree\"\n if np.random.rand() < 0.4\n and noise_grid[i][j] >= 0.4\n else \"\",\n \"tone\": 1,\n \"color\": colorscale(\n biome(noise_grid[i][j]), scale\n ),\n \"position\": [j, i],\n }\n row.append(square)\n grid_data.append(row)\n\n # graph = AStarGraph(grid_data)\n # path, _ = AStarSearch((5, 5), (35, 35), graph)\n # for step in path:\n # scale = 1 + np.random.rand() / 4\n # grid_data[step[1]][step[0]][\"color\"] = colorscale(\n # \"#654321\", scale\n # )\n return grid_data\n\n","sub_path":"backend/apps/emojirama/utils/generation.py","file_name":"generation.py","file_ext":"py","file_size_in_byte":2565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"246803625","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nG = 6.67*10**(-11) # Gravitational Constant\nmEarth = 5.972*10**(24) # Mass of Earth\nrEarth = 6378100 # Radius of Earth\ndeltaT = 0.01*(rEarth**3/(G*mEarth))**0.5 # Timestep\n\n# Stores the position and velocity information of the body\nclass body:\n \n # Initializes the object\n def __init__(self, x, y, velX, velY, mass, time):\n self.x = x\n self.y = y\n self.velX = velX\n self.velY = velY\n self.mass = mass\n self.time = time\n self.r = (x**2 + y**2)**(0.5)\n self.theta = np.angle(complex(x, y))\n \n # Updates the position and velocity of the body after time 'delta'\n # 'fx' and 'fy' are the forces in the x and y directions\n # 'boostX' and 'boostY' are arrays which specifies the values of the boosts to be given\n # 'start' and 'end' arrays specify the start and end times for each boost\n def updateBody(self, fx, fy, delta, boostX, boostY, start, end):\n prevBody = self\n self.x = self.x + self.velX*delta + 0.5*fx(self, boostX, start, end)*(delta)**2/(self.mass)\n self.y = self.y + self.velY*delta + 0.5*fy(self, boostY, start, end)*(delta)**2/(self.mass)\n self.velX = self.velX + 0.5*(fx(self, boostX, start, end) + fx(prevBody, boostX, start, end))*delta/(self.mass)\n self.velY = self.velY + 0.5*(fy(self, boostY, start, end) + fy(prevBody, boostY, start, end))*delta/(self.mass)\n self.time = self.time + delta\n self.r = (self.x**2 + self.y**2)**(0.5)\n self.theta = np.angle(complex(self.x, self.y))\n\n# Returns the force in the x direction on the body 'sat' by all the other bodies\ndef fXSat(sat, boost, start, end):\n giveBoost = 0\n for i in range(len(boost)):\n if sat.time <= end[i] and sat.time >= start[i]:\n giveBoost = boost[i]\n break\n force = 0\n for otherBody in bodies:\n if otherBody != sat:\n force = force - G*otherBody.mass*sat.mass*(sat.x - otherBody.x)/((sat.x - otherBody.x)**2 + (sat.y - otherBody.y)**2)**(3.0/2)\n return force + giveBoost\n\n# Returns the force in the y direction on the body 'sat' by all the other bodies\ndef fYSat(sat, boost, start, end):\n giveBoost = 0\n for i in range(len(boost)):\n if sat.time <= end[i] and sat.time >= start[i]:\n giveBoost = boost[i]\n break\n force = 0\n for otherBody in bodies:\n if otherBody != sat:\n force = force - G*otherBody.mass*sat.mass*(sat.y - otherBody.y)/((sat.x - otherBody.x)**2 + (sat.y - otherBody.y)**2)**(3.0/2)\n return force + giveBoost\n\n# Returns the cosine of the angle the velocity of 'sat' makes with the x-axis\ndef velXAng(sat):\n return sat.velX/(sat.velX**2 + sat.velY**2)**0.5\n\n# Returns the sine of the angle the velocity of 'sat' makes with the x-axis\ndef velYAng(sat):\n return sat.velY/(sat.velX**2 + sat.velY**2)**0.5\n\n# Defines the initial orbit of the satellite\nsatPerigee = 6714700.0\nsatApogee = 74715000.0\nsatSemiMajor = (satPerigee + satApogee)/2\nsatPerigeeVel = (G*mEarth*((2/satPerigee) - (1/satSemiMajor)))**0.5\n\n# Defines the initial position of the Moon\nMoonPos = 385000000.0\nMoonVel = 1000.0\nmMoon = 7.342*10**(22)\ntheta0 = -0.803589979409645\n\n# Defines the initial velocity of the Earth\nEarthVel = mMoon*MoonVel/mEarth\n\n# Creates the bodies 'sat', 'Moon', and 'Earth'\nsat = body(satPerigee, 0.0, 0.0, satPerigeeVel + EarthVel, 1000, 0.0)\nMoon = body(MoonPos*np.cos(theta0), MoonPos*np.sin(theta0), -MoonVel*np.sin(theta0), MoonVel*np.cos(theta0), mMoon, 0.0)\nEarth = body(0.0, 0.0, EarthVel*np.sin(theta0), -EarthVel*np.cos(theta0), 5.972*10**(24), 0.0)\n\n# Creates an array which stores all the bodies\nbodies = [sat, Earth, Moon]\n\n# Creates arrays which store the state of the satellite, Moon, and Earth\nlength = 300000\nx1 = np.zeros(length)\ny1 = np.zeros(length)\nt1 = np.zeros(length)\nx2 = np.zeros(length)\ny2 = np.zeros(length)\nt2 = np.zeros(length)\nx3 = np.zeros(length)\ny3 = np.zeros(length)\nt3 = np.zeros(length)\n\n# Defines the values of the boosts in Newtons\nboostVal1 = 50\nboostVal2 = 50\nboostVal3 = 50\nboostVal4 = 500\nboostVal5 = -50\nboostVal6 = -50\n\n# Defines the times at which the boosts are provided in seconds\nboostStartTime1 = 80957.7444938\nboostStartTime2 = 385483.47625\nboostStartTime3 = 1050594.40474\nboostStartTime4 = 1412382.1439953118\nboostStartTime5 = 1540707.1502211716\nboostStartTime6 = 1595588.284959275\n\n# Defines the duration of the boosts in seconds\nboostTime1 = 1200*deltaT\nboostTime2 = 330*deltaT\nboostTime3 = 115*deltaT\nboostTime4 = 350*deltaT\nboostTime5 = 100*deltaT\nboostTime6 = 200*deltaT\n\n# Iterate over time\nfor i in range(length):\n x1[i] = sat.x\n y1[i] = sat.y\n t1[i] = sat.time\n \n x2[i] = Moon.x\n y2[i] = Moon.y\n t2[i] = Moon.time\n \n x3[i] = Earth.x\n y3[i] = Earth.y\n t3[i] = Earth.time\n \n sat.updateBody(fXSat, fYSat, deltaT, [boostVal1*velXAng(sat), boostVal2*velXAng(sat), boostVal3*velXAng(sat), 0, boostVal5*velXAng(sat), boostVal6*velXAng(sat)], [boostVal1*velYAng(sat), boostVal2*velYAng(sat), boostVal3*velYAng(sat), -boostVal4, boostVal5*velYAng(sat), boostVal6*velYAng(sat)], [boostStartTime1 - boostTime1/2.0, boostStartTime2 - boostTime2/2.0, boostStartTime3 - boostTime3/2.0, boostStartTime4, boostStartTime5 - boostTime5/2.0, boostStartTime6 - boostTime6/2.0], [boostStartTime1 + boostTime1/2.0, boostStartTime2 + boostTime2/2.0, boostStartTime3 + boostTime3/2.0, boostStartTime4 + boostTime4, boostStartTime5 + boostTime5/2.0, boostStartTime6 + boostTime6/2.0])\n Moon.updateBody(fXSat, fYSat, deltaT, [0], [0], [0], [0])\n Earth.updateBody(fXSat, fYSat, deltaT, [0], [0], [0], [0])\n\n# Plot the trajectories of all the bodies\n# For making the video, we exported the arrays to MATLAB.\nplt.plot(x1, y1, x2, y2, x3, y3)\n","sub_path":"ChandrayaanMissionCODE.py","file_name":"ChandrayaanMissionCODE.py","file_ext":"py","file_size_in_byte":5821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"179220962","text":"# This file is part of COFFEE\n#\n# COFFEE is Copyright (c) 2014, Imperial College London.\n# Please see the AUTHORS file in the main source directory for\n# a full list of copyright holders. 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\n# are met:\n#\n# * Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n# * Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in the\n# documentation and/or other materials provided with the distribution.\n# * The name of Imperial College London or that of other\n# contributors may not be used to endorse or promote products\n# derived from this software without specific prior written\n# permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTERS\n# ''AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS\n# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE\n# COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,\n# INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)\n# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,\n# STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED\n# OF THE POSSIBILITY OF SUCH DAMAGE.\n\nfrom math import ceil\nfrom copy import deepcopy as dcopy\nfrom collections import OrderedDict\nfrom itertools import product\n\nfrom base import *\nfrom utils import *\nimport plan\nfrom coffee.visitors import FindInstances\n\n\nclass VectStrategy():\n\n \"\"\"Supported vectorization modes.\"\"\"\n\n \"\"\"Generate scalar code suitable to compiler auto-vectorization\"\"\"\n AUTO = 1\n\n \"\"\"Specialized (intrinsics-based) vectorization using padding\"\"\"\n SPEC_PADD = 2\n\n \"\"\"Specialized (intrinsics-based) vectorization using peel loop\"\"\"\n SPEC_PEEL = 3\n\n \"\"\"Specialized (intrinsics-based) vectorization composed with unroll-and-jam\n of outer loops, padding (to enforce data alignment), and peeling of padded\n iterations\"\"\"\n SPEC_UAJ_PADD = 4\n\n \"\"\"Specialized (intrinsics-based) vectorization composed with unroll-and-jam\n of outer loops and padding (to enforce data alignment)\"\"\"\n SPEC_UAJ_PADD_FULL = 5\n\n\nclass LoopVectorizer(object):\n\n def __init__(self, loop_opt):\n self.header = loop_opt.header\n self.decls = loop_opt.decls\n self.exprs = loop_opt.exprs\n self.expr_graph = loop_opt.expr_graph\n self.nz_syms = loop_opt.nz_syms\n\n def pad_and_align(self):\n \"\"\"Padding consists of three major steps:\n\n * Pad the innermost dimension of all n-dimensional arrays to the nearest\n multiple of the vector length.\n * Round up, to the nearest multiple of the vector length, bounds of all\n innermost loops in which padded arrays are accessed.\n * Since padding may induce data alignment of multi-dimensional arrays\n (in practice, this depends on the presence of offsets as well), add\n suitable '#pragma' to innermost loops to tell the backend compiler\n about this property.\n\n Padding works as follows. Assume a vector length of size 4, and consider\n the following piece of code: ::\n\n void foo(int A[10][10]):\n int B[10] = ...\n for i = 0 to 10:\n for j = 0 to 10:\n A[i][j] = B[i][j]\n\n Once padding is applied, the code will look like: ::\n\n void foo(int A[10][10]):\n int _A[10][12] = {{0.0}};\n int B[10][12] = ...\n for i = 0 to 10:\n for j = 0 to 12:\n _A[i][j] = B[i][j]\n\n for i = 0 to 10:\n for j = 0 to 10:\n A[i][j] = _A[i][j]\n\n Extra care is taken if offsets (e.g. A[i+3][j+3] ...) are used. In such\n a case, the buffer array '_A' in the example above can be vector-expanded: ::\n\n int _A[x][10][12];\n ...\n\n Where 'x' corresponds to the number of different offsets used in a given\n iteration space along the innermost dimension.\n\n Finally, all arrays are decorated with suitable attributes to enforce\n alignment to (the size in bytes of) the vector length.\n \"\"\"\n # Aliases\n info = visit(self.header, info_items=['symbols_dep', 'symbols_mode',\n 'symbol_refs', 'fors'])\n symbols_dep = info['symbols_dep']\n symbols_mode = info['symbols_mode']\n symbol_refs = info['symbol_refs']\n retval = FindInstances.default_retval()\n to_invert = FindInstances(Invert).visit(self.header, ret=retval)[Invert]\n # Vectorization aliases\n vector_length = plan.isa[\"dp_reg\"]\n align = plan.compiler['align'](plan.isa['alignment'])\n\n # 0) Under some circumstances, do not pad\n # A- Loop increments must be equal to 1, because at the moment the\n # machinery for ensuring the correctness of the transformation for\n # non-uniform and non-unitary increments is missing\n if any([l.increment != 1 for l, _ in flatten(info['fors'])]):\n return\n\n # Padding occurs along the innermost dimension\n p_dim = -1\n\n # 1) Pad arrays by extending the innermost dimension\n buffer = None\n for decl_name, decl in self.decls.items():\n if not decl.sym.rank:\n continue\n p_rank = decl.sym.rank[:p_dim] + (vect_roundup(decl.sym.rank[p_dim]),)\n if decl.scope == LOCAL:\n if p_rank != decl.sym.rank:\n # Padding\n decl.pad(p_rank)\n # Alignment\n decl.attr.append(align)\n continue\n # Examined symbol is a FunDecl argument, so a buffer might be required\n access_modes, dataspaces = [], []\n p_info = OrderedDict()\n # A- Analyze occurrences of the FunDecl argument in the AST\n for s, _ in symbol_refs[decl_name]:\n if s is decl.sym or not s.rank:\n continue\n # ... the access mode (READ, WRITE, ...)\n access_modes.append(symbols_mode[s])\n # ... the offset along the innermost dimension\n p_offset = s.offset[p_dim][1]\n # ... and the iteration and the data spaces\n loops = tuple(l for l in symbols_dep[s] if l.dim in s.rank)\n itspace = tuple((l.start, l.end) for l in loops)\n dataspace = [None for r in s.rank]\n for l in loops:\n # Assume, initially, the dataspace spans the whole dimension,\n # then try to limit it based on available information\n index = s.rank.index(l.dim)\n l_dataspace = (0, decl.sym.rank[index])\n offset = s.offset[index][1]\n if not isinstance(offset, str):\n l_dataspace = (l.start + offset, l.end + offset)\n dataspace[index] = l_dataspace\n dataspaces.append(tuple(dataspace))\n p_info.setdefault((itspace, p_offset), (loops, []))[1].append(s)\n # B- Check dataspace overlap. Dataspaces ...\n # ... should either completely overlap (will be mapped to the same buffer)\n # ... or be disjoint\n will_break = False\n for ds1, ds2 in product(dataspaces, dataspaces):\n for d1, d2 in zip(ds1, ds2):\n if ItSpace(mode=0).intersect([d1, d2]) not in [(0, 0), d1]:\n will_break = True\n if will_break:\n continue\n # C- Create a padded temporary buffer for efficient vectorization\n buf_name, buf_rank = '_%s' % decl_name, 0\n itspace_mapper = OrderedDict()\n for (itspace, p_offset), (loops, syms) in p_info.items():\n if isinstance(p_offset, str):\n # Dangerous to pad since can't resolve the offset value\n continue\n if not (p_rank != decl.sym.rank or vect_roundup(p_offset) > p_offset):\n # Useless to pad in this case\n continue\n mapped = set()\n for s in syms:\n original_s = dcopy(s)\n s.symbol = buf_name\n s.rank = (buf_rank,) + s.rank\n s.offset = ((1, 0),) + s.offset[:p_dim] + ((1, 0),)\n if s.offset not in mapped:\n # Map buffer symbol to each FunDecl symbol occurrence\n # avoiding duplicate, useless copies\n mapping = (original_s, dcopy(s))\n itspace_mapper.setdefault(itspace, (loops, []))[1].append(mapping)\n mapped.add(s.offset)\n # Track the non zero-valued regions\n nz = ((1, buf_rank),) + tuple((j-i, i+p_offset) for i, j in itspace)\n self.nz_syms.setdefault(buf_name, []).append(nz)\n # Prepare for the next buffer dimension\n buf_rank += 1\n if buf_rank == 0:\n continue\n buf_pad = (buf_rank,) + p_rank\n buf_rank = (buf_rank,) + decl.sym.rank\n init = ArrayInit(np.ndarray(shape=(1,)*len(buf_rank), buffer=np.array(0.0)))\n buffer = Decl(decl.typ, Symbol(buf_name, buf_rank), init, attributes=[align])\n buffer.scope = BUFFER\n buffer.pad(buf_pad)\n self.header.children.insert(0, buffer)\n # D- Create and append a loop nest(s) for copying data into/from\n # the temporary buffer. Depending on how the symbol is accessed\n # (read only, read and write, incremented, etc.), different sort\n # of copies are made\n first, last = access_modes[0], access_modes[-1]\n for itspace, (loops, mapper) in itspace_mapper.items():\n if first[0] == READ:\n stmts = [Assign(b, s) for s, b in mapper]\n copy_back = ItSpace(mode=2).to_for(loops, stmts=stmts)\n self.header.children.insert(0, copy_back[0])\n if last[0] == WRITE:\n # If extra information (a pragma) is present, telling that\n # the argument does not need to be incremented because it does\n # not contain any meaningful values, then we can safely write\n # to it. This is an optimization to avoid increments when not\n # necessarily required\n could_incr = WRITE in decl.pragma and len(itspace_mapper) == 1\n op = Assign if could_incr else last[1]\n stmts = [op(s, b) for s, b in mapper]\n copy_back = ItSpace(mode=2).to_for(loops, stmts=stmts)\n if to_invert:\n insert_at_elem(self.header.children, to_invert[0], copy_back[0])\n else:\n self.header.children.append(copy_back[0])\n # E) Update the global data structures\n self.decls[buf_name] = buffer\n\n # 2) Round up the bounds (i.e. /start/ and /end/ points) of innermost\n # loops such that memory accesses get aligned to the vector length\n for l in inner_loops(self.header):\n should_round, should_vectorize = True, True\n for stmt in l.body:\n sym, expr = stmt.children\n sym_decl = self.decls.get(sym.symbol)\n # Condition A: all lvalues must have the innermost loop as fastest\n # varying dimension\n if not (sym.rank and sym.rank[p_dim] == l.dim):\n should_round = False\n should_vectorize = False\n break\n # Condition B: all lvalues must be paddable; that is, they cannot be\n # kernel parameters\n if sym_decl and sym_decl.scope == EXTERNAL:\n should_round = False\n break\n # Condition C: statements using offsets to write buffers should\n # not be aligned\n if sym_decl and sym_decl.scope == BUFFER and sym.offset[p_dim][1] > 0:\n should_round = False\n break\n # Condition D: extra iterations induced by bounds and offset rounding\n # should /not/ alter the result.\n lvalues, aligned_l = {}, dcopy(l)\n for stmt in aligned_l.body:\n sym, expr = stmt.children\n lvalues[sym] = (0, 0, False)\n symbols = [sym] + FindInstances(Symbol).visit(expr)[Symbol]\n symbols = [s for s in symbols if any(r == l.dim for r in s.rank)]\n for s in symbols:\n # First of all, we need to be sure we can inspect the symbol\n # declaration\n decl = self.decls.get(s.symbol)\n if not decl:\n should_round = False\n break\n # We can skip loop constants\n if not decl.sym.rank:\n continue\n # Now we check if lowering the start point would be unsafe\n # because it would result in /not/ executing iterations\n # that should be executed\n offset = s.offset[p_dim][1]\n if isinstance(offset, str):\n # Unknown offset, cannot infer anything so must break\n should_round = False\n break\n start = vect_rounddown(offset)\n end = start + vect_roundup(l.end)\n if end < offset + l.end:\n should_round = False\n break\n # It remains to check if the extra iterations would alter the\n # result because they would access non zero-valued entries\n extra = range(start, offset) + range(offset + l.end + 1, end + 1)\n for i in extra:\n if i >= decl.core[p_dim]:\n # In the padded region, safe\n continue\n # If /s/ is the buffer, need to access the actual dimension\n # written by /stmt/\n nz = self.nz_syms.get(s.symbol, [tuple((r, 0) for r in decl.core)])\n if buffer and s.symbol == buffer.sym.symbol:\n for j in list(nz):\n buf_dim = j[0]\n if s.rank[0] not in range(buf_dim[1], buf_dim[0]+buf_dim[1]):\n nz.remove(j)\n # Now we can finally check if the i-th extra iteration falls in a\n # zero-valued region (in case, we are happy and round), or not\n if any(i in range(k, j + k) for j, k in [j[p_dim] for j in nz]):\n should_round = False\n # Round down the start point\n ast_update_ofs(s, {l.dim: start})\n # Track the rounding in the lvalue and infer if the zero-valued\n # region in /sym/ has now become a /non/ zero-valued region.\n # Note: /sym/ is the first element in the /symbols/ list\n if s is sym:\n # Assume there's no need to remove the non zero-valued region ...\n lvalues[s] = (start, offset, False)\n else:\n # ... and then, maybe, change it as the rvalue is examined: ...\n if start == offset and lvalues[sym][0] < lvalues[sym][1] or \\\n any(v[2] for k, v in lvalues.items() if str(k) == str(s)):\n # ... this is the case if at least one symbol in the rvalue\n # was not rounded while the lvalue was, /or/\n # if at least one symbol in the rvalue appeared as an lvalue\n # in a previoud /stmt/ and such lvalue was rounded down\n lvalues[sym] = lvalues[sym][:-1] + (True,)\n if should_round:\n l.body = aligned_l.body\n # Round up the end point\n l.end = vect_roundup(l.end)\n # It was safe to round an lvalue S, but now all subsequent\n # accesses to the same symbol S /might/ have to be rounded too.\n # This is the case /iff/:\n # 1) in rounding, we are writing to the rounded region (e.g.,\n # A[i+2] += B[i] ===> A[i] += B[i]: since B[i] wasn't rounded,\n # it means that B[i] is != 0, so we are now writing to A[i],\n # which was previously 0, instead of A[i+2]), /and/\n # 2) the offset used by S', which is a later reference to S, falls\n # in the rounded region (e.g., starting from the example above,\n # if there's another line D[i] = A[i+2], then it must be D[i] = A[i],\n # otherwise if it were D[i] = A[i+5] we should not round). Note\n # how such an S' /cannot/ be an lvalue, since we have already\n # enforced that rounding only happens over zero-valued regions.\n # The first property is captured by /remove_nz/\n for lvalue, (start, orig_ofs, remove_nz) in lvalues.items():\n if not remove_nz:\n # Property 1) above does not hold\n continue\n references = SymbolReferences().visit(self.header)[lvalue.symbol]\n references = [r for r, p in references]\n for r in references[references.index(lvalue)+1:]:\n r_rank, r_ofs = r.rank[p_dim], r.offset[p_dim][1]\n if r_ofs in range(orig_ofs, orig_ofs + l.end):\n ast_update_ofs(r, {r_rank: start - r_ofs}, increase=True)\n # The corresponding /nz_syms/ info should also be updated\n nz_lvalue = zip(*self.nz_syms.get(r.symbol, [((0, 0),)]))\n for i, (size, offset) in enumerate(nz_lvalue[p_dim]):\n if orig_ofs in range(offset, offset + size):\n self.nz_syms[r.symbol][i] = \\\n self.nz_syms[r.symbol][i][:p_dim] + \\\n ((size, offset-(orig_ofs-start)),)\n if l.start % vector_length == 0 and l.size % vector_length == 0:\n l.pragma.add(plan.compiler[\"align_forloop\"])\n # Enforce vectorization if loop size is a multiple of the vector length\n if should_vectorize and l.size % vector_length == 0:\n l.pragma.add(plan.compiler['force_simdization'])\n\n def specialize(self, opts, factor=1):\n \"\"\"Generate code for specialized expression vectorization. Check for peculiar\n memory access patterns in an expression and replace scalar code with highly\n optimized vector code. Currently, the following patterns are supported:\n\n * Outer products - e.g. A[i]*B[j]\n\n Also, code generation is supported for the following instruction sets:\n\n * AVX\n\n The parameter ``opts`` can be used to drive the transformation process by\n specifying one of the vectorization strategies in :class:`VectStrategy`.\n \"\"\"\n layout = None\n for stmt, expr_info in self.exprs.items():\n if expr_info.dimension != 2:\n continue\n parent = expr_info.parent\n domain_loops = expr_info.domain_loops\n domain_loops_parents = expr_info.domain_loops_parents\n\n # Check if outer-product vectorization is actually doable\n vect_len = plan.isa[\"dp_reg\"]\n rows = domain_loops[0].size\n if rows < vect_len:\n continue\n\n op = OuterProduct(stmt, domain_loops, 'STORE')\n\n # Vectorisation\n vs = VectStrategy\n unroll_factor = factor if opts in [vs.SPEC_UAJ_PADD, vs.SPEC_UAJ_PADD_FULL] else 1\n rows_per_it = vect_len*unroll_factor\n if opts == vs.SPEC_UAJ_PADD:\n if rows_per_it <= rows:\n body, layout = op.generate(rows_per_it)\n else:\n # Unroll factor too big\n body, layout = op.generate(vect_len)\n elif opts == SPEC_UAJ_PADD_FULL:\n if rows <= rows_per_it or vect_roundup(rows) % rows_per_it > 0:\n # Cannot unroll too much\n body, layout = op.generate(vect_len)\n else:\n body, layout = op.generate(rows_per_it)\n elif opts in [vs.SPEC_PADD, vs.SPEC_PEEL]:\n body, layout = op.generate(vect_len)\n else:\n raise RuntimeError(\"Don't know how to vectorize option %s\" % opts)\n\n # Construct the remainder loop\n if opts != vs.SPEC_UAJ_PADD_FULL and rows > rows_per_it and rows % rows_per_it > 0:\n # Adjust bounds and increments of the main, layout and remainder loops\n domain_outerloop = domain_loops[0]\n peel_loop = dcopy(domain_loops)\n bound = domain_outerloop.end\n bound -= bound % rows_per_it\n domain_outerloop.end, layout.end = bound, bound\n peel_loop[0].init.init = Symbol(bound)\n peel_loop[0].increment, peel_loop[1].increment = 1, 1\n # Append peeling loop after the main loop\n domain_outerparent = domain_loops_parents[0].children\n insert_at_elem(domain_outerparent, domain_outerloop, peel_loop[0], 1)\n\n # Replace scalar with vector code\n ofs = parent.children.index(stmt)\n parent.children[ofs:ofs] = body\n parent.children.remove(stmt)\n\n # Insert the layout code right after the loop nest enclosing the expression\n if layout:\n insert_at_elem(self.header.children, expr_info.loops[0], layout, 1)\n\n\nclass OuterProduct():\n\n \"\"\"Generate an intrinsics-based outer product vectorisation of a statement.\"\"\"\n\n def __init__(self, stmt, loops, mode):\n self.stmt = stmt\n self.loops = loops\n self.mode = mode\n\n class Alloc(object):\n\n \"\"\"Handle allocation of register variables. \"\"\"\n\n def __init__(self, tensor_size):\n nres = max(plan.isa[\"dp_reg\"], tensor_size)\n self.ntot = plan.isa[\"avail_reg\"]\n self.res = [plan.isa[\"reg\"](v) for v in range(nres)]\n self.var = [plan.isa[\"reg\"](v) for v in range(nres, self.ntot)]\n self.i = plan.isa\n\n def get_reg(self):\n if len(self.var) == 0:\n l = self.ntot * 2\n self.var += [self.i[\"reg\"](v) for v in range(self.ntot, l)]\n self.ntot = l\n return self.var.pop(0)\n\n def free_regs(self, regs):\n for r in reversed(regs):\n self.var.insert(0, r)\n\n def get_tensor(self):\n return self.res\n\n def _swap_reg(self, step, vrs):\n \"\"\"Swap values in a vector register. \"\"\"\n\n # Find inner variables\n regs = [reg for node, reg in vrs.items()\n if node.rank and node.rank[-1] == self.loops[1].dim]\n\n if step in [0, 2]:\n return [Assign(r, plan.isa[\"l_perm\"](r, \"5\")) for r in regs]\n elif step == 1:\n return [Assign(r, plan.isa[\"g_perm\"](r, r, \"1\")) for r in regs]\n elif step == 3:\n return []\n\n def _vect_mem(self, vrs, decls):\n \"\"\"Return a list of vector variable declarations representing\n loads, sets, broadcasts.\n\n :arg vrs: dictionary that associates scalar variables to vector.\n variables, for which it will be generated a corresponding\n intrinsics load/set/broadcast.\n :arg decls: list of scalar variables for which an intrinsics load/\n set/broadcast has already been generated, possibly updated\n by this method.\n \"\"\"\n stmt = []\n for node, reg in vrs.items():\n if node.rank and node.rank[-1] in [l.dim for l in self.loops]:\n exp = plan.isa[\"symbol_load\"](node.symbol, node.rank, node.offset)\n else:\n exp = plan.isa[\"symbol_set\"](node.symbol, node.rank, node.offset)\n if not decls.get(node.gencode()):\n decls[node.gencode()] = reg\n stmt.append(Decl(plan.isa[\"decl_var\"], reg, exp))\n return stmt\n\n def _vect_expr(self, node, ofs, regs, decls, vrs):\n \"\"\"Turn a scalar expression into its intrinsics equivalent.\n\n :arg node: AST expression to be vectorized.\n :arg ofs: contains the offset of the entry in the left hand side that\n is being vectorized.\n :arg regs: register allocator.\n :arg decls: list of scalar variables for which an intrinsics load/\n set/broadcast has already been generated.\n :arg vrs: dictionary that associates scalar variables to vector variables.\n Updated every time a new scalar variable is encountered.\n \"\"\"\n if isinstance(node, Symbol):\n if node.rank and self.loops[0].dim == node.rank[-1]:\n # The symbol depends on the outer loop dimension, so add offset\n n_ofs = tuple([(1, 0) for i in range(len(node.rank)-1)]) + ((1, ofs),)\n node = Symbol(node.symbol, dcopy(node.rank), n_ofs)\n node_ide = node.gencode()\n if node_ide not in decls:\n reg = [k for k in vrs.keys() if k.gencode() == node_ide]\n if not reg:\n vrs[node] = Symbol(regs.get_reg())\n return vrs[node]\n else:\n return vrs[reg[0]]\n else:\n return decls[node_ide]\n elif isinstance(node, Par):\n return self._vect_expr(node.child, ofs, regs, decls, vrs)\n else:\n left = self._vect_expr(node.left, ofs, regs, decls, vrs)\n right = self._vect_expr(node.right, ofs, regs, decls, vrs)\n if isinstance(node, Sum):\n return plan.isa[\"add\"](left, right)\n elif isinstance(node, Sub):\n return plan.isa[\"sub\"](left, right)\n elif isinstance(node, Prod):\n return plan.isa[\"mul\"](left, right)\n elif isinstance(node, Div):\n return plan.isa[\"div\"](left, right)\n\n def _incr_tensor(self, tensor, ofs, regs, out_reg):\n \"\"\"Add the right hand side contained in out_reg to tensor.\n\n :arg tensor: the left hand side of the expression being vectorized.\n :arg ofs: contains the offset of the entry in the left hand side that\n is being computed.\n :arg regs: register allocator.\n :arg out_reg: register variable containing the left hand side.\n \"\"\"\n if self.mode == 'STORE':\n # Store in memory\n sym = tensor.symbol\n rank = tensor.rank\n ofs = tensor.offset[:-2] + ((1, ofs),) + tensor.offset[-1:]\n load = plan.isa[\"symbol_load\"](sym, rank, ofs)\n return plan.isa[\"store\"](Symbol(sym, rank, ofs),\n plan.isa[\"add\"](load, out_reg))\n elif self.mode == 'MOVE':\n # Accumulate on a vector register\n reg = Symbol(regs.get_tensor()[ofs], ())\n return Assign(reg, plan.isa[\"add\"](reg, out_reg))\n\n def _restore_layout(self, regs, tensor):\n \"\"\"Restore the storage layout of the tensor.\n\n :arg regs: register allocator.\n :arg tensor: the left hand side of the expression being vectorized.\n \"\"\"\n code = []\n t_regs = [Symbol(r, ()) for r in regs.get_tensor()]\n n_regs = len(t_regs)\n\n # Create tensor symbols\n tensor_syms = []\n for i in range(n_regs):\n ofs = tensor.offset[:-2] + ((1, i),) + tensor.offset[-1:]\n tensor_syms.append(Symbol(tensor.symbol, tensor.rank, ofs))\n\n # Load LHS values from memory\n if self.mode == 'STORE':\n for i, j in zip(tensor_syms, t_regs):\n load_sym = plan.isa[\"symbol_load\"](i.symbol, i.rank, i.offset)\n code.append(Decl(plan.isa[\"decl_var\"], j, load_sym))\n\n # In-register restoration of the tensor layout\n perm = plan.isa[\"g_perm\"]\n uphi = plan.isa[\"unpck_hi\"]\n uplo = plan.isa[\"unpck_lo\"]\n typ = plan.isa[\"decl_var\"]\n vect_len = plan.isa[\"dp_reg\"]\n # Do as many times as the unroll factor\n spins = int(ceil(n_regs / float(vect_len)))\n for i in range(spins):\n # In-register permutations\n tmp = [Symbol(regs.get_reg(), ()) for r in range(vect_len)]\n code.append(Decl(typ, tmp[0], uphi(t_regs[1], t_regs[0])))\n code.append(Decl(typ, tmp[1], uplo(t_regs[0], t_regs[1])))\n code.append(Decl(typ, tmp[2], uphi(t_regs[2], t_regs[3])))\n code.append(Decl(typ, tmp[3], uplo(t_regs[3], t_regs[2])))\n code.append(Assign(t_regs[0], perm(tmp[1], tmp[3], 32)))\n code.append(Assign(t_regs[1], perm(tmp[0], tmp[2], 32)))\n code.append(Assign(t_regs[2], perm(tmp[3], tmp[1], 49)))\n code.append(Assign(t_regs[3], perm(tmp[2], tmp[0], 49)))\n regs.free_regs([s.symbol for s in tmp])\n\n # Store LHS values in memory\n for j in range(min(vect_len, n_regs - i * vect_len)):\n ofs = i * vect_len + j\n code.append(plan.isa[\"store\"](tensor_syms[ofs], t_regs[ofs]))\n\n return code\n\n def generate(self, rows):\n \"\"\"Generate the outer-product intrinsics-based vectorisation code.\n\n By default, the tensor computed by the outer product vectorization is\n kept in memory, so the layout is restored by means of explicit load and\n store instructions. The resulting code will therefore look like: ::\n\n for ...\n for j\n for k\n for ...\n A[j][k] = ...intrinsics-based outer product along ``j-k``...\n for j\n for k\n A[j][k] = ...intrinsics-based code for layout restoration...\n\n The other possibility would be to keep the computed values in temporaries\n after a suitable permutation of the loops in the nest; this variant can be\n activated by passing ``mode='MOVE'``, but it is not recommended unless\n loops are very small *and* a suitable permutation of the nest has been\n chosen to minimize register spilling.\n \"\"\"\n cols = plan.isa[\"dp_reg\"]\n tensor, expr = self.stmt.children\n tensor_size = cols\n\n # Get source-level variables\n regs = self.Alloc(tensor_size)\n\n # Adjust loops' increment\n self.loops[0].incr.children[1] = Symbol(rows)\n self.loops[1].incr.children[1] = Symbol(cols)\n\n stmts, decls, vrs = [], {}, {}\n rows_per_col = rows / cols\n rows_to_peel = rows % cols\n peeling = 0\n for i in range(cols):\n # Handle extra rows\n if peeling < rows_to_peel:\n nrows = rows_per_col + 1\n peeling += 1\n else:\n nrows = rows_per_col\n for j in range(nrows):\n # Vectorize, declare allocated variables, increment tensor\n ofs = j * cols\n v_expr = self._vect_expr(expr, ofs, regs, decls, vrs)\n stmts.extend(self._vect_mem(vrs, decls))\n incr = self._incr_tensor(tensor, i + ofs, regs, v_expr)\n stmts.append(incr)\n # Register shuffles\n if rows_per_col + (rows_to_peel - peeling) > 0:\n stmts.extend(self._swap_reg(i, vrs))\n\n # Set initialising and tensor layout code\n layout = self._restore_layout(regs, tensor)\n if self.mode == 'STORE':\n # Tensor layout\n layout_loops = dcopy(self.loops)\n layout_loops[0].incr.children[1] = Symbol(cols)\n layout_loops[0].children = [Block([layout_loops[1]], open_scope=True)]\n layout_loops[1].children = [Block(layout, open_scope=True)]\n layout = layout_loops[0]\n elif self.mode == 'MOVE':\n # Initialiser\n for r in regs.get_tensor():\n decl = Decl(plan.isa[\"decl_var\"], Symbol(r, ()), plan.isa[\"setzero\"])\n self.loops[1].body.insert(0, decl)\n # Tensor layout\n self.loops[1].body.extend(layout)\n layout = None\n\n return (stmts, layout)\n\n\n# Utility functions\n\ndef vect_roundup(x):\n \"\"\"Return x rounded up to the vector length. \"\"\"\n word_len = plan.isa.get(\"dp_reg\") or 1\n return int(ceil(x / float(word_len))) * word_len\n\n\ndef vect_rounddown(x):\n \"\"\"Return x rounded down to the vector length. \"\"\"\n word_len = plan.isa.get(\"dp_reg\") or 1\n return x - (x % word_len)\n","sub_path":"coffee/vectorizer.py","file_name":"vectorizer.py","file_ext":"py","file_size_in_byte":34144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"582458477","text":"# !/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport tornado\nimport time\nfrom base import ParaBaseHandler, BaseHandler\nfrom ..modules import ParaFeedNews\nfrom ..common import upload_img\nfrom sqlalchemy import func\n\nclass NewsHandler(ParaBaseHandler):\n\t@tornado.web.authenticated\n\tdef get(self, page=1):\n\t\tself.check_user()\n\t\tif page is None or page == '':\n\t\t\tpage = 1\n\t\telse:\n\t\t\tpage = int(page)\n\t\tstartIndex = (page-1)*15\n\t\tendIndex = 15\n\t\tnum = self.db.query(func.count(ParaFeedNews.id)).filter(ParaFeedNews.status !='0').scalar()\n\t\tcount = num / 15\n\t\tif num%15 != 0:\n\t\t\tcount += 1\n\n\t\tsql = \"select id, title, icon_url, status, source, pp, atime, type, count \"\\\n\t\t\t\"from para_feed_news where status!='0' order by atime desc limit %d, %d\" %(startIndex, endIndex)\n\t\tlist = self.db.execute(sql).fetchall()\n\t\tself.db.flush()\n\t\tself.render('admin-news-main.html', list=list, page=page, count=count)\n\n\tdef post(self, page=None):\n\t\tstatus = self.get_argument('status')\n\t\tid = self.get_argument('id')\n\t\tcs = self.db.query(ParaFeedNews).filter_by(id=id).first()\n\t\td = {}\n\t\tif cs:\n\t\t\tcs.status = status\n\t\t\tcs.time = time.strftime(\"%Y%m%d%H%M%S\",\n\t\t\t\t\t\t\t time.localtime(time.time()))\n\t\t\tself.db.merge(cs)\n\t\t\tself.db.flush()\n\t\t\tself.db.commit()\n\t\t\td = {\"id\":id, \"status\":status}\n\t\tself.write(d)\n\nclass NewsAddHandler(ParaBaseHandler):\n\t@tornado.web.authenticated\n\tdef get(self, *args, **kwargs):\n\t\tself.check_user()\n\t\tself.render('admin-news-add.html')\n\t@tornado.web.authenticated\n\tdef post(self, *args, **kwargs):\n\t\tself.check_user()\n\t\tnews = ParaFeedNews()\n\t\tnews.type = self.get_argument('type')\n\t\tnews.title = self.get_argument('title')\n\t\tnews.source = self.get_argument('source')\n\t\tnews.content = self.get_argument('review')\n\t\tnews.pp = self.get_argument('pp')\n\t\tif 'img_url' in self.request.files:\n\t\t\timg_url = self.request.files['img_url'][0]\n\t\t\tnews.img_url = upload_img(img_url, 'para', 'news', 'img')\n\t\tif 'ico_url' in self.request.files:\n\t\t\tico_url = self.request.files['ico_url'][0]\n\t\t\tnews.icon_url = upload_img(ico_url, 'para', 'news', 'ico')\n\t\tnews.cont = self.get_argument('content')\n\t\td = self.get_argument('time')\n\t\tif d:\n\t\t\tnews.atime = d\n\t\t\tnews.atime = d\n\t\t\td1 = d.replace('-','')\n\t\t\tnews.time = d1\n\t\t\tif d1 > time.strftime('%Y%m%d', time.localtime(time.time())):\n\t\t\t\tnews.status = '2'\n\t\tself.db.add(news)\n\t\tself.db.commit()\n\t\tself.db.flush()\n\t\tself.redirect('/index/ency/result/new')\n\nclass NewsEditHandler(ParaBaseHandler):\n\t@tornado.web.authenticated\n\tdef get(self, id):\n\t\tself.check_user()\n\t\tn = self.db.query(ParaFeedNews).filter_by(id=id).first()\n\t\tif n:\n\t\t\tself.render('admin-news-edit.html', n=n)\n\t\telse:\n\t\t\tself.write('不要调皮！')\n\t@tornado.web.authenticated\n\tdef post(self, id):\n\t\tnews = self.db.query(ParaFeedNews).filter_by(id=id).first()\n\t\tif news is None:\n\t\t\tnews = ParaFeedNews()\n\t\tnews.type = self.get_argument('type')\n\t\tnews.title = self.get_argument('title')\n\t\tnews.source = self.get_argument('source')\n\t\tnews.content = self.get_argument('review')\n\t\tif 'img_url' in self.request.files:\n\t\t\timg_url = self.request.files['img_url'][0]\n\t\t\tnews.img_url = upload_img(img_url, 'para', 'news', 'img')\n\t\tif 'ico_url' in self.request.files:\n\t\t\tico_url = self.request.files['ico_url'][0]\n\t\t\tnews.icon_url = upload_img(ico_url, 'para', 'news', 'ico')\n\t\tnews.pp = self.get_argument('pp')\n\t\tnews.cont = self.get_argument('content')\n\t\td = self.get_argument('time')\n\t\tif d:\n\t\t\tnews.atime = d\n\t\t\td1 = d.replace('-','')\n\t\t\tnews.time = d1\n\t\t\tif d1 > time.strftime('%Y%m%d', time.localtime(time.time())):\n\t\t\t\tnews.status = '2'\n\t\t\telse:\n\t\t\t\tnews.status = '1'\n\t\tself.db.merge(news)\n\t\tself.db.commit()\n\t\tself.db.flush()\n\t\tself.redirect('/index/ency/result/new')\n\n\nclass ImageUploadHandler(BaseHandler):\n\t# tornado.web.authenticated\n\tdef post(self, *args, **kwargs):\n\t\turl = None\n\t\ttype = 'News'\n\t\tif 'img_url' in self.request.files:\n\t\t\timg_url = self.request.files['img_url'][0]\n\t\t\tif type == 'News':\n\t\t\t\turl = upload_img(img_url, 'para', 'news', 'img')\n\t\t\telse:\n\t\t\t\tpass\n\t\tif url:\n\t\t\tfrom settings import IP_SERVER\n\t\t\turl = 'http://' + IP_SERVER + url\n\t\tself.write(url)","sub_path":"cn/parament/feeds/news.py","file_name":"news.py","file_ext":"py","file_size_in_byte":4084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"484488559","text":"#-------author@manik---------\r\nfrom socket import *\r\nhost = \"\"\r\nport = 4444\r\nserver_s = socket(AF_INET, SOCK_STREAM)\r\nserver_s.connect((host , port))\r\n\r\nwhile true:\r\n message = raw_input(\"Message\")\r\n server_s.send(message)\r\n print(\"Awaiting reply\")\r\n reply = server_s.recv(1024)#max data that can be recieved\r\n print(\" recieved\" , repr(reply))\r\n\r\nserver_s.close()\r\n","sub_path":"Client.py","file_name":"Client.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"625022978","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport math\nimport numpy as np\n\n# add cross modality attention module\nclass CrossAttention(nn.Module):\n def __init__(self, num_heads=8, embedding_dim=768, subspace_dim=32, relation_base=115, dropout_rate=0.1):\n super().__init__()\n\n self.num_attention_heads = num_heads\n self.attention_head_size = int(embedding_dim / num_heads)\n self.all_head_size = self.num_attention_heads * self.attention_head_size\n self.subspace_dim = subspace_dim\n\n self.key_visual = nn.Linear(embedding_dim, self.all_head_size, bias=False)\n self.value_visual = nn.Linear(embedding_dim, self.all_head_size)\n self.query_language = nn.Linear(embedding_dim, self.all_head_size, bias=False)\n self.dropout = nn.Dropout(dropout_rate)\n\n self.object_transform = nn.Linear(embedding_dim, self.subspace_dim) # linear downsampling to the relational sapce\n self.relational_matrix = nn.Parameter(torch.zeros(relation_base, subspace_dim, subspace_dim), requires_grad=True)\n torch.nn.init.xavier_uniform_(self.relational_matrix)\n\n def transpose_for_scores(self, x):\n new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)\n x = x.view(*new_x_shape)\n return x.permute(0, 2, 1, 3)\n # x shape: (batchsize, head_num, positions, head_size)\n\n def forward(self, x, y, attention_mask=None):\n # x: visual feature; y: language feature\n # reshape spatial features; flatten height and width dimensions\n x = x.permute(0, 2, 3, 1)\n x_shape = x.size() # (batchsize, height, width, channel)\n x = x.view(x.size()[0], 1, -1, x.size(3)).squeeze(1)\n\n # attention_input: (batchsize, positions, channel)\n mixed_query_language_layer = self.query_language(y)\n query_language = mixed_query_language_layer # save for output\n query_language_layer = self.transpose_for_scores(mixed_query_language_layer)\n mixed_key_visual_layer = self.key_visual(x)\n key_visual_layer = self.transpose_for_scores(mixed_key_visual_layer)\n key_visual = mixed_key_visual_layer.view(*x_shape).permute(0, 3, 1, 2).contiguous()\n # calculate cross attention scores\n attention_l2v_scores = torch.matmul(query_language_layer, key_visual_layer.transpose(-1, -2))\n attention_l2v_scores = attention_l2v_scores / math.sqrt(self.attention_head_size)\n if attention_mask is not None:\n # language to visual\n extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(3)\n extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0\n attention_l2v_scores = attention_l2v_scores + extended_attention_mask\n # Normalize the attention scores to probabilities.\n attention_l2v_probs = nn.Softmax(dim=-1)(attention_l2v_scores)\n\n # value and contexual representation\n mixed_value_visual_layer = self.value_visual(x)\n value_visual_layer = self.transpose_for_scores(mixed_value_visual_layer)\n # get contextual language layer from visual stream features\n context_language_layer = torch.matmul(attention_l2v_probs, value_visual_layer) # (batchsize, head_num, language_positions, embedding_feature)\n context_language_layer = context_language_layer.permute(0, 2, 1, 3).contiguous() # (batchsize, positions, head_num, subspace_feature)\n new_context_language_layer_shape = context_language_layer.size()[:-2] + (self.all_head_size,)\n object_representation = context_language_layer.view(*new_context_language_layer_shape) # (batchsize, positions, channel)\n\n # down sample to relational space\n object_for_relation = self.object_transform(object_representation)\n\n # normalizing each relational matrices\n normalized_relational_matrix = self.relational_matrix\n normalized_relational_matrix = normalized_relational_matrix.view(normalized_relational_matrix.size()[0], -1)\n normalized_relational_matrix = F.normalize(normalized_relational_matrix, dim=1, p=2)\n normalized_relational_matrix = normalized_relational_matrix.view(self.relational_matrix.size())\n normalized_relational_matrix = self.dropout(normalized_relational_matrix)\n\n # calculate prediction for relations\n object_for_relation = object_for_relation.unsqueeze(1)\n relation_prediction = torch.matmul(torch.matmul(object_for_relation,normalized_relational_matrix),object_for_relation.transpose(-1,-2))\n # object_for_relation = object_for_relation.squeeze()\n\n return query_language, key_visual, object_representation, relation_prediction, normalized_relational_matrix\n","sub_path":"models/CrossModalityModels.py","file_name":"CrossModalityModels.py","file_ext":"py","file_size_in_byte":4747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"239950755","text":"#Слово\ns = input()\nup = 0\nlow = 0\nfor i in s:\n if(i.isupper()):\n up = up + 1\n elif(i.islower()):\n low = low + 1\nif(up>low):\n print(s.upper())\nelse:\n print(s.lower())\n","sub_path":"ICPC_Beg_Contest1_H.py","file_name":"ICPC_Beg_Contest1_H.py","file_ext":"py","file_size_in_byte":197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"503776877","text":"import django_filters\nfrom django.forms import DateField, IntegerField, NullBooleanField\n\nfrom .models import Tag\nfrom .choices import *\n\n__all__ = (\n 'CustomFieldFilter',\n 'TagFilter',\n)\n\nEXACT_FILTER_TYPES = (\n CustomFieldTypeChoices.TYPE_BOOLEAN,\n CustomFieldTypeChoices.TYPE_DATE,\n CustomFieldTypeChoices.TYPE_INTEGER,\n CustomFieldTypeChoices.TYPE_SELECT,\n CustomFieldTypeChoices.TYPE_MULTISELECT,\n)\n\n\nclass CustomFieldFilter(django_filters.Filter):\n \"\"\"\n Filter objects by the presence of a CustomFieldValue. The filter's name is used as the CustomField name.\n \"\"\"\n def __init__(self, custom_field, *args, **kwargs):\n self.custom_field = custom_field\n\n if custom_field.type == CustomFieldTypeChoices.TYPE_INTEGER:\n self.field_class = IntegerField\n elif custom_field.type == CustomFieldTypeChoices.TYPE_BOOLEAN:\n self.field_class = NullBooleanField\n elif custom_field.type == CustomFieldTypeChoices.TYPE_DATE:\n self.field_class = DateField\n\n super().__init__(*args, **kwargs)\n\n self.field_name = f'custom_field_data__{self.field_name}'\n\n if custom_field.type == CustomFieldTypeChoices.TYPE_MULTISELECT:\n self.lookup_expr = 'has_key'\n elif custom_field.type not in EXACT_FILTER_TYPES:\n if custom_field.filter_logic == CustomFieldFilterLogicChoices.FILTER_LOOSE:\n self.lookup_expr = 'icontains'\n\n\nclass TagFilter(django_filters.ModelMultipleChoiceFilter):\n \"\"\"\n Match on one or more assigned tags. If multiple tags are specified (e.g. ?tag=foo&tag=bar), the queryset is filtered\n to objects matching all tags.\n \"\"\"\n def __init__(self, *args, **kwargs):\n\n kwargs.setdefault('field_name', 'tags__slug')\n kwargs.setdefault('to_field_name', 'slug')\n kwargs.setdefault('conjoined', True)\n kwargs.setdefault('queryset', Tag.objects.all())\n\n super().__init__(*args, **kwargs)\n","sub_path":"netbox/extras/filters.py","file_name":"filters.py","file_ext":"py","file_size_in_byte":1980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"557224847","text":"import os\r\nimport discord\r\nfrom discord.ext import commands\r\n\r\ntoken = \"\"\r\n\r\nclient = commands.Bot(command_prefix=\".\")\r\n\r\n@client.event\r\nasync def on_ready():\r\n await client.change_presence(status=discord.Status.do_not_disturb, activity=discord.Game(\"DO NOT DISTURB!! Gameing in progression..\"))\r\n print(\"Team Wire bot is ready...\")\r\n\r\n@client.event\r\nasync def on_command_error(ctx, error):\r\n if isinstance(error, commands.CommandNotFound):\r\n await ctx.send(\"The Command does not exists!!\")\r\n print(f\"{error}\")\r\n\r\n# Ping Function\r\n@client.command()\r\nasync def ping(ctx):\r\n await ctx.send(f\"Latency is {round(client.latency * 1000)}ms\")\r\n\r\n@ping.error\r\nasync def clear_error(self, ctx, error):\r\n if isinstance(error, commands.MissingPermissions):\r\n await ctx.send(\"You do not have the premission.\")\r\n\r\n# Load and Unload an extension.\r\n@client.command()\r\nasync def load(ctx, extension):\r\n client.load_extension(f\"cogs.{extension}\")\r\n\r\n@client.command()\r\nasync def unload(ctx, extension):\r\n client.unload_extension(f\"cogs.{extension}\")\r\n\r\nfor filename in os.listdir(\"./cogs\"):\r\n if filename.endswith(\".py\"):\r\n client.load_extension(f\"cogs.{filename[:-3]}\")\r\n\r\nclient.run(token)","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"517079992","text":"\"\"\"\nMask R-CNN\nConfigurations and data loading code for MS COCO.\n\nCopyright (c) 2017 Matterport, Inc.\nLicensed under the MIT License (see LICENSE for details)\nWritten by Waleed Abdulla\n\n------------------------------------------------------------\n\nUsage: import the module (see Jupyter notebooks for examples), or run from\n the command line as such:\n\n # Train a new model starting from pre-trained COCO weights\n python3 dsb2018.py train --dataset=/path/to/coco/ --model=coco\n\n # Train a new model starting from ImageNet weights\n python3 dsb2018.py train --dataset=/path/to/coco/ --model=imagenet\n\n # Continue training a model that you had trained earlier\n python3 dsb2018.py train --dataset=/path/to/coco/ --model=/path/to/weights.h5\n\n # Continue training the last model you trained\n python3 dsb2018.py train --dataset=/path/to/coco/ --model=last\n\n # Run COCO evaluatoin on the last model you trained\n python3 dsb2018.py evaluate --dataset=/path/to/coco/ --model=last\n\"\"\"\n\nimport os\nimport time\nimport numpy as np\n\nfrom config import Config\nimport utils\nimport model as modellib\n\nimport skimage\nimport pandas as pd\n\n# Disallow eager use of GPU memory\nimport tensorflow as tf\n\nconfig = tf.ConfigProto()\nconfig.gpu_options.allow_growth = True\nconfig.gpu_options.visible_device_list = \"0\"\ntf.Session(config=config)\n\n# Root directory of the project\nROOT_DIR = os.getcwd()\n\n# Path to trained weights file\nCOCO_MODEL_PATH = os.path.join(ROOT_DIR, \"mask_rcnn_coco.h5\")\n\n# Directory to save logs and model checkpoints, if not provided\n# through the command line argument --logs\nDEFAULT_LOGS_DIR = os.path.join(ROOT_DIR, \"logs_dsb2018\")\n# DEFAULT_DATASET_YEAR = \"2014\"\n\n# Datasets for Kaggle Data Science Bowl 2018\nTRAIN_PATH = '/home/yschoi/work/challenges/Kaggle_Data_Science_Bowl_2018/input/stage1_train/'\nTEST_PATH = '/home/yschoi/work/challenges/Kaggle_Data_Science_Bowl_2018/input/stage1_test/'\n\n############################################################\n# Configurations\n############################################################\n\nclass Dsb2018Config(Config):\n NAME = \"dsb2018\"\n GPU_COUNT = 1\n IMAGES_PER_GPU = 1\n NUM_CLASSES = 1 + 1\n IMAGE_MIN_DIM = 256\n IMAGE_MAX_DIM = 512 # 1024?\n RPN_ANCHOR_SCALES = (4, 8, 16, 32, 64)\n TRAIN_ROIS_PER_IMAGE = 500\n STEPS_PER_EPOCH = 600 // (IMAGES_PER_GPU * GPU_COUNT)\n VALIDATION_STEPS = 70 // (IMAGES_PER_GPU * GPU_COUNT)\n MEAN_PIXEL = [0, 0, 0]\n LEARNING_RATE = 0.01\n USE_MINI_MASK = True\n MAX_GT_INSTANCES = 500\n # # Minimum probability value to accept a detected instance\n # # ROIs below this threshold are skipped\n # DETECTION_MIN_CONFIDENCE = 0.7\n\n\n############################################################\n# Dataset\n############################################################\n\nclass Dsb2018Dataset(utils.Dataset):\n\n def load_dsb2018(self, dataset_dir, data_type):\n # Add classes\n self.add_class(\"dsb2018\", 1, \"nucleus\")\n\n # Add images\n data_ids = next(os.walk(dataset_dir))[1]\n data_split = np.int32(len(data_ids)*0.9) # 10% validataion\n data_start = 0\n\n if data_type==\"train\":\n data_ids = data_ids[:data_split]\n elif data_type==\"val\":\n data_ids = data_ids[data_split:]\n data_start = data_split\n else: # \"test\" and othres\n pass\n\n for i, id_ in enumerate(data_ids, start=data_start):\n img_path = dataset_dir + id_ + '/images/' + id_ + '.png'\n self.add_image(\n source=\"dsb2018\", image_id=i, path=img_path,\n mask_dir=dataset_dir + id_ + '/masks/',\n width=256, height=256 # Dummy placeholders (will be updated in 'load_image()')\n )\n\n def load_image(self, image_id):\n \"\"\"Load the specified image and return a [H,W,3] Numpy array.\n \"\"\"\n # Load image\n image = skimage.io.imread(self.image_info[image_id]['path'])\n\n # Add\n self.image_info[image_id][\"height\"] = image.shape[0]\n self.image_info[image_id][\"width\"] = image.shape[1]\n\n # If grayscale. Convert to RGB for consistency.\n if image.ndim < 3:\n image = skimage.color.gray2rgb(image)\n return image[:,:,:3]\n\n def load_mask(self, image_id):\n image_info = self.image_info[image_id]\n instance_masks = []\n class_ids = []\n\n for mask_file in next(os.walk(image_info['mask_dir']))[2]:\n mask_ = skimage.io.imread(image_info['mask_dir'] + mask_file)\n # mask_ = mask_[:, :, np.newaxis]\n instance_masks.append(mask_)\n class_ids.append(1) # Only one class with 'class_id'=1\n\n # Pack instance masks into an array\n if class_ids:\n mask = np.stack(instance_masks, axis=2)\n class_ids = np.array(class_ids, dtype=np.int32)\n return mask, class_ids\n else:\n # Call super class to return an empty mask\n return super(Dsb2018Dataset, self).load_mask(image_id)\n\n\n############################################################\n# DSB-2018 Evaluation\n############################################################\n\ndef build_dsb2018_results(dataset, image_ids, rois, class_ids, scores, masks):\n # If no results, return an empty list\n if rois is None:\n return []\n\n results = []\n for image_id in image_ids:\n # Loop through detections\n for i in range(rois.shape[0]):\n class_id = class_ids[i]\n score = scores[i]\n bbox = np.around(rois[i], 1)\n mask = masks[:, :, i]\n\n result = {\n \"image_id\": image_id,\n \"category_id\": dataset.get_source_class_id(class_id, \"dsb2018\"),\n \"bbox\": [bbox[1], bbox[0], bbox[3] - bbox[1], bbox[2] - bbox[0]],\n \"score\": score,\n \"segmentation\": maskUtils.encode(np.asfortranarray(mask))\n }\n results.append(result)\n return results\n\ndef evaluate_dsb2018(model, dataset, eval_type=\"bbox\", limit=0, image_ids=None):\n \"\"\"Runs evaluation.\n dataset: A Dataset object with valiadtion data\n eval_type: \"bbox\" or \"segm\" for bounding box or segmentation evaluation\n limit: if not 0, it's the number of images to use for evaluation\n \"\"\"\n # Pick images from the dataset\n image_ids = image_ids or dataset.image_ids\n\n # Limit to a subset\n if limit:\n image_ids = image_ids[:limit]\n\n # Get corresponding image IDs.\n dsb2018_image_ids = [dataset.image_info[id][\"id\"] for id in image_ids]\n\n t_prediction = 0\n t_start = time.time()\n\n results = []\n for i, image_id in enumerate(image_ids):\n # Load image\n image = dataset.load_image(image_id)\n\n # Run detection\n t = time.time()\n r = model.detect([image], verbose=0)[0]\n t_prediction += (time.time() - t)\n\n pred_masks_combined = np.max(r['masks'], axis=2)\n # skimage.io.imshow(pred_masks_combined)\n\n results.append(pred_masks_combined)\n\n print(\"Prediction time: {}. Average {}/image\".format(\n t_prediction, t_prediction / len(image_ids)))\n print(\"Total time: \", time.time() - t_start)\n\n return results\n\n\n# Define a run-length encoding function\nfrom skimage.morphology import label\n\ndef rle_encoding(x):\n dots = np.where(x.T.flatten() == 1)[0]\n run_lengths = []\n prev = -2\n\n for b in dots:\n if (b > prev + 1): run_lengths.extend((b + 1, 0))\n run_lengths[-1] += 1\n prev = b\n\n return run_lengths\n\ndef generate_rle_code(x):\n lab_img = label(x)\n for i in range(1, lab_img.max() + 1):\n yield rle_encoding(lab_img == i)\n\n\n############################################################\n# Training\n############################################################\n\nfrom sklearn.model_selection import train_test_split\n\nif __name__ == '__main__':\n import argparse\n\n # Parse command line arguments\n parser = argparse.ArgumentParser(\n description='Train Mask R-CNN on DSB-2018 dataset.')\n parser.add_argument(\"command\",\n metavar=\"\",\n help=\"'train' or 'evaluate' on DSB-2018\")\n # parser.add_argument('--dataset', required=True,\n # metavar=\"/path/to/coco/\",\n # help='Directory of the MS-COCO dataset')\n # parser.add_argument('--year', required=False,\n # default=DEFAULT_DATASET_YEAR,\n # metavar=\"\",\n # help='Year of the MS-COCO dataset (2014 or 2017) (default=2014)')\n parser.add_argument('--model', required=True,\n metavar=\"/path/to/weights.h5\",\n help=\"Path to weights .h5 file or 'coco'\")\n parser.add_argument('--logs', required=False,\n default=DEFAULT_LOGS_DIR,\n metavar=\"/path/to/logs/\",\n help='Logs and checkpoints directory (default=logs/)')\n parser.add_argument('--limit', required=False,\n default=65,\n metavar=\"\",\n help='Images to use for evaluation (default=500)')\n # parser.add_argument('--download', required=False,\n # default=False,\n # metavar=\"\",\n # help='Automatically download and unzip MS-COCO files (default=False)',\n # type=bool)\n args = parser.parse_args()\n print(\"Command: \", args.command)\n print(\"Model: \", args.model)\n # print(\"Dataset: \", args.dataset)\n #print(\"Year: \", args.year)\n print(\"Logs: \", args.logs)\n #print(\"Auto Download: \", args.download)\n\n # Configurations\n if args.command == \"train\":\n # config = CocoConfig()\n config = Dsb2018Config()\n else:\n #class InferenceConfig(CocoConfig):\n class InferenceConfig(Dsb2018Config):\n # Set batch size to 1 since we'll be running inference on\n # one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU\n GPU_COUNT = 1\n IMAGES_PER_GPU = 1\n DETECTION_MIN_CONFIDENCE = 0\n config = InferenceConfig()\n config.display()\n\n # Create model\n if args.command == \"train\":\n model = modellib.MaskRCNN(mode=\"training\", config=config,\n model_dir=args.logs)\n else:\n model = modellib.MaskRCNN(mode=\"inference\", config=config,\n model_dir=args.logs)\n\n # Select weights file to load\n excluded_layers = []\n if args.model.lower() == \"coco\": # Transfer learning from \"coco\"\n model_path = COCO_MODEL_PATH\n excluded_layers=['mrcnn_class_logits', 'mrcnn_bbox_fc', 'mrcnn_mask']\n elif args.model.lower() == \"last\":\n # Find last trained weights\n model_path = model.find_last()[1]\n elif args.model.lower() == \"imagenet\":\n # Start from ImageNet trained weights\n model_path = model.get_imagenet_weights()\n else:\n model_path = args.model\n\n # Load weights\n print(\"Loading weights \", model_path)\n # model.load_weights(model_path, by_name=True)\n model.load_weights(model_path, by_name=True, exclude=excluded_layers)\n\n # Train or evaluate\n if args.command == \"train\":\n # Training dataset. \n dataset_train = Dsb2018Dataset()\n dataset_train.load_dsb2018(TRAIN_PATH, \"train\")\n dataset_train.prepare()\n\n # Validation dataset\n dataset_val = Dsb2018Dataset()\n dataset_val.load_dsb2018(TRAIN_PATH, \"val\")\n dataset_val.prepare()\n\n # *** This training schedule is an example. Update to your needs ***\n\n # Training - Stage 1\n print(\"Training network heads\")\n model.train(dataset_train, dataset_val,\n learning_rate=config.LEARNING_RATE,\n epochs=40,\n layers='heads')\n\n # Training - Stage 2\n # Finetune layers from ResNet stage 4 and up\n print(\"Fine tune Resnet stage 4 and up\")\n model.train(dataset_train, dataset_val,\n learning_rate=config.LEARNING_RATE,\n epochs=120,\n layers='4+')\n\n # Training - Stage 3\n # Fine tune all layers\n print(\"Fine tune all layers\")\n model.train(dataset_train, dataset_val,\n learning_rate=config.LEARNING_RATE / 10,\n epochs=160,\n layers='all')\n\n elif args.command == \"evaluate\":\n # Validation dataset\n dataset_test = Dsb2018Dataset()\n dataset_test.load_dsb2018(TEST_PATH, \"test\")\n dataset_test.prepare()\n print(\"Running DSB-2018 evaluation on {} images.\".format(args.limit))\n evaluate_dsb2018(model, dataset_test, \"bbox\", limit=int(args.limit))\n\n elif args.command == \"submit\":\n # Validation dataset\n dataset_test = Dsb2018Dataset()\n dataset_test.load_dsb2018(TEST_PATH, \"test\")\n dataset_test.prepare()\n print(\"Running DSB-2018 evaluation on {} images.\".format(args.limit))\n pred_results = evaluate_dsb2018(model, dataset_test, \"bbox\")\n\n test_ids = next(os.walk(TEST_PATH))[1]\n new_test_ids = []\n rles = []\n for n, id_ in enumerate(test_ids):\n rle = list(generate_rle_code(pred_results[n]))\n rles.extend(rle)\n new_test_ids.extend([id_] * len(rle))\n\n import datetime\n\n sub = pd.DataFrame()\n sub['ImageId'] = new_test_ids\n sub['EncodedPixels'] = pd.Series(rles).apply(lambda x: ' '.join(str(y) for y in x))\n sub.to_csv('./output/mask_rcnn_submission_' + datetime.datetime.now().strftime(\"%Y%m%d_%H%M\") + '.csv', index=False)\n\n else:\n print(\"'{}' is not recognized. \"\n \"Use 'train' or 'evaluate'\".format(args.command))\n","sub_path":"dsb2018.py","file_name":"dsb2018.py","file_ext":"py","file_size_in_byte":13995,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"471456815","text":"def Setup(Settings,DefaultModel):\n # set7_dataset-aggressive-expansion/aggresive_expand_dataset_minlen30_kfold.py\n\n\n Settings[\"experiment_name\"] = \"aggresive_expand_dataset_minlen30_kfold\"\n\n Settings[\"graph_histories\"] = ['together']\n\n n=0\n\n from keras.preprocessing.image import ImageDataGenerator\n from DatasetHandler.custom_image import ImageDataGenerator as custom_ImageDataGenerator\n\n image_generator = custom_ImageDataGenerator(\n featurewise_center = False, # set input mean to 0 over the dataset\n samplewise_center = False, # set each sample mean to 0\n featurewise_std_normalization = False, # divide inputs by std of the dataset\n samplewise_std_normalization = False, # divide each input by its std\n zca_whitening = False, # apply ZCA whitening\n rotation_range = 10.0, # randomly rotate images in the range (degrees, 0 to 180)\n width_shift_range = 0.2, # randomly shift images horizontally (fraction of total width)\n height_shift_range = 0.2, # randomly shift images vertically (fraction of total height)\n horizontal_flip = True, # randomly flip images\n vertical_flip = False, # randomly flip images\n shear_range=0.2,\n zoom_range=0.2,\n )\n\n # Set these values:\n number_of_images_from_one = 2\n source_dataset = \"5556x_minlen30_640px\"\n target_dataset = \"5556x_minlen30_640px_2x_agressive_expanded\"\n pixels = 640\n epochs = 500\n use_dump_file = 'SegmentsData_marked_R100_4Tables.dump' # -> * new XYZ_expanded.dump\n\n model_type = 'img_osm_mix'\n\n # Feed the monkey and don't touch anything!\n Settings[\"models\"][n][\"noncanon_dataset\"] = 'expand_existing_dataset'\n Settings[\"models\"][n][\"noncanon_dataset_imagegenerator\"] = image_generator\n Settings[\"models\"][n][\"noncanon_dataset_genfrom1\"] = number_of_images_from_one\n\n Settings[\"models\"][n][\"model_type\"] = model_type\n Settings[\"models\"][n][\"dataset_name\"] = target_dataset\n Settings[\"models\"][n][\"source_dataset\"] = source_dataset\n Settings[\"models\"][n][\"pixels\"] = pixels\n Settings[\"models\"][n][\"cnn_model\"] = 'resnet50'\n Settings[\"models\"][n][\"unique_id\"] = 'expanded: ' + target_dataset\n Settings[\"models\"][n][\"cooking_method\"] = 'generators' # 'direct' or 'generators'\n Settings[\"models\"][n][\"epochs\"] = epochs\n\n Settings[\"models\"][n][\"dump_file_override\"] = use_dump_file\n Settings[\"models\"][n][\"dump_file_expanded\"] = use_dump_file[:-5] + '_expanded.dump'\n\n Settings[\"models\"][n][\"k_fold_crossvalidation\"] = True\n Settings[\"models\"][n][\"crossvalidation_k\"] = 10\n\n Settings[\"graph_histories\"] = []\n\n return Settings\n","sub_path":"Settings/set7_dataset-aggressive-expansion/aggresive_expand_dataset_minlen30_kfold.py","file_name":"aggresive_expand_dataset_minlen30_kfold.py","file_ext":"py","file_size_in_byte":2718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"521143004","text":"import os\nimport sys\nfrom os.path import abspath, dirname, join\nfrom django.conf import settings\nsys.path.insert(0, abspath(join(dirname(__file__), \"../\")))\nos.environ['DJANGO_SETTINGS_MODULE'] = 'uarepocamp.settings'\nos.chdir(settings.BASE_DIR)\n###############################################################\nfrom uaemailmanager.utils import str_to_cls\nfrom django.contrib.auth.models import User\nfrom uaemailmanager.settings import ContactTables\nfrom django.core import mail as django_mail\nfrom uaemailmanager.models import SentEmail, Template, People\nimport django.template\nfrom account.models import UAUser\n\nmandrill_connection = django_mail.get_connection(\"djrill.mail.backends.djrill.DjrillBackend\")\n\n\ndef get_name_fieldname(obj):\n name_fieldname = None\n for table in ContactTables:\n if type(obj) == str_to_cls(table['table']):\n name_fieldname = table['name_field']\n break\n if not name_fieldname:\n raise Exception(\"No name field for this class: \"+object.__class__)\n else:\n return getattr(obj, name_fieldname)\n\ndef get_email_fieldname(self):\n email_fieldname = None\n\n for table in ContactTables:\n if type(obj) == str_to_cls(table['table']):\n email_fieldname = table['email_field']\n break\n if not email_fieldname:\n raise Exception(\"No name field for this class: \"+object.__class__)\n else:\n\n return getattr(obj, email_fieldname)\n\n\n# Sent to One\ndef render_one_recipient(html=None, sender=None, recipients_iter=None):\n html = django.template.Template(html)\n context = django.template.Context({'sender': get_name_fieldname(sender), 'recipient': get_name_fieldname(next(recipients_iter))})\n html = html.render(context)\n return html\n\n\ndef send_mail(subject, template, sender, recipient_objects, render_html_function=None, render_html_func_kwargs=None):\n\n\n recipient_list = [recipient.email for recipient in recipient_objects]\n\n msg = django_mail.EmailMultiAlternatives(subject=subject, body=template.text, from_email=sender.email,\n to=recipient_list)\n if render_html_function:\n html = render_html_function(**render_html_func_kwargs)\n msg.attach_alternative(html, \"text/html\")\n\n msg.connection = mandrill_connection\n msg.track_opens = True\n msg.track_clicks = True\n\n msg.send()\n response = msg.mandrill_response\n\n for recipient_object, result in zip(recipient_objects, response):\n sent_email = SentEmail(template=template, subject=subject, sender_object=sender, recipient_object=recipient_object)\n if result['_id']:\n sent_email.mandrill_id = result['_id']\n if result['reject_reason']:\n sent_email.reject_reason = result['reject_reason']\n if result['status']:\n sent_email.status = result['status']\n sent_email.save()\n return response\n\n\ndef send_mass_mail(subject, template, sender, recipient_objects, render_html_function=None, render_html_func_kwargs=None):\n response = []\n for recipient in recipient_objects:\n response.append(send_mail(subject, template, sender, [recipient], render_html_function, render_html_func_kwargs))\n\n if len(recipient_objects) == 1:\n response = response[0]\n return response\n\n# #\n# test_template = Template.objects.get(pk=1)\n#\n# cosmin = People.objects.get(pk=3)\n# zahariesergiu = People.objects.get(pk=2)\n# covrig = People.objects.get(pk=4)\n# sergiuUA = UAUser.objects.get(pk=1)\n# #\n# recipient_objects = [cosmin, zahariesergiu, covrig]\n# # # response = send_mass_mail(\"FINAL TEST\", test_template, sergiuUA, recipient_objects, render_html_function=render_one_recipient, render_html_func_kwargs={'sender': sergiuUA, 'recipient': it, 'html': test_template.html})\n# response = send_mass_mail(\"TEST no IT3\", test_template, sergiuUA, recipient_objects, render_html_function=render_one_recipient, render_html_func_kwargs={'sender': sergiuUA, 'recipients_iter': iter(recipient_objects), 'html': test_template.html})\n# print(response)\n\n# from django.core.mail import EmailMultiAlternatives\n# from django.template import Context\n# from django.template.loader import render_to_string\n#\n# template_data = {\n# 'ORDERNO': \"12345\", 'TRACKINGNO': \"1Z987\"\n# }\n#\n# plaintext_context = Context(autoescape=False) # HTML escaping not appropriate in plaintext\n# subject = render_to_string(\"message_subject.txt\", template_data, plaintext_context)\n# text_body = render_to_string(\"message_body.txt\", template_data, plaintext_context)\n# html_body = render_to_string(\"message_body.html\", template_data)\n#\n# msg = EmailMultiAlternatives(subject=subject, from_email=\"sergiu.zaharie@united-academics.org\",\n# to=[\"zahariesergiu@gmail.com\"], body=text_body)\n# msg.attach_alternative(html_body, \"text/html\")\n# msg.send()","sub_path":"uaemailmanager/uaemailmanager.py","file_name":"uaemailmanager.py","file_ext":"py","file_size_in_byte":4847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"588342341","text":"# Contains the application factory and tells Python /flaskr should be treated as a package\nimport os\n\nfrom flask import Flask\n\n# application factory function\ndef create_app(test_config=None):\n # create and configure this app\n app = Flask(__name__, instance_relative_config=True) #__name__ is name of current Python module ; instance_relative_config tells app config files are relative to instance folder\n # sets default config the app will use\n app.config.from_mapping(\n SECRET_KEY='dev', # key should be changed when deploying\n DATABASE=os.path.join(app.instance_path, 'flaskr.sqlite'),\n )\n\n if test_config is None:\n # load the instance config, if it exists, when not testing\n app.config.from_pyfile('config.py', silent=True)\n else:\n # load the test config if passed in\n app.config.from_mapping(test_config)\n\n # ensure the instance folder exists\n try:\n os.makedirs(app.instance_path)\n except OSError:\n pass\n\n # a simple page that says hello\n @app.route('/hello')\n def hello():\n return 'Hello, World!'\n\n return app\n","sub_path":"flaskr/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"439985396","text":"# Views is the file that contains information and functions of different html-views.\n\n\nfrom django.shortcuts import render\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth.decorators import login_required\nfrom rest_framework.response import Response\n\nfrom .models import Player, Team, Game, Level, Position, Line, LiveData, Shot, Time\nfrom django.http import HttpResponseRedirect\nfrom accounts.forms import AddNewPlayer\nfrom rest_framework import viewsets, generics\nfrom django.forms import modelformset_factory\nfrom .serializers import UserSerializer, TeamSerializer, LineSerializer, PositionSerializer, LevelSerializer, TimeSerializer\nfrom .serializers import GameSerializer, PlayerSerializer, PlayerUpdateSerializer, LiveDataSerializer, ShotSerializer\n\n@login_required\ndef index(request):\n return render(request,'accounts/index.html')\n\n@login_required\ndef new_game(request):\n levels = Level.objects.all().order_by('name')\n\n context = {\n 'levels': levels,\n }\n return render(request, 'accounts/newgame.html', context=context)\n\n@login_required\ndef edit_players(request):\n teams = Team.objects.all().order_by('name')\n levels = Level.objects.all().order_by('name')\n players = Player.objects.all().order_by('jersey_number')\n\n context = {\n 'teams': teams,\n 'levels': levels,\n 'players': players,\n }\n\n return render(request, 'accounts/edit_players.html', context=context)\n\n@login_required\ndef edit_levels(request):\n levels = Level.objects.all().order_by('name')\n\n LevelFormSet = modelformset_factory(Level, fields=('name', 'country', 'isSenior', 'isMale', 'isNational'))\n if request.method == 'POST':\n formset = LevelFormSet(request.POST, request.FILES)\n if formset.is_valid():\n formset.save()\n # do something.\n else:\n formset = LevelFormSet()\n\n context = {\n 'levels': levels,\n 'formset': formset,\n }\n\n return render(request, 'accounts/edit_levels.html', context=context)\n\n@login_required\ndef edit_teams(request):\n teams = Team.objects.all().order_by('name')\n levels = Level.objects.all().order_by('name')\n\n context = {\n 'teams': teams,\n 'levels': levels,\n }\n\n return render(request, 'accounts/edit_teams.html', context=context)\n\ndef analyse(request):\n\n if request.user.id == 3 or request.user.id == 2:\n games = Game.objects.order_by('date')\n else:\n games = Game.objects.filter(user=request.user).order_by('date')\n\n teams = Team.objects.all().order_by('name')\n levels = Level.objects.all().order_by('name')\n players = Player.objects.all().order_by('jersey_number')\n\n context = {\n 'teams': teams,\n 'levels': levels,\n 'players': players,\n 'games': games,\n }\n\n return render(request, 'accounts/analysis.html', context = context)\n\ndef lite(request):\n return render(request, 'accounts/lite.html')\n\ndef add_new_player(request):\n\n \"\"\"View function for adding a new player to the team.\"\"\"\n player_instance = Player()\n\n # If this is a POST request then process the Form data\n if request.method == 'POST':\n\n # Create a form instance and populate it with data from the request (binding):\n form = AddNewPlayer(request.POST)\n # Check if the form is valid:\n if form.is_valid():\n # process the data in form.cleaned_data as required (here we just write it to the model due_back field)\n player_instance.jersey_number = form.clean_jersey_number()\n player_instance.first_name = form.clean_first_name()\n player_instance.last_name = form.clean_last_name()\n player_instance.save()\n\n # If this is a GET (or any other method) create the default form.\n else:\n form = AddNewPlayer()\n\n players = Player.objects.all().order_by('jersey_number')\n context = {\n 'players': players,\n }\n\n return HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\n # return render(request, 'accounts/edit_playersedit_players.html', context)\n\n# ViewSets define the view behavior.\n\nclass UserViewSet(viewsets.ModelViewSet):\n queryset = User.objects.all()\n serializer_class = UserSerializer\n\nclass TeamViewSet(viewsets.ModelViewSet):\n queryset = Team.objects.all().order_by('name')\n serializer_class = TeamSerializer\n\nclass PositionViewSet(viewsets.ModelViewSet):\n queryset = Position.objects.all().order_by(\"id\")\n serializer_class = PositionSerializer\n\nclass LineViewSet(viewsets.ModelViewSet):\n queryset = Line.objects.all().order_by(\"id\")\n serializer_class = LineSerializer\n\nclass GameViewSet(viewsets.ModelViewSet):\n queryset = Game.objects.all().order_by(\"id\")\n serializer_class = GameSerializer\n\nclass LiveDataViewSet(viewsets.ModelViewSet):\n queryset = LiveData.objects.all()\n serializer_class = LiveDataSerializer\n\nclass LevelViewSet(viewsets.ModelViewSet):\n queryset = Level.objects.all()\n serializer_class = LevelSerializer\n\nclass ShotViewSet(viewsets.ModelViewSet):\n queryset = Shot.objects.all()\n serializer_class = ShotSerializer\n\nclass TimeViewSet(viewsets.ModelViewSet):\n queryset = Time.objects.all()\n serializer_class = TimeSerializer\n\nclass PlayerViewSet(viewsets.ModelViewSet):\n queryset = Player.objects.all().order_by('jersey_number')\n serializer_class = PlayerSerializer\n\nclass TeamList(generics.ListAPIView):\n serializer_class = TeamSerializer\n\n def get_queryset(self):\n\n queryset = Team.objects.all().order_by('name')\n level = self.request.query_params.get('level_id')\n if level is not None:\n queryset = queryset.filter(level__id=level)\n return queryset\n\nclass PlayerList(generics.ListAPIView):\n serializer_class = PlayerSerializer\n\n def get_queryset(self):\n\n queryset = Player.objects.all().order_by('jersey_number')\n team = self.request.query_params.get('team_id')\n if team is not None:\n queryset = queryset.filter(team__id=team)\n return queryset\n\nclass GameList(generics.ListAPIView):\n serializer_class = GameSerializer\n\n def get_queryset(self):\n\n queryset = Game.objects.all().order_by('-date')\n user = self.request.query_params.get('user_id')\n if user is not None:\n queryset = queryset.filter(user__id=user)\n return queryset\n\n@login_required\ndef premium_game(request):\n teams = Team.objects.all().order_by('name')\n levels = Level.objects.all().order_by('name')\n players = Player.objects.all().order_by('jersey_number')\n\n context = {\n 'teams': teams,\n 'levels': levels,\n 'players': players,\n }\n return render(request, 'accounts/premiumgame.html', context=context)\n\n@login_required\ndef edit_data(request):\n\n return render(request, 'accounts/editdata.html')\n\nclass UpdatePlayer(generics.UpdateAPIView):\n serializer_class = PlayerUpdateSerializer\n queryset = Player.objects.all()\n\n def update(self, request, *args, **kwargs):\n instance = self.get_object()\n data = request.data\n serializer = self.get_serializer(instance, data, partial=True)\n serializer.is_valid(raise_exception=True)\n self.perform_update(serializer)\n\n return Response(serializer.data)\n","sub_path":"FBAnalyzer/accounts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"120297390","text":"# https://leetcode.com/problems/keyboard-row/\n# using letters of alphabet on only one row's of American keyboard\n\nclass Solution:\n def findWords(self, words: List[str]) -> List[str]:\n A = 'QWERTYUIOP'\n a = A.lower()\n B = 'ASDFGHJKL'\n b = B.lower()\n C = 'ZXCVBNM'\n c = C.lower()\n res = []\n for el in words:\n if all(ch in A or ch in a for ch in el) or all(ch in B or ch in b for ch in el) or all(ch in C or ch in c for ch in el):\n res.append(el)\n return res\n\nif __name__==\"__main__\":\n obj = Solution()\n param_1 = obj.isMonotonic([\"Hello\", \"Alaska\", \"Dad\", \"Peace\"])\n print(param_1)\n","sub_path":"leetcode/500_KeyboardRow.py","file_name":"500_KeyboardRow.py","file_ext":"py","file_size_in_byte":681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"53460160","text":"## script to load the aedat4 file and save into the images and *.txt files\r\n\r\nfrom dv import AedatFile\r\nimport cv2\r\nimport os\r\nimport numpy as np\r\nfrom PIL import Image\r\nimport pdb \r\n\r\n\r\ndef extract_davis(aedat_file_path, filename_txt_path, croped_events_path, save_path_dvs, save_path_aps, dvs_img_interval):\r\n \r\n frame_no = 0\r\n frame_all = []\r\n frame_exposure_time = []\r\n frame_interval_time = []\r\n # use_mode = 'frame_exposure_time' #### default mode \r\n use_mode = 'frame_interval_time'\r\n with AedatFile(aedat_file_path) as f:\r\n # list all the names of streams in the file\r\n print(f.names)\r\n # extract timestamps of each frame \r\n for frame in f['frames']:\r\n frame_all.append(frame.image) \r\n frame_exposure_time.append([frame.timestamp_start_of_exposure, frame.timestamp_end_of_exposure]) ## [1607928583397102, 1607928583401102]\r\n frame_interval_time.append([frame.timestamp_start_of_frame, frame.timestamp_end_of_frame]) ## [1607928583387944, 1607928583410285]\r\n\r\n # pdb.set_trace()\r\n \r\n if use_mode == 'frame_exposure_time':\r\n frame_timestamp = frame_exposure_time\r\n elif use_mode == 'frame_interval_time':\r\n frame_timestamp = frame_interval_time\r\n\r\n frame_num = len(frame_timestamp)\r\n # Access dimensions of the event stream\r\n height, width = f['events'].size \r\n event_frame = 255 * np.ones((height, width, 3), dtype=np.uint8)\r\n # loop through the \"events\" stream\r\n # events = np.hstack([packet for packet in f['events'].numpy()])\r\n\r\n # # Access information of all events by type\r\n # timestamps, x, y, polarities = events['timestamp'], events['x'], events['y'], events['polarity']\r\n\r\n\r\n # pdb.set_trace() \r\n # save event_img\r\n idx = np.round(np.linspace(0, len(frame_timestamp) - 1, int(frame_num/dvs_img_interval))).astype(int) ## frame index [0, 1, 2, ... , 3847] \r\n frame_timestamp = np.array(frame_timestamp)[idx] ## (3848, 2) \r\n \r\n # one reader #\r\n # for e in f['events']: \r\n # if e.timestamp >= frame_timestamp[frame_no][0]:\r\n # event_frame[int(e.y),int(e.x), :] = [30, 30, 220] * int(e.polarity) + [200, 30, 30] * int(not e.polarity)\r\n # #event_window.append([e.timestamp, e.x, e.y, e.polarity])\r\n # if e.timestamp > frame_timestamp[frame_no][1]:\r\n # cv2.imwrite(os.path.join(save_path_dvs, 'frame{:04d}'.format(frame_no*dvs_img_interval)+'.bmp'), event_frame)\r\n # frame_no = frame_no + 1\r\n # event_frame = 255 * np.ones((height, width, 3), dtype=np.uint8)\r\n # if frame_no > frame_num-1:\r\n # break\r\n # continue\r\n \r\n file = open(filename_txt_path, 'w')\r\n\r\n # another reader # speed up\r\n # events will be a named numpy array\r\n events = np.hstack([packet for packet in f['events'].numpy()])\r\n timestamps, x, y, polarities = events['timestamp'], events['x'], events['y'], events['polarity']\r\n\r\n # start_frame = 0 \r\n # end_frame = 50 \r\n # frame_num = end_frame - start_frame \r\n\r\n # start_idx = np.where(events['timestamp'] >= frame_timestamp[start_frame][0])[0][0]\r\n # end_idx = np.where(events['timestamp'] >= frame_timestamp[end_frame][1])[0][0]\r\n # cropped_event = events[start_idx:end_idx]\r\n # timestamps, x, y, polarities = cropped_event['timestamp'], cropped_event['x'], cropped_event['y'], cropped_event['polarity']\r\n\r\n event_file = open(croped_events_path, 'w')\r\n for ii in range(timestamps.shape[0]):\r\n event_file.write(('{}, {}, {}, {}'.format(timestamps[ii], x[ii], y[ii], polarities[ii]) + \"\\n\")) \r\n\r\n event_file.close()\r\n\r\n # pdb.set_trace() \r\n\r\n\r\n\r\n # for frame_no in range(start_frame, end_frame):\r\n for frame_no in range(0, int(frame_num/dvs_img_interval)-1):\r\n \t\r\n event_frame = 255 * np.ones((height, width, 3), dtype=np.uint8)\r\n start_idx = np.where(events['timestamp'] >= frame_timestamp[frame_no][0])[0][0]\r\n end_idx = np.where(events['timestamp'] >= frame_timestamp[frame_no][1])[0][0]\r\n event = events[start_idx:end_idx]\r\n \r\n on_idx = np.where(event['polarity'] == 1) ## (array([ 3, 4, 5, ..., 10633, 10635, 10636]),) \r\n off_idx = np.where(event['polarity'] == 0) ## (array([ 0, 1, 2, ..., 10629, 10632, 10634]),)\r\n event_frame[event['y'][on_idx], event['x'][on_idx], :] = [30, 30, 220] * event['polarity'][on_idx][:, None]\r\n event_frame[event['y'][off_idx], event['x'][off_idx], :] = [200, 30, 30] * (event['polarity'][off_idx]+1)[:, None]\r\n \r\n cv2.imwrite(os.path.join(save_path_dvs, 'frame{:04d}'.format(frame_no*dvs_img_interval)+'.bmp'), event_frame)\r\n cv2.imshow('Event Image', event_frame)\r\n cv2.waitKey(1)\r\n print('The {} timestamp is {}'.format(frame_no, events['timestamp'][frame_no]))\r\n file.write('The {} frame, the timestamp is {}'.format(frame_no, events['timestamp'][frame_no]) + \"\\n\") # save the timestamp\r\n \r\n # pdb.set_trace() \r\n \r\n file.close()\r\n\r\n # save aps_img\r\n # for frame_no in range(start_frame, end_frame):\r\n for frame_no in range(0, int(frame_num/dvs_img_interval)-1):\r\n this_frame = frame_all[frame_no]\r\n event_img = np.zeros((height, width))\r\n cv2.imwrite(os.path.join(save_path_aps, 'frame{:04d}'.format(frame_no)+'.bmp'), this_frame)\r\n cv2.imshow('APS Image', this_frame)\r\n cv2.waitKey(1)\r\n\r\n # pdb.set_trace()\r\n\r\n\r\ndef extract_rgb(rgb_file_path, save_path_rgb):\r\n cap = cv2.VideoCapture(rgb_file_path)\r\n width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\r\n height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\r\n fourcc = cv2.VideoWriter_fourcc(*'XVID')\r\n fps = cap.get(cv2.CAP_PROP_FPS)\r\n frame_num = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\r\n frame_no = 0\r\n while(True):\r\n ret, frame = cap.read()\r\n if not ret:\r\n break\r\n cv2.imwrite(os.path.join(save_path_rgb, 'frame{:04d}'.format(frame_no)+'.bmp'), frame)\r\n frame_no = frame_no + 1\r\n\r\n\r\n\r\ndef main():\r\n aedat_path = '/home/wangxiao/Downloads'\r\n dvs_img_interval = 1\r\n for root, dirs, files in os.walk(aedat_path):\r\n for file in files:\r\n (filename, extension) = os.path.splitext(file)\r\n\r\n print(\"==>> filename: \", filename)\r\n\r\n if (extension == '.aedat4'):\r\n\r\n if filename+'_aps' in os.listdir(root) or filename+'_dvs' in os.listdir(root):\r\n print(\"==>> Skip this video ... \")\r\n continue \r\n \r\n save_path_dvs = os.path.join(root, filename, filename+'_dvs')\r\n save_path_aps = os.path.join(root, filename, filename+'_aps')\r\n aedat_file_path = os.path.join(root, filename+'.aedat4')\r\n filename_txt_path = os.path.join(root, filename+'_timestamp.txt')\r\n croped_events_path = os.path.join(root, filename+'_events.txt')\r\n \r\n if not os.path.exists(save_path_dvs):\r\n os.makedirs(save_path_dvs)\r\n if not os.path.exists(save_path_aps):\r\n os.makedirs(save_path_aps)\r\n\r\n extract_davis(aedat_file_path, filename_txt_path, croped_events_path, save_path_dvs, save_path_aps, dvs_img_interval) \r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"scripts/read_aedat4.py","file_name":"read_aedat4.py","file_ext":"py","file_size_in_byte":7842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"389299147","text":"import asyncio\nimport base64\nimport os\nimport random\nimport sqlite3\nimport math\nimport re\nfrom datetime import datetime, timedelta\nfrom io import BytesIO\nfrom PIL import Image\nfrom . import sv\nfrom hoshino import Service, priv\nfrom hoshino.modules.priconne import _pcr_data\nfrom hoshino.modules.priconne import duel_chara as chara\nfrom hoshino.config import NICKNAME\nfrom hoshino.typing import CQEvent\nfrom hoshino.util import DailyNumberLimiter\nimport copy\nimport json\nfrom .CECounter import *\nfrom .ScoreCounter import *\nfrom .DuelCounter import *\nfrom .duelconfig import *\n\n@sv.on_fullmatch(['贵族决斗帮助','贵族帮助','贵族指令'])\nasync def duel_help(bot, ev: CQEvent):\n msg='''\n╔ ╗ \n 贵族决斗相关指令\n\n 1.贵族签到(每日一次)\n 2.查询贵族\n 3.贵族决斗+艾特\n 4.领金币/查金币\n 5.贵族舞会(招募女友)\n 6.查女友+角色名\n 7.升级贵族\n 8.重置金币+qq (限群主)\n 9.重置角色+qq (限群主) \n 10.重置决斗(限管理，决\n 斗卡住时用)\n 11.分手+角色名(需分手费)\n 12.贵族等级表\n 13.兑换声望+数量(兑换比例1：10000)\n 14.转账(为@qq转账xxx金币)\n 15.女友交易(用xxx金币与@qq交易女友+角色名)，需要收10%交易手续费\n 16.dlc帮助(增加dlc角色)\n 17.声望帮助(查询声望系统指令)\n 18.时装帮助(查询时装系统指令)\n 19.副本帮助(查询副本系统指令)\n 20.战斗帮助(查询战斗系统指令)\n 21.会战帮助(查询会战系统指令)\n \n \n 一个女友只属于一位群友\n╚ ╝\n''' \n tas_list=[]\n data = {\n \"type\": \"node\",\n \"data\": {\n \"name\": str(NICKNAME[0]),\n \"uin\": str(ev.self_id),\n \"content\":msg\n }\n }\n tas_list.append(data)\n await bot.send_group_forward_msg(group_id=ev['group_id'], messages=tas_list)\n\n\n@sv.on_prefix(['加载dlc','加载DLC','开启dlc','开启DLC'])\nasync def add_dlc(bot, ev: CQEvent):\n gid = ev.group_id\n if not priv.check_priv(ev, priv.OWNER):\n await bot.finish(ev, '只有群主才能加载dlc哦。', at_sender=True)\n args = ev.message.extract_plain_text().split()\n if len(args)>= 2:\n await bot.finish(ev, '指令格式错误。', at_sender=True)\n if len(args)==0:\n await bot.finish(ev, '请输入加载dlc+dlc名。', at_sender=True)\n dlcname = args[0]\n if dlcname not in dlcdict.keys():\n await bot.finish(ev, 'DLC名填写错误。', at_sender=True) \n\n if gid in dlc_switch[dlcname]:\n await bot.finish(ev, '本群已开启此dlc哦。', at_sender=True)\n dlc_switch[dlcname].append(gid)\n save_dlc_switch()\n await bot.finish(ev, f'加载dlc {dlcintro[dlcname]} 成功!', at_sender=True)\n \n \n\n@sv.on_prefix(['卸载dlc','卸载DLC','关闭dlc','关闭DLC'])\nasync def delete_dlc(bot, ev: CQEvent):\n gid = ev.group_id\n if not priv.check_priv(ev, priv.OWNER):\n await bot.finish(ev, '只有群主才能卸载dlc哦。', at_sender=True)\n args = ev.message.extract_plain_text().split()\n if len(args)>= 2:\n await bot.finish(ev, '指令格式错误', at_sender=True)\n if len(args)==0:\n await bot.finish(ev, '请输入卸载dlc+dlc名。', at_sender=True)\n dlcname = args[0]\n if dlcname not in dlcdict.keys():\n await bot.finish(ev, 'DLC名填写错误', at_sender=True) \n\n if gid not in dlc_switch[dlcname]:\n await bot.finish(ev, '本群没有开启此dlc哦。', at_sender=True)\n dlc_switch[dlcname].remove(gid)\n save_dlc_switch()\n await bot.finish(ev, f'卸载dlc {dlcname} 成功!', at_sender=True)\n \n\n\n@sv.on_fullmatch(['dlc列表','DLC列表','dlc介绍','DLC介绍'])\nasync def intro_dlc(bot, ev: CQEvent):\n msg = '可用DLC列表：\\n\\n'\n i=1\n for dlc in dlcdict.keys():\n msg+=f'{i}.{dlc}:\\n'\n intro = dlcintro[dlc]\n msg+=f'介绍:{intro}\\n'\n num = len(dlcdict[dlc]) + 1\n msg+=f'共有{num}名角色\\n\\n'\n i+=1\n msg+= '发送 加载\\卸载dlc+dlc名\\n可加载\\卸载dlc\\n卸载的dlc不会被抽到，但是角色仍留在玩家仓库，可以被抢走。' \n \n tas_list=[]\n data = {\n \"type\": \"node\",\n \"data\": {\n \"name\": str(NICKNAME[0]),\n \"uin\": str(ev.self_id),\n \"content\":msg\n }\n }\n tas_list.append(data)\n await bot.send_group_forward_msg(group_id=ev['group_id'], messages=tas_list)\n\n@sv.on_fullmatch(['dlc帮助','DLC帮助','dlc指令','DLC指令'])\nasync def help_dlc(bot, ev: CQEvent):\n msg = '''\n╔ ╗\n DLC帮助\n \n 1.加载\\卸载dlc+dlc名\n 2.dlc列表(查看介绍)\n \n 卸载的dlc不会被抽到\n 但是角色仍留在仓库\n 可以被他人抢走\n \n╚ ╝ \n'''\n await bot.finish(ev, msg)\n\n\n\n@sv.on_fullmatch(['贵族表','贵族等级表'])\nasync def duel_biao(bot, ev: CQEvent):\n msg='''\"1\": \"平民\", 最多可持有1名女友，每日签到额外获得100金币，初始等级。\n\"2\": \"骑士\", 升级需要100金币，最多可持有2名女友，每日签到额外获得200金币，保持等级最少持有1名女友。\n\"3\": \"准男爵\", 升级需要300金币，最多可持有3名女友，每日签到额外获得300金币，保持等级最少持有2名女友。\n\"4\": \"男爵\",升级需要500金币，最多可持有5名女友，每日签到额外获得400金币，保持等级最少持有3名女友。\n\"5\": \"子爵\",升级需要1000金币，最多可持有7名女友，每日签到额外获得500金币，保持等级最少持有5名女友。\n\"6\": \"伯爵\",升级需要3000金币，最多可持有9名女友，每日签到额外获得600金币，保持等级最少持有7名女友。\n\"7\": \"侯爵\",升级需要1000声望和5000金币，最多可持有10名女友，每日签到额外获得700金币，保持等级最少持有9名女友。\n\"8\": \"公爵\",升级需要1500声望和10000金币，最多可持有12名女友，每日签到额外获得800金币，不再会掉级，可拥有一名妻子。\n\"9\": \"国王\",升级需要2000声望和15000金币，最多可持有14名女友，每日签到额外获得900金币，不再会掉级，可拥有一名妻子。\n\"10\": \"皇帝\"升级需要2500声望和20000金币，最多可持有15名女友，每日签到额外获得1000金币，不再会掉级，可拥有一名妻子。\n\"11\": \"神\"升级需要4000声望和30000金币，最多可持有99名女友，每日签到额外获得2000金币，当输光女友时贬为平民，可拥有一名妻子。\n''' \n tas_list=[]\n data = {\n \"type\": \"node\",\n \"data\": {\n \"name\": str(NICKNAME[0]),\n \"uin\": str(ev.self_id),\n \"content\":msg\n }\n }\n tas_list.append(data)\n await bot.send_group_forward_msg(group_id=ev['group_id'], messages=tas_list)\n\n\n\n# 记录决斗和下注数据\n\n\nclass DuelJudger:\n def __init__(self):\n self.on = {}\n self.accept_on = {}\n self.support_on = {}\n self.fire_on = {}\n self.deadnum = {}\n self.support = {}\n self.turn = {}\n self.duelid = {}\n self.isaccept = {}\n self.hasfired_on = {}\n\n def set_support(self, gid):\n self.support[gid] = {}\n\n def get_support(self, gid):\n return self.support[gid] if self.support.get(gid) is not None else 0\n\n def add_support(self, gid, uid, id, score):\n self.support[gid][uid] = [id, score]\n\n def get_support_id(self, gid, uid):\n if self.support[gid].get(uid) is not None:\n return self.support[gid][uid][0]\n else:\n return 0\n\n def get_support_score(self, gid, uid):\n if self.support[gid].get(uid) is not None:\n return self.support[gid][uid][1]\n else:\n return 0\n\n # 五个开关：决斗，接受，下注， 开枪, 是否已经开枪\n\n def get_on_off_status(self, gid):\n return self.on[gid] if self.on.get(gid) is not None else False\n\n def turn_on(self, gid):\n self.on[gid] = True\n\n def turn_off(self, gid):\n self.on[gid] = False\n\n def get_on_off_accept_status(self, gid):\n return self.accept_on[gid] if self.accept_on.get(gid) is not None else False\n\n def turn_on_accept(self, gid):\n self.accept_on[gid] = True\n\n def turn_off_accept(self, gid):\n self.accept_on[gid] = False\n\n def get_on_off_support_status(self, gid):\n return self.support_on[gid] if self.support_on.get(gid) is not None else False\n\n def turn_on_support(self, gid):\n self.support_on[gid] = True\n\n def turn_off_support(self, gid):\n self.support_on[gid] = False\n\n def get_on_off_fire_status(self, gid):\n return self.fire_on[gid] if self.fire_on.get(gid) is not None else False\n\n def turn_on_fire(self, gid):\n self.fire_on[gid] = True\n\n def turn_off_fire(self, gid):\n self.fire_on[gid] = False\n\n def get_on_off_hasfired_status(self, gid):\n return self.hasfired_on[gid] if self.hasfired_on.get(gid) is not None else False\n\n def turn_on_hasfired(self, gid):\n self.hasfired_on[gid] = True\n\n def turn_off_hasfired(self, gid):\n self.hasfired_on[gid] = False\n\n # 记录决斗者id\n def init_duelid(self, gid):\n self.duelid[gid] = []\n\n def set_duelid(self, gid, id1, id2):\n self.duelid[gid] = [id1, id2]\n\n def get_duelid(self, gid):\n return self.duelid[gid] if self.accept_on.get(gid) is not None else [0, 0]\n \n # 查询一个决斗者是1号还是2号\n def get_duelnum(self, gid, uid):\n return self.duelid[gid].index(uid) + 1\n\n # 记录由谁开枪\n def init_turn(self, gid):\n self.turn[gid] = 1\n\n def get_turn(self, gid):\n return self.turn[gid] if self.turn[gid] is not None else 0\n\n def change_turn(self, gid):\n if self.get_turn(gid) == 1:\n self.turn[gid] = 2\n return 2\n else:\n self.turn[gid] = 1\n return 1\n\n # 记录子弹位置\n def init_deadnum(self, gid):\n self.deadnum[gid] = None\n\n def set_deadnum(self, gid, num):\n self.deadnum[gid] = num\n\n def get_deadnum(self, gid):\n return self.deadnum[gid] if self.deadnum[gid] is not None else False\n\n # 记录是否接受\n def init_isaccept(self, gid):\n self.isaccept[gid] = False\n\n def on_isaccept(self, gid):\n self.isaccept[gid] = True\n\n def off_isaccept(self, gid):\n self.isaccept[gid] = False\n\n def get_isaccept(self, gid):\n return self.isaccept[gid] if self.isaccept[gid] is not None else False\n\nclass GiftChange:\n def __init__(self): \n self.giftchange_on={}\n self.waitchange={}\n self.isaccept = {}\n self.changeid = {}\n\n #礼物交换开关\n def get_on_off_giftchange_status(self, gid):\n return self.giftchange_on[gid] if self.giftchange_on.get(gid) is not None else False\n\n def turn_on_giftchange(self, gid):\n self.giftchange_on[gid] = True\n\n def turn_off_giftchange(self, gid):\n self.giftchange_on[gid] = False\n #礼物交换发起开关 \n def get_on_off_waitchange_status(self, gid):\n return self.waitchange[gid] if self.waitchange.get(gid) is not None else False\n\n def turn_on_waitchange(self, gid):\n self.waitchange[gid] = True\n\n def turn_off_waitchange(self, gid):\n self.waitchange[gid] = False\n #礼物交换是否接受开关\n def turn_on_accept_giftchange(self, gid):\n self.isaccept[gid] = True\n\n def turn_off_accept_giftchange(self, gid):\n self.isaccept[gid] = False\n\n def get_isaccept_giftchange(self, gid):\n return self.isaccept[gid] if self.isaccept[gid] is not None else False\n #记录礼物交换请求接收者id\n def init_changeid(self, gid):\n self.changeid[gid] = []\n\n def set_changeid(self, gid, id2):\n self.changeid[gid] = id2\n\n def get_changeid(self, gid):\n return self.changeid[gid] if self.changeid.get(gid) is not None else 0\n\n\n\nduel_judger = DuelJudger()\ngift_change = GiftChange()\n\nclass NvYouJiaoYi:\n def __init__(self):\n self.jiaoyion = {}\n self.jiaoyiflag = {}\n self.jiaoyiid = {}\n self.jiaoyiname = {}\n self.jiaoyi_on = {}\n \n def get_jiaoyi_on_off_status(self, gid):\n return self.jiaoyion[gid] if self.jiaoyion.get(gid) is not None else False\n # 记录群交易开关\n def turn_jiaoyion(self, gid):\n self.jiaoyion[gid] = True\n\n def turn_jiaoyioff(self, gid):\n self.jiaoyion[gid] = False\n \n # 记录群交易是否接受开关\n def turn_on_jiaoyi(self, gid):\n self.jiaoyi_on[gid] = True\n\n def turn_off_jiaoyi(self, gid):\n self.jiaoyi_on[gid] = False\n \n # 记录交易者id\n def init_jiaoyiid(self, gid):\n self.jiaoyiid[gid] = []\n\n def set_jiaoyiid(self, gid, id1, id2, cid):\n self.jiaoyiid[gid] = [id1, id2, cid]\n\n def get_jiaoyiid(self, gid):\n return self.jiaoyiid[gid] if self.jiaoyi_on.get(gid) is not None else [0, 0, 0]\n # 记录是否接受交易\n def init_jiaoyiflag(self, gid):\n self.jiaoyiflag[gid] = False\n\n def on_jiaoyiflag(self, gid):\n self.jiaoyiflag[gid] = True\n\n def off_jiaoyiflag(self, gid):\n self.jiaoyiflag[gid] = False\n\n def get_jiaoyiflag(self, gid):\n return self.jiaoyiflag[gid] if self.jiaoyiflag[gid] is not None else False\n \n \nduel_jiaoyier = NvYouJiaoYi()\n\n\n@sv.on_rex(f'^以(\\d+)金币上架女友(.*)$')\nasync def store_shangjia(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n match = ev['match']\n name = str(match.group(2))\n num = int(match.group(1))\n duel = DuelCounter()\n if not name:\n await bot.send(ev, '请输入查女友+pcr角色名。', at_sender=True)\n return\n cid = chara.name2id(name)\n if cid == 1000:\n await bot.send(ev, '请输入正确的pcr角色名。', at_sender=True)\n return\n owner = duel._get_card_owner(gid, cid)\n c = chara.fromid(cid)\n #判断是否是妻子。\n if duel._get_queen_owner(gid,cid) !=0 :\n await bot.finish(ev, f'\\n{c.name}现在是\\n[CQ:at,qq={owner}]的妻子，无法上架哦。', at_sender=True)\n\n if owner == 0:\n await bot.send(ev, f'{c.name}现在还是单身哦，快去约到她吧。', at_sender=True)\n return\n if uid!=owner:\n msg = f'{c.name}现在正在\\n[CQ:at,qq={owner}]的身边哦，您没有上架权限哦。'\n await bot.send(ev, msg)\n return\n duel._add_store(gid, uid, cid, num)\n nvmes = get_nv_icon(cid)\n msg = f'您以{num}金币的价格上架了女友{c.name}{nvmes}。'\n await bot.send(ev, msg, at_sender=True)\n\n\n@sv.on_rex(f'^用(\\d+)金币与(.*)交易女友(.*)$')\nasync def nobleduel(bot, ev: CQEvent):\n if duel_jiaoyier.get_jiaoyi_on_off_status(ev.group_id):\n await bot.send(ev, \"此轮交易还没结束，请勿重复使用指令。\")\n return\n gid = ev.group_id\n match = ev['match']\n try:\n id2 = int(match.group(2))\n except ValueError:\n id2 = int(ev.message[1].data['qq'])\n except:\n await bot.finish(ev, '参数格式错误')\n name = str(match.group(3))\n num = int(match.group(1))\n duel_jiaoyier.turn_jiaoyion(gid)\n id1 = ev.user_id\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n level2 = duel._get_level(gid, id2)\n noblename = get_noblename(level2)\n is_overtime = 0\n num2 = 500\n if duel._get_level(gid, id2) < 7:\n msg = f'该用户等级较低，交易需要扣除您双倍声望喔'\n num2 = 1000\n await bot.send(ev, msg, at_sender=True)\n score = score_counter._get_score(gid, id1)\n prestige = score_counter._get_prestige(gid,id1)\n if score < num:\n msg = f'您的金币不足{num}，无法交易哦。'\n duel_jiaoyier.turn_jiaoyioff(ev.group_id)\n await bot.send(ev, msg, at_sender=True)\n return\n if prestige < num2:\n msg = f'您的声望不足{num2}，无法交易哦。'\n duel_jiaoyier.turn_jiaoyioff(ev.group_id)\n await bot.send(ev, msg, at_sender=True)\n return \n if id2 == id1:\n await bot.send(ev, \"不能和自己交易！\", at_sender=True)\n duel_jiaoyier.turn_jiaoyioff(ev.group_id)\n return \n if girl_outlimit(gid,id1):\n await bot.send(ev, \"您的女友超过了爵位上限，无法进行交易哦！\", at_sender=True)\n duel_jiaoyier.turn_jiaoyioff(ev.group_id)\n return \n \n if duel._get_level(gid, id1) == 0:\n msg = f'[CQ:at,qq={id1}]交易发起者还未在创建过贵族\\n请发送 创建贵族 开始您的贵族之旅。'\n duel_jiaoyier.turn_jiaoyioff(ev.group_id)\n await bot.send(ev, msg)\n return\n if duel._get_cards(gid, id1) == {}:\n msg = f'[CQ:at,qq={id1}]您没有女友，不能参与交易哦。'\n duel_jiaoyier.turn_jiaoyioff(ev.group_id)\n await bot.send(ev, msg)\n return\n\n if duel._get_level(gid, id2) == 0:\n msg = f'[CQ:at,qq={id2}]被交易者还未在本群创建过贵族\\n请发送 创建贵族 开始您的贵族之旅。'\n duel_jiaoyier.turn_jiaoyioff(ev.group_id)\n await bot.send(ev, msg)\n return\n if duel._get_cards(gid, id2) == {}:\n msg = f'[CQ:at,qq={id2}]您没有女友，不能参与交易哦。'\n duel_jiaoyier.turn_jiaoyioff(ev.group_id)\n await bot.send(ev, msg)\n return\n \n if not name:\n await bot.send(ev, '请输入查女友+pcr角色名。', at_sender=True)\n duel_jiaoyier.turn_jiaoyioff(ev.group_id)\n return\n cid = chara.name2id(name)\n if cid == 1000:\n await bot.send(ev, '请输入正确的pcr角色名。', at_sender=True)\n duel_jiaoyier.turn_jiaoyioff(ev.group_id)\n return\n owner = duel._get_card_owner(gid, cid)\n c = chara.fromid(cid)\n #判断是否是妻子。\n if duel._get_queen_owner(gid,cid) !=0 :\n owner = duel._get_queen_owner(gid,cid)\n duel_jiaoyier.turn_jiaoyioff(ev.group_id)\n await bot.finish(ev, f'\\n{c.name}现在是\\n[CQ:at,qq={owner}]的妻子，无法交易哦。', at_sender=True)\n\n if owner == 0:\n await bot.send(ev, f'{c.name}现在还是单身哦，快去约到她吧。', at_sender=True)\n duel_jiaoyier.turn_jiaoyioff(ev.group_id)\n return\n if id2!=owner:\n msg = f'{c.name}现在正在\\n[CQ:at,qq={owner}]的身边哦，您需要与此人进行交易哦。'\n duel_jiaoyier.turn_jiaoyioff(ev.group_id)\n await bot.send(ev, msg)\n return\n duel_jiaoyier.init_jiaoyiflag(gid)\n duel_jiaoyier.set_jiaoyiid(gid, id1, id2, cid)\n duel_jiaoyier.turn_on_jiaoyi(gid)\n msg = f'[CQ:at,qq={id2}]尊敬的{noblename}您好\\n[CQ:at,qq={id1}]试图以{num}金币的价格购买您的女友{c.name}，请在{WAIT_TIME_jy}秒内[接受交易/拒绝交易]，女友交易需要收5%手续��哦。'\n await bot.send(ev, msg)\n \n await asyncio.sleep(WAIT_TIME_jy)\n duel_jiaoyier.turn_off_jiaoyi(gid)\n if duel_jiaoyier.get_jiaoyiflag(gid) is False:\n msg = '交易被拒绝。'\n duel_jiaoyier.turn_jiaoyioff(gid)\n await bot.send(ev, msg, at_sender=True)\n return\n \n duel = DuelCounter()\n get_num=num*0.95\n score_counter._add_score(gid, id2, get_num)\n score = score_counter._get_score(gid, id2)\n \n score_counter._reduce_score(gid, id1, num)\n scoreyou = score_counter._get_score(gid, id1)\n duel._delete_card(gid, id2, cid)\n duel._add_card(gid, id1, cid)\n duel_jiaoyier.turn_jiaoyioff(gid)\n nvmes = get_nv_icon(cid)\n score_counter._reduce_prestige(gid,id1,num2)\n msg = f'[CQ:at,qq={id1}]以{num}金币的价格购买了[CQ:at,qq={id2}]的女友{c.name}，交易成功\\n[CQ:at,qq={id1}]您失去了{num}金币，{num2}声望，剩余{scoreyou}金币\\n[CQ:at,qq={id2}]扣除5%手续费，您能得到了{get_num}金币，剩余{score}金币。{nvmes}'\n await bot.send(ev, msg)\n\n\n@sv.on_fullmatch('接受交易')\nasync def duelaccept(bot, ev: CQEvent):\n gid = ev.group_id\n if duel_jiaoyier.get_jiaoyi_on_off_status(gid):\n if ev.user_id == duel_jiaoyier.get_jiaoyiid(gid)[1]:\n gid = ev.group_id\n msg = '交易接受成功，请耐心等待交易开始。'\n await bot.send(ev, msg, at_sender=True)\n duel_jiaoyier.turn_off_jiaoyi(gid)\n duel_jiaoyier.on_jiaoyiflag(gid)\n else:\n print('不是被交易者')\n else:\n print('现在不在交易期间')\n\n\n@sv.on_fullmatch('重置交易')\nasync def init_duel(bot, ev: CQEvent):\n if not priv.check_priv(ev, priv.ADMIN):\n await bot.finish(ev, '只有群管理才能使用重置交易哦。', at_sender=True)\n duel_jiaoyier.turn_jiaoyioff(ev.group_id)\n msg = '已重置本群交易状态！'\n await bot.send(ev, msg, at_sender=True)\n\n@sv.on_fullmatch('拒绝交易')\nasync def duelrefuse(bot, ev: CQEvent):\n gid = ev.group_id\n if duel_jiaoyier.get_jiaoyi_on_off_status(gid):\n if ev.user_id == duel_jiaoyier.get_jiaoyiid(gid)[1]:\n gid = ev.group_id\n msg = '您已拒绝女友交易。'\n await bot.send(ev, msg, at_sender=True)\n duel_jiaoyier.turn_off_jiaoyi(gid)\n duel_jiaoyier.off_jiaoyiflag(gid)\n\n@sv.on_fullmatch('贵族签到')\nasync def noblelogin(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n guid = gid, uid\n if not daily_sign_limiter.check(guid):\n await bot.send(ev, '今天已经签到过了哦，明天再来吧。', at_sender=True)\n return\n duel = DuelCounter()\n \n if duel._get_level(gid, uid) == 0:\n msg = '您还未在本群创建过贵族，请发送 创建贵族 开始您的贵族之旅。'\n await bot.send(ev, msg, at_sender=True)\n return\n #根据概率随机获得收益。 \n score_counter = ScoreCounter2()\n prestige = score_counter._get_prestige(gid,uid)\n if prestige == None :\n score_counter._set_prestige(gid,uid,0)\n daily_sign_limiter.increase(guid) \n loginnum_ = ['1','2', '3', '4'] \n r_ = [0.3, 0.4, 0.2, 0.1] \n sum_ = 0\n ran = random.random()\n for num, r in zip(loginnum_, r_):\n sum_ += r\n if ran < sum_ :break\n Bonus = {'1':[200,Login100],\n '2':[500,Login200],\n '3':[700,Login300], \n '4':[1000,Login600]\n } \n score1 = Bonus[num][0]\n score1 = 3 * score1\n text1 = random.choice(Bonus[num][1])\n \n #根据爵位的每日固定收入\n level = duel._get_level(gid, uid)\n score2 = 300*level\n SW2 = 100*level\n scoresum = score1+score2\n noblename = get_noblename(level)\n score = score_counter._get_score(gid, uid) \n if duel._get_QC_CELE(gid) == 1:\n scoresum = scoresum * QD_Gold_Cele_Num\n SW2 = SW2 * QD_SW_Cele_Num\n msg = f'\\n{text1}\\n签到成功！\\n[庆典举办中]\\n您领取了：\\n\\n{score1}金币(随机)和\\n{score2}金币以及{SW2}声望({noblename}爵位)'\n else:\n msg = f'\\n{text1}\\n签到成功！\\n您领取了：\\n\\n{score1}金币(随机)和\\n{score2}金币以及{SW2}声望({noblename}爵位)'\n score_counter._add_prestige(gid,uid,SW2)\n score_counter._add_score(gid, uid, scoresum)\n cidlist = duel._get_cards(gid, uid)\n cidnum = len(cidlist)\n \n if cidnum > 0:\n cid = random.choice(cidlist)\n c = chara.fromid(cid)\n nvmes = get_nv_icon(cid)\n msg +=f'\\n\\n今天向您请安的是\\n{c.name}{nvmes}' \n #随机获得一件礼物\n new_dict = {v : k for k, v in GIFT_DICT.items()}\n gfid = random.choice((11,12,13,14))\n select_gift = new_dict[gfid]\n duel._add_gift(gid,uid,gfid)\n msg +=f'\\n随机获得了礼物[{select_gift}]'\n await bot.send(ev, msg, at_sender=True)\n \n@sv.on_fullmatch('免费招募')\nasync def noblelogin(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter() \n if duel._get_FREE_CELE(gid) != 1:\n await bot.send(ev, '当前未开放免费招募庆典！', at_sender=True)\n return\n else:\n guid = gid, uid\n if not daily_free_limiter.check(guid):\n await bot.send(ev, '今天已经免费招募过了喔，明天再来吧。(免费招募次数每天0点刷新)', at_sender=True)\n return \n if duel._get_level(gid, uid) == 0:\n msg = '您还未在本群创建过贵族，请发送 创建贵族 开始您的贵族之旅。'\n await bot.send(ev, msg, at_sender=True)\n return \n score_counter = ScoreCounter2()\n if duel_judger.get_on_off_status(ev.group_id):\n msg = '现在正在决斗中哦，请决斗后再参加舞会吧。'\n await bot.send(ev, msg, at_sender=True)\n return \n else:\n # 防止女友数超过上限\n level = duel._get_level(gid, uid)\n noblename = get_noblename(level)\n girlnum = get_girlnum_buy(gid,uid)\n cidlist = duel._get_cards(gid, uid)\n cidnum = len(cidlist)\n if cidnum >= girlnum:\n msg = '您的女友已经满了哦，您转为获得500声望。'\n score_counter._add_prestige(gid, uid, 500)\n daily_free_limiter.increase(guid) \n await bot.send(ev, msg, at_sender=True)\n return\n score = score_counter._get_score(gid, uid)\n prestige = score_counter._get_prestige(gid,uid)\n if prestige == None:\n score_counter._set_prestige(gid,uid,0)\n newgirllist = get_newgirl_list(gid)\n # 判断女友是否被抢没和该用户是否已经没有女友\n if len(newgirllist) == 0:\n if cidnum!=0:\n await bot.send(ev, '这个群已经没有可以约到的新女友了哦。', at_sender=True)\n return \n else : \n score_counter._reduce_score(gid, uid, GACHA_COST)\n cid = 9999\n c = chara.fromid(1059)\n duel._add_card(gid, uid, cid)\n msg = f'本群已经没有可以约的女友了哦，一位神秘的可可萝在你孤单时来到了你身边。{c.icon.cqcode}。'\n await bot.send(ev, msg, at_sender=True)\n return\n\n # 招募女友成功\n daily_free_limiter.increase(guid) \n cid = random.choice(newgirllist)\n c = chara.fromid(cid)\n nvmes = get_nv_icon(cid)\n duel._add_card(gid, uid, cid)\n wintext = random.choice(Addgirlsuccess)\n msg = f'\\n{wintext}\\n招募女友成功！\\n新招募的女友为：{c.name}{nvmes}'\n await bot.send(ev, msg, at_sender=True)\n \n@sv.on_fullmatch(['本群贵族状态','查询本群贵族','本群贵族'])\nasync def group_noble_status(bot, ev: CQEvent):\n gid = ev.group_id\n duel = DuelCounter()\n newgirllist = get_newgirl_list(gid)\n newgirlnum = len(newgirllist)\n l1_num = duel._get_level_num(gid,1)\n l2_num = duel._get_level_num(gid,2)\n l3_num = duel._get_level_num(gid,3)\n l4_num = duel._get_level_num(gid,4)\n l5_num = duel._get_level_num(gid,5)\n l6_num = duel._get_level_num(gid,6)\n l7_num = duel._get_level_num(gid,7)\n l8_num = duel._get_level_num(gid,8)\n l9_num = duel._get_level_num(gid,9)\n lA_num = duel._get_level_num(gid,10)\n lB_num = duel._get_level_num(gid,20)\n dlctext=''\n for dlc in dlcdict.keys():\n if gid in dlc_switch[dlc]:\n dlctext += f'{dlcintro[dlc]}\\n'\n msg=f'''\n╔ ╗\n 本群贵族\n 神：{lB_num}名 \n 皇帝：{lA_num}名 \n 国王：{l9_num}名 \n 公爵：{l8_num}名 \n 侯爵：{l7_num}名\n 伯爵：{l6_num}名\n 子爵：{l5_num}名\n 男爵：{l4_num}名\n 准男爵：{l3_num}名\n 骑士：{l2_num}名\n 平民：{l1_num}名\n 已开启DLC:\n {dlctext}\n 还有{newgirlnum}名单身女友 \n╚ ╝\n'''\n tas_list=[]\n data = {\n \"type\": \"node\",\n \"data\": {\n \"name\": str(NICKNAME[0]),\n \"uin\": str(ev.self_id),\n \"content\":msg\n }\n }\n tas_list.append(data)\n await bot.send_group_forward_msg(group_id=ev['group_id'], messages=tas_list)\n \n@sv.on_fullmatch('创建贵族')\nasync def add_noble(bot, ev: CQEvent):\n try:\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n if duel._get_level(gid, uid) != 0:\n msg = '您已经在本群创建过贵族了，请发送 查询贵族 查询。'\n await bot.send(ev, msg, at_sender=True)\n return\n \n #判定本群女友是否已空，如果空则分配一个复制人可可萝。\n newgirllist = get_newgirl_list(gid)\n if len(newgirllist) == 0:\n cid = 9999\n c = chara.fromid(1059)\n girlmsg = f'���群已经没有可以约的女友了哦，一位神秘的可可萝在你孤单时来到了你身边。{c.icon.cqcode}。'\n else:\n cid = random.choice(newgirllist)\n c = chara.fromid(cid)\n girlmsg = f'为您分配的初始女友为：{c.name}{c.icon.cqcode}'\n duel._add_card(gid, uid, cid)\n duel._set_level(gid, uid, 1)\n msg = f'\\n创建贵族成功！\\n您的初始爵位是平民\\n可以拥有1名女友。\\n初始金币为1000，初始声望为0\\n{girlmsg}'\n score_counter = ScoreCounter2()\n score_counter._set_prestige(gid,uid,0)\n score_counter._add_score(gid, uid, 1000)\n \n await bot.send(ev, msg, at_sender=True) \n \n\n except Exception as e:\n await bot.send(ev, '错误:\\n' + str(e))\n\n\n@sv.on_fullmatch(['增加容量', '增加女友上限'])\nasync def add_warehouse(bot, ev: CQEvent):\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n gid = ev.group_id\n uid = ev.user_id\n current_score = score_counter._get_score(gid, uid)\n prestige = score_counter._get_prestige(gid,uid)\n if duel._get_level(gid, uid) <= 9:\n msg = '只有成为皇帝后，才能扩充女友上限喔'\n await bot.send(ev, msg, at_sender=True)\n return\n if prestige < SHANGXIAN_SW:\n msg = '扩充女友上限，需要{SHANGXIAN_SW}声望，您的声望不足喔'\n await bot.send(ev, msg, at_sender=True)\n return\n if current_score < SHANGXIAN_NUM:\n msg = f'增加女友上限需要消耗{SHANGXIAN_NUM}金币，您的金币不足哦'\n await bot.send(ev, msg, at_sender=True)\n return\n else:\n housenum=duel._get_warehouse(gid, uid)\n if housenum>=WAREHOUSE_NUM:\n msg = f'您已增加{WAREHOUSE_NUM}次上限，无法继续增加了哦'\n await bot.send(ev, msg, at_sender=True)\n return\n duel._add_warehouse(gid, uid, 1)\n score_counter._reduce_score(gid, uid, SHANGXIAN_NUM)\n score_counter._reduce_prestige(gid, uid, SHANGXIAN_SW)\n last_score = current_score-SHANGXIAN_NUM\n myhouse = get_girlnum_buy(gid, uid)\n msg = f'您消耗了{SHANGXIAN_NUM}金币，{SHANGXIAN_SW}声望，增加了1个女友上限，目前的女友上限为{myhouse}名'\n await bot.send(ev, msg, at_sender=True)\n\n@sv.on_fullmatch(['查询贵族', '贵族查询', '我的贵族'])\nasync def inquire_noble(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n CE = CECounter()\n score_counter = ScoreCounter2()\n if duel._get_level(gid, uid) == 0:\n msg = '您还未在本群创建过贵族，请发送 创建贵族 开始您的贵族之旅。'\n await bot.send(ev, msg, at_sender=True)\n return\n level = duel._get_level(gid, uid)\n noblename = get_noblename(level)\n girlnum = get_girlnum_buy(gid,uid)\n score = score_counter._get_score(gid, uid)\n charalist = []\n\n cidlist = duel._get_cards(gid, uid)\n cidnum = len(cidlist)\n prestige = score_counter._get_prestige(gid,uid)\n if prestige == None:\n prestige = 0\n partmsg = f'您的声望为{prestige}点'\n else:\n partmsg = f'您的声望为{prestige}点'\n nv_names=''\n if cidnum == 0:\n msg = f'''\n╔ ╗\n 您的爵位为{noblename}\n 您的金币为{score}\n {partmsg}\n 您共可拥有{girlnum}名女友\n 您目前没有女友。\n 发送[贵族约会]\n 可以招募女友哦。\n \n╚ ╝\n'''\n await bot.send(ev, msg, at_sender=True)\n\n else:\n shuzi_flag=0\n for cid in cidlist:\n #替换复制人可可萝\n if cid == 9999:\n cid = 1059\n star = CE._get_cardstar(gid,uid,cid)\n charalist.append(chara.Chara(cid, star, 0))\n c = chara.fromid(cid)\n shuzi_flag=shuzi_flag+1\n nv_names=nv_names+c.name+' '\n if shuzi_flag==6:\n nv_names=nv_names+'\\n'\n shuzi_flag=0\n \n \n #制图部分，六个一行\n num = copy.deepcopy(cidnum)\n position = 6\n if num <= 6:\n res = chara.gen_team_pic(charalist, star_slot_verbose=False)\n else:\n num -= 6\n res = chara.gen_team_pic(charalist[0:position], star_slot_verbose=False)\n while(num > 0):\n if num>=6:\n res1 = chara.gen_team_pic(charalist[position:position+6], star_slot_verbose=False)\n else: \n res1 = chara.gen_team_pic(charalist[position:], star_slot_verbose=False)\n res = concat_pic([res, res1])\n position +=6\n num -= 6\n \n\n bio = BytesIO()\n res.save(bio, format='PNG')\n base64_str = 'base64://' + base64.b64encode(bio.getvalue()).decode()\n mes = f\"[CQ:image,file={base64_str}]\"\n \n #判断是否开启声望\n\n \n \n \n \n \n msg = f'''\n╔ ╗\n 您的爵位为{noblename}\n 您的金币为{score}\n {partmsg}\n 您共可拥有{girlnum}名女友\n 您已拥有{cidnum}名女友\n 她们是：\n {nv_names}\n {mes} \n╚ ╝\n'''\n #判断有无妻子\n queen = duel._search_queen(gid,uid)\n if queen != 0:\n c = chara.fromid(queen)\n \n msg = f'''\n╔ ╗\n 您的爵位为{noblename}\n 您的金币为{score}\n {partmsg}\n 您的妻子是{c.name}\n 您共可拥有{girlnum}名女友\n 您已拥有{cidnum}名女友\n 她们是：\n {nv_names}\n {mes} \n \n╚ ╝\n'''\n\n\n tas_list=[]\n data = {\n \"type\": \"node\",\n \"data\": {\n \"name\": str(NICKNAME[0]),\n \"uin\": str(ev.self_id),\n \"content\":msg\n }\n }\n tas_list.append(data)\n await bot.send_group_forward_msg(group_id=ev['group_id'], messages=tas_list)\n\n\n@sv.on_fullmatch(['招募女友', '贵族舞会'])\nasync def add_girl(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n if duel_judger.get_on_off_status(ev.group_id):\n msg = '现在正在决斗中哦，请决斗后再参加舞会吧。'\n await bot.send(ev, msg, at_sender=True)\n return \n if duel._get_level(gid, uid) == 0:\n msg = '您还未在本群创建过贵族，请发送 创建贵族 开始您的贵族之旅。'\n duel_judger.turn_off(ev.group_id)\n await bot.send(ev, msg, at_sender=True)\n return\n else:\n # 防止女友数超过上限\n level = duel._get_level(gid, uid)\n noblename = get_noblename(level)\n girlnum = get_girlnum_buy(gid,uid)\n cidlist = duel._get_cards(gid, uid)\n cidnum = len(cidlist)\n if cidnum >= girlnum:\n msg = '您的女友已经满了哦，快点发送[升级贵族]进行升级吧。'\n await bot.send(ev, msg, at_sender=True)\n return\n score = score_counter._get_score(gid, uid)\n if score < GACHA_COST:\n msg = f'您的金币不足{GACHA_COST}哦。'\n await bot.send(ev, msg, at_sender=True)\n return\n prestige = score_counter._get_prestige(gid,uid)\n if prestige == None:\n score_counter._set_prestige(gid,uid,0)\n if prestige < 0 and level >7:\n msg = f'您现在身败名裂（声望为负），无法招募女友！。'\n await bot.send(ev, msg, at_sender=True)\n return\n newgirllist = get_newgirl_list(gid)\n # 判断女友是否被抢没和该用户是否已经没有女友\n if len(newgirllist) == 0:\n if cidnum!=0:\n await bot.send(ev, '这个群已经没有可以约到的新女友了哦。', at_sender=True)\n return \n else : \n score_counter._reduce_score(gid, uid, GACHA_COST)\n cid = 9999\n c = chara.fromid(1059)\n duel._add_card(gid, uid, cid)\n msg = f'本群已经没有可以约的女友了哦，一位神秘的可可萝在你孤单时来到了你身边。{c.icon.cqcode}。'\n await bot.send(ev, msg, at_sender=True)\n return\n\n score_counter._reduce_score(gid, uid, GACHA_COST)\n\n # 招募女友失败\n if random.random() < 0.4:\n losetext = random.choice(Addgirlfail)\n msg = f'\\n{losetext}\\n您花费了{GACHA_COST}金币，但是没有约到新的女友。获得了{GACHA_COST_Fail}金币补偿。'\n score_counter._add_score(gid, uid, GACHA_COST_Fail)\n score = score_counter._get_score(gid, uid)\n await bot.send(ev, msg, at_sender=True)\n return\n\n # 招募女友成功\n cid = random.choice(newgirllist)\n c = chara.fromid(cid)\n nvmes = get_nv_icon(cid)\n duel._add_card(gid, uid, cid)\n wintext = random.choice(Addgirlsuccess)\n \n msg = f'\\n{wintext}\\n招募女友成功！\\n您花费了{GACHA_COST}金币\\n新招募的女友为：{c.name}{nvmes}'\n await bot.send(ev, msg, at_sender=True)\n \n@sv.on_fullmatch('声望招募')\nasync def add_girl(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n if duel._get_SW_CELE(gid) != 1 and duel._get_level(gid, uid) != 20:\n msg = '目前不在限时开放声望招募期间，只有神能参与！'\n duel_judger.turn_off(ev.group_id)\n await bot.send(ev, msg, at_sender=True)\n return\n if duel_judger.get_on_off_status(ev.group_id):\n msg = '现在正在决斗中哦，请决斗后再参加舞会吧。'\n await bot.send(ev, msg, at_sender=True)\n return \n else:\n # 防止女友数超过上限\n level = duel._get_level(gid, uid)\n noblename = get_noblename(level)\n girlnum = get_girlnum_buy(gid,uid) + 10\n cidlist = duel._get_cards(gid, uid)\n cidnum = len(cidlist)\n if cidnum >= girlnum:\n msg = '您的女友已经满了哦，快点发送[升级贵族]进行升级吧。'\n await bot.send(ev, msg, at_sender=True)\n return\n score = score_counter._get_score(gid, uid)\n needSW2 = SW_COST\n prestige = score_counter._get_prestige(gid,uid)\n if prestige == None:\n score_counter._set_prestige(gid,uid,0)\n if prestige < needSW2:\n msg = f'您的声望不足{needSW2}哦。'\n await bot.send(ev, msg, at_sender=True)\n return\n\n\n newgirllist = get_newgirl_list(gid)\n # 判断女友是否被抢没和该用户是否已经没有女友\n if len(newgirllist) == 0:\n if cidnum!=0:\n await bot.send(ev, '这个群已经没有可以约到的新女友了哦。', at_sender=True)\n return \n else : \n score_counter._reduce_prestige(gid, uid, needSW2)\n cid = 9999\n c = chara.fromid(1059)\n duel._add_card(gid, uid, cid)\n msg = f'本群已经没有可以约的女友了哦，一位神秘的可可萝在你孤单时来到了你身边。{c.icon.cqcode}。'\n await bot.send(ev, msg, at_sender=True)\n return\n\n score_counter._reduce_prestige(gid, uid, needSW2)\n # 招募女友成功\n cid = random.choice(newgirllist)\n c = chara.fromid(cid)\n nvmes = get_nv_icon(cid)\n duel._add_card(gid, uid, cid)\n wintext = random.choice(Addgirlsuccess)\n \n msg = f'\\n{wintext}\\n招募女友成功！\\n您花费了{needSW2}声望\\n新招募的女友为：{c.name}{nvmes}'\n await bot.send(ev, msg, at_sender=True)\n\n\n\n@sv.on_fullmatch(['升级爵位', '升级贵族','贵族升级'])\nasync def add_girl(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n score = score_counter._get_score(gid, uid)\n level = duel._get_level(gid, uid)\n noblename = get_noblename(level)\n girlnum = get_girlnum(level)\n cidlist = duel._get_cards(gid, uid)\n cidnum = len(cidlist)\n\n if duel_judger.get_on_off_status(ev.group_id):\n msg = '现在正在决斗中哦，请决斗后再升级爵位吧。'\n await bot.send(ev, msg, at_sender=True)\n return \n if duel._get_level(gid, uid) == 0:\n msg = '您还未在本群创建过贵族，请发送 创建贵族 开始您的贵族之旅。'\n await bot.send(ev, msg, at_sender=True)\n return \n if level == 9:\n msg = f'您已经是国王了， 需要通过声望加冕称帝哦。'\n await bot.send(ev, msg, at_sender=True)\n return\n\n if level == 10:\n msg = f'您是本群的皇帝， 再往前一步就能成神了，请飞升成神。'\n await bot.send(ev, msg, at_sender=True)\n return\n\n if level == 20:\n msg = f'您已经到达了世界的巅峰， 无法再继续提升了。'\n await bot.send(ev, msg, at_sender=True)\n return\n \n if cidnum < girlnum:\n msg = f'您的女友没满哦。\\n需要达到{girlnum}名女友\\n您现在有{cidnum}名。'\n await bot.send(ev, msg, at_sender=True)\n return\n prestige = score_counter._get_prestige(gid,uid) \n needscore = get_noblescore(level + 1)\n futurename = get_noblename(level + 1)\n needSW = get_noblesw(level + 1)\n if score < needscore:\n msg = f'您的金币不足哦。\\n升级到{futurename}需要{needscore}金币'\n await bot.send(ev, msg, at_sender=True)\n return\n \n if level > 5 :\n if prestige == None:\n score_counter._set_prestige(gid,uid,0)\n await bot.finish(ev, '您还未开启声望系统哦，已为您开启！', at_sender=True)\n \n if prestige < needSW: \n await bot.finish(ev, '您的声望不足哦。', at_sender=True)\n\n score_counter._reduce_prestige(gid, uid, needSW)\n score_counter._reduce_score(gid, uid, needscore)\n duel._add_level(gid, uid)\n newlevel = duel._get_level(gid, uid)\n newnoblename = get_noblename(newlevel)\n newgirlnum = get_girlnum_buy(gid,uid)\n msg = f'花费了{needscore}金币和{needSW}声望\\n您成功由{noblename}升到了{newnoblename}\\n可以拥有{newgirlnum}名女友了哦。'\n await bot.send(ev, msg, at_sender=True)\n\n\n@sv.on_prefix('贵族决斗')\nasync def nobleduel(bot, ev: CQEvent):\n if ev.message[0].type == 'at':\n id2 = int(ev.message[0].data['qq'])\n else:\n await bot.finish(ev, '参数格式错误, 请重试')\n if duel_judger.get_on_off_status(ev.group_id):\n await bot.send(ev, \"此轮决斗还没结束，请勿重复使用指令。\")\n return\n \n gid = ev.group_id\n duel_judger.turn_on(gid)\n duel = DuelCounter()\n args = ev.message.extract_plain_text().split()\n score_counter = ScoreCounter2()\n id1 = ev.user_id\n duel = DuelCounter()\n is_overtime = 0\n prestige = score_counter._get_prestige(gid,id1)\n prestige2 = score_counter._get_prestige(gid,id2)\n level1 = duel._get_level(gid, id1)\n level2 = duel._get_level(gid, id2)\n if id2 == id1:\n await bot.send(ev, \"不能和自己决斗！\", at_sender=True)\n duel_judger.turn_off(ev.group_id)\n return \n\n if duel._get_level(gid, id1) == 0:\n msg = f'[CQ:at,qq={id1}]决斗发起者还未在创建过贵族\\n请发送 创建贵族 开始您的贵族之旅。'\n duel_judger.turn_off(ev.group_id)\n await bot.send(ev, msg)\n return\n if duel._get_cards(gid, id1) == {}:\n msg = f'[CQ:at,qq={id1}]您没有女友，不能参与决斗哦。'\n duel_judger.turn_off(ev.group_id)\n await bot.send(ev, msg)\n return\n\n if duel._get_level(gid, id2) == 0:\n msg = f'[CQ:at,qq={id2}]被决斗者还未在本群创建过贵族\\n请发送 创建贵族 开始您的贵族之旅。'\n duel_judger.turn_off(ev.group_id)\n await bot.send(ev, msg)\n return\n if duel._get_cards(gid, id2) == {}:\n msg = f'[CQ:at,qq={id2}]您没有女友，不能参与决斗哦。'\n duel_judger.turn_off(ev.group_id)\n await bot.send(ev, msg)\n return\n #判定每日上限\n guid = gid ,id1\n if not daily_duel_limiter.check(guid):\n await bot.send(ev, '今天的决斗次数已经超过上限了哦，明天再来吧。', at_sender=True)\n duel_judger.turn_off(ev.group_id)\n return\n daily_duel_limiter.increase(guid)\n if not args:\n force = 0\n else:\n if args[0] == '强制':\n gfid = 11\n if duel._get_gift_num(gid,id1,gfid)==0:\n duel_judger.turn_off(ev.group_id)\n await bot.finish(ev, '您并未持有强制决斗卡！')\n duel._reduce_gift(gid,id1,gfid)\n force = 1\n\n\n\n # 判定双方的女友是否已经超过上限\n\n # 这里设定大于才会提醒，就是可以超上限1名，可以自己改成大于等于。\n if girl_outlimit(gid,id1):\n msg = f'[CQ:at,qq={id1}]您的女友超过了爵位上限，本次决斗获胜只能获得金币哦。'\n await bot.send(ev, msg)\n if girl_outlimit(gid,id2):\n msg = f'[CQ:at,qq={id2}]您的女友超过了爵位上限，本次决斗获胜只能获得金币哦。'\n await bot.send(ev, msg)\n duel_judger.init_isaccept(gid)\n duel_judger.set_duelid(gid, id1, id2)\n duel_judger.turn_on_accept(gid)\n msg = f'[CQ:at,qq={id2}]对方向您发起了优雅的贵族决斗，请在{WAIT_TIME}秒内[接受/拒绝]。'\n\n await bot.send(ev, msg)\n await asyncio.sleep(WAIT_TIME)\n duel_judger.turn_off_accept(gid)\n if duel_judger.get_isaccept(gid) is False and force != 1:\n msg = '决斗被拒绝。'\n duel_judger.turn_off(gid)\n await bot.send(ev, msg, at_sender=True)\n return\n duel = DuelCounter()\n level1 = duel._get_level(gid, id1)\n noblename1 = get_noblename(level1)\n level2 = duel._get_level(gid, id2)\n noblename2 = get_noblename(level2)\n if duel._get_GOLD_CELE(gid) == 1:\n msg = f'''对方接受了决斗！ \n1号：[CQ:at,qq={id1}]\n爵位为：{noblename1}\n2号：[CQ:at,qq={id2}]\n爵位为：{noblename2}\n其他人请在{DUEL_SUPPORT_TIME}秒选择支持的对象\n[庆典举办中]支持成功时，金币的获取量将会变为{Gold_Cele_Num * WIN_NUM}倍！\n[支持1/2号xxx金币]'''\n else:\n msg = f'''对方接受了决斗！ \n1号：[CQ:at,qq={id1}]\n爵位为：{noblename1}\n2号：[CQ:at,qq={id2}]\n爵位为：{noblename2}\n其他人请在{DUEL_SUPPORT_TIME}秒选择支持的对象\n支持成功时，金币的获取量将会变为{WIN_NUM}倍！\n[支持1/2号xxx金币]'''\n\n await bot.send(ev, msg)\n duel_judger.turn_on_support(gid)\n deadnum = int(math.floor( random.uniform(1,7) ))\n print (\"死的位置是\", deadnum)\n duel_judger.set_deadnum(gid, deadnum)\n await asyncio.sleep(DUEL_SUPPORT_TIME)\n duel_judger.turn_off_support(gid)\n duel_judger.init_turn(gid)\n duel_judger.turn_on_fire(gid)\n duel_judger.turn_off_hasfired(gid)\n msg = f'支持环节结束，下面请决斗双方轮流[开枪]。\\n[CQ:at,qq={id1}]先开枪，30秒未开枪自动认输'\n\n await bot.send(ev, msg)\n n = 1\n while (n <= 6):\n wait_n = 0\n while (wait_n < 30):\n if duel_judger.get_on_off_hasfired_status(gid):\n break\n\n wait_n += 1\n await asyncio.sleep(1)\n if wait_n >= 30:\n # 超时未开枪的胜负判定\n loser = duel_judger.get_duelid(gid)[duel_judger.get_turn(gid) - 1]\n winner = duel_judger.get_duelid(gid)[2 - duel_judger.get_turn(gid)]\n msg = f'[CQ:at,qq={loser}]\\n你明智的选择了认输。'\n await bot.send(ev, msg)\n \n #记录本局为超时局。\n is_overtime = 1\n \n \n break\n else:\n if n == duel_judger.get_deadnum(gid):\n # 被子弹打到的胜负判定\n loser = duel_judger.get_duelid(gid)[duel_judger.get_turn(gid) - 1]\n winner = duel_judger.get_duelid(gid)[2 - duel_judger.get_turn(gid)]\n msg = f'[CQ:at,qq={loser}]\\n砰！你死了。'\n await bot.send(ev, msg)\n break\n else:\n id = duel_judger.get_duelid(gid)[duel_judger.get_turn(gid) - 1]\n id2 = duel_judger.get_duelid(gid)[2 - duel_judger.get_turn(gid)]\n msg = f'[CQ:at,qq={id}]\\n砰！松了一口气，你并没有死。\\n[CQ:at,qq={id2}]\\n轮到你开枪了哦。'\n await bot.send(ev, msg)\n n += 1\n duel_judger.change_turn(gid)\n duel_judger.turn_off_hasfired(gid)\n duel_judger.turn_on_fire(gid)\n score_counter = ScoreCounter2()\n \n cidlist = duel._get_cards(gid, loser)\n selected_girl = random.choice(cidlist)\n queen = duel._search_queen(gid,loser)\n \n CE = CECounter()\n bangdinwin = CE._get_guaji(gid,winner)\n bangdinlose = CE._get_guaji(gid,loser)\n #判断决斗胜利者是否有绑定角色,有则增加经验值\n bd_msg=''\n if bangdinwin:\n bd_info = chara.fromid(bangdinwin)\n card_level=add_exp(gid,winner,bangdinwin,WIN_EXP)\n nvmes = get_nv_icon(bangdinwin)\n up_info = duel._get_fashionup(gid,winner,bangdinwin,0)\n if up_info: \n fashion_info = get_fashion_info(up_info)\n nvmes = fashion_info['icon']\n bd_msg = f\"\\n您绑定的女友{bd_info.name}获得了{WIN_EXP}点经验，{card_level[2]}\\n{nvmes}\"\n \n baohu = 0\n if duel._get_gift_num(gid,loser,12) !=0:\n baohu = 1\n #判定被输掉的是否是复制人可可萝，是则换成金币。\n if baohu == 0:\n #判定被输掉的是否是复制人可可萝，是则换成金币。\n if selected_girl==9999:\n score_counter._add_score(gid, winner, 300)\n c = chara.fromid(1059)\n nvmes = get_nv_icon(1059)\n duel._delete_card(gid, loser, selected_girl)\n msg = f'[CQ:at,qq={winner}]\\n您赢得了神秘的可可萝，但是她微笑着消失了。\\n本次决斗获得了300金币。'\n await bot.send(ev, msg)\n msg = f'[CQ:at,qq={loser}]\\n您输掉了贵族决斗，被抢走了女友\\n{c.name}，\\n只要招募，她就还会来到你的身边哦。{nvmes}'\n await bot.send(ev, msg)\n\n #判断被输掉的是否为妻子。 \n elif selected_girl==queen:\n score_counter._add_score(gid, winner, 1000)\n msg = f'[CQ:at,qq={winner}]您赢得的角色为对方的妻子，\\n您改为获得1000金币。'\n await bot.send(ev, msg)\n score_counter._reduce_prestige(gid,loser,300)\n msg = f'[CQ:at,qq={loser}]您差点输掉了妻子，额外失去了300声望。'\n await bot.send(ev, msg)\n \n #判断被输掉的是否为绑定经验获取角色。 \n elif selected_girl==bangdinlose:\n score_counter._add_score(gid, winner, 1000)\n msg = f'[CQ:at,qq={winner}]您赢得的角色为对方的绑定女友，\\n您改为获得2000金币。'\n await bot.send(ev, msg)\n score_counter._reduce_prestige(gid,loser,500)\n msg = f'[CQ:at,qq={loser}]您差点输掉了绑定女友，额外失去了500声望。'\n await bot.send(ev, msg)\n\n\n elif girl_outlimit(gid,winner):\n score_counter._add_score(gid, winner, 1000)\n msg = f'[CQ:at,qq={winner}]您的女友超过了爵位上限，\\n本次决斗获得了300金币。'\n c = chara.fromid(selected_girl)\n #判断好感是否足够，足够则扣掉好感\n favor = duel._get_favor(gid,loser,selected_girl)\n if favor>=favor_reduce:\n c = chara.fromid(selected_girl)\n duel._reduce_favor(gid,loser,selected_girl,favor_reduce)\n msg = f'[CQ:at,qq={loser}]您输掉了贵族决斗，您与{c.name}的好感下降了50点。\\n{c.icon.cqcode}'\n await bot.send(ev, msg) \n else:\n duel._delete_card(gid, loser, selected_girl)\n msg = f'[CQ:at,qq={loser}]您输掉了贵族决斗且对方超过了爵位上限，您的女友恢复了单身。\\n{c.name}{c.icon.cqcode}'\n await bot.send(ev, msg)\n\n else:\n #判断好感是否足够，足够则扣掉好感\n favor = duel._get_favor(gid,loser,selected_girl) \n if favor>=favor_reduce:\n duel._reduce_favor(gid,loser,selected_girl,favor_reduce)\n msg = f'[CQ:at,qq={loser}]您输掉了贵族决斗，您与{c.name}的好感下降了50点。\\n{c.icon.cqcode}'\n await bot.send(ev, msg) \n score_counter._add_score(gid, winner, 300)\n msg = f'[CQ:at,qq={winner}]您赢得了决斗，对方女友仍有一定好感。\\n本次决斗获得了300金币。'\n await bot.send(ev, msg) \n else:\n c = chara.fromid(selected_girl)\n duel._delete_card(gid, loser, selected_girl)\n duel._add_card(gid, winner, selected_girl)\n msg = f'[CQ:at,qq={loser}]您输掉了贵族决斗，您被抢走了女友\\n{c.name}{c.icon.cqcode}'\n await bot.send(ev, msg)\n #判断赢家的角色列表里是否有复制人可可萝。\n wincidlist = duel._get_cards(gid, winner)\n if 9999 in wincidlist:\n duel._delete_card(gid, winner, 9999)\n score_counter._add_score(gid, winner, 300)\n msg = f'[CQ:at,qq={winner}]\\n“主人有了女友已经不再孤单了，我暂时离开了哦。”\\n您赢得了{c.name},可可萝微笑着消失了。\\n您获得了300金币。'\n await bot.send(ev, msg)\n else:\n msg = f'[CQ:at,qq={winner}]\\n对方使用了保护卡，您没能抢夺到对方的女友。'\n await bot.send(ev, msg)\n msg = f'[CQ:at,qq={loser}]\\n您使用了保护卡，本次决斗未损失女友'\n await bot.send(ev, msg)\n duel._reduce_gift(gid,loser,12)\n\n #判断胜者败者是否需要增减声望\n level_loser = duel._get_level(gid, loser)\n level_winner = duel._get_level(gid, winner)\n wingold = 800 + (level_loser * 100)\n if is_overtime == 1:\n if n !=6:\n wingold = 100\n score_counter._add_score(gid, winner, wingold)\n msg = f'[CQ:at,qq={winner}]本次决斗胜利获得了{wingold}金币。{bd_msg}'\n await bot.send(ev, msg)\n winprestige = score_counter._get_prestige(gid,winner)\n if winprestige == None:\n winprestige == 0\n if winprestige != None:\n level_cha = level_loser - level_winner\n level_zcha = max(level_cha,0)\n winSW = WinSWBasics + (level_zcha * 20)\n if is_overtime == 1:\n if n !=6:\n if level_loser < 6:\n winSW = 0\n else:\n winSW = 150\n score_counter._add_prestige(gid,winner,winSW)\n msg = f'[CQ:at,qq={winner}]决斗胜利使您的声望上升了{winSW}点。'\n await bot.send(ev, msg)\n loseprestige = score_counter._get_prestige(gid,loser)\n if loseprestige == -1:\n loseprestige == 0\n if loseprestige != -1:\n level_cha = level_loser - level_winner\n level_zcha = max(level_cha,0)\n LOSESW = LoseSWBasics + (level_zcha * 10)\n score_counter._reduce_prestige(gid,loser,LOSESW)\n msg = f'[CQ:at,qq={loser}]决斗失败使您的声望下降了{LOSESW}点。'\n await bot.send(ev, msg)\n\n\n #判定败者是否掉爵位，皇帝不会因为决斗掉爵位。\n level_loser = duel._get_level(gid, loser)\n if level_loser > 1 and level_loser < 8:\n noblename_loser = get_noblename(level_loser)\n girlnum_loser = get_girlnum(level_loser - 1)\n cidlist_loser = duel._get_cards(gid, loser)\n cidnum_loser = len(cidlist_loser)\n if cidnum_loser < girlnum_loser:\n duel._reduce_level(gid, loser)\n new_noblename = get_noblename(level_loser - 1)\n msg = f'[CQ:at,qq={loser}]\\n您的女友数为{cidnum_loser}名\\n小于爵位需要的女友数{girlnum_loser}名\\n您的爵位下降到了{new_noblename}'\n await bot.send(ev, msg)\n\n #结算下注金币，判定是否为超时局。\n if is_overtime == 1:\n if n !=6:\n if level_loser < 6:\n msg = '认输警告！本局为超时局/认输局，不进行金币结算，支持的金币全部返还。胜者获得的声望为0，金币大幅减少。'\n else:\n msg = '认输警告！本局为超时局/认输局，不进行金币结算，支持的金币全部返还。胜者获得的声望减半，金币大幅减少，不计等级差。'\n await bot.send(ev, msg)\n duel_judger.set_support(ev.group_id)\n duel_judger.turn_off(ev.group_id)\n return\n \n support = duel_judger.get_support(gid)\n winuid = []\n supportmsg = '金币结算:\\n'\n winnum = duel_judger.get_duelnum(gid, winner)\n\n if support != 0:\n for uid in support:\n support_id = support[uid][0]\n support_score = support[uid][1]\n if support_id == winnum:\n winuid.append(uid)\n #这里是赢家获得的金币结算，可以自己修改倍率。\n if duel._get_GOLD_CELE(gid) == 1:\n winscore = support_score * WIN_NUM * Gold_Cele_Num\n else:\n winscore = support_score * WIN_NUM\n score_counter._add_score(gid, uid, winscore)\n supportmsg += f'[CQ:at,qq={uid}]+{winscore}金币\\n'\n else:\n score_counter._reduce_score(gid, uid, support_score)\n supportmsg += f'[CQ:at,qq={uid}]-{support_score}金币\\n'\n await bot.send(ev, supportmsg)\n duel_judger.set_support(ev.group_id)\n duel_judger.turn_off(ev.group_id)\n return\n\n\n@sv.on_fullmatch('接受')\nasync def duelaccept(bot, ev: CQEvent):\n gid = ev.group_id\n if duel_judger.get_on_off_accept_status(gid):\n if ev.user_id == duel_judger.get_duelid(gid)[1]:\n gid = ev.group_id\n msg = '贵族决斗接受成功，请耐心等待决斗开始。'\n await bot.send(ev, msg, at_sender=True)\n duel_judger.turn_off_accept(gid)\n duel_judger.on_isaccept(gid)\n else:\n print('不是被决斗者')\n else:\n print('现在不在决斗期间')\n\n\n@sv.on_fullmatch('拒绝')\nasync def duelrefuse(bot, ev: CQEvent):\n gid = ev.group_id\n if duel_judger.get_on_off_accept_status(gid):\n if ev.user_id == duel_judger.get_duelid(gid)[1]:\n gid = ev.group_id\n msg = '您已拒绝贵族决斗。'\n await bot.send(ev, msg, at_sender=True)\n duel_judger.turn_off_accept(gid)\n duel_judger.off_isaccept(gid)\n\n\n@sv.on_fullmatch('开枪')\nasync def duelfire(bot, ev: CQEvent):\n gid = ev.group_id\n if duel_judger.get_on_off_fire_status(gid):\n if ev.user_id == duel_judger.get_duelid(gid)[duel_judger.get_turn(gid) - 1]:\n duel_judger.turn_on_hasfired(gid)\n duel_judger.turn_off_fire(gid)\n\n\n@sv.on_rex(r'^支持(1|2)号(\\d+)(金币|币)$')\nasync def on_input_duel_score(bot, ev: CQEvent):\n try:\n if duel_judger.get_on_off_support_status(ev.group_id):\n gid = ev.group_id\n uid = ev.user_id\n\n match = ev['match']\n select_id = int(match.group(1))\n input_score = int(match.group(2))\n print(select_id, input_score)\n score_counter = ScoreCounter2()\n # 若下注该群下注字典不存在则创建\n if duel_judger.get_support(gid) == 0:\n duel_judger.set_support(gid)\n support = duel_judger.get_support(gid)\n # 检查是否重复下注\n if uid in support:\n msg = '您已经支持过了。'\n await bot.send(ev, msg, at_sender=True)\n return\n # 检查是否是决斗人员\n duellist = duel_judger.get_duelid(gid)\n if uid in duellist:\n msg = '决斗参与者不能支持。'\n await bot.send(ev, msg, at_sender=True)\n return\n\n # 检查金币是否足够下注\n if score_counter._judge_score(gid, uid, input_score) == 0:\n msg = '您的金币不足。'\n await bot.send(ev, msg, at_sender=True)\n return\n else:\n duel_judger.add_support(gid, uid, select_id, input_score)\n msg = f'支持{select_id}号成功。'\n await bot.send(ev, msg, at_sender=True)\n except Exception as e:\n await bot.send(ev, '错误:\\n' + str(e))\n\n@sv.on_rex(r'^梭哈支持(1|2)号$')\nasync def on_input_duel_score2(bot, ev: CQEvent):\n try:\n if duel_judger.get_on_off_support_status(ev.group_id):\n gid = ev.group_id\n duel = DuelCounter()\n uid = ev.user_id\n if Suo_allow != True:\n msg = '管理员禁止梭哈。'\n await bot.send(ev, msg, at_sender=True)\n return\n score_counter = ScoreCounter2()\n match = ev['match']\n select_id = int(match.group(1))\n current_score = score_counter._get_score(gid, uid)\n input_score = current_score\n print(select_id, input_score)\n score_counter = ScoreCounter2()\n # 若下注该群下注字典不存在则创建\n if duel_judger.get_support(gid) == 0:\n duel_judger.set_support(gid)\n support = duel_judger.get_support(gid)\n # 检查是否重复下注\n if uid in support:\n msg = '您已经支持过了。'\n await bot.send(ev, msg, at_sender=True)\n return\n # 检查是否是决斗人员\n duellist = duel_judger.get_duelid(gid)\n if uid in duellist:\n msg = '决斗参与者不能支持。'\n await bot.send(ev, msg, at_sender=True)\n return\n # 检查金币是否足够下注\n if score_counter._judge_score(gid, uid, input_score) == 0:\n msg = '您的金币不足。'\n await bot.send(ev, msg, at_sender=True)\n return\n else:\n if duel._get_SUO_CELE(gid) == 1:\n input_score = Suo * current_score * Suo_Cele_Num\n duel_judger.add_support(gid, uid, select_id, input_score)\n msg = f'梭哈支持{select_id}号{current_score}金币成功，[庆典举办中]胜利时，将获得相对于平常值{Suo*Suo_Cele_Num}倍的金币！'\n await bot.send(ev, msg, at_sender=True)\n else:\n input_score = Suo * current_score\n duel_judger.add_support(gid, uid, select_id, input_score)\n msg = f'梭哈支持{select_id}号{current_score}金币成功，胜利时，将获得相对于平常值{Suo}倍的金币！'\n await bot.send(ev, msg, at_sender=True)\n except Exception as e:\n await bot.send(ev, '错误:\\n' + str(e))\n\n\n# 以下部分与赛跑的重合，有一个即可，两个插件都装建议注释掉。\n\n@sv.on_prefix(['领金币', '领取金币'])\nasync def add_score(bot, ev: CQEvent):\n try:\n score_counter = ScoreCounter2()\n gid = ev.group_id\n uid = ev.user_id\n\n current_score = score_counter._get_score(gid, uid)\n if current_score == 0:\n score_counter._add_score(gid, uid, ZERO_GET_AMOUNT)\n msg = f'您已领取{ZERO_GET_AMOUNT}金币'\n await bot.send(ev, msg, at_sender=True)\n return\n else:\n msg = '金币为0才能领取哦。'\n await bot.send(ev, msg, at_sender=True)\n return\n except Exception as e:\n await bot.send(ev, '错误:\\n' + str(e))\n\n\n@sv.on_prefix(['查金币', '查询金币', '查看金币'])\nasync def get_score(bot, ev: CQEvent):\n try:\n score_counter = ScoreCounter2()\n gid = ev.group_id\n uid = ev.user_id\n\n current_score = score_counter._get_score(gid, uid)\n msg = f'您的金币为{current_score}'\n await bot.send(ev, msg, at_sender=True)\n return\n except Exception as e:\n await bot.send(ev, '错误:\\n' + str(e))\n \n\n\n@sv.on_rex(f'^为(.*)充值(\\d+)金币$')\nasync def cheat_score(bot, ev: CQEvent):\n if not priv.check_priv(ev, priv.SUPERUSER):\n await bot.finish(ev, '不要想着走捷径哦。', at_sender=True)\n gid = ev.group_id\n match = ev['match']\n try:\n id = int(match.group(1))\n except ValueError:\n id = int(ev.message[1].data['qq'])\n except:\n await bot.finish(ev, '参数格式错误')\n num = int(match.group(2))\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n if duel._get_level(gid, id) == 0:\n await bot.finish(ev, '该用户还未在本群创建贵族哦。', at_sender=True)\n score_counter._add_score(gid, id, num)\n score = score_counter._get_score(gid, id)\n msg = f'已为[CQ:at,qq={id}]充值{num}金币。\\n现在共有{score}金币。'\n await bot.send(ev, msg)\n \n@sv.on_rex(f'^设定群(.*)为(\\d+)号死$')\nasync def cheat_num(bot, ev: CQEvent):\n if not priv.check_priv(ev, priv.SUPERUSER):\n await bot.finish(ev, '不要想着走捷径哦。', at_sender=True)\n match = ev['match']\n try:\n gid = int(match.group(1))\n except ValueError:\n gid = int(ev.message[1].data['qq'])\n except:\n await bot.finish(ev, '参数格式错误')\n deadnum = int(match.group(2))\n duel_judger.set_deadnum(gid, deadnum)\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n msg = f'已将群{gid}本次决斗死亡位置修改为{deadnum}号。\\n'\n print (\"死的位置是\", duel_judger.get_deadnum(gid))\n await bot.send(ev, msg)\n self.deadnum[gid] = deadnum\n \n@sv.on_rex(f'^为(.*)转账(\\d+)金币$')\nasync def cheat_score(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n match = ev['match']\n try:\n id = int(match.group(1))\n except ValueError:\n id = int(ev.message[1].data['qq'])\n except:\n await bot.finish(ev, '参数格式错误')\n num = int(match.group(2))\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n if duel._get_level(gid, id) == 0:\n await bot.finish(ev, '该用户还未在本群创建贵族哦。', at_sender=True)\n if duel._get_level(gid, id) < 7:\n await bot.finish(ev, '该用户等级过低，无法接受转账喔（接受转账需要等级达到伯爵）。', at_sender=True)\n score = score_counter._get_score(gid, uid)\n if score < num:\n msg = f'您的金币不足{num}哦。'\n await bot.send(ev, msg, at_sender=True)\n return\n else:\n score_counter._reduce_score(gid, uid, num)\n scoreyou = score_counter._get_score(gid, uid)\n num2 = num * (1-Zhuan_Need)\n score_counter._add_score(gid, id, num2)\n score = score_counter._get_score(gid, id)\n msg = f'已为[CQ:at,qq={id}]转账{num}金币。\\n扣除{Zhuan_Need*100}%手续费，您的金币剩余{scoreyou}金币，对方金币剩余{score}金币。'\n await bot.send(ev, msg)\n\n\n@sv.on_fullmatch('重置决斗')\nasync def init_duel(bot, ev: CQEvent):\n if not priv.check_priv(ev, priv.ADMIN):\n await bot.finish(ev, '只有群管理才能使用重置决斗哦。', at_sender=True)\n duel_judger.turn_off(ev.group_id)\n msg = '已重置本群决斗状态！'\n await bot.send(ev, msg, at_sender=True)\n\n@sv.on_prefix(['查女友', '查询女友', '查看女友'])\nasync def search_girl(bot, ev: CQEvent):\n args = ev.message.extract_plain_text().split()\n gid = ev.group_id\n if not args:\n await bot.send(ev, '请输入查女友+pcr角色名。', at_sender=True)\n return\n name = args[0]\n cid = chara.name2id(name)\n if cid == 1000:\n await bot.send(ev, '请输入正确的pcr角色名。', at_sender=True)\n return\n duel = DuelCounter()\n owner = duel._get_card_owner(gid, cid)\n c = chara.fromid(cid)\n #判断是否是妻子。\n print(duel._get_queen_owner(gid,cid))\n nvmes = get_nv_icon(cid)\n lh_msg = ''\n fashioninfo = get_fashion(cid)\n jishu=0\n if fashioninfo:\n lh_msg = lh_msg+'\\n角色目前拥有时装(只显示前3个)：'\n for fashion in fashioninfo:\n jishu=jishu+1\n if jishu<4:\n lh_msg =lh_msg+f\"\\n{fashion['icon']}\\n{fashion['name']}\\n获取途径{fashion['content']}\"\n if duel._get_queen_owner(gid,cid) !=0 :\n owner = duel._get_queen_owner(gid,cid)\n await bot.finish(ev, f'\\n{c.name}现在是\\n[CQ:at,qq={owner}]的妻子哦。{nvmes}{lh_msg}', at_sender=True)\n\n if owner == 0:\n await bot.send(ev, f'{c.name}现在还是单身哦，快去约到她吧。{nvmes}{lh_msg}', at_sender=True)\n return\n else:\n store_flag = duel._get_store(gid, owner, cid)\n if store_flag>0:\n msg = f'{c.name}现在正在以{store_flag}金币的价格寄售中哦\\n寄售人为[CQ:at,qq={owner}]哦。{nvmes}{lh_msg}'\n else:\n msg = f'{c.name}现在正在\\n[CQ:at,qq={owner}]的身边哦。{nvmes}{lh_msg}'\n await bot.send(ev, msg)\n \n \n\n\n#重置某一用户的金币，只用做必要时删库用。\n@sv.on_prefix('重置金币')\nasync def reset_score(bot, ev: CQEvent):\n gid = ev.group_id\n if not priv.check_priv(ev, priv.OWNER):\n await bot.finish(ev, '只有群主才能使用重置金币功能哦。', at_sender=True)\n args = ev.message.extract_plain_text().split()\n if len(args)>=2:\n await bot.finish(ev, '指令格式错误', at_sender=True)\n if len(args)==0:\n await bot.finish(ev, '请输入重置金币+被重置者QQ', at_sender=True)\n else :\n id = args[0]\n duel = DuelCounter()\n if duel._get_level(gid, id) == 0:\n await bot.finish(ev, '该用户还未在本群创建贵族哦。', at_sender=True)\n score_counter = ScoreCounter2() \n current_score = score_counter._get_score(gid, id)\n score_counter._reduce_score(gid, id,current_score)\n await bot.finish(ev, f'已清空用户{id}的金币。', at_sender=True)\n \n#注意会清空此人的角色以及贵族等级，只用做必要时删库用。 \n@sv.on_prefix('重置角色')\nasync def reset_chara(bot, ev: CQEvent):\n gid = ev.group_id\n if not priv.check_priv(ev, priv.OWNER):\n await bot.finish(ev, '只有群主才能使用重置角色功能哦。', at_sender=True)\n args = ev.message.extract_plain_text().split()\n if len(args)>=2:\n await bot.finish(ev, '指令格式错误', at_sender=True)\n if len(args)==0:\n await bot.finish(ev, '请输入重置角色+被重置者QQ', at_sender=True)\n else :\n id = args[0]\n duel = DuelCounter()\n if duel._get_level(gid, id) == 0:\n await bot.finish(ev, '该用户还未在本群创建贵族哦。', at_sender=True)\n cidlist = duel._get_cards(gid, id)\n for cid in cidlist:\n duel._delete_card(gid, id, cid)\n score_counter = ScoreCounter2() \n current_score = score_counter._get_score(gid, id)\n score_counter._reduce_score(gid, id,current_score)\n queen = duel._search_queen(gid,id)\n duel._delete_queen_owner(gid,queen)\n duel._set_level(gid, id, 0) \n await bot.finish(ev, f'已清空用户{id}的女友和贵族等级。', at_sender=True)\n\n\n@sv.on_prefix('确认重开')\nasync def reset_CK(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n if Remake_allow == False:\n await bot.finish(ev, '管理员不允许自行重开。', at_sender=True)\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n prestige = score_counter._get_prestige(gid,uid)\n if prestige < 0:\n await bot.finish(ev, '您现在身败名裂（声望为负），无法重开！请联系管理员重开！', at_sender=True)\n if duel._get_level(gid, uid) == 0:\n await bot.finish(ev, '该用户还未在本群创建贵族哦。', at_sender=True)\n cidlist = duel._get_cards(gid, uid)\n for cid in cidlist:\n duel._delete_card(gid, uid, cid)\n score_counter = ScoreCounter2() \n current_score = score_counter._get_score(gid, uid)\n score_counter._reduce_score(gid, uid,current_score)\n queen = duel._search_queen(gid,uid)\n duel._delete_queen_owner(gid,queen)\n duel._set_level(gid, uid, 0) \n await bot.finish(ev, f'已清空您的女友和贵族等级，金币等。', at_sender=True)\n\n@sv.on_prefix('分手')\nasync def breakup(bot, ev: CQEvent):\n if BREAK_UP_SWITCH == True:\n args = ev.message.extract_plain_text().split()\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n level = duel._get_level(gid, uid)\n if duel_judger.get_on_off_status(ev.group_id):\n msg = '现在正在决斗中哦，请决斗后再来谈分手事宜吧。'\n await bot.finish(ev, msg, at_sender=True)\n if level == 0:\n await bot.finish(ev, '该用户还未在本群创建贵族哦。', at_sender=True)\n if not args:\n await bot.finish(ev, '请输入分手+pcr角色名。', at_sender=True)\n name = args[0]\n cid = chara.name2id(name)\n if cid == 1000:\n await bot.finish(ev, '请输入正确的pcr角色名。', at_sender=True)\n score_counter = ScoreCounter2()\n needscore = 400+level*100\n needSW = 100+level*15\n if level == 20:\n needSW = 600\n score = score_counter._get_score(gid, uid)\n prestige = score_counter._get_prestige(gid,uid)\n cidlist = duel._get_cards(gid, uid)\n if cid not in cidlist:\n await bot.finish(ev, '该角色不在你的身边哦。', at_sender=True)\n #检测是否是妻子\n queen = duel._search_queen(gid,uid)\n if cid==queen:\n await bot.finish(ev, '不可以和您的妻子分手哦。', at_sender=True)\n if score < needscore:\n msg = f'您的爵位分手一位女友需要{needscore}金币和{needSW}声望哦。\\n分手不易，做好准备再来吧。'\n await bot.finish(ev, msg, at_sender=True)\n if prestige < needSW:\n msg = f'您的爵位分手一位女友需要{needscore}金币和{needSW}声望哦。\\n分手不易，做好准备再来吧。'\n await bot.finish(ev, msg, at_sender=True)\n score_counter._reduce_score(gid, uid, needscore)\n score_counter._reduce_prestige(gid, uid, needSW)\n duel._delete_card(gid, uid, cid)\n c = chara.fromid(cid)\n msg = f'\\n“真正离开的那次，关门声最小。”\\n你和{c.name}分手了。失去了{needscore}金币分手费,声望减少了{needSW}。\\n{c.icon.cqcode}'\n await bot.send(ev, msg, at_sender=True)\n \n \n\n@sv.on_rex(f'^一键分手(.*)$')\nasync def breakup_yj(bot, ev: CQEvent):\n if BREAK_UP_SWITCH == True:\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n level = duel._get_level(gid, uid)\n # 处理输入数据\n match = ev['match']\n defen = str(match.group(1))\n defen = re.sub(r'[?？，,_]', '', defen)\n defen, unknown = chara.roster.parse_team(defen)\n duel = DuelCounter()\n if unknown:\n _, name, score = chara.guess_id(unknown)\n if score < 70 and not defen:\n return # 忽略无关对话\n msg = f'无法识别\"{unknown}\"' if score < 70 else f'无法识别\"{unknown}\" 您说的有{score}%可能是{name}'\n await bot.finish(ev, msg)\n return\n if not defen:\n await bot.finish(ev, '请发送\"进入副本+编组队伍\"，无需+号', at_sender=True)\n return\n if len(defen) > 10:\n await bot.finish(ev, '不能多于10名角色', at_sender=True)\n return\n if len(defen) != len(set(defen)):\n await bot.finish(ev, '编队中含重复角色', at_sender=True)\n return\n if 1004 in defen:\n await bot.send(ev, '\\n⚠️您正在查询普通版炸弹人\\n※万圣版可用万圣炸弹人/瓜炸等别称', at_sender=True)\n return\n if duel_judger.get_on_off_status(ev.group_id):\n msg = '现在正在决斗中哦，请决斗后再来谈分手事宜吧。'\n await bot.finish(ev, msg, at_sender=True)\n tas_list = []\n cidlist = duel._get_cards(gid, uid)\n for cid in defen:\n c = chara.fromid(cid)\n \n needscore = 400+level*100\n needSW = 100+level*15\n if level == 20:\n needSW = 600\n score = score_counter._get_score(gid, uid)\n prestige = score_counter._get_prestige(gid,uid)\n if cid not in cidlist:\n await bot.finish(ev, f'{c.name}不在你的身边哦。', at_sender=True)\n #检测是否是妻子\n queen = duel._search_queen(gid,uid)\n if cid==queen:\n await bot.finish(ev, '不可以和您的妻子分手哦。', at_sender=True)\n if score < needscore:\n msg = f'您的爵位分手一位女友需要{needscore}金币和{needSW}声望哦。\\n分手不易，做好准备再来吧。'\n await bot.finish(ev, msg, at_sender=True)\n if prestige < needSW:\n msg = f'您的爵位分手一位女友需要{needscore}金币和{needSW}声望哦。\\n分手不易，做好准备再来吧。'\n await bot.finish(ev, msg, at_sender=True)\n duel._delete_card(gid, uid, cid)\n score_counter._reduce_score(gid, uid, needscore)\n score_counter._reduce_prestige(gid, uid, needSW)\n msg = f\"真正离开的那次，关门声最小。\\n你和{c.name}分手了\\n{c.icon.cqcode}\"\n data = {\n \"type\": \"node\",\n \"data\": {\n \"name\": str(NICKNAME[0]),\n \"uin\": str(ev.self_id),\n \"content\":msg\n }\n }\n tas_list.append(data)\n await bot.send_group_forward_msg(group_id=gid, messages=tas_list)\n \n \n#国王以上声望部分。\n\n@sv.on_fullmatch('开启声望系统')\nasync def open_prestige(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n level = duel._get_level(gid, uid)\n score_counter = ScoreCounter2()\n prestige = score_counter._get_prestige(gid,uid)\n if prestige != None:\n await bot.finish(ev, '您已经开启了声望系统哦。', at_sender=True) \n score_counter._set_prestige(gid,uid,0)\n msg = '成功开启声望系统！殿下，向着成为皇帝的目标进发吧。'\n await bot.send(ev, msg, at_sender=True)\n \n@sv.on_fullmatch(['声望系统帮助','声望帮助'])\nasync def prestige_help(bot, ev: CQEvent):\n msg='''\n成为伯爵后才可以开启声望系统\n开启后可以通过决斗等方式获取声望\n声望系统相关指令如下\n1. 开启声望系统\n2. 查询声望\n3. 加冕仪式(需要4000声望，20000金币）\n4. 皇室婚礼+角色名(需5000金币，公爵以上)\n\n决斗胜利+300声望\n决斗失败-100声望\n皇室婚礼需公爵才能举办\n每个人只能举办一次\n妻子不会因决斗被抢走\n\n ''' \n tas_list=[]\n data = {\n \"type\": \"node\",\n \"data\": {\n \"name\": str(NICKNAME[0]),\n \"uin\": str(ev.self_id),\n \"content\":msg\n }\n }\n tas_list.append(data)\n await bot.send_group_forward_msg(group_id=ev['group_id'], messages=tas_list)\n\n@sv.on_fullmatch('查询声望')\nasync def inquire_prestige(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n level = duel._get_level(gid, uid)\n score_counter = ScoreCounter2()\n prestige = score_counter._get_prestige(gid,uid)\n if prestige == None:\n await bot.finish(ev, '您还未开启声望系统哦。', at_sender=True)\n msg = f'您的声望为{prestige}点。' \n await bot.send(ev, msg, at_sender=True) \n \n@sv.on_fullmatch(['加冕称帝','加冕仪式'])\nasync def be_emperor(bot, ev: CQEvent): \n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n level = duel._get_level(gid, uid)\n score_counter = ScoreCounter2() \n prestige = score_counter._get_prestige(gid,uid)\n \n if prestige == None:\n await bot.finish(ev, '您还未开启声望系统哦。', at_sender=True)\n if level!=9:\n await bot.finish(ev, '只有国王才能进行加冕仪式哦。', at_sender=True)\n if prestige < DJ_NEED_SW: \n await bot.finish(ev, f'加冕仪式需要{DJ_NEED_SW}声望，您的声望不足哦。', at_sender=True)\n score = score_counter._get_score(gid, uid)\n if score < DJ_NEED_GOLD:\n await bot.finish(ev, f'加冕仪式需要{DJ_NEED_GOLD}金币，您的金币不足哦。', at_sender=True)\n score_counter._reduce_score(gid,uid,DJ_NEED_GOLD)\n score_counter._reduce_prestige(gid,uid,DJ_NEED_SW)\n duel._set_level(gid, uid, 10)\n msg = f'\\n礼炮鸣响，教皇领唱“感恩赞美歌”。“皇帝万岁！”\\n在民众的欢呼声中，你加冕为了皇帝。\\n花费了{DJ_NEED_SW}点声望，{DJ_NEED_GOLD}金币。'\n await bot.send(ev, msg, at_sender=True)\n \n@sv.on_fullmatch(['飞升成神','成神飞升'])\nasync def be_feisheng(bot, ev: CQEvent): \n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n level = duel._get_level(gid, uid)\n score_counter = ScoreCounter2() \n prestige = score_counter._get_prestige(gid,uid)\n \n if level!=10:\n await bot.finish(ev, '只有皇帝才能飞升哦。', at_sender=True)\n if prestige < FS_NEED_SW: \n await bot.finish(ev, f'飞升成神需要{FS_NEED_SW}声望，您的声望不足哦。', at_sender=True)\n score = score_counter._get_score(gid, uid)\n if score < FS_NEED_GOLD:\n await bot.finish(ev, f'飞升成神需要{FS_NEED_GOLD}金币，您的金币不足哦。', at_sender=True)\n score_counter._reduce_score(gid,uid,FS_NEED_GOLD)\n score_counter._reduce_prestige(gid,uid,FS_NEED_SW)\n duel._set_level(gid, uid, 20)\n msg = f'\\n光柱冲天，你感觉无尽的力量涌入了自己的体内。\\n在民众的惊讶的目光中，你飞升成神了。\\n花费了{FS_NEED_SW}点声望，{FS_NEED_GOLD}金币。'\n await bot.send(ev, msg, at_sender=True)\n \n \n@sv.on_prefix('皇室婚礼')\nasync def marry_queen(bot, ev: CQEvent):\n args = ev.message.extract_plain_text().split()\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n level = duel._get_level(gid, uid)\n score_counter = ScoreCounter2() \n prestige = score_counter._get_prestige(gid,uid)\n if prestige == None:\n await bot.finish(ev, '您还未开启声望系统哦。', at_sender=True) \n if level <= 7:\n await bot.finish(ev, '只有8级(公爵)及以上才可以举办皇室婚礼哦。', at_sender=True) \n if duel._search_queen(gid,uid)!=0:\n await bot.finish(ev, '皇帝只可以举办一次皇室婚礼哦。', at_sender=True)\n if not args:\n await bot.finish(ev, '请输入皇室婚礼+pcr角色名。', at_sender=True)\n name = args[0]\n cid = chara.name2id(name)\n if cid == 1000:\n await bot.finish(ev, '请输入正确的pcr角色名。', at_sender=True)\n cidlist = duel._get_cards(gid, uid) \n if cid not in cidlist:\n await bot.finish(ev, '该角色不在你的身边哦。', at_sender=True) \n if prestige < 1000:\n await bot.finish(ev, '您现在名声不好，不能结婚哦（结婚需要声望大于1000）。', at_sender=True)\n if prestige < 0:\n await bot.finish(ev, '您现在身败名裂，不能结婚哦（结婚需要声望大于1000）。', at_sender=True)\n score = score_counter._get_score(gid, uid)\n if score < 3000:\n await bot.finish(ev, '皇室婚礼需要3000金币，您的金币不足哦。', at_sender=True) \n favor = duel._get_favor(gid,uid,cid)\n if favor < NEED_favor:\n await bot.finish(ev, f'举办婚礼的女友需要达到{NEED_favor}好感，您的好感不足哦。', at_sender=True) \n score_counter._reduce_score(gid,uid,3000) \n duel._set_queen_owner(gid,cid,uid)\n nvmes = get_nv_icon(cid)\n c = chara.fromid(cid)\n msg = f'繁杂的皇室礼仪过后\\n\\n{c.name}与[CQ:at,qq={uid}]\\n\\n正式踏上了婚礼的殿堂\\n成为了他的妻子。\\n让我们为他们表示祝贺！\\n妻子不会因决斗被抢走了哦。\\n{nvmes}'\n await bot.send(ev, msg)\n\n\n@sv.on_prefix(['查好感','查询好感'])\nasync def girl_story(bot, ev: CQEvent):\n args = ev.message.extract_plain_text().split()\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n if not args:\n await bot.finish(ev, '请输入查好感+女友名。', at_sender=True)\n name = args[0]\n cid = chara.name2id(name)\n if cid == 1000:\n await bot.finish(ev, '请输入正确的女友名。', at_sender=True)\n cidlist = duel._get_cards(gid, uid) \n if cid not in cidlist:\n await bot.finish(ev, '该女友不在你的身边哦。', at_sender=True)\n\n if duel._get_favor(gid,uid,cid)== None:\n duel._set_favor(gid,uid,cid,0)\n favor= duel._get_favor(gid,uid,cid)\n relationship,text = get_relationship(favor)\n c = chara.fromid(cid) \n nvmes = get_nv_icon(cid)\n msg = f'\\n{c.name}对你的好感是{favor}\\n你们的关系是{relationship}\\n“{text}”\\n{nvmes}'\n await bot.send(ev, msg, at_sender=True)\n\n@sv.on_prefix(['每日约会','女友约会', '贵族约会'])\nasync def daily_date(bot, ev: CQEvent):\n args = ev.message.extract_plain_text().split()\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n if not args:\n await bot.finish(ev, '请输入贵族约会+女友名。', at_sender=True)\n name = args[0]\n cid = chara.name2id(name)\n if cid == 1000:\n await bot.finish(ev, '请输入正确的女友名。', at_sender=True)\n cidlist = duel._get_cards(gid, uid) \n if cid not in cidlist:\n await bot.finish(ev, '该女友不在你的身边哦。', at_sender=True) \n guid = gid ,uid\n if not daily_date_limiter.check(guid):\n await bot.finish(ev, '今天已经和女友约会过了哦，明天再来吧。', at_sender=True)\n\n loginnum_ = ['1','2', '3', '4'] \n r_ = [0.2, 0.4, 0.35, 0.05] \n sum_ = 0\n ran = random.random()\n for num, r in zip(loginnum_, r_):\n sum_ += r\n if ran < sum_ :break\n Bonus = {'1':[5,Date5],\n '2':[10,Date10],\n '3':[15,Date15], \n '4':[20,Date20]\n } \n favor = Bonus[num][0]\n text = random.choice(Bonus[num][1])\n duel._add_favor(gid,uid,cid,favor)\n c = chara.fromid(cid)\n nvmes = get_nv_icon(cid)\n current_favor = duel._get_favor(gid,uid,cid)\n relationship = get_relationship(current_favor)[0]\n msg = f'\\n\\n{text}\\n\\n你和{c.name}的好感上升了{favor}点\\n她现在对你的好感是{current_favor}点\\n你们现在的关系是{relationship}\\n{nvmes}'\n daily_date_limiter.increase(guid)\n await bot.send(ev, msg, at_sender=True)\n\n\n@sv.on_prefix(['送礼物','送礼','赠送礼物'])\nasync def give_gift(bot, ev: CQEvent):\n args = ev.message.extract_plain_text().split()\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n if gift_change.get_on_off_giftchange_status(ev.group_id):\n await bot.finish(ev, \"有正在进行的礼物交换，礼物交换结束再来送礼物吧。\") \n if len(args)!=2:\n await bot.finish(ev, '请输入 送礼物+女友名+礼物名 中间用空格隔开。', at_sender=True)\n name = args[0]\n cid = chara.name2id(name)\n if cid == 1000:\n await bot.finish(ev, '请输入正确的女友名。', at_sender=True)\n cidlist = duel._get_cards(gid, uid) \n if cid not in cidlist:\n await bot.finish(ev, '该女友不在你的身边哦。', at_sender=True) \n gift = args[1]\n if gift not in GIFT_DICT.keys():\n await bot.finish(ev, '请输入正确的礼物名。', at_sender=True)\n gfid = GIFT_DICT[gift]\n if duel._get_gift_num(gid,uid,gfid)==0:\n await bot.finish(ev, '你的这件礼物的库存不足哦。', at_sender=True)\n duel._reduce_gift(gid,uid,gfid)\n favor,text = check_gift(cid,gfid)\n duel._add_favor(gid,uid,cid,favor)\n current_favor = duel._get_favor(gid,uid,cid)\n relationship = get_relationship(current_favor)[0]\n c = chara.fromid(cid)\n nvmes = get_nv_icon(cid)\n msg = f'\\n{c.name}:“{text}”\\n\\n你和{c.name}的好感上升了{favor}点\\n她现在对你的好感是{current_favor}点\\n你们现在的关系是{relationship}\\n{nvmes}'\n await bot.send(ev, msg, at_sender=True) \n\n@sv.on_prefix(['批量送礼','一键送礼'])\nasync def give_gift_all(bot, ev: CQEvent):\n args = ev.message.extract_plain_text().split()\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n if gift_change.get_on_off_giftchange_status(ev.group_id):\n await bot.finish(ev, \"有正在进行的礼物交换，礼物交换结束再来送礼物吧。\") \n if len(args)!=2:\n await bot.finish(ev, '请输入 送礼物+女友名+礼物名 中间用空格隔开。', at_sender=True)\n name = args[0]\n cid = chara.name2id(name)\n if cid == 1000:\n await bot.finish(ev, '请输入正确的女友名。', at_sender=True)\n cidlist = duel._get_cards(gid, uid) \n if cid not in cidlist:\n await bot.finish(ev, '该女友不在你的身边哦。', at_sender=True) \n gift = args[1]\n if gift not in GIFT_DICT.keys():\n await bot.finish(ev, '请输入正确的礼物名。', at_sender=True)\n gfid = GIFT_DICT[gift]\n if gfid > 10:\n await bot.finish(ev, '这个物品不能作为礼物哦。', at_sender=True)\n gift_num = duel._get_gift_num(gid,uid,gfid)\n if gift_num==0:\n await bot.finish(ev, '你的这件礼物的库存不足哦。', at_sender=True)\n duel._reduce_gift(gid,uid,gfid,gift_num)\n favor,text = check_gift(cid,gfid)\n favor = gift_num * favor\n duel._add_favor(gid,uid,cid,favor)\n current_favor = duel._get_favor(gid,uid,cid)\n relationship = get_relationship(current_favor)[0]\n c = chara.fromid(cid)\n nvmes = get_nv_icon(cid)\n msg = f'\\n{c.name}:“{text}”\\n您送给了{c.name}{gift}x{gift_num}\\n你和{c.name}的好感上升了{favor}点\\n她现在对你的好感是{current_favor}点\\n你们现在的关系是{relationship}\\n{nvmes}'\n await bot.send(ev, msg, at_sender=True) \n\n@sv.on_fullmatch(['抽礼物','买礼物','购买礼物'])\nasync def buy_gift(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n guid = gid ,uid\n if duel_judger.get_on_off_status(ev.group_id):\n msg = '现在正在决斗中哦，请决斗后再来买礼物吧。'\n await bot.finish(ev, msg, at_sender=True)\n score = score_counter._get_score(gid, uid)\n if score < 300:\n await bot.finish(ev, '购买礼物需要300金币，您的金币不足哦。', at_sender=True) \n if not daily_gift_limiter.check(guid):\n await bot.finish(ev, f'今天购买礼物已经超过{GIFT_DAILY_LIMIT}次了哦，明天再来吧。', at_sender=True) \n select_gift = random.choice(list(GIFT_DICT.keys()))\n gfid = GIFT_DICT[select_gift]\n while(gfid >= 10):\n select_gift = random.choice(list(GIFT_DICT.keys()))\n gfid = GIFT_DICT[select_gift]\n duel._add_gift(gid,uid,gfid)\n msg = f'\\n您花费了300金币，\\n买到了[{select_gift}]哦，\\n欢迎下次惠顾。'\n score_counter._reduce_score(gid,uid,300)\n daily_gift_limiter.increase(guid)\n await bot.send(ev, msg, at_sender=True) \n\n\n@sv.on_fullmatch(['我的礼物','礼物仓库','查询礼物','礼物列表'])\nasync def my_gift(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n msg = f'\\n您的礼物仓库如下:'\n giftmsg =''\n for gift in GIFT_DICT.keys():\n gfid = GIFT_DICT[gift]\n num = duel._get_gift_num(gid,uid,gfid)\n if num!=0:\n #补空格方便对齐\n length = len(gift)\n msg_part = ' '*(4-length)\n giftmsg+=f'\\n{gift}{msg_part}: {num}件'\n if giftmsg == '':\n await bot.finish(ev, '您现在没有礼物哦，快去发送 买礼物 购买吧。', at_sender=True) \n msg+=giftmsg\n await bot.send(ev, msg, at_sender=True) \n\n@sv.on_rex(f'^用(.*)与(.*)交换(.*)$')\nasync def change_gift(bot, ev: CQEvent):\n gid = ev.group_id \n duel = DuelCounter()\n if gift_change.get_on_off_giftchange_status(ev.group_id):\n await bot.finish(ev, \"有正在进行的礼物交换，请勿重复使用指令。\")\n gift_change.turn_on_giftchange(gid)\n id1 = ev.user_id\n match = ev['match']\n try:\n id2 = int(ev.message[1].data['qq'])\n except:\n gift_change.turn_off_giftchange(ev.group_id)\n await bot.finish(ev, '参数格式错误')\n if id2 == id1:\n await bot.send(ev, \"不能和自己交换礼物！\", at_sender=True)\n gift_change.turn_off_giftchange(ev.group_id)\n return \n gift1 = match.group(1)\n gift2 = match.group(3)\n if gift1 not in GIFT_DICT.keys():\n gift_change.turn_off_giftchange(ev.group_id)\n await bot.finish(ev, f'礼物1不存在。')\n if gift2 not in GIFT_DICT.keys():\n gift_change.turn_off_giftchange(ev.group_id)\n await bot.finish(ev, f'礼物2不存在。') \n gfid1 = GIFT_DICT[gift1]\n gfid2 = GIFT_DICT[gift2] \n if gfid2 == gfid1:\n await bot.send(ev, \"不能交换相同的礼物！\", at_sender=True)\n gift_change.turn_off_giftchange(ev.group_id)\n return \n\n if duel._get_gift_num(gid,id1,gfid1)==0:\n gift_change.turn_off_giftchange(ev.group_id) \n await bot.finish(ev, f'[CQ:at,qq={id1}]\\n您的{gift1}的库存不足哦。') \n if duel._get_gift_num(gid,id2,gfid2)==0:\n gift_change.turn_off_giftchange(ev.group_id) \n await bot.finish(ev, f'[CQ:at,qq={id2}]\\n您的{gift2}的库存不足哦。') \n level2 = duel._get_level(gid, id2)\n noblename = get_noblename(level2)\n gift_change.turn_on_waitchange(gid)\n gift_change.set_changeid(gid,id2)\n gift_change.turn_off_accept_giftchange(gid) \n msg = f'[CQ:at,qq={id2}]\\n尊敬的{noblename}您好：\\n\\n[CQ:at,qq={id1}]试图用[{gift1}]交换您的礼物[{gift2}]。\\n\\n请在{WAIT_TIME_CHANGE}秒内[接受交换/拒绝交换]。'\n await bot.send(ev, msg)\n await asyncio.sleep(WAIT_TIME_CHANGE) \n gift_change.turn_off_waitchange(gid)\n if gift_change.get_isaccept_giftchange(gid) is False:\n msg = '\\n礼物交换被拒绝。'\n gift_change.init_changeid(gid)\n gift_change.turn_off_giftchange(gid)\n await bot.finish(ev, msg, at_sender=True) \n duel._reduce_gift(gid,id1,gfid1)\n duel._add_gift(gid,id1,gfid2) \n duel._reduce_gift(gid,id2,gfid2)\n duel._add_gift(gid,id2,gfid1) \n msg = f'\\n礼物交换成功！\\n您使用[{gift1}]交换了\\n[CQ:at,qq={id2}]的[{gift2}]。' \n gift_change.init_changeid(gid)\n gift_change.turn_off_giftchange(gid)\n await bot.finish(ev, msg, at_sender=True) \n\n\n\n@sv.on_fullmatch('接受交换')\nasync def giftchangeaccept(bot, ev: CQEvent):\n gid = ev.group_id\n if gift_change.get_on_off_waitchange_status(gid):\n if ev.user_id == gift_change.get_changeid(gid):\n msg = '\\n礼物交换接受成功，请耐心等待礼物交换结束。'\n await bot.send(ev, msg, at_sender=True)\n gift_change.turn_off_waitchange(gid)\n gift_change.turn_on_accept_giftchange(gid)\n\n\n\n@sv.on_fullmatch('拒绝交换')\nasync def giftchangerefuse(bot, ev: CQEvent):\n gid = ev.group_id\n if gift_change.get_on_off_waitchange_status(gid):\n if ev.user_id == gift_change.get_changeid(gid):\n msg = '\\n礼物交换拒绝成功，请耐心等待礼物交换结束。'\n await bot.send(ev, msg, at_sender=True)\n gift_change.turn_off_waitchange(gid)\n gift_change.turn_off_accept_giftchange(gid)\n\n\n\n@sv.on_prefix(['购买情报','买情报'])\nasync def buy_information(bot, ev: CQEvent):\n args = ev.message.extract_plain_text().split()\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n if duel_judger.get_on_off_status(ev.group_id):\n msg = '现在正在决斗中哦，请决斗后再来买情报吧。'\n await bot.finish(ev, msg, at_sender=True) \n if not args:\n await bot.finish(ev, '请输入买情报+女友名。', at_sender=True)\n name = args[0]\n cid = chara.name2id(name)\n if cid == 1000:\n await bot.finish(ev, '请输入正确的女友名。', at_sender=True)\n score = score_counter._get_score(gid, uid)\n if score < 500:\n await bot.finish(ev, '购买女友情报需要500金币，您的金币不足哦。', at_sender=True) \n score_counter._reduce_score(gid,uid,500)\n last_num = cid%10\n like = ''\n normal = ''\n dislike = ''\n for gift in GIFT_DICT.keys():\n if GIFT_DICT[gift]==last_num:\n favorite = gift\n continue\n num1 = last_num%3\n num2 = GIFT_DICT[gift]%3\n choicelist = GIFTCHOICE_DICT[num1]\n if GIFT_DICT[gift] >= 10:\n continue\n if num2 == choicelist[0]:\n like+=f'{gift}\\n'\n continue\n if num2 == choicelist[1]:\n normal+=f'{gift}\\n'\n continue\n if num2 == choicelist[2]:\n dislike+=f'{gift}\\n'\n continue \n c = chara.fromid(cid) \n nvmes = get_nv_icon(cid)\n msg = f'\\n花费了500金币，您买到了以下情报：\\n{c.name}最喜欢的礼物是:\\n{favorite}\\n喜欢的礼物是:\\n{like}一般喜欢的礼物是:\\n{normal}不喜欢的礼物是:\\n{dislike}{nvmes}'\n await bot.send(ev, msg, at_sender=True) \n\n\n@sv.on_fullmatch('重置礼物交换')\nasync def init_change(bot, ev: CQEvent):\n if not priv.check_priv(ev, priv.ADMIN):\n await bot.finish(ev, '只有群管理才能使用重置礼物交换哦。', at_sender=True)\n gift_change.turn_off_giftchange(ev.group_id)\n msg = '已重置本群礼物交换状态！'\n await bot.send(ev, msg, at_sender=True)\n\n@sv.on_fullmatch(['好感系统帮助','礼物系统帮助','好感帮助','礼物帮助'])\nasync def gift_help(bot, ev: CQEvent):\n msg='''\n╔ ╗ \n 好感系统帮助\n1. 查好感+女友名\n2. 贵族约会+女友名(1天限1次)\n3. 买礼物(300金币，一天限5次）\n4. 送礼+女友名\n5. 用xx与[艾特对象]交换xx\n例: 用热牛奶与@妈宝交换书\n6. 买情报+女友名(500金币，可了解女友喜好)\n7. 礼物仓库(查询礼物仓库)\n8. 好感列表\n9. 重置礼物交换(限管理，交换卡住时用)\n注:\n通过约会或者送礼可以提升好感\n决斗输掉某女友会扣除50好感，不够则被抢走\n女友喜好与原角色无关，只是随机生成，仅供娱乐\n╚ ╝\n ''' \n tas_list=[]\n data = {\n \"type\": \"node\",\n \"data\": {\n \"name\": str(NICKNAME[0]),\n \"uin\": str(ev.self_id),\n \"content\":msg\n }\n }\n tas_list.append(data)\n await bot.send_group_forward_msg(group_id=ev['group_id'], messages=tas_list)\n\n\n@sv.on_fullmatch(['好感列表','女友好感列表'])\nasync def get_favorlist(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter() \n if duel._get_level(gid, uid) == 0:\n msg = '您还未在本群创建过贵族，请发送 创建贵族 开始您的贵族之旅。'\n await bot.send(ev, msg, at_sender=True)\n return\n cidlist = duel._get_cards(gid, uid)\n if len(cidlist)==0:\n await bot.finish(ev, '您现在还没有女友哦。', at_sender=True)\n favorlist = []\n for cid in cidlist:\n favor = duel._get_favor(gid,uid,cid)\n if favor !=0 and favor!=None:\n favorlist.append({\"cid\":cid,\"favor\":favor})\n if len(favorlist)==0:\n await bot.finish(ev, '您的女友好感全部为0哦。', at_sender=True) \n rows_by_favor = sorted(favorlist, key=lambda r: r['favor'],reverse=True)\n msg = '\\n您好感0以上的女友的前15名如下所示:\\n'\n num = min(len(rows_by_favor),15)\n for i in range(0,num):\n cid = rows_by_favor[i][\"cid\"]\n favor = rows_by_favor[i][\"favor\"]\n c = chara.fromid(cid)\n msg+=f'{c.name}:{favor}点\\n'\n await bot.send(ev, msg, at_sender=True)\n\n \n@sv.on_prefix('确认离婚')\nasync def lihun_queen(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n level = duel._get_level(gid, uid)\n score_counter = ScoreCounter2() \n prestige = score_counter._get_prestige(gid,uid)\n if duel._search_queen(gid,uid) ==0:\n await bot.finish(ev, '您没有妻子！。', at_sender=True) \n score = score_counter._get_score(gid, uid)\n if prestige < 3000: \n await bot.finish(ev, '离婚需要3000声望，您的声望现在离婚可能身败名裂哦。', at_sender=True)\n if score < 20000:\n await bot.finish(ev, '离婚需要20000金币，您的金币不足哦。', at_sender=True) \n score_counter._reduce_score(gid,uid,20000) \n score_counter._reduce_prestige(gid,uid,3000)\n queen = duel._search_queen(gid,uid)\n duel._delete_card(gid, uid, queen)\n c = chara.fromid(queen)\n nvmes = get_nv_icon(queen)\n msg = f'花费了20000金币，[CQ:at,qq={uid}]总算将他的妻子{c.name}赶出家门\\n，引起了众人的不满，损失3000声望。{nvmes}'\n duel._delete_queen_owner(gid,queen)\n await bot.send(ev, msg)\n \n@sv.on_rex(f'^为(.*)充值(\\d+)声望$')\nasync def cheat_SW(bot, ev: CQEvent):\n if not priv.check_priv(ev, priv.SUPERUSER):\n await bot.finish(ev, '不要想着走捷径哦。', at_sender=True)\n gid = ev.group_id\n match = ev['match']\n try:\n id = int(match.group(1))\n except ValueError:\n id = int(ev.message[1].data['qq'])\n except:\n await bot.finish(ev, '参数格式错误')\n num = int(match.group(2))\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n prestige = score_counter._get_prestige(gid,id)\n if duel._get_level(gid, id) == 0:\n await bot.finish(ev, '该用户还未在本群创建贵族哦。', at_sender=True)\n if prestige == None:\n await bot.finish(ev, '该用户尚未开启声望系统哦！。', at_sender=True) \n score_counter._add_prestige(gid,id,num)\n msg = f'已为[CQ:at,qq={id}]充值{num}声望。'\n await bot.send(ev, msg)\n \n@sv.on_rex(f'^扣除(.*)的(\\d+)声望$')\nasync def cheat_SW2(bot, ev: CQEvent):\n if not priv.check_priv(ev, priv.SUPERUSER):\n await bot.finish(ev, '不要想着走捷径哦。', at_sender=True)\n gid = ev.group_id\n match = ev['match']\n try:\n id = int(match.group(1))\n except ValueError:\n id = int(ev.message[1].data['qq'])\n except:\n await bot.finish(ev, '参数格式错误')\n num = int(match.group(2))\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n prestige = score_counter._get_prestige(gid,id)\n if duel._get_level(gid, id) == 0:\n await bot.finish(ev, '该用户还未在本群创建贵族哦。', at_sender=True)\n if prestige == None:\n await bot.finish(ev, '该用户尚未开启声望系统哦！。', at_sender=True) \n score_counter._reduce_prestige(gid,id,num)\n msg = f'已扣除[CQ:at,qq={id}]的{num}声望。'\n await bot.send(ev, msg)\n \nasync def get_user_card_dict(bot, group_id):\n mlist = await bot.get_group_member_list(group_id=group_id)\n d = {}\n for m in mlist:\n d[m['user_id']] = m['card'] if m['card']!='' else m['nickname']\n return d \n\n@sv.on_fullmatch(('金币排行榜', '金币排行'))\nasync def Race_ranking(bot, ev: CQEvent):\n try:\n user_card_dict = await get_user_card_dict(bot, ev.group_id)\n score_dict = {}\n score_counter = ScoreCounter2()\n gid = ev.group_id\n for uid in user_card_dict.keys():\n if uid != ev.self_id:\n score_dict[user_card_dict[uid]] = score_counter._get_score(gid, uid)\n group_ranking = sorted(score_dict.items(), key = lambda x:x[1], reverse = True)\n msg = '此群贵族决斗金币排行为:\\n'\n for i in range(min(len(group_ranking), 10)):\n if group_ranking[i][1] != 0:\n msg += f'第{i+1}名: {group_ranking[i][0]}, 金币: {group_ranking[i][1]}\\n'\n tas_list=[]\n data = {\n \"type\": \"node\",\n \"data\": {\n \"name\": str(NICKNAME[0]),\n \"uin\": str(ev.self_id),\n \"content\":msg.strip()\n }\n }\n tas_list.append(data)\n await bot.send_group_forward_msg(group_id=ev['group_id'], messages=tas_list)\n except Exception as e:\n await bot.send(ev, '错误:\\n' + str(e)) \n \n@sv.on_fullmatch(('声望排行榜', '声望排行'))\nasync def SW_ranking(bot, ev: CQEvent):\n try:\n user_card_dict = await get_user_card_dict(bot, ev.group_id)\n score_dict = {}\n score_counter = ScoreCounter2()\n gid = ev.group_id\n for uid in user_card_dict.keys():\n if uid != ev.self_id:\n score_dict[user_card_dict[uid]] = score_counter._get_prestige(gid, uid)\n if score_dict[user_card_dict[uid]] == None:\n score_dict[user_card_dict[uid]] = 0\n group_ranking = sorted(score_dict.items(), key = lambda x:x[1], reverse = True)\n msg = '此群贵族对决声望排行为:\\n'\n for i in range(min(len(group_ranking), 10)):\n if group_ranking[i][1] != 0:\n msg += f'第{i+1}名: {group_ranking[i][0]}, 声望: {group_ranking[i][1]}\\n'\n tas_list=[]\n data = {\n \"type\": \"node\",\n \"data\": {\n \"name\": str(NICKNAME[0]),\n \"uin\": str(ev.self_id),\n \"content\":msg.strip()\n }\n }\n tas_list.append(data)\n await bot.send_group_forward_msg(group_id=ev['group_id'], messages=tas_list)\n except Exception as e:\n await bot.send(ev, '错误:\\n' + str(e)) \n\n@sv.on_fullmatch(('女友排行榜', '女友排行'))\nasync def SW_ranking(bot, ev: CQEvent):\n try:\n user_card_dict = await get_user_card_dict(bot, ev.group_id)\n score_dict = {}\n score_counter = ScoreCounter2()\n duel = DuelCounter()\n gid = ev.group_id\n for uid in user_card_dict.keys():\n if uid != ev.self_id:\n cidlist = duel._get_cards(gid, uid)\n score_dict[user_card_dict[uid]] = cidnum = len(cidlist)\n group_ranking = sorted(score_dict.items(), key = lambda x:x[1], reverse = True)\n msg = '此群贵族对决女友数排行为:\\n'\n for i in range(min(len(group_ranking), 10)):\n if group_ranking[i][1] != 0:\n msg += f'第{i+1}名: {group_ranking[i][0]}, 女友数: {group_ranking[i][1]}\\n'\n tas_list=[]\n data = {\n \"type\": \"node\",\n \"data\": {\n \"name\": str(NICKNAME[0]),\n \"uin\": str(ev.self_id),\n \"content\":msg.strip()\n }\n }\n tas_list.append(data)\n await bot.send_group_forward_msg(group_id=ev['group_id'], messages=tas_list)\n except Exception as e:\n await bot.send(ev, '错误:\\n' + str(e)) \n\n@sv.on_rex(f'^用(\\d+)声望兑换金币$')\nasync def cheat_score(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n match = ev['match']\n num = int(match.group(1))\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n prestige = score_counter._get_prestige(gid,uid)\n if duel._get_level(gid, uid) == 0:\n await bot.finish(ev, '您还没有在本群创建贵族哦。', at_sender=True)\n if prestige == None:\n await bot.finish(ev, '您未开启声望系统哦！。', at_sender=True) \n if num < 200:\n await bot.finish(ev, '200声望起兑换哦！。', at_sender=True)\n score = score_counter._get_score(gid, uid)\n pay_score=num\n num2 = num * 10000\n if prestige < pay_score:\n msg = f'您的声望只有{score}，无法兑换哦。'\n await bot.send(ev, msg, at_sender=True)\n return\n else:\n score_counter._reduce_prestige(gid, uid, pay_score)\n score_counter._add_score(gid,uid,num2)\n scoreyou = score_counter._get_score(gid, uid)\n prestige = score_counter._get_prestige(gid,uid)\n msg = f'使用{num}声望兑换{num2}金币成功\\n您的声望剩余{prestige}，金币为{scoreyou}。'\n await bot.send(ev, msg, at_sender=True)\n\n@sv.on_fullmatch(('查询庆典','庆典状况','当前庆典'))\nasync def GET_Cele(bot, ev: CQEvent):\n duel = DuelCounter()\n gid = ev.group_id\n if Show_Cele_Not == True:\n if duel._get_GOLD_CELE(gid) == 1:\n msg = f'\\n当前正举办押注金币庆典，当支持成功时，获得的金币将变为原来的{Gold_Cele_Num}倍\\n'\n else:\n msg = f'\\n当前未举办金币庆典\\n'\n if duel._get_QC_CELE(gid) == 1:\n msg += f'当前正举办贵族签到庆典，签到时获取的声望将变为{QD_SW_Cele_Num}倍，金币将变为{QD_Gold_Cele_Num}倍\\n'\n else:\n msg += f'当前未举办签到庆典\\n'\n if duel._get_SUO_CELE(gid) == 1:\n msg += f'当前正举办梭哈倍率庆典，梭哈时的倍率将额外提升{Suo_Cele_Num}倍\\n'\n else:\n msg += f'当前未举办梭哈倍率庆典\\n'\n if duel._get_FREE_CELE(gid) == 1:\n msg += f'当前正举办免费招募庆典，每日可免费招募{FREE_DAILY_LIMIT}次\\n'\n else:\n msg += f'当前未举办免费招募庆典\\n'\n if duel._get_SW_CELE(gid) == 1:\n msg += f'当前正举办限时开启声望招募庆典'\n else:\n msg += f'当前未举办限时开启声望招募庆典'\n tas_list=[]\n data = {\n \"type\": \"node\",\n \"data\": {\n \"name\": str(NICKNAME[0]),\n \"uin\": str(ev.self_id),\n \"content\":msg\n }\n }\n tas_list.append(data)\n await bot.send_group_forward_msg(group_id=ev['group_id'], messages=tas_list)\n else: \n if duel._get_GOLD_CELE(gid) == 1:\n msg = f'\\n当前正举办押注金币庆典，当支持成功时，获得的金币将变为原来的{Gold_Cele_Num}倍\\n'\n else:\n msg = f'\\n'\n if duel._get_QC_CELE(gid) == 1:\n msg += f'当前正举办贵族签到庆典，签到时获取的声望将变为{QD_SW_Cele_Num}倍，金币将变为{QD_Gold_Cele_Num}倍\\n'\n if duel._get_SUO_CELE(gid) == 1:\n msg += f'当前正举办梭哈倍率庆典，梭哈时的倍率将额外提升{Suo_Cele_Num}倍\\n'\n if duel._get_FREE_CELE(gid) == 1:\n msg += f'当前正举办免费招募庆典，每日可免费招募{FREE_DAILY_LIMIT}次\\n'\n if duel._get_SW_CELE(gid) == 1:\n msg += f'当前正举办限时开启声望招募庆典'\n tas_list=[]\n data = {\n \"type\": \"node\",\n \"data\": {\n \"name\": str(NICKNAME[0]),\n \"uin\": str(ev.self_id),\n \"content\":msg\n }\n }\n tas_list.append(data)\n await bot.send_group_forward_msg(group_id=ev['group_id'], messages=tas_list)\n \n@sv.on_rex(r'^开启本群(金币|签到|梭哈倍率|免费招募|声望招募)庆典$')\nasync def ON_Cele_SWITCH(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n if not priv.check_priv(ev, priv.SUPERUSER):\n await bot.finish(ev, '您无权开放庆典！', at_sender=True)\n duel = DuelCounter()\n if duel._get_SW_CELE(gid) == None:\n await bot.finish(ev, '本群庆典未初始化，请先发\"初始化本群庆典\"初始化数据！', at_sender=True)\n match = (ev['match'])\n cele = (match.group(1))\n if cele == '金币':\n QC_Data = duel._get_QC_CELE(gid)\n SUO_Data = duel._get_SUO_CELE(gid)\n SW_Data = duel._get_SW_CELE(gid)\n FREE_Data = duel._get_FREE_CELE(gid)\n duel._initialization_CELE(gid,1,QC_Data,SUO_Data,SW_Data,FREE_Data)\n msg = f'已开启本群金币庆典，当支持成功时，获得的金币将变为原来的{Gold_Cele_Num}倍\\n'\n await bot.send(ev, msg, at_sender=True)\n return\n elif cele == '签到':\n GC_Data = duel._get_GOLD_CELE(gid)\n SUO_Data = duel._get_SUO_CELE(gid)\n SW_Data = duel._get_SW_CELE(gid)\n FREE_Data = duel._get_FREE_CELE(gid)\n duel._initialization_CELE(gid,GC_Data,1,SUO_Data,SW_Data,FREE_Data)\n msg = f'已开启本群贵族签到庆典，签到时获取的声望将变为{QD_SW_Cele_Num}倍，金币将变为{QD_Gold_Cele_Num}倍\\n'\n await bot.send(ev, msg, at_sender=True)\n return\n elif cele == '梭哈倍率':\n GC_Data = duel._get_GOLD_CELE(gid)\n QC_Data = duel._get_QC_CELE(gid)\n SW_Data = duel._get_SW_CELE(gid)\n FREE_Data = duel._get_FREE_CELE(gid)\n duel._initialization_CELE(gid,GC_Data,QC_Data,1,SW_Data,FREE_Data)\n msg = f'已开启本群梭哈倍率庆典，梭哈时的倍率将额外提升{Suo_Cele_Num}倍\\n'\n await bot.send(ev, msg, at_sender=True)\n return\n elif cele == '免费招募':\n GC_Data = duel._get_GOLD_CELE(gid)\n QC_Data = duel._get_QC_CELE(gid)\n SUO_Data = duel._get_SUO_CELE(gid)\n SW_Data = duel._get_SW_CELE(gid)\n duel._initialization_CELE(gid,GC_Data,QC_Data,SUO_Data,SW_Data,1)\n msg = f'已开启本群免费招募庆典，每日可免费招募{FREE_DAILY_LIMIT}次\\n'\n await bot.send(ev, msg, at_sender=True)\n return\n elif cele == '声望招募':\n GC_Data = duel._get_GOLD_CELE(gid)\n QC_Data = duel._get_QC_CELE(gid)\n SUO_Data = duel._get_SUO_CELE(gid)\n FREE_Data = duel._get_FREE_CELE(gid)\n duel._initialization_CELE(gid,GC_Data,QC_Data,SUO_Data,1,FREE_Data)\n msg = f'已开启本群限时开启声望招募庆典\\n'\n await bot.send(ev, msg, at_sender=True)\n return\n msg = f'庆典名匹配出错！请输入金币/签到/梭哈/免费招募/声望招募庆典中的一个！'\n await bot.send(ev, msg, at_sender=True)\n\n\n@sv.on_rex(r'^关闭本群(金币|签到|梭哈倍率|免费招募|声望招募)庆典$')\nasync def OFF_Cele_SWITCH(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n if not priv.check_priv(ev, priv.SUPERUSER):\n await bot.finish(ev, '您无权开放庆典！', at_sender=True)\n match = (ev['match'])\n cele = (match.group(1))\n duel = DuelCounter()\n if duel._get_SW_CELE(gid) == None:\n await bot.finish(ev, '本群庆典未初始化，请先发\"初始化本群庆典\"初始化数据！', at_sender=True)\n if cele == '金币':\n QC_Data = duel._get_QC_CELE(gid)\n SUO_Data = duel._get_SUO_CELE(gid)\n SW_Data = duel._get_SW_CELE(gid)\n FREE_Data = duel._get_FREE_CELE(gid)\n duel._initialization_CELE(gid,0,QC_Data,SUO_Data,SW_Data,FREE_Data)\n msg = f'\\n已关闭本群金币庆典'\n await bot.send(ev, msg, at_sender=True)\n return\n elif cele == '签到':\n GC_Data = duel._get_GOLD_CELE(gid)\n SUO_Data = duel._get_SUO_CELE(gid)\n SW_Data = duel._get_SW_CELE(gid)\n FREE_Data = duel._get_FREE_CELE(gid)\n duel._initialization_CELE(gid,GC_Data,0,SUO_Data,SW_Data,FREE_Data)\n msg = f'\\n已关闭本群贵族签到庆典'\n await bot.send(ev, msg, at_sender=True)\n return\n elif cele == '梭哈倍率':\n GC_Data = duel._get_GOLD_CELE(gid)\n QC_Data = duel._get_QC_CELE(gid)\n SW_Data = duel._get_SW_CELE(gid)\n FREE_Data = duel._get_FREE_CELE(gid)\n duel._initialization_CELE(gid,GC_Data,QC_Data,0,SW_Data,FREE_Data)\n msg = f'\\n已关闭本群梭哈倍率庆典'\n await bot.send(ev, msg, at_sender=True)\n return\n elif cele == '免费招募':\n GC_Data = duel._get_GOLD_CELE(gid)\n QC_Data = duel._get_QC_CELE(gid)\n SUO_Data = duel._get_SUO_CELE(gid)\n SW_Data = duel._get_SW_CELE(gid)\n duel._initialization_CELE(gid,GC_Data,QC_Data,SUO_Data,SW_Data,0)\n msg = f'\\n已关闭本群免费招募庆典'\n await bot.send(ev, msg, at_sender=True)\n return\n elif cele == '声望招募':\n GC_Data = duel._get_GOLD_CELE(gid)\n QC_Data = duel._get_QC_CELE(gid)\n SUO_Data = duel._get_SUO_CELE(gid)\n FREE_Data = duel._get_FREE_CELE(gid)\n duel._initialization_CELE(gid,GC_Data,QC_Data,SUO_Data,0,FREE_Data)\n msg = f'\\n已关闭本群限时声望招募庆典'\n await bot.send(ev, msg, at_sender=True)\n return\n msg = f'庆典名匹配出错！请输入金币/签到/梭哈/免费招募/声望招募庆典中的一个！'\n await bot.send(ev, msg, at_sender=True)\n\n@sv.on_fullmatch('初始化本群庆典')\nasync def initialization(bot, ev: CQEvent):\n uid = ev.user_id\n gid = ev.group_id\n if not priv.check_priv(ev, priv.SUPERUSER):\n await bot.finish(ev, '您无权初始化庆典！', at_sender=True)\n duel = DuelCounter()\n duel._initialization_CELE(gid,Gold_Cele,QD_Cele,Suo_Cele,SW_add,FREE_DAILY)\n msg = f'已成功初始化本群庆典！\\n您可发送查询庆典来查看本群庆典情况！\\n'\n await bot.send(ev, msg, at_sender=True)\n \n@sv.on_rex(f'^兑换(\\d+)声望$')\nasync def cheat_score(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n match = ev['match']\n num = int(match.group(1))\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n prestige = score_counter._get_prestige(gid,uid)\n if duel._get_level(gid, uid) == 0:\n await bot.finish(ev, '您还没有在本群创建贵族哦。', at_sender=True)\n if prestige == None:\n await bot.finish(ev, '您未开启声望系统哦！。', at_sender=True) \n score = score_counter._get_score(gid, uid)\n pay_score=num*10000\n if score < pay_score:\n msg = f'兑换{num}声望需要{pay_score}金币，您的金币只有{score}，无法兑换哦。'\n await bot.send(ev, msg, at_sender=True)\n return\n else:\n score_counter._reduce_score(gid, uid, pay_score)\n score_counter._add_prestige(gid,uid,num)\n scoreyou = score_counter._get_score(gid, uid)\n prestige = score_counter._get_prestige(gid,uid)\n msg = f'兑换{num}声望成功，扣除{pay_score}金币\\n您的声望为{prestige}，金币剩余{scoreyou}。'\n await bot.send(ev, msg, at_sender=True)\n\n#时装系统模块\n@sv.on_fullmatch(['时装系统帮助','时装帮助'])\nasync def fashion_help(bot, ev: CQEvent):\n msg='''\n╔ ╗ \n 时装系统帮助\n1. 我的女友+女友名\n2. 时装商城\n3. 购买时装+时装名称\n4. 穿戴时装+时装名称\n5. 还原穿戴+女友名\n注:\n通过购买时装可以提升好感\n╚ ╝\n ''' \n tas_list=[]\n data = {\n \"type\": \"node\",\n \"data\": {\n \"name\": str(NICKNAME[0]),\n \"uin\": str(ev.self_id),\n \"content\":msg\n }\n }\n tas_list.append(data)\n await bot.send_group_forward_msg(group_id=ev['group_id'], messages=tas_list)\n\n@sv.on_fullmatch('时装商城')\nasync def fashion_list(bot, ev: CQEvent):\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n if duel._get_level(gid, uid) == 0:\n msg = '您还未在本群创建过贵族，请发送 创建贵族 开始您的贵族之旅。'\n await bot.send(ev, msg, at_sender=True)\n return\n \n cidlist = duel._get_cards(gid, uid)\n cidnum = len(cidlist)\n\n if cidnum == 0:\n msg = '您还没有女友，无法购买时装哦。'\n await bot.send(ev, msg, at_sender=True)\n return\n else:\n jishu=0\n tas_list = []\n tat_list = (ev, f'[CQ:at,qq={uid}]您可以购买的时装为：\\r\\n')\n for tat in tat_list:\n data = {\n \"type\": \"node\",\n \"data\": {\n \"name\": str(NICKNAME[0]),\n \"uin\": str(ev.self_id),\n \"content\":tat\n }\n }\n tas_list.append(data)\n for fashion in fashionlist:\n if fashionlist[fashion]['cid'] in cidlist:\n if fashionlist[fashion]['xd_flag']==0:\n buy_info = duel._get_fashionbuy(gid,uid,fashionlist[fashion]['cid'],fashionlist[fashion]['fid'])\n if buy_info==0:\n jishu=jishu+1\n #if jishu<7:\n lh_msg = ''\n icon=get_fashion_icon(fashionlist[fashion]['fid'])\n lh_msg =lh_msg+f\"\\n{icon}\\n{fashionlist[fashion]['name']}\"\n if fashionlist[fashion]['pay_score']>0:\n lh_msg =lh_msg+f\"\\n需要金币:{fashionlist[fashion]['pay_score']}\"\n if fashionlist[fashion]['pay_sw']>0:\n lh_msg =lh_msg+f\"\\n需要声望:{fashionlist[fashion]['pay_sw']}\"\n data = {\n \"type\": \"node\",\n \"data\": {\n \"name\": str(NICKNAME[0]),\n \"uin\": str(ev.self_id),\n \"content\":lh_msg\n }\n }\n tas_list.append(data)\n #else:\n # break\n if jishu>0:\n await bot.send_group_forward_msg(group_id=ev['group_id'], messages=tas_list)\n #await bot.send(ev, lh_msg, at_sender=True)\n else:\n msg = '您的女友中目前没有出售中的时装哦。'\n await bot.send(ev, msg, at_sender=True)\n \n@sv.on_prefix(['购买时装','买时装'])\nasync def buy_fashion(bot, ev: CQEvent):\n args = ev.message.extract_plain_text().split()\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n \n if duel_judger.get_on_off_status(ev.group_id):\n msg = '现在正在决斗中哦，请决斗后再来买时装吧。'\n await bot.finish(ev, msg, at_sender=True) \n return\n if not args:\n await bot.finish(ev, '请输入购买时装+时装名。', at_sender=True)\n return\n \n name = args[0]\n fashioninfo = get_fashion_buy(name)\n if fashioninfo:\n cid = fashioninfo['cid']\n buy_info = duel._get_fashionbuy(gid,uid,cid,fashioninfo['fid'])\n if buy_info:\n await bot.finish(ev, f\"您已购买过时装{fashioninfo['name']}请勿重复够吗哦。\", at_sender=True)\n return\n owner = duel._get_card_owner(gid, fashioninfo['cid'])\n c = chara.fromid(fashioninfo['cid'])\n if uid!=owner:\n msg = f'{c.name}现在正在\\n[CQ:at,qq={owner}]的身边哦，您没有购买时装的权限哦。'\n await bot.send(ev, msg)\n return\n if fashioninfo['xd_flag'] == 1:\n await bot.finish(ev, f\"{fashioninfo['name']}为限定时装无法购买哦。\", at_sender=True)\n return\n msg_score = \"\"\n if fashioninfo['pay_score']>0:\n score = score_counter._get_score(gid, uid)\n if score < fashioninfo['pay_score']:\n await bot.finish(ev, f\"购买{fashioninfo['name']}需要{fashioninfo['pay_score']}金币，您的金币不足哦。\", at_sender=True)\n return\n score_counter._reduce_score(gid,uid,fashioninfo['pay_score'])\n msg_score = msg_score+f\"{fashioninfo['pay_score']}金币，\"\n msg_prestige = \"\"\n if fashioninfo['pay_sw']>0:\n prestige = score_counter._get_prestige(gid,uid)\n if prestige == None:\n await bot.finish(ev, '您未开启声望系统哦！。', at_sender=True)\n return\n if prestige < fashioninfo['pay_sw']:\n await bot.finish(ev, f\"购买{fashioninfo['name']}需要{fashioninfo['pay_sw']}声望，您的声望不足哦。\", at_sender=True)\n return\n score_counter._reduce_prestige(gid,uid,fashioninfo['pay_sw'])\n msg_prestige = msg_prestige+f\"{fashioninfo['pay_sw']}声望，\"\n duel._add_favor(gid,uid,cid,fashioninfo['favor'])\n duel._add_fashionbuy(gid,uid,cid,fashioninfo['fid'])\n current_favor = duel._get_favor(gid,uid,cid)\n relationship = get_relationship(current_favor)[0]\n msg = f\"您花费了{msg_score}{msg_prestige}为您的女友{c.name}购买了时装{fashioninfo['name']}\\n您与{c.name}的好感上升了{fashioninfo['favor']}点\\n她现在对你的好感是{current_favor}点\\n你们现在的关系是{relationship}\\n{fashioninfo['icon']}\"\n await bot.send(ev, msg, at_sender=True) \n else:\n await bot.finish(ev, '请输入正确的时装名。', at_sender=True)\n\n@sv.on_prefix(['穿戴时装','穿时装'])\nasync def up_fashion(bot, ev: CQEvent):\n args = ev.message.extract_plain_text().split()\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n \n if not args:\n await bot.finish(ev, '请输入穿戴时装+时装名。', at_sender=True)\n return\n name = args[0]\n fashioninfo = get_fashion_buy(name)\n if fashioninfo:\n cid = fashioninfo['cid']\n c = chara.fromid(cid)\n buy_info = duel._get_fashionbuy(gid,uid,cid,fashioninfo['fid'])\n if buy_info:\n duel._add_fashionup(gid,uid,cid,fashioninfo['fid'])\n msg = f\"您为女友{c.name}穿上了时装{fashioninfo['name']}\\n{fashioninfo['icon']}\"\n await bot.send(ev, msg, at_sender=True) \n else:\n await bot.finish(ev, f\"您还没有该时装哦，请输入购买时装+时装名进行购买哦。\", at_sender=True)\n return\n else:\n await bot.finish(ev, '请输入正确的时装名。', at_sender=True)\n\n@sv.on_prefix(['我的女友'])\nasync def my_fashion(bot, ev: CQEvent):\n args = ev.message.extract_plain_text().split()\n gid = ev.group_id\n uid = ev.user_id\n if not args:\n await bot.send(ev, '请输入我的女友+pcr角色名。', at_sender=True)\n return\n name = args[0]\n cid = chara.name2id(name)\n if cid == 1000:\n await bot.send(ev, '请输入正确的pcr角色名。', at_sender=True)\n return\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n CE = CECounter()\n c = chara.fromid(cid)\n nvmes = get_nv_icon(cid)\n lh_msg = ''\n fashioninfo = get_fashion(cid)\n up_icon=''\n up_info = duel._get_fashionup(gid,uid,cid,0)\n jishu=0\n up_name=''\n if fashioninfo:\n for fashion in fashioninfo:\n buy_info = duel._get_fashionbuy(gid,uid,cid,fashion['fid'])\n if up_info == fashion['fid']:\n up_icon = fashion['icon']\n up_name = fashion['name']\n if buy_info:\n if up_info != fashion['fid']:\n jishu=jishu+1\n if jishu<3:\n lh_msg =lh_msg+f\"\\n{fashion['icon']}\\n{fashion['name']}\"\n owner = duel._get_card_owner(gid, cid)\n if uid!=owner:\n msg = f'{c.name}现在正在\\n[CQ:at,qq={owner}]的身边哦，您无法查询哦。'\n await bot.send(ev, msg)\n return\n if owner == 0:\n await bot.send(ev, f'{c.name}现在还是单身哦，快去约到她吧。{nvmes}', at_sender=True)\n return\n if uid==owner:\n queen_msg=''\n if duel._get_queen_owner(gid,cid) !=0:\n queen_msg=f'现在是您的妻子\\n'\n if duel._get_favor(gid,uid,cid)== None:\n duel._set_favor(gid,uid,cid,0)\n #获取角色星级\n cardstar = CE._get_cardstar(gid, uid, cid)\n zllevel = CE._get_zhuansheng(gid,uid,cid)\n equip_list = ''\n equip_msg = ''\n dreeslist = CE._get_dress_list(gid, uid, cid)\n for eid in dreeslist:\n equipinfo = get_equip_info_id(eid)\n if equipinfo:\n equip_list = equip_list + f\"\\n{equipinfo['icon']}{equipinfo['type']}:{equipinfo['name']}({equipinfo['model']})\"\n if equip_list:\n equip_msg = f\"\\n目前穿戴的装备为:{equip_list}\"\n favor= duel._get_favor(gid,uid,cid)\n relationship,text = get_relationship(favor)\n card_ce=get_card_ce(gid,uid,cid)\n level_info = CE._get_card_level(gid, uid, cid)\n rank = CE._get_rank(gid, uid, cid)\n if up_icon:\n nvmes=up_icon\n up_msg=''\n if up_name:\n up_msg=f\"\\n目前穿戴的时装是{up_name}\\n\"\n if lh_msg:\n lh_msg = f\"\\n您为{c.name}购买的时装有(只显示未穿的2件)：\"+lh_msg\n msg = f'\\n{c.name}目前的等级是{level_info}级，{zllevel}转，星级为{cardstar}星，rank等级为：{rank}级，战斗力为{card_ce}点\\n{queen_msg}对你的好感是{favor}\\n你们的关系是{relationship}\\n“{text}”{equip_msg}{up_msg}{nvmes}{lh_msg}'\n await bot.send(ev, msg, at_sender=True)\n \n@sv.on_prefix(['还原穿戴','取消穿戴'])\nasync def up_fashion(bot, ev: CQEvent):\n args = ev.message.extract_plain_text().split()\n gid = ev.group_id\n uid = ev.user_id\n if not args:\n await bot.send(ev, '请输入取消穿戴+pcr角色名。', at_sender=True)\n return\n name = args[0]\n cid = chara.name2id(name)\n if cid == 1000:\n await bot.send(ev, '请输入正确的pcr角色名。', at_sender=True)\n return\n duel = DuelCounter()\n score_counter = ScoreCounter2()\n c = chara.fromid(cid)\n nvmes = get_nv_icon(cid)\n owner = duel._get_card_owner(gid, cid)\n if uid!=owner:\n msg = f'{c.name}现在正在\\n[CQ:at,qq={owner}]的身边哦，您无法处理哦。'\n await bot.send(ev, msg)\n return\n if owner == 0:\n await bot.send(ev, f'{c.name}现在还是单身哦，快去约到她吧。{nvmes}', at_sender=True)\n return\n if uid==owner:\n up_info = duel._get_fashionup(gid,uid,cid,0)\n if up_info:\n duel._delete_fashionup(gid,uid,cid)\n msg = f\"您为您的女友{c.name}取消了时装的穿戴\\n{nvmes}\"\n else:\n msg = f\"您的女友{c.name}没有穿戴时装，无法取消哦\\n{nvmes}\"\n await bot.send(ev, msg, at_sender=True) \n \n \nclass duelrandom():\n def __init__(self):\n self.random_gold_on = {}\n self.random_gold = {}\n self.rdret = {}\n self.user = {}\n \n def turn_on_random_gold(self, gid):\n self.random_gold_on[gid] = True\n\n def turn_off_random_gold(self, gid):\n self.random_gold_on[gid] = False\n\n def set_gold(self, gid):\n self.user[gid] = []\n\n def add_gold(self, gid, gold, num):\n self.random_gold[gid] = {'GOLD' : gold, 'NUM' : num}\n\n def get_gold(self, gid):\n return self.random_gold[gid]['GOLD']\n \n def get_num(self, gid):\n return self.random_gold[gid]['NUM']\n\n def add_user(self, gid, uid):\n self.user[gid].append(uid)\n\n def get_on_off_random_gold(self, gid):\n return self.random_gold_on[gid] if self.random_gold_on.get(gid) is not None else False\n \n def random_g(self, gid, gold, num):\n z = []\n ret = random.sample(range(1, gold), num - 1)\n ret.append(0)\n ret.append(gold)\n ret.sort()\n for i in range(len(ret) - 1):\n z.append(ret[i+1] - ret[i])\n self.rdret[gid] = z\n \n def get_user_random_gold(self, gid, num):\n rd = random.randint(0, num - 1)\n print(rd)\n ugold = self.rdret[gid][rd]\n self.rdret[gid].remove(ugold)\n return ugold\n\nr_gold = duelrandom()\n\n@sv.on_prefix('发送红包')\nasync def ramdom_gold(bot, ev:CQEvent):\n if not r_gold.get_on_off_random_gold(ev.group_id):\n if not priv.check_priv(ev, priv.SUPERUSER):\n await bot.finish(ev, '该功能仅限超级管理员使用')\n gid = ev.group_id\n msg = ev.message.extract_plain_text().split()\n if not msg[0].isdigit():\n await bot.finish(ev, '请输入正确的奖励金额')\n if not msg[1].isdigit():\n await bot.finish(ev, '请输入正确的奖励个数')\n gold = int(msg[0])\n num = int(msg[1])\n await bot.send(ev, f'已发放红包，金币总额为：{gold}\\n数量：{num}\\n请输入 领取红包')\n r_gold.turn_on_random_gold(gid)\n r_gold.set_gold(gid)\n r_gold.add_gold(gid, gold, num)\n r_gold.random_g(gid, gold, num)\n await asyncio.sleep(60)\n r_gold.turn_off_random_gold(gid)\n await bot.send(ev, '随机金币奖励活动已结束，请期待下次活动开启')\n r_gold.user = {}\n\n@sv.on_fullmatch('领取红包')\nasync def get_random_gold(bot, ev:CQEvent):\n if r_gold.get_on_off_random_gold(ev.group_id):\n uid = int(ev.user_id)\n gid = ev.group_id\n if uid in r_gold.user[gid]:\n await bot.finish(ev, '您已领取过红包', at_sender=True)\n score_counter = ScoreCounter2()\n #获取金币\n gold = r_gold.get_gold(gid)\n #获取个数\n num = r_gold.get_num(gid)\n #获取金额\n rd = r_gold.get_user_random_gold(gid, num)\n r_gold.add_gold(gid, gold-rd, num-1)\n newnum = r_gold.get_num(gid)\n newgold = r_gold.get_gold(gid)\n score_counter._add_score(gid, uid, rd)\n r_gold.add_user(gid, uid)\n await bot.send(ev, f'已领取红包：{rd}\\n剩余{newnum}个总额：{newgold}', at_sender=True)\n if newnum == 0:\n await bot.send(ev, f'红包已全部领取完毕')\n r_gold.turn_off_random_gold(gid)\n\n\n\n@sv.on_fullmatch('删bug')\nasync def shanbug(bot, ev:CQEvent):\n duel = DuelCounter()\n duel._delete_card(285991381, 375744371, 7801)\n await bot.send(ev, f'删掉了md')\n \n@sv.on_prefix(['使用道具'])\nasync def use(bot, ev: CQEvent):\n args = ev.message.extract_plain_text().split()\n gid = ev.group_id\n uid = ev.user_id\n duel = DuelCounter()\n CTUSE = [11,12,13]\n if not args:\n await bot.finish(ev, '请输入 使用道具+道具名+目标名 中间用空格隔开。', at_sender=True)\n gift = args[0]\n if gift not in GIFT_DICT.keys():\n await bot.finish(ev, '请输入正确的道具名', at_sender=True)\n gfid = GIFT_DICT[gift]\n if duel._get_gift_num(gid,uid,gfid)==0:\n await bot.finish(ev, '你未持有这个道具哦。', at_sender=True)\n if gfid <= 10:\n await bot.finish(ev, '请输入正确的道具名。', at_sender=True)\n if gfid in CTUSE:\n await bot.finish(ev, '这件道具不能这么使用。', at_sender=True)\n if gfid == 14:\n if len(args)!=2:\n await bot.finish(ev, '请输入 使用道具+道具名+目标名 中间用空格隔开。', at_sender=True)\n name = args[1]\n cid = chara.name2id(name)\n if cid == 1000:\n await bot.finish(ev, '请输入正确的女友名。', at_sender=True)\n owner = duel._get_card_owner(gid, cid)\n c = chara.fromid(cid)\n if owner == 0:\n await bot.send(ev, f'{c.name}还是单身哦，无法使用。', at_sender=True)\n return \n duel._reduce_gift(gid,uid,gfid)\n if duel._get_gift_num(gid,owner,13)==0:\n msg = f'\\n你使用了陷害卡，{c.name}与他的持有者[CQ:at,qq={owner}]的好感度降低了30！\\n{c.icon.cqcode}'\n duel._reduce_favor(gid,owner,cid,30)\n else:\n msg = f'\\n你使用了陷害卡，但对方使用无懈卡使你的道具无效了！'\n duel._reduce_gift(gid,owner,13)\n await bot.send(ev, msg, at_sender=True)\n if gfid == 15:\n await bot.finish(ev, '还不允许使用哦！', at_sender=True)\n# if len(args)!=2:\n# await bot.finish(ev, '请输入 使用道具+道具名+目标名 中间用空格隔开。', at_sender=True)\n# name = args[1]\n# cid = chara.name2id(name)\n# if cid == 1000:\n# await bot.finish(ev, '请输入正确的女友名。', at_sender=True)\n# level = duel._get_level(gid, uid)\n# noblename = get_noblename(level)\n# girlnum = get_girlnum_buy(gid,uid)\n# cidlist = duel._get_cards(gid, uid)\n# cidnum = len(cidlist)\n# if cidnum >= girlnum:\n# msg = '您的女友已经满了哦，无法使用这个道具，快点发送[升级贵族]进行升级吧。'\n# await bot.send(ev, msg, at_sender=True)\n# return\n# owner = duel._get_card_owner(gid, cid)\n# c = chara.fromid(cid)\n# if owner != 0:\n# await bot.send(ev, f'{c.name}不是单身哦，无法使用。', at_sender=True)\n# return \n# duel._reduce_gift(gid,uid,gfid)\n# msg = f'\\n你使用了指定招募卡，{c.name}成为了你的女友\\n{c.icon.cqcode}'\n# duel._add_card(gid, uid, cid)\n# await bot.send(ev, msg, at_sender=True) \n","sub_path":"zhuti.py","file_name":"zhuti.py","file_ext":"py","file_size_in_byte":140755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"113089201","text":"import urllib.request\nimport json\nimport psycopg2\nimport idigbio\nimport sys\nfrom DBInfo import connectDB, tableExists\n\n'''Python script for building a table schema for a PostgreSQL database based\non a query result from idigbio. Database details defined in DBInfo.py script's\nconnectDB() function.\n'''\n\n\ndef createSchema(result, table_name):\n '''Function that takes an idigbio query in dict format as argument \"result\" \n and creates a database table schema based on based on the fields\n (dictionary keys) present in the query. \n Goes through field names in each record in the query and adds distinct ones to table\n '''\n #Check that table does not already exist in database\n if tableExists(table_name):\n print(\"Table '\" + table_name + \"' already exists.\")\n sys.exit(0)\n \n #Connect to database, DB info can be set in DBInfo.py file\n connection = connectDB()\n\n #Initialize cursor, attribute of psycopg2 \"connection\" object\n cursor = connection.cursor()\n \n create_command = \"CREATE TABLE \" + table_name + \"()\"\n \n #Create a new table in the database\n try:\n cursor.execute(create_command)\n connection.commit()\n except psycopg2.ProgrammingError as e:\n print(\"Table not created successfully.\")\n print(e.pgerror)\n sys.exit()\n\n #Extract record type data from idigbio API endpoint\n raw_data = urllib.request.urlopen(\"http://search.idigbio.org/v2/meta/fields/records\")\n\n #Decode data and convert to JSON/ Python dictionary\n record_types_dict = json.loads(raw_data.read().decode())\n\n #Fields that require special treatment (longer than 200 chars, special type etc.)\n special_fields = [\"typestatus\", \"data\", \"datecollected\", \"datemodified\", \n \"indexData\", \"flags\", \"recordids\", \"locality\", \n \"verbatimlocality\", \"collector\", \"commonnames\", \n \"mediarecords\", \"highertaxon\"]\n\n\n #Iterate through each record in idigbio query result dictionary \n for record in result[\"items\"]:\n #Query database to find columns it currently contains\n select_command = \"SELECT * FROM \" + table_name + \" LIMIT 0\"\n cursor.execute(select_command)\n \n #Make a list of the fields in database currently\n table_fields = [desc[0] for desc in cursor.description]\n \n #Iterate through fields in current record\n for field in record[\"indexTerms\"]:\n #Base string for PSQL column addition command\n add_col = \"ALTER TABLE \" + table_name + \" ADD COLUMN \\\"\"\n \n #Handle special case fields (defined in special_fields list)\n if field in special_fields and field not in table_fields:\n '''\n TODO: Change below fields with JSON data type to jsonb type in database\n '''\n #Special case: data field with JSON datastructure (Change later)\n if field == \"indexData\":\n add_col += field + \"\\\" TEXT\"\n \n #Special case: Date fields\n elif field == \"datecollected\":\n add_col += field + \"\\\" DATE\"\n \n elif field == \"datemodified\":\n add_col += field + \"\\\" DATE\"\n \n #Special case: data field with JSON structure (Change to JSON type later)\n elif field == \"data\":\n add_col += field + \"\\\" TEXT\"\n \n #Other cases treated as strings of unknown length (TEXT)\n else:\n add_col += field + \"\\\" TEXT\"\n \n #Execute command built\n cursor.execute(add_col)\n continue\n \n \n #Handle remaining fields. Check that field doesn't already exist in DB\n if field not in special_fields and field not in table_fields:\n #Extract field's designated type from API data\n field_type = record_types_dict[field][\"type\"]\n \n #Construct appropriate add command\n if field_type == \"string\":\n #CREATE COLUMN VARCHAR 200\n add_col += field + \"\\\" VARCHAR(200)\"\n elif field_type == \"float\":\n #CREATE COLUMN DECIMAL\n add_col += field + \"\\\" DECIMAL\"\n elif field_type == \"integer\":\n #CREATE COLUMN INTEGER\n add_col += field + \"\\\" INTEGER\"\n elif field_type == \"boolean\":\n #CREATE COLUMN BOOLEAN\n add_col += field + \"\\\" BOOLEAN\"\n \n #Handles special types like geopoint etc.\n else:\n #CREATE COLUMN VARCHAR 200\n add_col += field + \"\\\" VARCHAR(200)\"\n \n #Execute column addition command and save changes to DB\n cursor.execute(add_col)\n continue\n \n \n #Close connection to DB\n connection.commit()\n cursor.close()\n connection.close()\n \n print(\"Database table \" + table_name + \" has been created successfully.\")\n \n\n \ndef main():\n '''Main function for testing purposes only\n ''' \n #Initialize idigbio API\n api = idigbio.json()\n \n #Define query dictionary\n rq = {\"genus\":\"panthera\"}\n \n #Assign query results\n result = api.search_records(rq, limit=30)\n \n table_name = \"records1\"\n \n #Create database table\n createSchema(result, table_name)\n \n\nif __name__==\"__main__\":\n main()","sub_path":"Jupyter Notebooks/Data Workflow/TableSchemaCreator.py","file_name":"TableSchemaCreator.py","file_ext":"py","file_size_in_byte":5704,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"529854537","text":"import re\r\n\r\n# Count the words in a string. Return a dictionary with the word as\r\n# the key and the number of ocurrences of the word as the value. Words\r\n# are separated by whitespace or punctuation and are case insensitive.\r\n#\r\n# for example: word_count(\"apple apple_orange,Apple-OrAnGe\")\r\n# would return: {\"apple:3, orange:2\"}\r\n#\r\n\r\ndef word_count(text):\r\n \r\n # Replace all punctuation with whitespace.\r\n # Literally, replace any \"non-word\" characters (\\W) or underscores (_) with a blank space\r\n text = re.sub('[\\W_]', ' ', text)\r\n\r\n # Set all characters to lowercase for case insensitive comparisons\r\n text = text.lower()\r\n\r\n # Split the text on whitespace into a list of words\r\n split_text = text.split()\r\n\r\n #\r\n # Create a dictionary of word occurrence counts\r\n # {:}\r\n #\r\n\r\n # Initialize the empty dictionary\r\n result = {}\r\n\r\n # Loop through each word in the text\r\n for word in split_text:\r\n # Check if this is the first occurrence of the string\r\n if word not in result:\r\n # This is the first occurrence, add to the dictionary\r\n result[word] = 1\r\n else:\r\n # This is a repeat occurrence, increment the occurrence count\r\n result[word] += 1\r\n\r\n return result\r\n\r\n\r\n#print (word_count('a,b,a,c'))\r\n","sub_path":"student_work/larrymw/python/word_count/word_count.py","file_name":"word_count.py","file_ext":"py","file_size_in_byte":1358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"126571263","text":"opening_file= open(\"words.txt\", \"r\")\nreadinglines = opening_file.readlines()\nwords_dict = {}\n#This section prepares each line in the file to broke down. It strips out the spaces and \\n\n#Then it splits the words on the spaces so it knows how far the string extends\nfor line in readinglines:\n line = line.strip()\n line = line.split(\" \")\n print(line)\n#States that if the words from the line are already present in the dictionary made, it will add a 1 to the count to be referenced later\n for word in line:\n if word in words_dict:\n words_dict[word] = words_dict[word] + 1\n else:\n words_dict[word] = 1\n print(word)\n print(words_dict)\n#Now that we have our dictionary set up, we will print the key(the word) and the associated number.\nfor key in list(words_dict.keys()):\n print(key, \":\", words_dict[key])\nopening_file.close()\n","sub_path":"Dictionaries and Sets/Dictionaries and Sets Exercise - Word Frequency.py","file_name":"Dictionaries and Sets Exercise - Word Frequency.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"75968738","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: html_and_xml.py\n@time: 15/12/28 19:14\n\"\"\"\n\nimport html\nimport xml\n\n\n'正确的转换特殊标记字符在处理html和xml文本时很重要'\n\ns = 'Elements are written as \"text\".'\nprint(s)\n# 替换文本字符串中的 ‘<’ 或者 ‘>’\nprint(html.escape(s))\nprint(html.escape(s, quote=False))\n\ns = 'Spicy "Jalapeño".'\nfrom html.parser import HTMLParser\n#p = HTMLParser()\n#p.unescape(s) # deprecated!!!\nrst = html.unescape(s)\nprint(rst)","sub_path":"python-cookbook/cp-2/html_and_xml.py","file_name":"html_and_xml.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"203303111","text":"#!/usr/bin/env python\nimport os\n\n# Uncommented parts are explained in : create_a_basic_document_with_a_list.py\n\nfrom odfdo import Document, List, ListItem\n\n# Create the document\nmy_document = Document(\"text\")\nbody = my_document.body\n\n# Adding List\nmy_list = List([\"Arthur\", \"Ford\", \"Trillian\"])\nitem = ListItem(\"Marvin\")\nmy_list.append_item(item)\nbody.append(my_list)\n\n# Adding Sublist¶\n# A sublist is simply a list as an item of another list:\nitem.append(List([\"Paranoid Android\", \"older than the universe\"]))\n\n# See the result:\nprint(my_document.get_formatted_text())\n# - Arthur\n# - Ford\n# - Trillian\n# - Marvin\n# - Paranoid Android\n# - older than the universe\n\n\n# Inserting List Item\n# In case your forgot to insert an item:\nmy_list.insert_item(\"some dolphins\", position=1)\n\n# Or you can insert it before another item:\nmarvin = my_list.get_item(content=\"Marvin\")\nmy_list.insert_item(\"Zaphod\", before=marvin)\n# Or after:\nmy_list.insert_item(\"and many others\", after=marvin)\n\n\nif not os.path.exists(\"test_output\"):\n os.mkdir(\"test_output\")\n\noutput = os.path.join(\"test_output\", \"my_document_with_sublist.odt\")\n\n# And finally save the document.\nmy_document.save(target=output, pretty=True)\n\n# See the result:\nprint(my_document.get_formatted_text())\n# - Arthur\n# - some dolphins\n# - Ford\n# - Trillian\n# - Zaphod\n# - Marvin\n# - Paranoid Android\n# - older than the universe\n# - and many others\n","sub_path":"recipes/create_a_basic_text_document_with_list_and_sublists.py","file_name":"create_a_basic_text_document_with_list_and_sublists.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"257728619","text":"def check(v, div):\n while v > 0:\n digit = v % div\n if digit == 7:\n return True\n v = v // div\n return False\n\n\ndef main():\n N = int(input())\n ct = 0\n for v in range(N + 1):\n if check(v, 10) or check(v, 8):\n ct += 1\n print(N - ct)\n\n\nmain()\n","sub_path":"abc/abc186c.py","file_name":"abc186c.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"639103553","text":"def rndnmbr():\n import random\n return random.randrange(1,50)\ndef tab_evenness(tab):\n even = 0\n odd = 0\n for x in tab:\n if(x % 2 == 0):\n even += 1\n else:\n odd += 1\n percent = round(even / len(tab) * 100, 2)\n print(f\"Liczby parzyste: {percent}%\")\n print(f\"Liczby nieparzyste: {100 - percent}%\")\ntab = [rndnmbr()]\nfor x in range(999):\n tab.append(rndnmbr())\ntab_evenness(tab)\n\n","sub_path":"04-Subroutines/Zadanie30.py","file_name":"Zadanie30.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"21460908","text":"# -*- coding: utf-8 -*-\r\n#@author:huan\r\n\r\nfrom sklearn.metrics import auc,roc_curve,confusion_matrix,f1_score,recall_score\r\n\r\nimport sys\r\nsys.path.append('/home/longpeiji/utils')\r\n\r\nfrom user_datagenerator import User_Generator\r\nimport pandas as pd\r\nfrom glob import glob\r\nimport numpy as np\r\nfrom keras.models import load_model\r\nimport time\r\nimport os\r\n\r\ndef evaluate_model(data_dir1,data_dir2,model,im_num,pred_save_path,evaluate_save_path):\r\n data_gene=User_Generator(data_dir1,data_dir2)\r\n y_true,y_pred=[]\r\n for x_test,label in data_gene.test_or_validation(200):\r\n y_p=model.predict([x_test])\r\n for y1,y2 in zip(y_p,label):\r\n y_pred.append(y1)\r\n y_true.append(y2)\r\n pd.DataFrame(y_pred).to_excel(pred_save_path)\r\n fpr,tpr,thresholds=roc_curve(y_true,y_pred)\r\n auc_value=auc(fpr,tpr) \r\n \r\n score=[np.where(x>=0.5,1,0) for x in y_pred]\r\n u=confusion_matrix(y_true,score)\r\n Accuracy=(u[0][0]+u[1][1])/(u[0][0]+u[0][1]+u[1][0]+u[1][1])\r\n Sensitivity=u[1][1]/(u[1][1]+u[1][0]) #敏感度,tpr\r\n Specificity=u[0][0]/(u[0][1]+u[0][0]) #特异性，FPR\r\n f1_v=f1_score(y_true,score)\r\n pd.DataFrame(data={'--Accuracy--':[str(Accuracy)+'**'],\\\r\n '--auc--':[str(auc_value)+'**'],\\\r\n '--Sensitivity--':[str(Sensitivity)+'**'],\\\r\n '--Specificity--':[str(Specificity)+'**'],\\\r\n '--f1_v':[str(f1_v)+'**']})\\\r\n .to_excel(evaluate_save_path)\r\n \r\ndef write_result(model_base_path,x,y,i,save_path):\r\n model=load_model(model_base_path)\r\n y_pred=model.predict([x])\r\n# pd.DataFrame(y_pred).to_excel(save_path+'/'+'y_pred'+str(i)+'.xlsx')\r\n fpr,tpr,thresholds=roc_curve(y,y_pred)\r\n auc_value=auc(fpr,tpr) \r\n \r\n score=[np.where(x>=0.5,1,0) for x in y_pred]\r\n u=confusion_matrix(y,score)\r\n Accuracy=(u[0][0]+u[1][1])/(u[0][0]+u[0][1]+u[1][0]+u[1][1])\r\n Sensitivity=u[1][1]/(u[1][1]+u[1][0]) #敏感度,tpr\r\n Specificity=u[0][0]/(u[0][1]+u[0][0]) #特异性，FPR\r\n f1_v=f1_score(y,score)\r\n r=recall_score(y,score)\r\n pd.DataFrame(data={'Accuracy':[Accuracy],\\\r\n 'auc':[auc_value],\\\r\n 'recall_score':[r],\\\r\n 'Sensitivity':[Sensitivity],\\\r\n 'Specificity':[Specificity],\\\r\n 'f1_v':[f1_v]})\\\r\n .to_excel(save_path+'/'+'validation_y_pred'+str(i)+'.xlsx')\r\n\r\n#print(time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())) \r\n#path='D:/learning/keras_dr_image_assessment/data_augmentation'\r\n#type1_path='D:/huan/image_assessment/validation/blur'\r\n#type2_path='D:/huan/image_assessment/validation/no_blur'\r\n\r\npath='/home/longpeiji/keras_user_resnet/save_model'\r\nsave_path='/home/longpeiji/keras_user_resnet/validation'\r\ntype1_path='/home/longpeiji/image_assessment/validation/blur'\r\ntype2_path='/home/longpeiji/image_assessment/validation/no_blur'\r\nim_num=2000\r\n\r\ndata_gen=User_Generator(type1_path,type2_path)\r\nfor x,y in data_gen.test_or_validation(im_num):\r\n while 1:\r\n try: \r\n l=len(glob(save_path+'/*.xlsx'))\r\n if l==0:\r\n for p in glob(path+'/*.h5'): \r\n num=int(os.path.basename(p).split('_')[1].split('.')[0]) \r\n write_result(p,x,y,num,save_path)\r\n print(time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())) \r\n else:\r\n for p in glob(path+'/*.h5'): \r\n num=int(os.path.basename(p).split('_')[1].split('.')[0])\r\n if num>l-1: \r\n write_result(p,x,y,num,save_path)\r\n print(time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())) \r\n \r\n except OSError : #处理文件没有传完就去读的异常\r\n time.sleep(60) #给model.save 60s时间进行模型保存\r\n","sub_path":"layers/model_predict.py","file_name":"model_predict.py","file_ext":"py","file_size_in_byte":3991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"82416707","text":"# procedure:\n# run reduction_2014Jul01\n# in same terminal copy and paste code below\n\nplt.figure(108)\nplt.clf()\nplt.plot(g[4],g[1],'g.',label='g\\'')\nplt.plot(r[4],r[1],'r.',label='r\\'')\nplt.plot(i[4],i[1],'k.',label='i\\'')\nplt.legend()\n\njdred=g[4]\nplt.xlim(np.min(jdred),np.max(jdred))\njdref=np.floor(np.min(g[0]))\nplt.xlabel('JD - '+str(jdref)+' (days)')\nplt.ylabel('Normalized Flux')\nplt.title('KIC9821078')\nplt.savefig(outdir+'KIC9821078.png',dpi=300)\n\nplt.text(0.74,0.77, r'$\\sigma_{i}$'+' = %.4f' % i[5],fontsize=15)\nplt.text(0.74,0.80, r'$\\sigma_{r}$'+' = %.4f' % r[5],fontsize=15)\nplt.text(0.74,0.83, r'$\\sigma_{g}$'+' = %.4f' % g[5],fontsize=15)\n","sub_path":"multiband.py","file_name":"multiband.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"569742421","text":"from django.shortcuts import render\nfrom basket.models import Player\nfrom basket.forms import PlayerForm\nfrom django.shortcuts import redirect\n\n\ndef index(request):\n data = {}\n data = Player.objects.all().order_by('-id')\n template_name = 'player/listar.html'\n return render(request, template_name, {\"data\": data})\n\n\ndef agregar(request):\n template_name = 'player/agregar.html'\n playerForm = PlayerForm()\n if request.POST:\n playerForm = PlayerForm(request.POST, request.FILES)\n if playerForm.is_valid():\n playerForm = playerForm.save(commit=False)\n playerForm.save()\n return redirect('listar')\n return render(request, template_name, {'playerForm': playerForm})\n\n\ndef eliminar(request, player_id):\n template_name = 'player/eliminar.html'\n player = Player.objects.get(pk=player_id)\n if request.POST:\n player.delete()\n return redirect('listar')\n return render(request, template_name, {'player': player})\n\n\ndef editar(request, player_id):\n template_name = 'player/editar.html'\n player = Player.objects.get(pk=player_id)\n if request.GET:\n form = PlayerForm(instance=player)\n else:\n form = PlayerForm(request.POST, instance=player)\n if form.is_valid():\n form.save()\n return redirect('editar')\n return render(request, template_name, {'form': form})\n","sub_path":"basket/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"334331869","text":"import cv2\n\n\nimage = cv2.imread(\"data/1 (1).png\", 0) # read gray image\nimage = cv2.GaussianBlur(image, (5, 5), 0)\nimage = image[:50, :50]\nprint(image.shape)\n\nedged = cv2.Canny(image, 10, 50)\n\nkernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (10, 10))\nclosed = cv2.morphologyEx(edged, cv2.MORPH_CLOSE, kernel)\n\ncv2.imshow(\"Bacteria1\", closed)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n","sub_path":"python/main2.py","file_name":"main2.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"207454213","text":"def freq_table(s):\n result = {}\n for c in s:\n if c in result:\n result[c] += 1\n else:\n result[c] = 1\n return result\n\n\ndef solution(s1, s2):\n master = freq_table(s1)\n runtime = master.copy()\n\n matching = False\n for c in s2:\n if c in runtime.keys():\n matching = True\n runtime[c] -= 1\n else:\n if matching:\n matching = False\n runtime = master.copy()\n\n for _, v in runtime.items():\n if v != 0:\n return False\n\n return True\n\n\ns1 = 'ABCD'\ns2 = 'DCXXXBCDA'\n\nprint(solution(s1, s2))\n","sub_path":"Interviews/Jet/AnagramSubstringMatching.py","file_name":"AnagramSubstringMatching.py","file_ext":"py","file_size_in_byte":630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"216113893","text":"# on crée le tableau double dimension\ntable = [[\" \" for i in range(11)] for j in range(9)]\n\ni = 0\nmiddleTable = int(len(table[0]) / 2)\n# permet de remplir le tableau avec des étoiles\nwhile i < len(table):\n if i >= 0 and i < middleTable + 1:\n z = 0\n while z < i + 1:\n # permet de remplir la parti droite du tableau\n table[i][middleTable + z] = \"*\"\n # permet de remplir la parti gauche du tableau\n table[i][middleTable - z] = \"*\"\n z += 1\n if i >= middleTable + 1:\n k = middleTable - 1\n while k < middleTable + 2:\n table[i][k] = \"*\"\n k += 1\n\n i += 1\n\ny = 0\n\n\nprint(\"le Sapin de Noël : \")\nwhile y < len(table):\n print(\" \".join(table[y])) # permet d'afficher le tableau sans les crochets\n y += 1\n","sub_path":"loop.py","file_name":"loop.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"601606159","text":"import sys, os\n\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nfrom UcsBase import ManagedObject\nsys.path.remove(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\n\nclass ClitestTypeTest2(ManagedObject):\n\tdef __init__(self):\n\t\tManagedObject.__init__(self,\"ClitestTypeTest2\")\n\n\t@staticmethod\n\tdef ClassId():\n\t\treturn \"clitestTypeTest2\"\n\n\tA_PARTIAL_ENUM = \"APartialEnum\"\n\tABITMASK = \"Abitmask\"\n\tACHARBUF = \"Acharbuf\"\n\tDN = \"Dn\"\n\tFILE_DIR = \"FileDir\"\n\tFILE_HOST = \"FileHost\"\n\tFILE_NAME = \"FileName\"\n\tFILE_PASSWD = \"FilePasswd\"\n\tFILE_PATH = \"FilePath\"\n\tFILE_PORT = \"FilePort\"\n\tFILE_PROTO = \"FileProto\"\n\tFILE_USER = \"FileUser\"\n\tRN = \"Rn\"\n\tSTATUS = \"Status\"\n\n\tCONST_A_PARTIAL_ENUM_DEFAULT = \"default\"\n\tCONST_A_PARTIAL_ENUM_UNTAGGED = \"untagged\"\n\tCONST_FILE_PROTO_FTP = \"ftp\"\n\tCONST_FILE_PROTO_HTTP = \"http\"\n\tCONST_FILE_PROTO_NFS_COPY = \"nfs-copy\"\n\tCONST_FILE_PROTO_NONE = \"none\"\n\tCONST_FILE_PROTO_SCP = \"scp\"\n\tCONST_FILE_PROTO_SFTP = \"sftp\"\n\tCONST_FILE_PROTO_TFTP = \"tftp\"\n","sub_path":"UcsSdk-0.8.3/src/UcsSdk/MoMeta/ClitestTypeTest2.py","file_name":"ClitestTypeTest2.py","file_ext":"py","file_size_in_byte":1007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"408272479","text":"from main import test\nimport time\n\nweight=[0,1,2]\ngrouping= [0, 1]\ncopying=[0, 1]\nfile = open(\"accuracy.txt\", \"w\")\nfile1 = open(\"time.txt\", \"w\")\n\n\nfor i in range(0, len(weight)):\n for j in range(0, len(grouping)):\n for k in range(0, len(copying)):\n testing = test(weight=i, grouping=j, copying=k, random_flag=False)\n #testing.load_data(\"data/filmtrust/ratings.txt\")\n # testing.load_data(\"data/epinion/ratings_temp.txt\")\n testing.load_data(\"data/syntetic/ratings.txt\")\n tic = time.time()\n testing.calculate_rating()\n accuracy = testing.calculate_accuracy()\n file.write(str(i)+\",\"+str(j)+\",\"+str(k)+\":\" +str(accuracy)+\"\\n\")\n\n toc = time.time()\n print('time to make the predictis: %.2f secs' %(toc-tic)+\"\\n\")\n file1.write(str(i)+\",\"+str(j)+\",\"+str(k)+\":\" + '%.2f secs' %(toc-tic)+\"\\n\")\n #testing.print_statistic()","sub_path":"ablation.py","file_name":"ablation.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"224450706","text":"# Standard Python libraries\nfrom __future__ import (absolute_import, print_function,\n division, unicode_literals)\nimport os\n\nfrom .. import Calculation\n\nclass ElasticConstantsStatic(Calculation):\n \"\"\"\n Class for handling different calculation styles in the same fashion. The\n class defines the common methods and attributes, which are then uniquely\n implemented for each style. The available styles are loaded from the\n iprPy.calculations submodule.\n \"\"\"\n @property\n def files(self):\n \"\"\"\n iter of str: Path to each file required by the calculation.\n \"\"\"\n files = [\n 'calc_' + self.style + '.py',\n 'cij.template',\n 'potential.template',\n ]\n for i in range(len(files)):\n files[i] = os.path.join(self.directory, files[i])\n \n return files\n \n @property\n def singularkeys(self):\n \"\"\"list: Calculation keys that can have single values during prepare.\"\"\"\n return [\n 'lammps_command',\n 'mpi_command',\n 'length_unit',\n 'pressure_unit',\n 'energy_unit',\n 'force_unit',\n ]\n \n @property\n def multikeys(self):\n \"\"\"list: Calculation keys that can have multiple values during prepare.\"\"\"\n return [\n [\n 'potential_file',\n 'potential_content',\n 'potential_dir',\n 'load_file',\n 'load_content',\n 'load_style',\n 'family',\n 'load_options',\n 'symbols',\n 'box_parameters',\n ],\n [\n 'a_uvw',\n 'b_uvw',\n 'c_uvw',\n 'atomshift',\n 'sizemults',\n ],\n [\n 'strainrange',\n ],\n [\n 'energytolerance',\n 'forcetolerance',\n 'maxiterations',\n 'maxevaluations',\n 'maxatommotion',\n ],\n ]","sub_path":"iprPy/calculation/elastic_constants_static/ElasticConstantsStatic.py","file_name":"ElasticConstantsStatic.py","file_ext":"py","file_size_in_byte":2323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"16765965","text":"n = int(input())\narr = [int(input()) for i in range(n)]\n\n# chage = 0\n\n# for i in range(n):\n# chage = 0\n# for j in range(n-i-1):\n# if arr[j] > arr[j+1]:\n# chage = 1\n# arr[j], arr[j+1] = arr[j+1], arr[j]\n# if chage == 0:\n# print(i+1)\n# break\n\nearr = list(enumerate(arr))\nearr.sort(key = lambda x: x[1])\n# print(earr)\nrs = 0\nfor i in range(n):\n if earr[i][0] > i:\n rs = max(rs, earr[i][0]-i)\n\nprint(rs+1)","sub_path":"1377 버블소트.py","file_name":"1377 버블소트.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"554470298","text":"import abc\nfrom inspect import signature\nfrom typing import Callable\n\n\n__all__ = ['BaseEvent', 'EventProcessor']\n\n\nclass BaseEvent(metaclass=abc.ABCMeta):\n \"\"\"Базовый класс для всех событий, обрабатываемых ``EventProcessor``'ом.\"\"\"\n\n\nclass EventProcessor:\n \"\"\"Обработчик событий, поступающих ему на вход.\"\"\"\n def __init__(self):\n self.handlers = []\n\n def add_handler(self, handler: Callable):\n \"\"\"\n Добавляет новую функцию-обработчик для событий.\n Тип события автоматически определяется из типа первого аргумента искомой функции.\n\n Params:\n handler: функция, тип первого аргумента которой должен быть унаследован от ``BaseEvent``\n\n Raises:\n ValueError: если в сигнатуре функции нет аргументов или класс первого\n аргумента переданной функции не является наследником ``BaseEvent``\n \"\"\"\n event_type = self._get_handler_eventtype(handler)\n if not issubclass(event_type, BaseEvent):\n message = f\"{event_type} isn't one of ``BaseEvent``'s class inheritors\"\n raise ValueError(message)\n self.handlers.append(handler)\n\n def process_event(self, event: BaseEvent) -> None:\n \"\"\"\n Обрабатывает событие поступившее на вход.\n К событию будут применены все добавленные подходящие ему обработчики\n (с учётом наследования).\n\n Params:\n event: событие, появление которого необходимо обработать\n \"\"\"\n handler_types = map(self._get_handler_eventtype, self.handlers)\n for handler_type, handler in zip(handler_types, self.handlers):\n if isinstance(event, handler_type):\n handler(event)\n\n @staticmethod\n def _get_handler_eventtype(handler: Callable) -> type:\n \"\"\"\n Возвращает тип события, которое обрабатывает данная функция-обработчик.\n\n Params:\n handler: функция, тип первого аргумента которой должен быть унаследован от ``BaseEvent``\n\n Returns:\n type: тип первого аргумента из сингнатуры, полученной функции\n\n Raises:\n ValueError: если в сигнатуре функции нет аргументов или класс первого\n аргумента переданной функции не является наследником BaseEvent\n \"\"\"\n sig = signature(handler)\n if len(sig.parameters) < 1:\n message = (\"``handler`` должен содержать минимум 1 аргумент - \"\n \"событие для обработки\")\n raise ValueError(message)\n\n param, *_ = sig.parameters.values()\n event_type = param.annotation\n\n if not issubclass(event_type, BaseEvent):\n message = (\"Первый аргумент функции ``handler`` должен быть \"\n \"унаследован от ``BaseEvent``\")\n raise ValueError(message)\n\n return event_type\n","sub_path":"BarcodeQR_CamScanner/event_system/handling.py","file_name":"handling.py","file_ext":"py","file_size_in_byte":3703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"504510476","text":"# Copyright 2021 The TensorFlow Authors. 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\"\"\"Tests for the keras losses.\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\nfrom model import losses\n\n\nclass KerasLossesTest(tf.test.TestCase):\n\n def test_batch_softmax_loss(self):\n batch_softmax = losses.BatchSoftmax()\n true_label = tf.constant([[2], [0], [1]], dtype=tf.int32)\n logits = tf.constant([\n [0.8, 0.1, 0.2, 0.3],\n [0.2, 0.7, 0.1, 0.5],\n [0.5, 0.4, 0.9, 0.2]\n ], dtype=tf.float32)\n self.assertBetween(\n batch_softmax.call(y_true=true_label, y_pred=logits).numpy(),\n 1.3, 1.4)\n\n def test_global_softmax_loss(self):\n global_softmax = losses.GlobalSoftmax()\n true_label = tf.constant([[2], [0], [1]], dtype=tf.int32)\n logits = tf.constant([\n [0.8, 0.1, 0.2, 0.3],\n [0.2, 0.7, 0.1, 0.5],\n [0.5, 0.4, 0.9, 0.2]\n ], dtype=tf.float32)\n self.assertBetween(\n global_softmax.call(y_true=true_label, y_pred=logits).numpy(), 1.5, 1.6)\n\n\nif __name__ == '__main__':\n tf.test.main()\n","sub_path":"lite/examples/recommendation/ml/model/losses_test.py","file_name":"losses_test.py","file_ext":"py","file_size_in_byte":1698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"127219600","text":"from bs4 import BeautifulSoup\r\nimport requests\r\nimport codecs\r\n\r\nuser_agent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.108 Safari/537.36'\r\nheaders = {'User-Agent':user_agent}\r\n\r\nurl = \"http://cn.bing.com\"\r\nr = requests.get(url, headers = headers)\r\nr.encoding = 'utf-8'\r\nf = codecs.open('test.txt', 'w', encoding='utf-8')\r\nf.write(r.text)\r\nf.close()\r\n\r\nsoup = BeautifulSoup(r.text, \"html.parser\", from_encoding='utf-8')\r\n\r\na = soup.find('a', id='sh_cp')\r\n\r\nprint(a.attrs['title'])\r\n\r\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"304389565","text":"import pandas as pd\nimport linecache\nimport math\nimport os\nfrom data import ADVMData\n\n\ndef load_argonaut_data(data_path, filename):\n \"\"\"\n Loads a Argonaut File into an ADVMData class object.\n\n :param data_path: file path containing the Argonaut data files\n :param filename: root filename for the 3 Argonaut files\n :return: ADVMData object containing the Argonaut dataset information\n \"\"\"\n\n # Read the Argonaut '.dat' file into a DataFrame\n arg_dat_file = os.path.join(data_path, filename + \".dat\")\n dat_df = _read_argonaut_dat_file(arg_dat_file)\n\n # Read the Argonaut '.snr' file into a DataFrame\n arg_snr_file = os.path.join(data_path, filename + \".snr\")\n snr_df = _read_argonaut_snr_file(arg_snr_file)\n\n # Read specific configuration values from the Argonaut '.ctl' file into a dictionary.\n arg_ctl_file = os.path.join(data_path, filename + \".ctl\")\n config_dict = _read_argonaut_ctl_file(arg_ctl_file)\n\n # Combine the '.snr' and '.dat.' DataFrames into a single acoustic DataFrame, make the timestamp\n # the index, and return an instantiated ADVMData object\n # acoustic_df = pd.DataFrame(index=dat_df.index, data=(pd.concat([snr_df, dat_df], axis=1)))\n # acoustic_df.set_index('year', drop=True, inplace=True)\n # acoustic_df.index.names = ['Timestamp']\n acoustic_df = pd.concat([dat_df, snr_df], axis=1)\n\n return ADVMData(config_dict, acoustic_df)\n\n\ndef _read_argonaut_dat_file(arg_dat_filepath):\n \"\"\"\n Read the Argonaut '.dat' file into a DataFrame.\n\n :param arg_dat_filepath: Filepath containing the Argonaut '.dat' file\n :return: Timestamp formatted DataFrame containing '.dat' file contents\n \"\"\"\n\n # Read the Argonaut '.dat' file into a DataFrame\n dat_df = pd.read_table(arg_dat_filepath, sep='\\s+')\n\n # rename the relevant columns to the standard/expected names\n dat_df.rename(columns={\"Temperature\": \"Temp\", \"Level\": \"Vbeam\"}, inplace=True)\n\n # set dataframe index by using date/time information\n date_time_columns = [\"Year\", \"Month\", \"Day\", \"Hour\", \"Minute\", \"Second\"]\n datetime_index = pd.to_datetime(dat_df[date_time_columns])\n dat_df.set_index(datetime_index, inplace=True)\n\n # remove non-relevant columns\n relevant_columns = ['Temp', 'Vbeam']\n dat_df = dat_df.filter(regex=r'(' + '|'.join(relevant_columns) + r')$')\n\n dat_df.apply(pd.to_numeric)\n\n return dat_df\n\n\ndef _read_argonaut_snr_file(arg_snr_filepath):\n \"\"\"\n Read the Argonaut '.dat' file into a DataFrame.\n\n :param arg_snr_filepath: Filepath containing the Argonaut '.dat' file\n :return: Timestamp formatted DataFrame containing '.snr' file contents\n \"\"\"\n\n # Read the Argonaut '.snr' file into a DataFrame, combine first two rows to make column headers,\n # and remove unused datetime columns from the DataFrame.\n snr_df = pd.read_table(arg_snr_filepath, sep='\\s+', header=None)\n header = snr_df.ix[0] + snr_df.ix[1]\n snr_df.columns = header.str.replace(r\"\\(.*\\)\", \"\") # remove parentheses and everything inside them from headers\n snr_df = snr_df.ix[2:]\n\n # rename columns to recognizable date/time elements\n column_names = list(snr_df.columns)\n column_names[1] = 'Year'\n column_names[2] = 'Month'\n column_names[3] = 'Day'\n column_names[4] = 'Hour'\n column_names[5] = 'Minute'\n column_names[6] = 'Second'\n snr_df.columns = column_names\n\n # create a datetime index and set the dataframe index\n datetime_index = pd.to_datetime(snr_df.ix[:, 'Year':'Second'])\n snr_df.set_index(datetime_index, inplace=True)\n\n # remove non-relevant columns\n snr_df = snr_df.filter(regex=r'(^Cell\\d{2}(Amp|SNR)\\d{1})$')\n\n snr_df = snr_df.apply(pd.to_numeric)\n\n return snr_df\n\n\ndef _read_argonaut_ctl_file(arg_ctl_filepath):\n \"\"\"\n Read the Argonaut '.ctl' file into a configuration dictionary.\n\n :param arg_ctl_filepath: Filepath containing the Argonaut '.dat' file\n :return: Dictionary containing specific configuration parameters\n \"\"\"\n\n # Read specific configuration values from the Argonaut '.ctl' file into a dictionary.\n # The fixed formatting of the '.ctl' file is leveraged to extract values from foreknown file lines.\n config_dict = {}\n line = linecache.getline(arg_ctl_filepath, 10).strip()\n arg_type = line.split(\"ArgType ------------------- \")[-1:]\n\n if arg_type == \"SL\":\n config_dict['Beam Orientation'] = \"Horizontal\"\n else:\n config_dict['Beam Orientation'] = \"Vertical\"\n\n line = linecache.getline(arg_ctl_filepath, 12).strip()\n frequency = line.split(\"Frequency ------- (kHz) --- \")[-1:]\n config_dict['Frequency'] = float(frequency[0])\n\n # calculate transducer radius (m)\n if float(frequency[0]) == 3000:\n config_dict['Effective Transducer Diameter'] = 0.015\n elif float(frequency[0]) == 1500:\n config_dict['Effective Transducer Diameter'] = 0.030\n elif float(frequency[0]) == 500:\n config_dict['Effective Transducer Diameter'] = 0.090\n elif math.isnan(float(frequency[0])):\n config_dict['Effective Transducer Diameter'] = \"NaN\"\n\n config_dict['Number of Beams'] = int(2) # always 2; no need to check file for value\n\n line = linecache.getline(arg_ctl_filepath, 16).strip()\n slant_angle = line.split(\"SlantAngle ------ (deg) --- \")[-1:]\n config_dict['Slant Angle'] = float(slant_angle[0])\n\n line = linecache.getline(arg_ctl_filepath, 44).strip()\n slant_angle = line.split(\"BlankDistance---- (m) ------ \")[-1:]\n config_dict['Blanking Distance'] = float(slant_angle[0])\n\n line = linecache.getline(arg_ctl_filepath, 45).strip()\n cell_size = line.split(\"CellSize -------- (m) ------ \")[-1:]\n config_dict['Cell Size'] = float(cell_size[0])\n\n line = linecache.getline(arg_ctl_filepath, 46).strip()\n number_cells = line.split(\"Number of Cells ------------ \")[-1:]\n config_dict['Number of Cells'] = int(number_cells[0])\n\n return config_dict\n\n\ndef load_tab_delimited_data(data_path, filename):\n \"\"\"\n Loads a TAB-delimited ASCII File into an ADVMData class object.\n\n :param data_path: file path containing the TAB-delimited ASCII data file\n :param filename: root filename for the TAB-delimited ASCII file\n :return: DataFrame object containing the ASCII file dataset information\n \"\"\"\n\n # Read TAB-delimited txt file into a DataFrame.\n tab_delimited_file = os.path.join(data_path, filename + \".txt\")\n tab_delimited_df = pd.read_table(tab_delimited_file, sep='\\t')\n\n # Check the formatting of the date/time columns. If one of the correct formats is used, reformat\n # those date/time columns into a new timestamp column. If none of the correct formats are used,\n # return an invalid file format error to the user.\n if 'y' and 'm' and 'd' and 'H' and 'M' and 'S' in tab_delimited_df.columns:\n tab_delimited_df.rename(columns={\"y\": \"year\", \"m\": \"month\", \"d\": \"day\"}, inplace=True)\n tab_delimited_df.rename(columns={\"H\": \"hour\", \"M\": \"minute\", \"S\": \"second\"}, inplace=True)\n tab_delimited_df[\"year\"] = pd.to_datetime(tab_delimited_df[[\"year\", \"month\", \"day\", \"hour\",\n \"minute\", \"second\"]], errors=\"coerce\")\n tab_delimited_df.rename(columns={\"year\": \"Timestamp\"}, inplace=True)\n tab_delimited_df.drop([\"month\", \"day\", \"hour\", \"minute\", \"second\"], axis=1, inplace=True)\n elif 'Date' and 'Time' in tab_delimited_df.columns:\n tab_delimited_df[\"Date\"] = pd.to_datetime(tab_delimited_df[\"Date\"] + \" \" + tab_delimited_df[\"Time\"],\n errors=\"coerce\")\n tab_delimited_df.rename(columns={\"Date\": \"Timestamp\"}, inplace=True)\n tab_delimited_df.drop([\"Time\"], axis=1, inplace=True)\n elif 'DateTime' in tab_delimited_df.columns:\n tab_delimited_df.rename(columns={\"DateTime\": \"Timestamp\"}, inplace=True)\n tab_delimited_df[\"Timestamp\"] = pd.to_datetime(tab_delimited_df[\"Timestamp\"], errors=\"coerce\")\n else:\n raise ValueError(\"Date and time information is incorrectly formatted.\", tab_delimited_file)\n\n tab_delimited_df.set_index('Timestamp', drop=True, inplace=True)\n\n return tab_delimited_df\n","sub_path":"load.py","file_name":"load.py","file_ext":"py","file_size_in_byte":8212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"590823485","text":"# 一个案例\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nx = np.linspace(0, 10, 10)\n\nplt.figure(1)\nax1_211 = plt.subplot(221)\nax1_212 = plt.subplot(223)\nax1_122 = plt.subplot(122)\n\nplt.figure(2)\nax2_211 = plt.subplot(211)\nax2_212 = plt.subplot(212)\n\n# plt.sca(ax1_211)\nplt.title('hello!')\nl1=ax1_211.plot(x, np.sin(x))\nplt.setp(l1[0],'marker','+')\nplt.setp(l1[0],'linestyle','-')\nplt.setp(l1[0],'color','k')\n\nplt.setp(l1[0],'label','cos')\n\nprint(plt.getp(ax1_211,'legend'))\nax1_211.legend()\nprint(plt.getp(ax1_211,'legend'))\nprint(plt.getp(l1[0],'color'))\nplt.sca(ax1_212)\nplt.plot(x, np.cos(x))\nplt.sca(ax1_122)\nplt.plot(x, x)\n\nplt.sca(ax2_211)\nplt.plot(x, x)\nplt.plot(x, -x)\nplt.sca(ax2_212)\nplt.plot(x, np.sin(x))\n\nplt.show()\n","sub_path":"IpythonNoteBook/trymatplotlib/k3.py","file_name":"k3.py","file_ext":"py","file_size_in_byte":739,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"403161847","text":"\"\"\"\nScript Name: PyForecast_GUI.py\nScript Author: Kevin Foley, Civil Engineer, Reclamation\nLast Modified: Apr 2, 2018\n\nDescription: 'PyForecast_GUI' is a PyQt5 GUI for the PyForecast application. \n The GUI includes all the visual aspects of the application (menus,\n plots, tables, buttons, webmaps, etc.) as well as the functionality\n to add data to the plots, tables, and webmaps.\n\"\"\"\n\n#//////////////////////////// IMPORT LIBRARIES /////////////////////////////////////////\n#// Here we load the necessary packages and libraries that 'PyForecast_GUI.py' needs\n#// in order to run properly.\n\nfrom PyQt5 import QtCore, QtGui, QtWidgets, QtWebEngineWidgets\n\nimport os\nimport platform\n\nimport matplotlib\nmatplotlib.use('Qt5Agg')\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\nfrom matplotlib.backends.backend_qt5agg import NavigationToolbar2QT\nfrom matplotlib.widgets import TextBox\nfrom matplotlib.figure import Figure\nimport matplotlib.patches as patches\nimport matplotlib.dates as mdates\nimport matplotlib.font_manager as fm\n\nimport numpy as np\nimport pandas as pd\nfrom datetime import datetime\n\nimport ctypes\nimport subprocess\n\nfrom Resources.GIS import CLIMATE_DIVISIONS\n\n#///////////////////////// SET COMMON PROPERTIES ////////////////////////////////////////\n#// Here we set the common properties that will be used across classes and objects.\n#// These are essentially global variables.\n\n# Set the taskbar Icon\nmyappid = u'reclamation.PyForecast.2b'\nif platform.system() == 'Windows':\n ctypes.windll.shell32.SetCurrentProcessExplicitAppUserModelID(myappid)\n\n# Load the trebuchet font into a matplotlib property\nprop = fm.FontProperties(fname = r'Resources/Fonts_Icons_Images/Trebuchet MS.ttf')\n\n# Set up some validators to only allow integers in some forms\nonlyInt = QtGui.QIntValidator()\n\n\n#///////////////////////// DEFINE SPECIAL CLASSES ///////////////////////////////////////\n#// PyForecast has additional functionality on top of the default PyQt5 classes, \n#// requiring us to extend those classes to include things like context menus,\n#// javascript webmaps, and matplotlib plots.\n\n\n\"\"\"\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n||||||||||||||||||||||||||||||||||||||||| CUSTOM PLOTS |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n\"\"\"\n\n# Define class for a Matplotlib plot that displays one axes object\nclass PlotCanvas_1Plot(FigureCanvas):\n\n # Initialize the graph\n def __init__(self, parent=None, dpi = 100):\n \n # Add a figure and an axes object\n self.fig = Figure(dpi = dpi)\n self.fig.patch.set_facecolor(\"#e8e8e8\")\n self.axes = self.fig.add_subplot(111)\n\n # Intialize the FigureCanvas\n FigureCanvas.__init__(self, self.fig)\n FigureCanvas.setSizePolicy(self, QtWidgets.QSizePolicy.Maximum, QtWidgets.QSizePolicy.Maximum)\n FigureCanvas.updateGeometry(self)\n\n # Initialize the plot with no data\n self.add_to_plot([0],[0])\n self.draw_plot()\n \n # Define a function to add data to the plot\n def add_to_plot(self, X, Y, label='', color='blue'):\n #label = label[0:20]\n self.axes.plot(X,Y,color=color, label=label)\n \n # Define a function to draw the plot\n def draw_plot(self):\n \n self.axes.legend(loc = 2) # Place the legend in the top-left corner\n self.axes.minorticks_on() # Self explanatory\n self.axes.grid(True, which='major', color = 'k', alpha=0.4) # Set the major grid\n self.axes.grid(True, which='minor', color = 'k', alpha=0.2, linestyle='--') # Set the minor grid\n self.axes.set_xlabel('Date', fontproperties = prop) # Set the X-axis label\n self.axes.set_ylabel('Value', fontproperties = prop) # Set the Y-axis label\n self.fig.tight_layout() # Expand graph to fit window\n self.draw()\n\n # Define a function to add correlation information to the plot\n def draw_corr_plot(self, X, Y, xname=None, yname=None):\n\n self.clear_plot()\n self.draw()\n\n xname = xname[0:20]\n yname = yname[0:20]\n\n # Convert the X, Y to arrays\n x = np.array(X)\n y = np.array(Y)\n\n # Create a linear matrix\n A = np.vstack([x, np.ones(len(x))]).T\n\n # Get the least squares coeff and intercept\n model = np.linalg.lstsq(A, y, rcond=None)\n m = model[0][:-1]\n b = model[0][-1]\n\n # Create the y-hat line\n y_hat = m*x + b\n print(y_hat)\n\n # Generate statistics\n y_bar = np.sum(y)/len(y)\n ssReg = np.sum((y_hat-y_bar)**2)\n ssTot = np.sum((y-y_bar)**2)\n r2 = np.round((ssReg/ssTot), 3)\n\n # Add data to the plot\n self.axes.plot(X, Y, color='#0a85cc', marker = 'o', linestyle = '')\n self.axes.set_xlabel(xname)\n self.axes.set_ylabel(yname)\n self.axes.plot(x, y_hat, 'g-', label=\"r2: {0}\".format(str(r2)))\n self.axes.grid(True, which='major', color = 'k', alpha=0.4) # Set the major grid\n self.axes.grid(True, which='minor', color = 'k', alpha=0.2, linestyle='--') # Set the minor grid\n self.axes.legend(loc = 2) # Place the legend in the top-left corner\n self.fig.tight_layout()\n self.draw()\n\n\n # Define a function to clear the plot\n def clear_plot(self, a = None):\n\n self.axes.cla()\n\n# Define a class for displaying 3 vertical line plots\nclass PlotCanvas_3Plot(FigureCanvas):\n\n # Initialize the graph\n def __init__(self, parent=None, dpi = 100):\n \n # Add a figure and an axes object\n self.fig = plt.figure(dpi = dpi)\n self.fig.patch.set_facecolor(\"#e8e8e8\")\n self.axes1 = plt.subplot2grid((2,2),(0,0), rowspan=2)\n self.axes2 = plt.subplot2grid((2,2),(0,1))\n self.axes3 = plt.subplot2grid((2,2),(1,1))\n \n\n\n FigureCanvas.__init__(self, self.fig)\n self.setParent(parent)\n\n FigureCanvas.setSizePolicy(self,\n QtWidgets.QSizePolicy.Expanding,\n QtWidgets.QSizePolicy.Expanding)\n\n FigureCanvas.updateGeometry(self)\n self.add_to_plot1([],[])\n self.add_to_plot2([0],[0])\n self.add_to_plot3([0],[0],linefmt = 'k-', markerfmt ='ro', basefmt = 'k-')\n self.draw_plot()\n\n # Define a function to add horizontal lines and vertical lines to the plots\n def add_current_forecast(self, currentForecast, label = '', color = 'red', marker = 'o', linestyle = '-', alpha = 1):\n self.axes1.axvline(x=currentForecast, color = '#505359', linewidth=2)\n self.axes2.axhline(y=currentForecast, color = '#505359', linewidth=2)\n \n def draw_box(self, lowleft, width):\n print(lowleft)\n self.axes1.add_patch(patches.Rectangle(lowleft, width, width, fill=True, facecolor=(1,0,0,0.3), edgecolor=\"r\", linewidth=2,))\n \n # Define functions to add data to the plots\n # This is a scatter / line plot\n def add_to_plot1(self, X, Y, label = '', color = 'red', marker = 'o', linestyle = '-', alpha = 1):\n self.axes1.plot(X, Y, color = color, label = label, marker = marker, linestyle = linestyle, alpha = alpha)\n \n # This is a scatter / line plot\n def add_to_plot2(self, X, Y, color = 'red', label = '', linestyle = '-', marker = 'o', linewidth = 2, alpha = 1 ):\n self.axes2.plot(X, Y, color = color, label = label, linestyle = linestyle, marker = marker, linewidth = linewidth, alpha = alpha )\n\n # This is a stem plot\n def add_to_plot3(self, X, Y, linefmt = 'k-', markerfmt ='bo', basefmt = 'k-', color = '#00347c'):\n (markers, stemlines, baseline) = self.axes3.stem(X, Y, linefmt = linefmt, basefmt = basefmt)\n plt.setp(markers, color = color, marker = 'o')\n\n # Define a function to draw the axes objects and plot the figure\n def draw_plot(self):\n self.axes1.minorticks_on()\n self.axes1.grid(True, which='Major', color = 'k', linestyle = '-', alpha = 0.4)\n self.axes1.grid(True, which = 'Minor', color = 'k', linestyle = '--', alpha = 0.2)\n self.axes1.set_axisbelow(True)\n self.axes1.set_xlabel('Forecast', fontproperties = prop)\n self.axes1.set_ylabel('Observed', fontproperties = prop)\n\n years = mdates.YearLocator()\n\n self.axes2.legend(loc = 2)\n self.axes2.minorticks_on()\n self.axes2.grid(True, which='Major', color = 'k', alpha = 0.4)\n self.axes2.grid(True, which = 'Minor', color = 'k', alpha = 0.2, linestyle = '--')\n self.axes2.xaxis.set_minor_locator(years)\n self.axes2.set_axisbelow(True)\n self.axes2.set_xlabel('Year', fontproperties = prop)\n self.axes2.set_ylabel('Inflow', fontproperties = prop)\n self.axes2.legend()\n\n self.axes3.minorticks_on()\n self.axes3.xaxis.set_minor_locator(years)\n self.axes3.grid(True, which='Major', color = 'k', alpha = 0.4)\n self.axes3.grid(True, which = 'Minor', color = 'k', alpha = 0.2, linestyle = '--')\n self.axes3.set_axisbelow(True)\n self.axes3.set_xlabel('Year', fontproperties = prop)\n self.axes3.set_ylabel('Residual', fontproperties = prop)\n\n plt.tight_layout(rect=[0,0.03,1,0.93])\n self.draw()\n\n # Define a function to clear the plots\n def clear_plot(self):\n self.axes1.cla()\n self.axes2.cla()\n self.axes3.cla()\n\n# Define a class for a Matplotlib plot that displays three axes objects\nclass PlotCanvas_2Plot(FigureCanvas):\n\n # Initialize the graph\n def __init__(self, parent=None, dpi = 100):\n \n # Add a figure and an axes object\n self.fig = plt.figure(dpi = dpi)\n self.fig.patch.set_facecolor(\"#e8e8e8\")\n self.axes1 = plt.subplot2grid((2,2),(0,0), colspan=2)\n self.axes2 = plt.subplot2grid((2,2),(1,0), colspan=2)\n\n # Intialize the FigureCanvas\n FigureCanvas.__init__(self, self.fig)\n FigureCanvas.setSizePolicy(self, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)\n FigureCanvas.updateGeometry(self)\n\n # Initialize the plot with no data\n self.add_to_plot1([0],[0])\n self.add_to_plot2([0],[0])\n\n self.draw_plot()\n\n # Define a function to add horizontal lines and vertical lines to the plots\n def add_current_forecast(self, currentForecast):\n\n self.axes3.axhline(y=currentForecast, color = 'green', linewidth=2)\n \n # Define a function to add data to the scatter / line plot in axes item 1\n def add_to_plot1(self, X, Y, label = '', color = '#00347c', marker = 'o', linestyle = '-', alpha = 1, zorder=1):\n\n self.axes1.plot(X, Y, color = color, label = label, marker = marker, linestyle = linestyle, alpha = alpha, zorder=1)\n \n # Define a function to add data to the line plot in axes item 2\n def add_to_plot2(self, X, Y, color = '#00347c', label = '', linestyle = '-', marker = 'o', linewidth = 2, alpha = 1, zorder=1 ):\n\n self.axes2.plot(X, Y, color = color, label = label, linestyle = linestyle, marker = marker, linewidth = linewidth, alpha = alpha, zorder=1 )\n \n # Define a function to draw the plots\n def draw_plot(self):\n\n # Draw the first axes object\n self.axes1.minorticks_on()\n self.axes1.grid(True, which='Major', color = 'k', linestyle = '-', alpha = 0.4)\n #self.axes1.grid(True, which = 'Minor', color = 'k', linestyle = '--', alpha = 0.2)\n self.axes1.set_axisbelow(True)\n self.axes1.set_xlabel('Forecast', fontproperties = prop)\n self.axes1.set_ylabel('Probability', fontproperties = prop)\n\n self.axes2.minorticks_on()\n self.axes2.grid(True, which='Major', color = 'k', linestyle = '-', alpha = 0.4)\n #self.axes2.grid(True, which = 'Minor', color = 'k', linestyle = '--', alpha = 0.2)\n self.axes2.set_axisbelow(True)\n self.axes2.set_xlabel('Forecast', fontproperties = prop)\n self.axes2.set_ylabel('Cumulative Probability', fontproperties = prop)\n self.axes2.yaxis.set_ticks([0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0])\n self.axes2.set_ylim(0, 1)\n\n\n plt.tight_layout(rect=[0,0.03,1,0.93])\n self.draw()\n\n # Define a function to clear the plots\n def clear_plot(self):\n\n self.axes1.cla()\n self.axes2.cla()\n\n# Define a class to add a Nevigation bar to the bottom of plots\nclass NavigationToolbar(NavigationToolbar2QT):\n\n # We don't need all the buttons, so we specify the tools we need\n toolitems = [t for t in NavigationToolbar2QT.toolitems if t[0] in ( 'Home', 'Pan', 'Zoom', 'Save')]\n\nclass toggleButton(QtWidgets.QPushButton):\n\n def __init__(self, text=''):\n QtWidgets.QPushButton.__init__(self)\n self.toggleStatus = 0\n self.pressed.connect(self.updateToggleStatus)\n self.setText(text)\n \n def updateToggleStatus(self):\n \n self.toggleStatus += 1\n if self.toggleStatus == 3:\n self.toggleStatus = 0\n\n print(self.toggleStatus)\n\n return\n\n# Define a custom Tree View widget to be used to view equations and forecasts on the summary page\nclass SummTreeView(QtWidgets.QWidget):\n\n # Initialize a QTreeView and start with a blank tree\n def __init__(self, parent=None):\n\n # intitialize the treeview\n QtWidgets.QWidget.__init__(self)\n #QtWidgets.QTreeView.__init__(self)\n self.layout = QtWidgets.QVBoxLayout()\n self.header = QtWidgets.QTextEdit()\n self.header.setHtml(\"\"\"

    \"\"\")\n self.header.setReadOnly(True)\n self.treeView = CustomTreeView()\n self.treeView.setHeaderHidden(True)\n self.header.setFrameStyle(QtWidgets.QFrame.NoFrame)\n self.header.setMaximumHeight(90)\n self.treeView.setFrameStyle(QtWidgets.QFrame.NoFrame)\n\n # Set a custom context menu for the tree\n self.treeView.setContextMenuPolicy(QtCore.Qt.ActionsContextMenu)\n self.delAction = QtWidgets.QAction(\"Delete model from summary\", None)\n self.ensembleAction = QtWidgets.QAction(\"Send model to Ensembles Tab\", None)\n self.treeView.addAction(self.delAction)\n self.treeView.addAction(self.ensembleAction)\n\n # Set the widget layout\n self.layout.addWidget(self.header)\n self.layout.addWidget(self.treeView)\n self.setLayout(self.layout)\n\n\n\"\"\"\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n||||||||||||||||||||||||||||||||||||||||| CUSTOM TREE ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n\"\"\"\n\n\nclass CustomTreeView(QtWidgets.QTreeView):\n\n deletedItem = QtCore.pyqtSignal(list)\n droppedPredictor = QtCore.pyqtSignal(list)\n # Initialize a QTreeView and start with a blank tree\n def __init__(self, parent=None, dragFrom = False, dropTo = False, menuFunctions=['']):\n\n # intitialize the treeview\n QtWidgets.QTreeView.__init__(self)\n\n self.setAlternatingRowColors(True)\n self.setHeaderHidden(True)\n\n\n if dragFrom:\n self.setDragEnabled(True)\n \n if dropTo:\n print('accepts drops')\n self.setAcceptDrops(True)\n self.viewport().setAcceptDrops(True)\n\n self.setDropIndicatorShown(True)\n\n # Set the context menu\n self.setContextMenuPolicy(QtCore.Qt.ActionsContextMenu)\n if 'OPENEXCEL' in menuFunctions:\n self.openExcelAction = QtWidgets.QAction(\"Open in Excel\")\n self.addAction(self.openExcelAction)\n\n if 'DELETE' in menuFunctions:\n self.delAction = QtWidgets.QAction(\"Delete\")\n self.addAction(self.delAction)\n self.delAction.triggered.connect(self.deleteRow)\n \n if \"SENDDENS\" in menuFunctions:\n self.sendAction = QtWidgets.QAction(\"Send to Density Tab\")\n self.addAction(self.sendAction)\n \n if \"GENCURRENT\" in menuFunctions:\n self.genAction = QtWidgets.QAction(\"Generate Current Forecast\")\n self.addAction(self.genAction)\n\n # Set to be read-only\n self.setEditTriggers(QtWidgets.QAbstractItemView.NoEditTriggers)\n \n def dropEvent(self, event):\n\n prdID = -1\n pos = event.pos()\n\n # Get the item's text\n if event.mimeData().hasFormat('application/x-qabstractitemmodeldatalist'):\n mod = QtGui.QStandardItemModel()\n mod.dropMimeData(event.mimeData(),QtCore.Qt.CopyAction, 0, 0, QtCore.QModelIndex())\n item = mod.item(0,0)\n itemText = item.text()\n else:\n print('wrongMimeType')\n event.ignore()\n return\n \n # Ensure that the item is a valid predictor\n if item.hasChildren():\n\n numChildren = item.rowCount()\n print('item has {0} children'.format(numChildren))\n for i in range(numChildren):\n child = item.child(i)\n if 'prdID: ' in child.text():\n prdID = child.text()[7:]\n break\n \n if prdID == -1:\n print('no prdid')\n event.ignore()\n return\n \n else:\n print('no children')\n event.ignore()\n return\n\n # Make sure that the event's pos is OK\n dropIndex = self.indexAt(pos)\n dropItem = self.model.itemFromIndex(dropIndex)\n if dropItem.text() == 'PredictorPool':\n equation = dropItem.parent().text()\n self.lastIndex = dropIndex \n self.droppedPredictor.emit([prdID, equation])\n event.accept()\n else:\n event.ignore()\n return\n\n\n def deleteRow(self):\n print('deleteRow')\n currentIndex = self.currentIndex()\n item = self.model.itemFromIndex(currentIndex)\n parent = item.parent().parent()\n text = item.text()\n try:\n text = text.split(':')[0].strip(' ')\n test = int(text)\n if len(text) == 5:\n self.model.removeRow(currentIndex.row(), currentIndex.parent())\n self.deletedItem.emit([text, parent.text()])\n except Exception as e:\n print(e)\n return\n\n\n def addToTree(self, dict_, levels_in_max = None, exclude_keys=[]):\n self.model = QtGui.QStandardItemModel()\n self.addDictToModel(self.model, dict_, initial = True, levels_in_max = levels_in_max, levels_in = 1, exclude_keys = exclude_keys)\n self.setModel(self.model) \n\n def addDictToModel(self, model, dict_, initial=True, levels_in_max = None, levels_in = 1, exclude_keys = []):\n \n # Check for recursion\n if initial == True:\n parentItem = model.invisibleRootItem()\n else:\n parentItem = model\n try:\n if not isinstance(dict_, dict):\n if dict_ in exclude_keys:\n return\n item = QtGui.QStandardItem(str(dict_))\n parentItem.appendRow(item)\n return\n # Iterate through each key in the dictionary\n for key, values in dict_.items():\n\n if key in exclude_keys:\n continue\n\n # If the values are a dict, re-enter the funciton recursively\n if isinstance(values, dict):\n item = QtGui.QStandardItem(str(key))\n parentItem.appendRow(item)\n if levels_in <= levels_in_max:\n self.addDictToModel(item, values, initial=False, levels_in_max=levels_in_max, levels_in=levels_in+1,exclude_keys = exclude_keys)\n\n elif isinstance(values, list):\n item = QtGui.QStandardItem(str(key))\n parentItem.appendRow(item)\n for value in values:\n self.addDictToModel(item, value, initial=False, levels_in_max = levels_in_max, levels_in=levels_in+1,exclude_keys = exclude_keys)\n else:\n if isinstance(key, datetime):\n key = datetime.strftime(key, '%Y')\n item = QtGui.QStandardItem(str(key) + ': ' + str(values))\n parentItem.appendRow(item)\n except:\n print('\\nERROR:')\n print(dict_)\n print(\"\\n\")\n\n\n\"\"\"\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n||||||||||||||||||||||||||||||||||||||||| CUSTOM TABLE |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n\"\"\"\n# The custom table view can be initialized with many options:\n# rowLock / colLock (bool) sets the selection behavior so that users must select entire rows or columns, instead of individual cells\n# cols / rows (int) sets the initial number of rows and columns\n# headers (list of strings) sets the initial header labels\n# menuFunctions (list of strings from 'COPY','OPEN','DELETEROW','DELETECOL')\n# readOnly (bool) sets the entire table to be readonly\n\n\n\n# Define a custom QTableView widget for the regression tab for the data and forecastOptions\nclass CustomTableView(QtWidgets.QTableWidget):\n\n deletedRowEmission = QtCore.pyqtSignal(list)\n deletedColumnEmission = QtCore.pyqtSignal(str)\n\n def __init__(self, parent=None, rowLock = False, colLock = False, cols = 0, rows = 0, headers = [''], menuFunctions = [''], readOnly = True, dragFrom=False):\n\n # Initialize the tableview with options\n QtWidgets.QTableWidget.__init__(self)\n self.setColumnCount(cols)\n self.setRowCount(rows)\n self.readOnly = readOnly\n if rowLock:\n self.setSelectionBehavior(QtWidgets.QAbstractItemView.SelectRows)\n elif colLock:\n pass\n #self.setSelectionBehavior(QtWidgets.QAbstractItemView.SelectColumns)\n else:\n pass\n if dragFrom:\n self.setDragEnabled(True)\n\n if readOnly:\n self.readOnly = True\n else:\n self.readOnly = False\n\n\n\n #self.setGridStyle(QtCore.Qt.DotLine)\n self.setHorizontalScrollMode(QtWidgets.QAbstractItemView.ScrollPerPixel)\n self.setVerticalScrollMode(QtWidgets.QAbstractItemView.ScrollPerPixel)\n self.verticalHeader().setVisible(False)\n self.setFrameStyle(QtWidgets.QFrame.NoFrame)\n self.setHorizontalHeaderLabels(headers)\n self.resizeColumnsToContents()\n self.horizontalHeader().setStretchLastSection(True)\n\n\n # Create a context menu for the tableview\n self.setContextMenuPolicy(QtCore.Qt.ActionsContextMenu)\n\n if 'COPY' in menuFunctions:\n self.copyAction = QtWidgets.QAction(\"Copy selection\", None)\n self.addAction(self.copyAction)\n self.copyAction.triggered.connect(self.copyFromTable)\n \n if 'OPEN' in menuFunctions:\n self.openAction = QtWidgets.QAction(\"Open in spreadsheet\", None)\n self.addAction(self.openAction)\n self.openAction.triggered.connect(self.openInSpreadsheet)\n \n if 'DELETEROW' in menuFunctions:\n self.deleteRowAction = QtWidgets.QAction(\"Delete table row\", None)\n self.addAction(self.deleteRowAction)\n self.deleteRowAction.triggered.connect(lambda: self.deleteFromTable('row'))\n\n if 'DELETECOL' in menuFunctions:\n self.deleteColAction = QtWidgets.QAction(\"Delete table column\", None)\n self.addAction(self.deleteColAction)\n self.deleteColAction.triggered.connect(lambda: self.deleteFromTable('col'))\n \n if 'SAVEFCST' in menuFunctions:\n self.saveFcstAction = QtWidgets.QAction(\"Save Forecast\", None)\n self.addAction(self.saveFcstAction)\n \n if \"REGSTAT\" in menuFunctions:\n self.regStatAction = QtWidgets.QAction(\"View Principal Components\", None)\n self.addAction(self.regStatAction)\n\n\n # Defing a function to delete items from the table\n def deleteFromTable(self, option, rowID = None, colID = None):\n\n if option == 'row':\n items = self.selectedItems()\n self.deletedRowEmission.emit(items)\n self.removeRow(self.currentRow())\n\n elif option == 'col':\n currentCol = self.currentColumn()\n colName = self.horizontalHeaderItem(currentCol).text()\n self.deletedColumnEmission.emit(colName)\n self.removeColumn(self.currentColumn())\n \n elif option == 'customrow':\n self.removeRow(rowID)\n \n else:\n pass\n\n\n # Define a function to copy items from the table\n def copyFromTable(self):\n\n # Set up and clear the clipboard\n cb = QtWidgets.QApplication.clipboard()\n cb.clear(mode = cb.Clipboard)\n prev_row = -1\n\n # Get the selected items\n items = self.selectedItems()\n\n # Initialize a string to store the copied items\n clip_string = \"\"\n\n # Loop through items and add them to the clipboard\n for i, item in enumerate(items):\n\n if i == 0:\n clip_string += item.text()\n prev_row = item.row()\n continue\n \n if item.row() == prev_row:\n clip_string += '\\t'\n clip_string += item.text()\n prev_row = item.row()\n \n else:\n clip_string += '\\n'\n clip_string += item.text()\n prev_row = item.row()\n \n # Set the clipboard with the clip_text\n cb.setText(clip_string, mode = cb.Clipboard)\n\n return clip_string\n\n # Define a function to add a row to the table from a list\n def addRow(self, list_):\n\n # Get the current row\n currentRow = self.rowCount()\n\n # insert a new row\n self.insertRow(currentRow)\n\n # add the items from the list\n for i, listItem in enumerate(list_):\n item = QtWidgets.QTableWidgetItem(listItem)\n\n self.setItem(\n currentRow, i, item\n )\n if self.readOnly:\n self.setEditTriggers(QtWidgets.QTableWidget.NoEditTriggers)\n\n # Resize the columns and stretch the last column\n #self.resizeColumnsToContents()\n self.horizontalHeader().setStretchLastSection(True)\n self.resizeColumnsToContents()\n\n\n # Define a function to convert a table to a dataframe\n def toDataFrame(self):\n\n # get the number of rows and column, and the header\n rows = self.rowCount()\n cols = self.columnCount()\n headers = [self.horizontalHeaderItem(i).text() for i in range(cols)]\n\n # Convert to a stringIO csv file\n if rows != 0:\n self.selectAll()\n\n from io import StringIO\n rawString = self.copyFromTable()\n rawString = StringIO(rawString)\n\n df = pd.read_csv(rawString, sep=\"\\t\", names = headers, dtype = str)\n\n # If the first column contains datetimes, use that as the index\n try:\n df.set_index(pd.DatetimeIndex(pd.to_datetime(df[df.columns[0]])), inplace = True)\n del df[df.columns[0]]\n return df\n except:\n print('couldnt convert times')\n return df\n \n else:\n return pd.DataFrame()\n\n # Define a function to create a table from a dataset directory\n def TableFromDatasetDirectory(self, dataDir):\n\n # Create a dataframe from the first dataDir Entry\n df = pd.DataFrame().from_dict(dataDir[0]['Data'], orient='columns')\n print(df)\n df.columns = [dataDir[0]['Name'] + '|' + dataDir[0]['Parameter'] + '|' + dataDir[0]['Units']]\n\n # Iterate through the remaining datasets (through each dataset)\n for dataset in dataDir[1:]:\n\n # Convert the dataset into a series\n try:\n series = pd.DataFrame().from_dict(dataset['Data'], orient='columns')\n series.columns = [dataset['Name'] + '|' + dataset['Parameter'] + '|' + dataset['Units']]\n except:\n print(dataset['Name'])\n print(pd.DataFrame().from_dict(dataset['Data'], orient='columns'))\n # Append the series to the new dataframe\n df = pd.concat([df, series], axis=1)\n\n # Create a table from the dataframe\n self.createTableFromDataFrame(df)\n\n # Define a function to add a dataframe to the table\n def createTableFromDataFrame(self, data):\n\n # Clear any existing data\n self.setRowCount(0)\n self.setColumnCount(0)\n\n # Intitialize the dimensions and headers\n self.setRowCount(len(data.index))\n self.setColumnCount(len(data.columns)+1)\n\n self.setHorizontalHeaderItem(0, QtWidgets.QTableWidgetItem(str(\"Date\")))\n for i in range(len(data.columns)):\n colHeader = data.columns[i].split('|')\n name = colHeader[0]\n param = colHeader[1]\n unit = colHeader[2]\n self.setHorizontalHeaderItem(i+1, QtWidgets.QTableWidgetItem(str(\n \"{0}\\nParameter: {1}\\nUnits: {2}\".format(name,param,unit)\n )))\n\n # Iterate through the dataframe and set each item\n for i in range(len(data.index)):\n item = QtWidgets.QTableWidgetItem(str(data.index[i])[0:10])\n self.setItem(i, 0, item)\n for j in range(len(data.columns)): \n col = list(data.columns)[j]\n val = data[col].iloc[i]\n item = QtWidgets.QTableWidgetItem(str(val))\n if self.readOnly:\n item.setFlags(QtCore.Qt.ItemIsEnabled)\n self.setItem(i, j+1, item)\n #self.resizeColumnsToContents()\n self.horizontalHeader().setStretchLastSection(True)\n self.resizeColumnsToContents()\n \n \n \n # Open in spreadsheet\n def openInSpreadsheet(self):\n data = self.toDataFrame()\n tempFileName = \"tmp\"+str(int(np.random.random()*10000)) + '.csv'\n data.to_csv(path_or_buf='Resources/tempFiles/'+tempFileName)\n try:\n try:\n subprocess.check_call(['cmd','/c','start','Resources/tempFiles/'+tempFileName])\n except Exception as e:\n print(e)\n subprocess.check_call(['open','Resources/tempFiles/'+tempFileName])\n except:\n pass\n \n\n# Define a custom Web Map widget using MapBox\nclass WebMap(QtWebEngineWidgets.QWebEnginePage):\n\n # Create a signal to emit console messages from Javascript\n java_msg_signal = QtCore.pyqtSignal(str)\n \n def __init__(self, parent=None):\n\n # Initialize the web map\n QtWebEngineWidgets.QWebEnginePage.__init__(self)\n url = QtCore.QUrl.fromLocalFile(os.path.abspath('Resources/WebResources/WebMap.html'))\n self.load(url)\n\n # Override the 'acceptNavigationRequest' function to open href links in another browser\n def acceptNavigationRequest(self, url, _type, isMainFrame):\n\n # Screen nvaigation requests for href links\n if _type == QtWebEngineWidgets.QWebEnginePage.NavigationTypeLinkClicked:\n\n QtGui.QDesktopServices.openUrl(url) \n return False\n \n else: \n return True\n\n # Override the 'javaScriptConsoleMessage' function to send console messages to a signal/slot\n def javaScriptConsoleMessage(self, lvl, msg, line, source):\n\n # Only allow INFO level console messages to be emitted\n if lvl != 0:\n return\n\n # Split the recieved message by commas into a list and emit it through to the reciever.\n self.java_msg_signal.emit(msg)\n\n\"\"\"\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n||||||||||||||||||||||||||||||||||||||||| SUMMARY TAB |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n\"\"\"\n\n# Define a custom widget to display forecast information on the summary tab\nclass FcstInfoPane(QtWidgets.QWidget):\n\n # Initialize a custom QWidget\n def __init__(self, parent=None):\n\n QtWidgets.QWidget.__init__(self)\n self.setupUI()\n \n # Define the layout of the custom widget\n def setupUI(self):\n\n self.layout = QtWidgets.QHBoxLayout() # Set the layout for the custom widget\n \n # Create the Forecast metadata section\n #self.forecastMetaLayout = QtWidgets.QVBoxLayout() # Set a vertical layout for the forecast metadata section\n self.forecastMetaLayout = QtWidgets.QGridLayout()\n self.forecastMeta_ForecastID = QtWidgets.QLabel('Forecast ID: ')\n self.forecastMeta_ForecastID.setMaximumWidth(200)\n self.forecastMeta_ForecastIDLine = QtWidgets.QLineEdit()\n self.forecastMeta_ForecastIDLine.setReadOnly(True)\n self.forecastMeta_ForecastIDLine.setMinimumWidth(200)\n self.forecastMeta_ForecastIDLine.setMaximumWidth(400)\n self.forecastMetaLayout.addWidget(self.forecastMeta_ForecastID, 0, 0)\n self.forecastMetaLayout.addWidget(self.forecastMeta_ForecastIDLine, 0, 1)\n self.forecastMeta_Target = QtWidgets.QLabel('Forecast Target: ')\n self.forecastMeta_TargetLine = QtWidgets.QLineEdit()\n self.forecastMeta_TargetLine.setReadOnly(True)\n self.forecastMeta_TargetLine.setMaximumWidth(400)\n self.forecastMetaLayout.addWidget(self.forecastMeta_Target, 1, 0)\n self.forecastMetaLayout.addWidget(self.forecastMeta_TargetLine, 1, 1)\n self.forecastMeta_Period = QtWidgets.QLabel('Forecast Period: ')\n self.forecastMeta_PeriodLine = QtWidgets.QLineEdit()\n self.forecastMeta_PeriodLine.setReadOnly(True)\n self.forecastMeta_PeriodLine.setMaximumWidth(400)\n self.forecastMetaLayout.addWidget(self.forecastMeta_Period, 2, 0)\n self.forecastMetaLayout.addWidget(self.forecastMeta_PeriodLine, 2, 1)\n self.forecastMeta_Frequency = QtWidgets.QLabel('Forecast Frequency: ')\n self.forecastMeta_FrequencyLine = QtWidgets.QLineEdit()\n self.forecastMeta_FrequencyLine.setReadOnly(True)\n self.forecastMeta_FrequencyLine.setMaximumWidth(400)\n self.forecastMetaLayout.addWidget(self.forecastMeta_Frequency, 3, 0)\n self.forecastMetaLayout.addWidget(self.forecastMeta_FrequencyLine, 3, 1)\n self.forecastMeta_Forecaster = QtWidgets.QLabel('Forecaster: ')\n self.forecastMeta_ForecasterLine = QtWidgets.QLineEdit()\n self.forecastMeta_ForecasterLine.setReadOnly(True)\n self.forecastMeta_ForecasterLine.setMaximumWidth(400)\n self.forecastMetaLayout.addWidget(self.forecastMeta_Forecaster, 4, 0)\n self.forecastMetaLayout.addWidget(self.forecastMeta_ForecasterLine, 4, 1)\n \n self.forecastInfo_Heading1 = QtWidgets.QLabel('Forecast Notes: ')\n self.forecastInfo_Heading1Line = QtWidgets.QPlainTextEdit()\n self.forecastInfo_Heading1Line.setReadOnly(True)\n self.forecastInfo_Heading1Line.setMaximumWidth(400)\n widg = QtWidgets.QWidget()\n self.forecastMetaLayout.addWidget(self.forecastInfo_Heading1, 5, 0)\n self.forecastMetaLayout.addWidget(self.forecastInfo_Heading1Line, 5, 1)\n self.forecastInfo_ForecastLayout = QtWidgets.QHBoxLayout()\n self.forecastInfo_10Fcst = QtWidgets.QLabel('10%: ')\n self.forecastInfo_10FcstLine = QtWidgets.QLineEdit()\n self.forecastInfo_10FcstLine.setReadOnly(True)\n self.forecastInfo_30Fcst = QtWidgets.QLabel('30%: ')\n self.forecastInfo_30FcstLine = QtWidgets.QLineEdit()\n self.forecastInfo_30FcstLine.setReadOnly(True)\n self.forecastInfo_50Fcst = QtWidgets.QLabel('50%: ')\n self.forecastInfo_50FcstLine = QtWidgets.QLineEdit()\n self.forecastInfo_50FcstLine.setReadOnly(True)\n self.forecastInfo_70Fcst = QtWidgets.QLabel('70%: ')\n self.forecastInfo_70FcstLine = QtWidgets.QLineEdit()\n self.forecastInfo_70FcstLine.setReadOnly(True)\n self.forecastInfo_90Fcst = QtWidgets.QLabel('90%: ')\n self.forecastInfo_90FcstLine = QtWidgets.QLineEdit()\n self.forecastInfo_90FcstLine.setReadOnly(True)\n self.forecastInfo_ForecastLayout.addWidget(self.forecastInfo_10Fcst)\n self.forecastInfo_ForecastLayout.addWidget(self.forecastInfo_10FcstLine)\n self.forecastInfo_ForecastLayout.addWidget(self.forecastInfo_30Fcst)\n self.forecastInfo_ForecastLayout.addWidget(self.forecastInfo_30FcstLine)\n self.forecastInfo_ForecastLayout.addWidget(self.forecastInfo_50Fcst)\n self.forecastInfo_ForecastLayout.addWidget(self.forecastInfo_50FcstLine)\n self.forecastInfo_ForecastLayout.addWidget(self.forecastInfo_70Fcst)\n self.forecastInfo_ForecastLayout.addWidget(self.forecastInfo_70FcstLine)\n self.forecastInfo_ForecastLayout.addWidget(self.forecastInfo_90Fcst)\n self.forecastInfo_ForecastLayout.addWidget(self.forecastInfo_90FcstLine)\n widg.setLayout(self.forecastInfo_ForecastLayout)\n self.forecastMetaLayout.addWidget(widg, 6, 0, 1, 2)\n\n\n # Create the Forecast information section\n self.forecastInfoLayout = QtWidgets.QGridLayout()\n self.forecastInfo_Heading2 = QtWidgets.QLabel('Equation:')\n self.forecastInfoLayout.addWidget(self.forecastInfo_Heading2, 0, 0)\n self.forecastInfoTable = CustomTableView(self, rowLock=True, cols = 2, rows = 0, headers = ['Parameter','Coefficient'])\n self.forecastInfoTable.setMinimumWidth(400)\n self.forecastInfoLayout.addWidget(self.forecastInfoTable, 1, 0, 1, 2)\n\n # Create the current data view window\n self.forecastCurrentData = CustomTableView(self, rowLock=True, cols=2, rows=0, headers=['Predictor','Value'])\n self.forecastCurrentData.setMinimumSize(QtCore.QSize(300,200))\n self.forecastCurrentData.setMaximumSize(QtCore.QSize(500,1000))\n\n # Lay out the forecast information pane\n self.layout.addLayout(self.forecastMetaLayout)\n line1 = QtWidgets.QFrame()\n line1.setLineWidth(0)\n line1.setFrameShape(QtWidgets.QFrame.VLine)\n line1.setFrameShadow(QtWidgets.QFrame.Plain)\n self.layout.addWidget(line1)\n self.layout.addLayout(self.forecastInfoLayout)\n line2 = QtWidgets.QFrame()\n line2.setLineWidth(0)\n line2.setFrameShape(QtWidgets.QFrame.VLine)\n line2.setFrameShadow(QtWidgets.QFrame.Plain)\n self.layout.addWidget(line2)\n self.layout.addWidget(self.forecastCurrentData)\n self.setLayout(self.layout)\n\n\"\"\"\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n||||||||||||||||||||||||||||||||||||||||| STATIONS TAB ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n\"\"\"\n\n# Define a custom widget to display selected station information on the stations tab\nclass StationInfoPane(QtWidgets.QWidget):\n\n # Initialize a custom QWidget\n def __init__(self, parent=None):\n\n QtWidgets.QWidget.__init__(self)\n self.setupUI()\n \n # Define the layout of the custom widget\n def setupUI(self):\n\n # Create a layout for the widget\n self.layout = QtWidgets.QVBoxLayout()\n\n # Add a header and description \n self.stationHeader = QtWidgets.QTextEdit()\n self.stationHeader.setHtml(\"\"\"
    Select Datasets
    \n

    Use the map to select climatalogical stations that should be included in the analysis. The program will download period of record data for each dataset selected.

    \n
    \"\"\")\n self.stationHeader.setFrameStyle(QtWidgets.QFrame.NoFrame)\n self.stationHeader.setReadOnly(True)\n self.stationHeader.setMaximumHeight(125)\n\n # Add a table to display selected stations\n self.stationTable = CustomTableView(self, rowLock = True, cols = 5, rows = 0, headers = ['PYID','Type','ID','Name','Parameter'], menuFunctions=['COPY','OPEN','DELETEROW'])\n\n # Add a section to add other datasets\n self.otherDataLayout = QtWidgets.QGridLayout()\n self.stationLabel = QtWidgets.QLabel('Other Datasets:')\n self.otherDataLayout.addWidget(self.stationLabel, 0, 0, 1, 4)\n\n # NRCC dataset layout\n self.nrccLabel = QtWidgets.QLabel('NRCC')\n self.nrccLabel.setMinimumWidth(100)\n self.nrccLabel.setMaximumWidth(100)\n self.nrccInfoButton = QtWidgets.QLabel()\n self.nrccInfoButton.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.nrccInfoButton.setScaledContents(True)\n self.nrccInfoButton.setToolTip('

    NRCC gridded precipitation and temperature data, averaged by watershed.

    ')\n self.nrccInput = QtWidgets.QLineEdit()\n self.nrccInput.setPlaceholderText(\"Enter HUC8:\")\n self.nrccButton = QtWidgets.QPushButton('Add')\n self.otherDataLayout.addWidget(self.nrccLabel, 1, 0, 1, 1)\n self.otherDataLayout.addWidget(self.nrccInfoButton, 1, 1, 1, 1)\n self.otherDataLayout.addWidget(self.nrccInput, 1, 2, 1, 1)\n self.otherDataLayout.addWidget(self.nrccButton, 1, 3, 1, 1)\n\n # Prism dataset layout\n self.prismLabel = QtWidgets.QLabel('PRISM')\n self.prismLabel.setMinimumWidth(100)\n self.prismLabel.setMaximumWidth(100)\n self.prismInfoButton = QtWidgets.QLabel()\n self.prismInfoButton.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.prismInfoButton.setScaledContents(True)\n self.prismInfoButton.setToolTip('

    PRISM gridded precipitation and temperature data, averaged by watershed.

    ')\n self.prismInput = QtWidgets.QLineEdit()\n self.prismInput.setPlaceholderText(\"Enter HUC8:\")\n self.prismButton = QtWidgets.QPushButton('Add')\n self.otherDataLayout.addWidget(self.prismLabel, 2, 0, 1, 1)\n self.otherDataLayout.addWidget(self.prismInfoButton, 2, 1, 1, 1)\n self.otherDataLayout.addWidget(self.prismInput, 2, 2, 1, 1)\n self.otherDataLayout.addWidget(self.prismButton, 2, 3, 1, 1)\n\n # PDSI dataset \n self.pdsiLabel = QtWidgets.QLabel(\"PDSI\")\n self.pdsiLabel.setMinimumWidth(100)\n self.pdsiLabel.setMaximumWidth(100)\n self.pdsiInfoButton = QtWidgets.QLabel()\n self.pdsiInfoButton.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.pdsiInfoButton.setScaledContents(True)\n self.pdsiInfoButton.setToolTip('

    Palmer Drought Severity Index by Climate Division

    ')\n self.pdsiInput = QtWidgets.QComboBox()\n self.pdsiInput.addItems(list(CLIMATE_DIVISIONS.divisions.keys()))\n self.pdsiButton = QtWidgets.QPushButton('Add')\n self.otherDataLayout.addWidget(self.pdsiLabel, 3, 0, 1, 1)\n self.otherDataLayout.addWidget(self.pdsiInfoButton, 3, 1, 1, 1)\n self.otherDataLayout.addWidget(self.pdsiInput, 3, 2, 1, 1)\n self.otherDataLayout.addWidget(self.pdsiButton, 3, 3, 1, 1)\n\n # SOI / ENSO dataset layout\n self.ensoLabel = QtWidgets.QLabel('Climate')\n self.ensoLabel.setMinimumWidth(100)\n self.ensoLabel.setMaximumWidth(100)\n self.ensoInfoButton = QtWidgets.QLabel()\n self.ensoInfoButton.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.ensoInfoButton.setToolTip('

    Various climate indices.

    See www.cpc.ncep.noaa.gov for more information.

    ')\n self.ensoInput = QtWidgets.QComboBox()\n self.ensoInput.addItem('Nino3.4 SST')\n self.ensoInput.addItem('Nino3.4 SST Anomaly')\n self.ensoInput.addItem('PNA Teleconnection')\n self.ensoInput.addItem('AMO Teleconnection')\n self.ensoInput.addItem(\"PDO Teleconnection\")\n #self.ensoInput.addItem('Mauna Loa CO2')\n self.ensoButton = QtWidgets.QPushButton('Add')\n self.otherDataLayout.addWidget(self.ensoLabel, 4, 0, 1, 1)\n self.otherDataLayout.addWidget(self.ensoInfoButton, 4, 1, 1, 1)\n self.otherDataLayout.addWidget(self.ensoInput, 4, 2, 1, 1)\n self.otherDataLayout.addWidget(self.ensoButton, 4, 3, 1, 1)\n\n # Other Web Service data\n self.webServiceLabel = QtWidgets.QLabel(\"Web Dataset\")\n self.webServiceLabel.setMinimumWidth(100)\n self.webServiceLabel.setMaximumWidth(100)\n self.webServiceInfo = QtWidgets.QLabel()\n self.webServiceInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.webServiceInfo.setToolTip('

    Add a dataset using a custom dataloader

    ')\n self.webServiceButton = QtWidgets.QPushButton(\"Define Web Dataset\")\n self.otherDataLayout.addWidget(self.webServiceLabel, 5, 0, 1, 1)\n self.otherDataLayout.addWidget(self.webServiceInfo, 5, 1, 1, 1)\n self.otherDataLayout.addWidget(self.webServiceButton, 5, 2, 1, 2)\n\n\n # Build the widget\n self.layout.addWidget(self.stationHeader)\n self.layout.addWidget(self.stationTable)\n line1 = QtWidgets.QFrame()\n line1.setFrameShape(QtWidgets.QFrame.HLine)\n line1.setFrameShadow(QtWidgets.QFrame.Plain)\n line1.setLineWidth(0)\n self.layout.addWidget(line1)\n self.layout.addLayout(self.otherDataLayout)\n self.setLayout(self.layout)\n\n\"\"\"\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n||||||||||||||||||||||||||||||||||||||||| DATA TAB ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n\"\"\"\n\n# Define a custom widget to display options for the data tab\nclass DataOptionsPane(QtWidgets.QWidget):\n\n # Initialize a custom QWidget\n def __init__(self, parent=None):\n\n QtWidgets.QWidget.__init__(self)\n self.setupUI()\n \n # Define the layout of the custom widget\n def setupUI(self):\n\n # Create a layout for the widget\n self.layout = QtWidgets.QVBoxLayout()\n\n # Add a header and description\n self.header1 = QtWidgets.QTextEdit()\n self.header1.setHtml(\"\"\"
    Acquire Datasets
    \n

    Specify options to download and process selected datasets. Import your Excel and CSV spreadsheets.

    \n
    \"\"\")\n self.header1.setReadOnly(True)\n self.header1.setFrameStyle(QtWidgets.QFrame.NoFrame)\n self.header1.setMaximumHeight(100)\n\n # layout the options in a grid\n self.optionsGrid = QtWidgets.QGridLayout()\n self.optionsGrid.setColumnStretch(0, 1)\n self.optionsGrid.setColumnStretch(1, 0)\n self.optionsGrid.setColumnStretch(2, 1)\n self.optionsGrid.setColumnStretch(3, 1)\n \n # POR option\n self.porLabel = QtWidgets.QLabel(\"POR\")\n self.porInfo = QtWidgets.QLabel() \n self.porInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.porInfo.setScaledContents(True)\n self.porInfo.setToolTip('

    PyForecast will attempt to download daily station data up to the POR specified here.

    ')\n self.porInput = QtWidgets.QLineEdit()\n self.porInput.setPlaceholderText(\"POR in years:\")\n self.porInput.setValidator(onlyInt)\n self.optionsGrid.addWidget(self.porLabel, 0, 0, 1, 1)\n self.optionsGrid.addWidget(self.porInfo, 0, 1, 1, 1)\n self.optionsGrid.addWidget(self.porInput, 0, 2, 1, 2)\n\n # IMPUTE SWE option\n # self.sweImputeLabel = QtWidgets.QLabel(\"Impute SNOTEL\")\n # self.sweImputeInfo = QtWidgets.QLabel() \n # self.sweImputeInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n # self.sweImputeInfo.setScaledContents(True)\n # self.sweImputeInfo.setToolTip('

    Fill incomplete SNOTEL data using MICE Imputation. Works if 3 or more SWE stations are included.

    ')\n # self.sweImputeInputYes = QtWidgets.QCheckBox(\"Yes\")\n # self.sweImputeInputYes.setChecked(False)\n # self.sweImputeInputNo = QtWidgets.QCheckBox(\"No\")\n # self.sweImputeInputNo.setChecked(True)\n # self.sweGroup = QtWidgets.QButtonGroup(self)\n # self.sweGroup.addButton(self.sweImputeInputYes)\n # self.sweGroup.addButton(self.sweImputeInputNo)\n # self.optionsGrid.addWidget(self.sweImputeLabel, 1, 0, 1, 1)\n # self.optionsGrid.addWidget(self.sweImputeInfo, 1, 1, 1, 1)\n # self.optionsGrid.addWidget(self.sweImputeInputYes, 1, 2, 1, 1)\n # self.optionsGrid.addWidget(self.sweImputeInputNo, 1, 3, 1, 1)\n\n # Interpolate Option\n self.interpLabel = QtWidgets.QLabel(\"Fill NaN's\")\n self.interpInfo = QtWidgets.QLabel() \n self.interpInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.interpInfo.setScaledContents(True)\n self.interpInfo.setToolTip('

    Interpolate missing data using cubic splines. Fills a maximum of 3 days of missing data.

    ')\n self.interpInputYes = QtWidgets.QCheckBox(\"Yes\")\n self.interpInputYes.setChecked(True)\n self.interpInputNo = QtWidgets.QCheckBox(\"No\")\n self.interpInputNo.setChecked(False)\n self.interpGroup = QtWidgets.QButtonGroup(self)\n self.interpGroup.addButton(self.interpInputYes)\n self.interpGroup.addButton(self.interpInputNo)\n self.optionsGrid.addWidget(self.interpLabel, 2, 0, 1, 1)\n self.optionsGrid.addWidget(self.interpInfo, 2, 1, 1, 1)\n self.optionsGrid.addWidget(self.interpInputYes, 2, 2, 1, 1)\n self.optionsGrid.addWidget(self.interpInputNo, 2, 3, 1, 1)\n\n # Data download\n self.downloadButton = QtWidgets.QPushButton(\"Download\")\n self.optionsGrid.addWidget(self.downloadButton, 3, 0, 1, 4)\n self.progressBar = QtWidgets.QProgressBar()\n self.progressBar.setValue(0)\n self.optionsGrid.addWidget(self.progressBar, 4, 0, 1, 4)\n\n line1 = QtWidgets.QFrame()\n line1.setFrameShape(QtWidgets.QFrame.HLine)\n line1.setFrameShadow(QtWidgets.QFrame.Plain)\n line1.setLineWidth(0)\n spacer = QtWidgets.QWidget()\n spacer.setMinimumHeight(20)\n spacer.setMaximumHeight(20)\n self.optionsGrid.addWidget(line1, 5, 0, 1, 4)\n self.optionsGrid.addWidget(spacer, 6, 0, 1, 4)\n\n # Data update\n self.updateLabel = QtWidgets.QLabel('Update Data')\n self.updateInfo = QtWidgets.QLabel() \n self.updateInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.updateInfo.setScaledContents(True)\n self.updateInfo.setToolTip('

    Update the data table with current data measured since the last update.

    ')\n self.updateButton = QtWidgets.QPushButton('Update')\n self.optionsGrid.addWidget(self.updateLabel,7,0,1,1)\n self.optionsGrid.addWidget(self.updateInfo,7,1,1,1)\n self.optionsGrid.addWidget(self.updateButton,7,2,1,2)\n\n\n # Data import label\n self.importLabel = QtWidgets.QLabel(\"Import dataset\")\n self.importInfo = QtWidgets.QLabel() \n self.importInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.importInfo.setScaledContents(True)\n self.importInfo.setToolTip('

    Import data from a CSV or Excel File. Data is appended to the table. See the documentation for instructions.

    ')\n self.importButton = QtWidgets.QPushButton(\"Import\")\n self.optionsGrid.addWidget(self.importLabel, 8, 0, 1, 1)\n self.optionsGrid.addWidget(self.importInfo, 8, 1, 1, 1)\n self.optionsGrid.addWidget(self.importButton, 8, 2, 1, 2)\n \n\n # View missing data button\n self.missingLabel = QtWidgets.QLabel(\"View Missing\")\n self.missingInfo = QtWidgets.QLabel() \n self.missingInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.missingInfo.setScaledContents(True)\n self.missingInfo.setToolTip('

    View the serial completeness of your dataset.

    ')\n self.missingButton = QtWidgets.QPushButton(\"View\")\n self.optionsGrid.addWidget(self.missingLabel, 9, 0, 1, 1)\n self.optionsGrid.addWidget(self.missingInfo, 9, 1, 1, 1)\n self.optionsGrid.addWidget(self.missingButton, 9, 2, 1, 2)\n\n spacer = QtWidgets.QWidget()\n spacer.setMaximumHeight(1200)\n self.optionsGrid.addWidget(spacer, 10, 0, 1, 4)\n\n\n # Build the widget\n self.layout.addWidget(self.header1)\n self.layout.addLayout(self.optionsGrid)\n self.setLayout(self.layout)\n\n\"\"\"\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n||||||||||||||||||||||||||||||||||||||||| FORECAST OPTIONS TAB ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n\"\"\"\n \n# Define a custom widget to display options for the data tab\nclass FcstOptionsPane(QtWidgets.QWidget):\n\n # Initialize a custom QWidget\n def __init__(self, parent=None):\n\n QtWidgets.QWidget.__init__(self)\n self.setupUI()\n self.setFixedWidth(420)\n \n # Define the layout of the custom widget\n def setupUI(self):\n\n # Create a layout for the widget\n self.layout = QtWidgets.QVBoxLayout()\n\n # Create a header to top the pane\n self.header = QtWidgets.QTextEdit()\n self.header.setHtml(\"\"\"
    Set Options
    \n

    Specify the properties of your forecasts. Construct predictor variables to be used in forecast equations. View correlations between variables.

    \n
    \"\"\")\n self.header.setReadOnly(True)\n self.header.setFrameStyle(QtWidgets.QFrame.NoFrame)\n self.header.setMaximumHeight(90)\n\n # Build the left side of the options pane\n self.gridLayout1 = QtWidgets.QGridLayout()\n self.periodLabel = QtWidgets.QLabel(\"Forecast Period\")\n self.periodInfo = QtWidgets.QLabel() \n self.periodInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.periodInfo.setToolTip('

    Set the inflow volumne period for your forecast (defaults to April - July)

    ')\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.periodInfo)\n hlayout.addWidget(self.periodLabel)\n hlayout.addSpacerItem(QtWidgets.QSpacerItem(400,40,QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum))\n self.gridLayout1.addLayout(hlayout, 0, 0, 1, 4)\n self.periodStartInput = QtWidgets.QComboBox()\n self.periodStartInput.addItems(['January','February','March','April','May','June','July','August','September','October','November','December'])\n self.periodStartInput.setCurrentIndex(3)\n self.gridLayout1.addWidget(self.periodStartInput, 1, 0, 1, 2)\n self.periodEndInput = QtWidgets.QComboBox()\n self.periodEndInput.addItems(['January','February','March','April','May','June','July','August','September','October','November','December'])\n self.periodEndInput.setCurrentIndex(6)\n self.gridLayout1.addWidget(self.periodEndInput, 1, 2, 1, 2)\n self.freqLabel = QtWidgets.QLabel(\"Forecast Frequency\")\n self.freqInfo = QtWidgets.QLabel() \n self.freqInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.freqInfo.setToolTip('

    Specify how many forecast equations should be produced each water year. Default monthly.

    ')\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.freqInfo)\n hlayout.addWidget(self.freqLabel)\n hlayout.addSpacerItem(QtWidgets.QSpacerItem(400,40,QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum))\n self.gridLayout1.addLayout(hlayout, 2, 0, 1, 4)\n self.freqInput = QtWidgets.QComboBox()\n self.freqInput.addItems([\"Monthly\", \"Bi-Monthly\"])\n self.gridLayout1.addWidget(self.freqInput, 3, 0, 1, 4)\n self.eqStartLabel = QtWidgets.QLabel(\"Forecasts start on:\")\n self.eqStartInfo = QtWidgets.QLabel() \n self.eqStartInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.eqStartInfo.setToolTip('

    Specify the first month in which you will issue a forecast.

    ')\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.eqStartInfo)\n hlayout.addWidget(self.eqStartLabel)\n hlayout.addSpacerItem(QtWidgets.QSpacerItem(400,40,QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum))\n self.gridLayout1.addLayout(hlayout, 4, 0, 1, 4)\n self.eqStartInput = QtWidgets.QComboBox()\n self.eqStartInput.addItems(['January','February','March','April','May','June','July','August','September','October','November','December'])\n self.gridLayout1.addWidget(self.eqStartInput, 5, 0, 1, 4)\n self.wateryearStartInfo = QtWidgets.QLabel()\n self.wateryearStartInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.wateryearStartInfo.setToolTip('

    Specify the first month of your water year

    ')\n self.wateryearStartLabel = QtWidgets.QLabel(\"Wateryear starts on:\")\n self.wateryearStartInput = QtWidgets.QComboBox()\n self.wateryearStartInput.addItems(['January','February','March','April','May','June','July','August','September','October','November','December'])\n self.wateryearStartInput.setCurrentIndex(9)\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.wateryearStartInfo)\n hlayout.addWidget(self.wateryearStartLabel)\n hlayout.addSpacerItem(QtWidgets.QSpacerItem(400,40,QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum))\n self.gridLayout1.addLayout(hlayout, 6, 0, 1, 4)\n self.gridLayout1.addWidget(self.wateryearStartInput, 7, 0, 1, 4)\n self.targetLabel = QtWidgets.QLabel(\"Forecast Target:\")\n self.targetInfo = QtWidgets.QLabel() \n self.targetInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.targetInfo.setToolTip('

    Specify the dataset that you are forecasting. Only streamflow variables can be chosen.

    ')\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.targetInfo)\n hlayout.addWidget(self.targetLabel)\n hlayout.addSpacerItem(QtWidgets.QSpacerItem(400,40,QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum))\n self.gridLayout1.addLayout(hlayout, 8, 0, 1, 4)\n self.targetInput = QtWidgets.QComboBox()\n self.gridLayout1.addWidget(self.targetInput, 9, 0, 1, 4)\n self.precipLabel = QtWidgets.QLabel(\"Accumulate Precipitation\")\n self.precipInfo = QtWidgets.QLabel() \n self.precipInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.precipInfo.setToolTip('

    Create predictors that accumulates precipitation. User defines accumulation period.

    ')\n self.precipInputYes = QtWidgets.QCheckBox(\"Yes\")\n self.precipInputYes.setChecked(True)\n self.precipInputNo = QtWidgets.QCheckBox(\"No\")\n self.precipInputNo.setChecked(False)\n self.precipGroup = QtWidgets.QButtonGroup()\n self.precipGroup.addButton(self.precipInputYes)\n self.precipGroup.addButton(self.precipInputNo)\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.precipInfo)\n hlayout.addWidget(self.precipLabel)\n hlayout.addSpacerItem(QtWidgets.QSpacerItem(400,40,QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum))\n hlayout.addWidget(self.precipInputYes)\n hlayout.addWidget(self.precipInputNo)\n self.gridLayout1.addLayout(hlayout, 10, 0, 1, 4)\n self.accumLabel = QtWidgets.QLabel(\"Accumulate From:\")\n self.accumStart = QtWidgets.QComboBox()\n self.accumStart.addItems(['October','November','December','January','February','March','April','May','June','July','August','September'])\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.accumLabel)\n hlayout.addWidget(self.accumStart)\n self.gridLayout1.addLayout(hlayout, 11, 0, 1, 4)\n self.forecasterLabel = QtWidgets.QLabel(\"Forecaster: \")\n self.forecasterInput = QtWidgets.QLineEdit()\n self.forecasterInput.setPlaceholderText(\"Name:\")\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.forecasterLabel)\n hlayout.addWidget(self.forecasterInput)\n self.gridLayout1.addLayout(hlayout, 12, 0, 1, 4)\n self.forecastNotes = QtWidgets.QPlainTextEdit()\n self.forecastNotes.setMaximumHeight(50)\n self.forecastNotes.setPlaceholderText(\"Notes...\")\n self.forecastNotesLabel = QtWidgets.QLabel(\"Forecast Notes:\")\n self.gridLayout1.addWidget(self.forecastNotesLabel, 13, 0, 1, 4)\n self.gridLayout1.addWidget(self.forecastNotes, 14, 0, 1, 4)\n self.applyButton = QtWidgets.QPushButton(\"Apply Options\")\n self.gridLayout1.addWidget(self.applyButton, 15, 0, 1, 4)\n hline = QtWidgets.QFrame()\n hline.setFrameShape(QtWidgets.QFrame.HLine)\n hline.setFrameShadow(QtWidgets.QFrame.Plain)\n hline.setLineWidth(0)\n self.gridLayout1.addWidget(hline, 16, 0, 1, 4)\n self.updateButton = QtWidgets.QPushButton(\"Update Predictors\")\n self.gridLayout1.addWidget(self.updateButton, 17, 0, 1, 4)\n\n\n \n\n # Build the widget\n self.scroll = QtWidgets.QScrollArea()\n self.scrollContent = QtWidgets.QWidget()\n self.scrollContent.setFixedWidth(380)\n self.scroll.setFrameStyle(QtWidgets.QFrame.NoFrame)\n self.scrollLayout = QtWidgets.QVBoxLayout()\n self.scrollLayout.addWidget(self.header)\n self.scrollLayout.addLayout(self.gridLayout1)\n self.scrollContent.setLayout(self.scrollLayout)\n self.scroll.setWidget(self.scrollContent)\n\n self.layout.addWidget(self.scroll)\n self.setLayout(self.layout)\n\nclass FcstOptionsTrees(QtWidgets.QWidget):\n \n # Initialize a custom QWidget\n def __init__(self, parent=None):\n\n QtWidgets.QWidget.__init__(self)\n self.setupUI()\n \n # Define the layout of the custom widget\n def setupUI(self):\n\n # Create a layout for the widget\n self.layout = QtWidgets.QHBoxLayout()\n\n # Set up the first tree\n self.tree1Layout = QtWidgets.QVBoxLayout()\n self.tree1Label = QtWidgets.QLabel(\"All Available Predictors\")\n self.tree1Info = QtWidgets.QLabel() \n self.tree1Info.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.tree1Info.setToolTip('

    Drag available predictors into specific forecasts

    ')\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.tree1Info)\n hlayout.addWidget(self.tree1Label)\n hlayout.addSpacerItem(QtWidgets.QSpacerItem(400,40,QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum))\n self.tree1Layout.addLayout(hlayout)\n self.tree1 = CustomTreeView(self, dragFrom=True, dropTo=False, menuFunctions=['OPENEXCEL'])\n self.tree1.setFrameStyle(QtWidgets.QFrame.NoFrame)\n self.tree1Layout.addWidget(self.tree1)\n\n # Set up the second tree\n self.tree2Layout = QtWidgets.QVBoxLayout()\n self.tree2Label = QtWidgets.QLabel(\"Equation Pools\")\n self.tree2Info = QtWidgets.QLabel() \n self.tree2Info.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.tree2Info.setToolTip('

    Drag available predictors into specific forecasts

    ')\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.tree2Info)\n hlayout.addWidget(self.tree2Label)\n hlayout.addSpacerItem(QtWidgets.QSpacerItem(400,40,QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum))\n self.tree2Layout.addLayout(hlayout)\n self.tree2 = CustomTreeView(self, dragFrom=True, dropTo=True, menuFunctions=['DELETE'])\n self.tree2.setFrameStyle(QtWidgets.QFrame.NoFrame)\n self.tree2Layout.addWidget(self.tree2)\n\n # Lay out the widget\n self.layout.addLayout(self.tree1Layout)\n vline = QtWidgets.QFrame()\n vline.setFrameShape(QtWidgets.QFrame.VLine)\n vline.setFrameShadow(QtWidgets.QFrame.Plain)\n self.layout.addWidget(vline)\n self.layout.addLayout(self.tree2Layout)\n self.setLayout(self.layout)\n\nclass plotsPane(QtWidgets.QWidget):\n prdIDSignal = QtCore.pyqtSignal(str)\n # Initialize a custom QWidget\n def __init__(self, parent=None):\n\n QtWidgets.QWidget.__init__(self)\n self.setAcceptDrops(True)\n \n self.setupUI()\n \n # Define the layout of the custom widget\n def setupUI(self):\n\n self.layout = QtWidgets.QGridLayout()\n self.tsPlot = PlotCanvas_1Plot()\n self.tsPlot.setSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)\n self.corrPlot = PlotCanvas_1Plot()\n self.corrPlot.setSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)\n\n vlayout = QtWidgets.QVBoxLayout()\n self.clearButton = QtWidgets.QPushButton(\"Clear Plots\")\n self.clearButton.setMaximumWidth(40)\n self.axesButton = QtWidgets.QPushButton(\"Use 2 Axes\")\n self.axesButton.setMaximumWidth(40)\n self.corrButton = QtWidgets.QPushButton(\"Correlation\")\n self.corrButton.setMaximumWidth(40)\n vlayout.addWidget(self.clearButton)\n vlayout.addWidget(self.axesButton)\n vlayout.addWidget(self.corrButton)\n self.layout.addLayout(vlayout, 0, 0, 1, 1)\n self.layout.addWidget(self.tsPlot, 0, 1, 1, 1)\n self.layout.addWidget(self.corrPlot, 0, 2, 1, 1)\n self.tsNav = NavigationToolbar(self.tsPlot, self)\n self.corrNav = NavigationToolbar(self.corrPlot, self)\n self.layout.addWidget(self.tsNav, 1, 1, 1, 1)\n self.layout.addWidget(self.corrNav, 1, 2, 1, 1)\n self.setLayout(self.layout)\n\n # Define the behaviour for droping data into the graphs\n def dragEnterEvent(self, event):\n event.acceptProposedAction()\n\n def dropEvent(self, event):\n\n prdID = -1\n pos = event.pos()\n\n # Get the item's text\n if event.mimeData().hasFormat('application/x-qabstractitemmodeldatalist'):\n mod = QtGui.QStandardItemModel()\n mod.dropMimeData(event.mimeData(),QtCore.Qt.CopyAction, 0, 0, QtCore.QModelIndex())\n item = mod.item(0,0)\n itemText = item.text()\n else:\n print('wrongMimeType')\n event.ignore()\n return\n \n # Ensure that the item is a valid predictor\n if item.hasChildren():\n\n numChildren = item.rowCount()\n print('item has {0} children'.format(numChildren))\n for i in range(numChildren):\n child = item.child(i)\n if 'prdID: ' in child.text():\n prdID = child.text()[7:]\n if 'Name' in child.text():\n prdID = child.text()\n break\n \n if prdID == -1:\n print('no prdid')\n event.ignore()\n return\n \n else:\n print('no children')\n event.ignore()\n return\n \n \n self.prdIDSignal.emit(prdID)\n event.accept()\n\n\"\"\"\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n||||||||||||||||||||||||||||||||||||||||| REGRESSION TAB ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n\"\"\"\n\n# Set up a standard regression tab\nclass StandardRegressionTab(QtWidgets.QWidget):\n\n # Initialize a custom widget\n def __init__(self, parent=None, model = 'default'):\n\n QtWidgets.QWidget.__init__(self)\n self.setupUI(model)\n \n def setupUI(self,model='none'):\n\n # Set the tab layout\n self.layout = QtWidgets.QVBoxLayout()\n self.layout2 = QtWidgets.QVBoxLayout()\n\n # Add widgets to the layout\n self.eqSelectLabel = QtWidgets.QLabel(\"Select Equation\")\n self.eqSelectInfo = QtWidgets.QLabel() \n self.eqSelectInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.eqSelectInfo.setToolTip('

    Which forecast equation are you trying to generate.

    ')\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.eqSelectInfo)\n hlayout.addWidget(self.eqSelectLabel)\n hlayout.addSpacerItem(QtWidgets.QSpacerItem(400,40,QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum))\n self.layout.addLayout(hlayout)\n self.eqSelect = QtWidgets.QComboBox()\n self.eqSelect.addItems([])\n self.layout.addWidget(self.eqSelect)\n self.featSelMethodLabel = QtWidgets.QLabel(\"Feature Selection Method\")\n self.featSelMethodInfo = QtWidgets.QLabel() \n self.featSelMethodInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.featSelMethodInfo.setToolTip('

    Defines how predictors are added to models. See documentation for more info.

    ')\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.featSelMethodInfo)\n hlayout.addWidget(self.featSelMethodLabel)\n hlayout.addSpacerItem(QtWidgets.QSpacerItem(400,40,QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum))\n self.layout.addLayout(hlayout)\n self.featSelInput = QtWidgets.QComboBox()\n self.featSelInput.addItems([\"Sequential Floating Forward Selection\", \"Sequential Floating Backwards Selection\"])#[\"Forward Selection\",\"Backward Selection\",[\"Sequential Floating Forward Selection\"],\"Floating Backward\"])\n self.layout.addWidget(self.featSelInput)\n self.numModelsLabel = QtWidgets.QLabel(\"Number of Models\")\n self.numModelsInfo = QtWidgets.QLabel() \n self.numModelsInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.numModelsInfo.setToolTip('

    Defined how many models will be built in parallel using the feature selection scheme specified above.

    ')\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.numModelsInfo)\n hlayout.addWidget(self.numModelsLabel)\n hlayout.addSpacerItem(QtWidgets.QSpacerItem(400,40,QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum))\n self.layout.addLayout(hlayout)\n self.numModelsInput = QtWidgets.QLineEdit()\n self.numModelsInput.setText(\"50\")\n self.numModelsInput.setValidator(onlyInt)\n self.layout.addWidget(self.numModelsInput)\n self.crossValLabel = QtWidgets.QLabel(\"Cross Validation Method\")\n self.crossValInfo = QtWidgets.QLabel() \n self.crossValInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.crossValInfo.setToolTip('

    Defines the cross-validation scheme to score models as predictors are added. See documentation.

    ')\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.crossValInfo)\n hlayout.addWidget(self.crossValLabel)\n hlayout.addSpacerItem(QtWidgets.QSpacerItem(400,40,QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum))\n self.layout.addLayout(hlayout)\n self.crossValInput = QtWidgets.QComboBox()\n self.crossValInput.addItems([\"Leave One Out\",\"K-Fold (5 folds)\",\"K-Fold (10 folds)\"])\n self.layout.addWidget(self.crossValInput)\n self.scoreLabel = QtWidgets.QLabel(\"Model Scoring Method\")\n self.scoreInfo = QtWidgets.QLabel() \n self.scoreInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.scoreInfo.setToolTip('

    Defines the parameter used to score models as predictors are added.

    ')\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.scoreInfo)\n hlayout.addWidget(self.scoreLabel)\n hlayout.addSpacerItem(QtWidgets.QSpacerItem(400,40,QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum))\n self.layout.addLayout(hlayout)\n self.scoreInput = QtWidgets.QComboBox()\n self.scoreInput.addItems([\"Cross Validated Adjusted R2\",\"Root Mean Squared Prediction Error\",\"Cross Validated Nash-Sutcliffe\"])\n self.layout.addWidget(self.scoreInput)\n\n self.distLabel = QtWidgets.QLabel(\"Inflow Distribution\")\n self.distInfo = QtWidgets.QLabel() \n self.distInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.distInfo.setToolTip('

    Defines the assumed distribution of inflows to the reservoir

    ')\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.distInfo)\n hlayout.addWidget(self.distLabel)\n hlayout.addSpacerItem(QtWidgets.QSpacerItem(400,40,QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum))\n self.layout.addLayout(hlayout)\n self.distInput = QtWidgets.QComboBox()\n self.distInput.addItems([\"Normal\"])#, \"Lognormal\"])\n self.layout.addWidget(self.distInput)\n\n\n self.regrButton = QtWidgets.QPushButton(\"Run \" + model)\n self.layout.addWidget(self.regrButton)\n self.regrProgress = QtWidgets.QProgressBar()\n self.regrProgress.setValue(0)\n self.layout.addWidget(self.regrProgress)\n\n self.regModLabel = QtWidgets.QLabel(\"Models Analyzed: \")\n self.layout.addWidget(self.regModLabel)\n\n hline = QtWidgets.QFrame()\n hline.setFrameShape(QtWidgets.QFrame.HLine)\n hline.setFrameShadow(QtWidgets.QFrame.Plain)\n self.layout.addWidget(hline)\n\n self.bestModelTable = CustomTableView(self, rowLock = True, colLock = False, cols =4, rows = 0, headers = ['prdIDs','CV Adjusted R2','RMSPE','CV NSE'], menuFunctions = ['SAVEFCST', 'REGSTAT'], readOnly = True, dragFrom=False)\n self.bestModelTable.setMinimumHeight(250)\n self.layout.addWidget(self.bestModelTable)\n\n self.scroll = QtWidgets.QScrollArea()\n self.scrollContent = QtWidgets.QWidget()\n #self.scrollContent.setMinimumWidth(370)\n self.scroll.setFrameStyle(QtWidgets.QFrame.NoFrame)\n self.scrollLayout = self.layout\n #self.scrollLayout.addLayout(self.layout)\n self.scrollContent.setLayout(self.scrollLayout)\n self.scroll.setWidget(self.scrollContent)\n self.scroll.setWidgetResizable(True)\n\n self.layout2.addWidget(self.scroll)\n\n # Lay out the tab\n self.setLayout(self.layout2)\n\n# Set up a Artificial Neural Network regression tab\nclass ANNRegressionTab(QtWidgets.QWidget):\n\n # Initialize a custom widget\n def __init__(self, parent=None, model = 'default'):\n\n QtWidgets.QWidget.__init__(self)\n self.setupUI(model)\n \n def setupUI(self,model='none'):\n\n # Set the tab layout\n self.layout = QtWidgets.QVBoxLayout()\n\n # Add the options\n self.bigLabel = QtWidgets.QLabel(\"COMING SOON........\")\n self.layout.addWidget(self.bigLabel)\n self.structureLabel = QtWidgets.QLabel(\"Hidden Layers\")\n self.structureInfo = QtWidgets.QLabel() \n self.structureInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.structureInfo.setToolTip('

    How many hidden layers will the MLP neural network have.

    ')\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.structureInfo)\n hlayout.addWidget(self.structureLabel)\n hlayout.addSpacerItem(QtWidgets.QSpacerItem(400,40,QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum))\n self.layout.addLayout(hlayout)\n self.structureInput = QtWidgets.QComboBox()\n self.structureInput.addItems([\"1 Hidden Layer\",\"2 Hidden Layers\",\"3 Hidden Layers\"])\n self.layout.addWidget(self.structureInput)\n\n self.hiddenNeuronsLabel = QtWidgets.QLabel(\"Add Layer Neurons\")\n self.hiddenNeuronsInfo = QtWidgets.QLabel()\n self.hiddenNeuronsInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.hiddenNeuronsInfo.setToolTip('

    Define the number of neurons in each hidden layer

    ')\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.hiddenNeuronsInfo)\n hlayout.addWidget(self.hiddenNeuronsLabel)\n hlayout.addSpacerItem(QtWidgets.QSpacerItem(400,40,QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum))\n self.layout.addLayout(hlayout)\n self.hiddenNeuronsLayer1Label = QtWidgets.QLabel(\"Layer 1 Neurons: \")\n self.hiddenNeuronsLayer1Input = QtWidgets.QLineEdit()\n self.hiddenNeuronsLayer1Input.setValidator(onlyInt)\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.hiddenNeuronsLayer1Label)\n hlayout.addWidget(self.hiddenNeuronsLayer1Input)\n self.layout.addLayout(hlayout)\n self.hiddenNeuronsLayer2Label = QtWidgets.QLabel(\"Layer 2 Neurons: \")\n self.hiddenNeuronsLayer2Input = QtWidgets.QLineEdit()\n self.hiddenNeuronsLayer2Input.setValidator(onlyInt)\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.hiddenNeuronsLayer2Label)\n hlayout.addWidget(self.hiddenNeuronsLayer2Input)\n self.layout.addLayout(hlayout)\n self.hiddenNeuronsLayer3Label = QtWidgets.QLabel(\"Layer 3 Neurons: \")\n self.hiddenNeuronsLayer3Input = QtWidgets.QLineEdit()\n self.hiddenNeuronsLayer3Input.setValidator(onlyInt)\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.hiddenNeuronsLayer3Label)\n hlayout.addWidget(self.hiddenNeuronsLayer3Input)\n self.layout.addLayout(hlayout)\n \n # Build the widget\n self.setLayout(self.layout)\n\n# Set up a Gaussian Process regression tab\nclass GPRegressionTab(QtWidgets.QWidget):\n\n # Initialize a custom widget\n def __init__(self, parent=None, model = 'default'):\n\n QtWidgets.QWidget.__init__(self)\n self.setupUI(model)\n \n def setupUI(self,model='none'):\n\n # Set the tab layout\n self.layout = QtWidgets.QVBoxLayout()\n self.bigLabel = QtWidgets.QLabel('COMING SOON...')\n self.layout.addWidget(self.bigLabel)\n self.setLayout(self.layout)\n\n# Set up the regression selection pane\nclass RegressionSelectionPane(QtWidgets.QWidget):\n\n # Initialize a custom QWidget\n def __init__(self, parent=None):\n\n QtWidgets.QWidget.__init__(self)\n self.setupUI()\n self.setStyleSheet(\"\"\"\n QWidget {padding:0}\n QTabBar::tab { min-width: 50 }\n \"\"\")\n \n # Define the layout of the custom widget\n def setupUI(self):\n\n # Create a layout for the widget\n self.layout = QtWidgets.QVBoxLayout()\n\n # Create a header to top the pane\n self.header = QtWidgets.QTextEdit()\n self.header.setHtml(\"\"\"
    Perform Regressions
    \n

    Build regression models to generate forecast equations and forecast objects. View and save well-performing models.

    \n
    \"\"\")\n self.header.setReadOnly(True)\n self.header.setFrameStyle(QtWidgets.QFrame.NoFrame)\n self.header.setMaximumHeight(80)\n\n # Put in a tabwidget\n self.tabWidget = QtWidgets.QTabWidget(self)\n self.tabWidget.setStyleSheet(\"\"\"\n QTabBar {background: #e8e8e8}\n \"\"\")\n self.mlrTab = StandardRegressionTab(self, \"MLR\")\n self.pcarTab = StandardRegressionTab(self, \"PCAR\")\n self.zscrTab = StandardRegressionTab(self, \"ZSCR\")\n self.annTab = StandardRegressionTab(self, 'ANN')\n self.gprTab = GPRegressionTab(self)\n self.tabWidget.addTab(self.mlrTab, \"MLR\")\n self.tabWidget.addTab(self.pcarTab, \"PCAR\")\n self.tabWidget.addTab(self.zscrTab, \"ZSCR\")\n self.tabWidget.addTab(self.annTab, \"ANN\")\n self.tabWidget.addTab(self.gprTab, \"GPR\")\n\n # Build the widget\n self.layout.addWidget(self.header)\n self.layout.addWidget(self.tabWidget)\n self.setLayout(self.layout)\n\n\"\"\"\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n||||||||||||||||||||||||||||||||||||||||| DENSITY ANALYSIS TAB ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n\"\"\"\n\n# Set up the regression selection pane\nclass DensityPane(QtWidgets.QWidget):\n\n # Initialize a custom QWidget\n def __init__(self, parent=None):\n\n QtWidgets.QWidget.__init__(self)\n self.setupUI()\n self.setFixedWidth(350)\n \n # Define the layout of the custom widget\n def setupUI(self):\n\n # Create a layout for the widget\n self.layout = QtWidgets.QVBoxLayout()\n\n # Create a header to top the pane\n self.header = QtWidgets.QTextEdit()\n self.header.setHtml(\"\"\"
    Analyze Forecast Density
    \n

    Discover trends in forecasts by constructing forecast PDFs and CDFs. Store forecast density predictions and plots.

    \n
    \"\"\")\n self.header.setReadOnly(True)\n self.header.setFrameStyle(QtWidgets.QFrame.NoFrame)\n self.header.setMaximumHeight(90)\n self.layout.addWidget(self.header)\n\n # Table to display selected forecasts\n self.selectedFcstLabel = QtWidgets.QLabel(\"Selected Forecasts\")\n self.selectedFcstInfo = QtWidgets.QLabel()\n self.selectedFcstInfo.setPixmap(QtGui.QPixmap(os.path.abspath(\"Resources/Fonts_Icons_Images/infoHover.png\")).scaled(30,30, QtCore.Qt.KeepAspectRatio))\n self.selectedFcstInfo.setToolTip('

    Select forecasts to analyze and choose Run Analysis

    ')\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.selectedFcstInfo)\n hlayout.addWidget(self.selectedFcstLabel)\n hlayout.addSpacerItem(QtWidgets.QSpacerItem(400,40,QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum))\n self.layout.addLayout(hlayout)\n label = QtWidgets.QLabel(\"Equation\")\n self.forecastEquationSelect = QtWidgets.QComboBox()\n hlayout=QtWidgets.QHBoxLayout()\n hlayout.addWidget(label)\n hlayout.addWidget(self.forecastEquationSelect)\n self.layout.addLayout(hlayout)\n self.selectedFcstTable = CustomTableView(self, rowLock=True, cols=2, headers=['FcstID','Forecast'])\n self.selectedFcstTable.horizontalHeader().setStretchLastSection(True)\n self.layout.addWidget(self.selectedFcstTable)\n\n # Bandwidth selection\n bwidthlabel = QtWidgets.QLabel(\"Bandwidth:\")\n self.bwidthEdit = QtWidgets.QLineEdit()\n self.bwidthEdit.setText(\"AUTO\")\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(bwidthlabel)\n hlayout.addWidget(self.bwidthEdit)\n self.layout.addLayout(hlayout)\n\n # Buttons to run and clear the density estimation list\n self.runButton = QtWidgets.QPushButton(\"Run Analysis\")\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.runButton)\n self.layout.addLayout(hlayout)\n\n hline = QtWidgets.QFrame()\n hline.setFrameShape(QtWidgets.QFrame.HLine)\n hline.setFrameShadow(QtWidgets.QFrame.Plain)\n self.layout.addWidget(hline)\n\n # Define the output options\n\n self.saveButton = QtWidgets.QPushButton(\"Save Forecast\")\n self.clearPlotButton = QtWidgets.QPushButton(\"Clear Plots\")\n hlayout = QtWidgets.QHBoxLayout()\n hlayout.addWidget(self.saveButton)\n hlayout.addWidget(self.clearPlotButton)\n self.layout.addLayout(hlayout)\n\n\n # Build the widget\n self.setLayout(self.layout)\n\n\"\"\"\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n||||||||||||||||||||||||||||||||||||||||| WINDOW ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||\n\"\"\"\n\n#///////////////////////// DEFINE THE GUI ///////////////////////////////////////////////\n#// Here we actually layout the GUI, element by element. \n\nclass UI_MainWindow(object):\n \n def setupUi(self, MainWindow):\n\n #///////////// Set up the window geometry /////////////////\n MainWindow.resize(1200, 750)\n MainWindow.setMinimumSize(QtCore.QSize(900, 750))\n MainWindow.setWindowTitle(\"PyForecast v2.0\")\n\n #///////////// Set up the menus ///////////////////////////\n # Initiate a menu widget\n self.menu = self.menuBar()\n\n # Create a File Menu and add the File menu buttons\n self.fileMenu = self.menu.addMenu(\"File\")\n self.newAction = QtWidgets.QAction('New Forecast', MainWindow)\n self.saveAction = QtWidgets.QAction('Save Forecast', MainWindow)\n self.openAction = QtWidgets.QAction('Open Forecast', MainWindow)\n self.addLoaderAction = QtWidgets.QAction(\"Edit Dataloaders\",MainWindow)\n self.setCustomDatetimeAction = QtWidgets.QAction('Set custom datetime', MainWindow)\n #self.blueThemeAction = QtWidgets.QAction(\"Blue / Gray\")\n #self.yellowThemeAction = QtWidgets.QAction(\"Yellow / Black\")\n # self.ConnectAction = QtWidgets.QAction('Connect to HDB', MainWindow) <- May be added later\n # self.ExportFcstAction = QtWidgets.QAction('Export Forecast to Spreadsheet', MainWindow) <- May be added later\n self.exitAction = QtWidgets.QAction('Exit PyForecast', MainWindow)\n self.fileMenu.addAction(self.newAction)\n self.fileMenu.addAction(self.saveAction)\n self.fileMenu.addAction(self.openAction)\n self.fileMenu.addSeparator()\n self.fileMenu.addAction(self.addLoaderAction)\n #self.fileMenu.addSeparator()\n #self.editThemeMenu = self.fileMenu.addMenu(\"Change Color Theme\")\n #self.editThemeMenu.addAction(self.blueThemeAction)\n #self.editThemeMenu.addAction(self.yellowThemeAction)\n self.fileMenu.addSeparator()\n self.fileMenu.addAction(self.setCustomDatetimeAction)\n self.fileMenu.addSeparator()\n self.fileMenu.addAction(self.exitAction)\n\n # Create an About Menu and add the About menu buttons\n self.aboutMenu = self.menu.addMenu(\"About\")\n self.docAction = QtWidgets.QAction('Documentation', MainWindow)\n self.versionAction = QtWidgets.QAction('Version Info',MainWindow)\n self.aboutMenu.addAction(self.docAction)\n self.aboutMenu.addAction(self.versionAction)\n\n # Add the menubar to the window\n MainWindow.setMenuBar(self.menu)\n\n #///////////// Set up the Main Window layout ///////////////////////////\n # The Tab Widget will be the central widget of the application\n self.tabWidget = QtWidgets.QTabWidget(MainWindow)\n\n # There are 5 tabs in the application\n self.summaryTab = QtWidgets.QWidget() # The container for the Summary Tab\n self.stationsTab = QtWidgets.QWidget() # The container for the Stations Tab\n self.dataTab = QtWidgets.QWidget() # The container for the Data Tab\n self.fcstOptionsTab = QtWidgets.QWidget() # The container for the Forecast Options Tab\n self.regressionTab = QtWidgets.QWidget() # The container for the Regression Tab\n self.densityAnalysisTab = QtWidgets.QWidget() # The container for the Density Tab\n\n #/////////// Set up the Summary Tab ////////////////////////////////////\n #// The Summary tab had 3 main elements: The forecast selection tree,\n #// the forecast information window, and the forecast plot window.\n\n # Set up a layout for the tab\n self.summaryTab.layout = QtWidgets.QHBoxLayout(self)\n \n # Add a horizontal splitter to divide the forecast selection pane from the plots\n self.summaryTab.splitter1 = QtWidgets.QSplitter(QtCore.Qt.Horizontal)\n self.summaryTab.fcstSelectionPane = QtWidgets.QWidget()\n self.summaryTab.fcstSelectionPane.layout = QtWidgets.QVBoxLayout()\n self.summaryTab.fcstSelectionPane.header = QtWidgets.QTextEdit()\n self.summaryTab.fcstSelectionPane.header.setHtml(\"\"\"
    Select Forecast
    \n

    Use the list to select a forecast to view from this file.

    \n
    \"\"\")\n self.summaryTab.fcstSelectionPane.header.setReadOnly(True)\n self.summaryTab.fcstSelectionPane.header.setFrameStyle(QtWidgets.QFrame.NoFrame)\n self.summaryTab.fcstSelectionPane.header.setMaximumHeight(90)\n\n self.summaryTab.fcstTree = CustomTreeView(self, menuFunctions=[\"GENCURRENT\",\"DELETE\"], dragFrom=False, dropTo=False)\n self.summaryTab.fcstTree.setFrameStyle(QtWidgets.QFrame.NoFrame)\n \n self.summaryTab.fcstSelectionPane.layout.addWidget(self.summaryTab.fcstSelectionPane.header)\n self.summaryTab.fcstSelectionPane.layout.addWidget(self.summaryTab.fcstTree)\n self.summaryTab.fcstSelectionPane.setLayout(self.summaryTab.fcstSelectionPane.layout)\n\n self.summaryTab.plots = PlotCanvas_3Plot(self)\n self.summaryTab.splitter1.addWidget(self.summaryTab.fcstSelectionPane)\n self.summaryTab.splitter1.addWidget(self.summaryTab.plots)\n\n # Add a vertical splitter to dive the horizontal splitter from the forecast information pane\n self.summaryTab.splitter2 = QtWidgets.QSplitter(QtCore.Qt.Vertical)\n self.summaryTab.fcstInfoPane = FcstInfoPane(self)\n self.summaryTab.splitter2.addWidget(self.summaryTab.splitter1)\n self.summaryTab.splitter2.addWidget(self.summaryTab.fcstInfoPane)\n\n # Lay out the tab\n self.summaryTab.layout.addWidget(self.summaryTab.splitter2)\n self.summaryTab.setLayout(self.summaryTab.layout)\n\n # Add the tab to the tabWidget\n self.tabWidget.addTab(self.summaryTab, \"Summary\")\n\n #///////////// Set up the Stations Tab /////////////////////////////////\n #// The Stations Tab is divided into 2 main elements: The webmap station selector,\n #// and the selected stations pane.\n\n # Set up the layout for the tab\n self.stationsTab.layout = QtWidgets.QHBoxLayout(self)\n\n # Add a horizontal splitter to divide the map from the station information pane\n self.stationsTab.splitter1 = QtWidgets.QSplitter(QtCore.Qt.Horizontal)\n self.stationsTab.mapPane = QtWebEngineWidgets.QWebEngineView(self)\n self.stationsTab.mapPane.settings().setAttribute(QtWebEngineWidgets.QWebEngineSettings.LocalContentCanAccessRemoteUrls, True)\n self.stationsTab.page = WebMap(self)\n self.stationsTab.mapPane.setPage(self.stationsTab.page)\n self.stationsTab.page.java_msg_signal.connect(self.onNewData)\n self.stationsTab.stationInfoPane = StationInfoPane(self)\n self.stationsTab.splitter1.addWidget(self.stationsTab.mapPane)\n self.stationsTab.splitter1.addWidget(self.stationsTab.stationInfoPane)\n\n # Lay out the tab\n self.stationsTab.layout.addWidget(self.stationsTab.splitter1)\n self.stationsTab.setLayout(self.stationsTab.layout)\n\n # Add the tab to the tabwidget\n self.tabWidget.addTab(self.stationsTab, \"Stations\")\n\n #///////////// Set up the Data Tab /////////////////////////////////\n #// The Data Tab id divided into 3 main elements. The data options \n #// pane allows users to specify Period of record information, as well\n #// as specify alternate sources for data. The table displays the curent\n #// daily data, and the plot window plots the data over the POR\n\n # Set up the layout for the tab\n self.dataTab.layout = QtWidgets.QHBoxLayout(self)\n\n # Add a horizontal splitter to divide the data options pane from the data table\n self.dataTab.splitter1 = QtWidgets.QSplitter(QtCore.Qt.Horizontal)\n self.dataTab.dataOptions = DataOptionsPane(self)\n self.dataTab.dataOptions.setMinimumWidth(280)\n self.dataTab.dataOptions.setMaximumWidth(280)\n self.dataTab.dataTable = CustomTableView(self, colLock=True, cols=0, rows=0, menuFunctions=['COPY','OPEN','DELETECOL'], readOnly = False)\n self.dataTab.dataTable.setWordWrap(True)\n self.dataTab.splitter1.addWidget(self.dataTab.dataOptions)\n self.dataTab.splitter1.addWidget(self.dataTab.dataTable)\n\n # Add a vertical splitter to divide the table from the plots\n self.dataTab.splitter2 = QtWidgets.QSplitter(QtCore.Qt.Vertical)\n self.dataTab.plots = PlotCanvas_1Plot(self)\n self.dataTab.splitter2.addWidget(self.dataTab.splitter1)\n self.dataTab.splitter2.addWidget(self.dataTab.plots)\n self.dataTab.dataNav = NavigationToolbar(self.dataTab.plots, self.dataTab)\n self.dataTab.splitter2.addWidget(self.dataTab.dataNav)\n\n # Finish the tab\n self.dataTab.layout.addWidget(self.dataTab.splitter2)\n self.dataTab.setLayout(self.dataTab.layout)\n self.tabWidget.addTab(self.dataTab, \"Data\")\n\n #///////////// Set up the Forecast Options Tab /////////////////////////////////\n #// The Forecast options tab is divided into 3 sections. The Options section\n #// allows users to specify the options used to create predictors and equations.\n #// The table shows the transformed predictors. The Correlations plot \n #// shows correlations between 2 variables in the transform plot\n\n # Set up the layout of the Tab\n \n self.fcstOptionsTab.layout = QtWidgets.QHBoxLayout()\n\n # Add a verical spliiter to split the options pane from the correlations plot\n self.fcstOptionsTab.splitter1 = QtWidgets.QSplitter(QtCore.Qt.Vertical)\n self.fcstOptionsTab.optionsPane = FcstOptionsPane(self)\n self.fcstOptionsTab.splitter1.addWidget(self.fcstOptionsTab.optionsPane)\n\n # Add a horizontal splitter to split the table from splitter1\n self.fcstOptionsTab.splitter2 = QtWidgets.QSplitter(QtCore.Qt.Horizontal)\n self.fcstOptionsTab.splitter2.addWidget(self.fcstOptionsTab.splitter1)\n self.fcstOptionsTab.dualTreeView = FcstOptionsTrees(self)\n self.fcstOptionsTab.splitter2.addWidget(self.fcstOptionsTab.dualTreeView)\n\n # Add a vertical splitter to split top widgets from plots\n self.fcstOptionsTab.splitter3 = QtWidgets.QSplitter(QtCore.Qt.Vertical)\n self.fcstOptionsTab.plotsPane = plotsPane()\n self.fcstOptionsTab.splitter3.addWidget(self.fcstOptionsTab.splitter2)\n self.fcstOptionsTab.splitter3.addWidget(self.fcstOptionsTab.plotsPane)\n\n # Lay out the tab\n self.fcstOptionsTab.layout.addWidget(self.fcstOptionsTab.splitter3)\n\n self.fcstOptionsTab.setLayout(self.fcstOptionsTab.layout)\n self.tabWidget.addTab(self.fcstOptionsTab, \"Forecast Options\")\n\n #///////////// Set up the Regressions Tab /////////////////////////////////\n #// The Regressions tab is divided into 2 main sections with 1 subsection.\n #// The Plot and forecast information section shows the output of the \n #// regression models (both a plot and an equation), and the regression\n #// selection pane allows the user to choose regression types to run.\n\n # Set up the layou of the tab\n self.regressionTab.layout = QtWidgets.QHBoxLayout()\n self.regressionTab.layout2 = QtWidgets.QVBoxLayout()\n\n # Add a vertical splitter to split the plots from the options pane\n self.regressionTab.splitter = QtWidgets.QSplitter(QtCore.Qt.Horizontal)\n self.regressionTab.plots = PlotCanvas_3Plot(self)\n widget = QtWidgets.QWidget()\n layout = QtWidgets.QHBoxLayout()\n splitter = QtWidgets.QSplitter(QtCore.Qt.Vertical)\n splitter.addWidget(self.regressionTab.plots)\n widget1 = QtWidgets.QWidget()\n layout1 = QtWidgets.QHBoxLayout()\n self.regressionTab.toggleButton = toggleButton(\" Toggle Cross-Validation \")\n layout1.addWidget(self.regressionTab.toggleButton)\n layout1.setAlignment(QtCore.Qt.AlignLeft)\n widget1.setLayout(layout1)\n splitter.addWidget(widget1)\n self.regressionTab.equationBox = QtWidgets.QPlainTextEdit()\n self.regressionTab.equationBox.setReadOnly(True)\n splitter.addWidget(self.regressionTab.equationBox)\n layout.addWidget(splitter)\n widget.setLayout(layout)\n \n self.regressionTab.regrSelectPane = RegressionSelectionPane(self)\n self.regressionTab.regrSelectPane.setMinimumWidth(430)\n self.regressionTab.splitter.addWidget(widget)\n self.regressionTab.splitter.addWidget(self.regressionTab.regrSelectPane)\n\n # Lay out the tab\n self.regressionTab.layout.addWidget(self.regressionTab.splitter)\n self.regressionTab.setLayout(self.regressionTab.layout)\n self.tabWidget.addTab(self.regressionTab, \"Regression\")\n\n #///////////// Set up the Density Estimation Tab /////////////////////////////////\n #// The Density Estimation Tab allows users to visualize the distribution of forecasts \n #// from a particular month or from multiple months. \n\n # Set the layout for the tab\n self.densityAnalysisTab.layout = QtWidgets.QHBoxLayout()\n\n # Add a vertical spliiter to divide the options pane from the plots\n self.densityAnalysisTab.splitter = QtWidgets.QSplitter()\n self.densityAnalysisTab.densityPane = DensityPane(self)\n widget = QtWidgets.QWidget()\n layout = QtWidgets.QVBoxLayout()\n self.densityAnalysisTab.plots = PlotCanvas_2Plot(self)\n layout.addWidget(self.densityAnalysisTab.plots )\n hlayout = QtWidgets.QHBoxLayout()\n label = QtWidgets.QLabel(\"10%:\")\n self.densityAnalysisTab.pct10Edit = QtWidgets.QLineEdit()\n hlayout.addWidget(label)\n hlayout.addWidget(self.densityAnalysisTab.pct10Edit)\n label = QtWidgets.QLabel(\"30%:\")\n self.densityAnalysisTab.pct30Edit = QtWidgets.QLineEdit()\n hlayout.addWidget(label)\n hlayout.addWidget(self.densityAnalysisTab.pct30Edit)\n label = QtWidgets.QLabel(\"50%:\")\n self.densityAnalysisTab.pct50Edit = QtWidgets.QLineEdit()\n hlayout.addWidget(label)\n hlayout.addWidget(self.densityAnalysisTab.pct50Edit)\n label = QtWidgets.QLabel(\"70%:\")\n self.densityAnalysisTab.pct70Edit = QtWidgets.QLineEdit()\n hlayout.addWidget(label)\n hlayout.addWidget(self.densityAnalysisTab.pct70Edit)\n label = QtWidgets.QLabel(\"90%:\")\n self.densityAnalysisTab.pct90Edit = QtWidgets.QLineEdit()\n hlayout.addWidget(label)\n hlayout.addWidget(self.densityAnalysisTab.pct90Edit)\n layout.addLayout(hlayout)\n widget.setLayout(layout)\n self.densityAnalysisTab.splitter.addWidget(self.densityAnalysisTab.densityPane)\n self.densityAnalysisTab.splitter.addWidget(widget)\n\n # Lay out the tab\n self.densityAnalysisTab.layout.addWidget(self.densityAnalysisTab.splitter)\n self.densityAnalysisTab.setLayout(self.densityAnalysisTab.layout)\n self.tabWidget.addTab(self.densityAnalysisTab, \"Density Analysis\")\n\n #//////////// Finish GUI Layout ////////////////////////////////////////\n MainWindow.setCentralWidget(self.tabWidget)\n\n \n def onNewData(self, data):\n print(data)","sub_path":"Resources/GUI/PyForecast_GUI.py","file_name":"PyForecast_GUI.py","file_ext":"py","file_size_in_byte":106228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"617064316","text":"#Dog class is inherit from wolf class\nclass Wolf:\n price=5000\n def __init__(self,name,color):\n self.name=name\n self.color=color\n def bark(self):\n print(\"Grrr......\")\n\nclass Dog(Wolf):\n def bark1(self):\n print(\"woof\")\n\n\nif __name__==\"__main__\":\n pet1=Dog(\"tommy\",\"brown\")\n pet1.bark()\n pet1.bark1()\n print(pet1.name,\"is of color\",pet1.color)\n","sub_path":"object oriented/inheritance_002.py","file_name":"inheritance_002.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"587392168","text":"from first_follow import compute_firsts, compute_follows\n\n\ndef metodo_predictivo_no_recursivo(G, M):\n parser = deprecated_metodo_predictivo_no_recursivo(G, M)\n\n def updated(tokens):\n return parser([t.token_type for t in tokens])\n\n return updated\n\n\ndef deprecated_metodo_predictivo_no_recursivo(G, M=None, firsts=None, follows=None):\n if M is None:\n if firsts is None:\n firsts = compute_firsts(G)\n if follows is None:\n follows = compute_follows(G, firsts)\n M = build_parsing_table(G, firsts, follows)\n\n def parser(w):\n stack = [G.startSymbol]\n cursor = 0\n output = []\n while len(stack) != 0:\n top = stack.pop()\n a = w[cursor]\n if top.IsTerminal:\n if a == top:\n cursor += 1\n else:\n raise Exception(\"Parsing error\")\n elif top.IsNonTerminal:\n production = M[top, a]\n for i in reversed(production[0].Right):\n stack.append(i)\n output.append(production[0])\n return output\n\n return parser\n\n\ndef build_parsing_table(G, firsts, follows):\n M = {}\n\n for production in G.Productions:\n X = production.Left\n alpha = production.Right\n\n for terminal in G.terminals:\n if terminal in firsts[alpha].set:\n M[X, terminal] = [production]\n if firsts[alpha].contains_epsilon and terminal in follows[X]:\n M[X, terminal] = [production]\n\n if firsts[alpha].contains_epsilon and G.EOF in follows[X]:\n M[X, G.EOF] = [production]\n\n return M\n","sub_path":"ll1_parser.py","file_name":"ll1_parser.py","file_ext":"py","file_size_in_byte":1688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"473855647","text":"class Audioclip:\n\n def __init__ (self, clip_type, error_code, id, name, priority, status, player_id, stream_url, mySonos):\n self.app_id = mySonos.app_id\n self.clip_type = clip_type\n self.error_code = error_code\n self.id = id\n self.name = name\n self.priority = priority\n self.status = status\n self.player_id = player_id\n self.stream_url = stream_url\n self.mySonos = mySonos\n\n def load_audioclip (self, volume=-1):\n body = {\"appId\": self.app_id, \"name\": self.name, \"clipType\": self.clip_type}\n if volume != -1:\n body['volume'] = 30\n if self.stream_url != None:\n body['streamUrl'] = self.stream_url\n self.mySonos._post_request_to_sonos('/players/' + self.player_id + '/audioClip', body)\n\n def cancel_audioclip (self):\n self.mySonos._delete_request_to_sonos('/players/' + self.player_id + '/audioClip/' + self.id)\n","sub_path":"sonosrestapi/audioclip.py","file_name":"audioclip.py","file_ext":"py","file_size_in_byte":946,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"22068689","text":"'''\nBuild a single joint dynamic model, 2 different Festos, negative feedback based \non pressure/position curves. This is a numerical model where everything happens \nnicely.\n\nM(\\theta) \\ddot{\\theta} + C(\\theta, \\dot{\\theta}) \\dot{\\theta} + N(\\theta) \\theta\n= Torque\n\nMass Model:\nJoint is hung vertically for now\nM: A point mass at a distance from the joint (rotational inertia)\nC: A small damping factor is assumed\nN: gravity on the point mass at a distance, weight of robot\n\nNotes:\n- Pushing static stiffness too high causes clipping for the primary actuator, so less \n net torque is achieved and decreased line following is achieved\n- Dynamic gains that are too high cause sawtooth oscillation and instability\n- Dynamic gains that are too low cause lagging trajectory execution\n'''\nimport sys\nprint('--- %s ---' % (sys.argv[0],))\n\nimport datetime\nimport math\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom math import pi\nfrom numpy import arctan, sqrt, floor, ceil\nfrom functools import partial\n\nEXT_PRESSURE = 500\nFLX_PRESSURE = 550\n\nclass Simulator(object):\n def __init__(self, bang_bang=True, limit_pressure=True, **kwargs):\n '''\n Set defaults, and override extras with kwargs\n '''\n self.bang_bang = bang_bang\n self.limit_pressure = limit_pressure\n ### Simulation Parameters and Constants ###\n self.MAX_AMPLITUDE = math.pi / 16\n\n self.LINK_LENGTH = 0.25 # meters\n self.LINK_MASS = 0.25 # kg\n self.ROBOT_MASS = 0.6 # kg\n\n self.JOINT_LIMIT_MAX = pi / 4\n self.JOINT_LIMIT_MIN = -pi / 4\n\n self.TORQUE_MAX = 2.5\n self.TORQUE_MIN = 0.1\n\n self.PRESSURE_MAX = 620\n self.PRESSURE_MIN = 0\n\n self.PRESSURE_RATE_MAX = 500 # 200 kPa per sec works\n\n self.PRESSURE_RESOLUTION = 17.0 # hysterisis gap, # 17 works\n\n self.TIME_RESOLUTION = 0.001\n self.TIME_START = 0\n self.TIME_END = 2.0\n\n self.CONTROL_RATE = 100\n\n ## Actuator Model Parameters ##\n\n self.a0 = 254.3 # kpa\n self.a1 = 192.0 # kpa\n self.a2 = 2.0625\n self.a3 = -0.461\n self.a4 = -0.331 # 1 / Nm\n self.a5 = 1.230\n self.a6 = 15.6 # kpa\n\n ## Mutual Actuator Parameters ##\n\n self.l_rest = .189 # m\n self.l_620 = round(-((.17 * self.l_rest) - self.l_rest), 3)\n self.k_max = 0.17\n self.l_max = self.l_rest\n self.l_min = self.l_620\n\n self.d = 0.005 # m\n self.offset = 0.015 # m\n self.l1 = round(sqrt(self.d**2 + self.offset**2), 3)\n self.l0 = floor((self.l_max - self.l1) * 1000.0) / 1000.0\n\n ## Specific Actuator Parameters ##\n # Actuator L is the negative (flexion) actuator\n # Actuator R is the positive (extension) actuator\n # /////////////\n # l | r\n # l | r\n # l | r\n # l(o) r\n # x>\\= 619.5:\n base /= 2\n step /= 2\n if step < 0.01:\n step = 0.01\n elif ext_p_1 < 0.5:\n base += 0.1\n if step < 0.01:\n step = 0.01\n else:\n raise ValueError()\n ext_p_0 = self.ext_torque_to_pressure(base - step, state)\n ext_p_1 = self.ext_torque_to_pressure(base, state)\n\n condition = (ext_p_1 == ext_p_0 or\n (not 0.5 <= ext_p_1 <= 619.5) or\n (not 0.5 <= ext_p_0 <= 619.5))\n \n if condition and i == 9:\n print('Ps_2_T(', extp, flxp, state, ')')\n print('failed to fix the problem.')\n input(('not fixed', base, step, 'got', ext_p_1, ext_p_0))\n if not condition:\n print(('fixed', base, step, 'got', ext_p_1, ext_p_0))\n\n if ext_p_1 == 0 and ext_p_0 == 0:\n dTdeP = 0\n else:\n dTdeP = (step) / (ext_p_1 - ext_p_0)\n flx_p_0 = self.ext_torque_to_pressure(base - step, state)\n flx_p_1 = self.ext_torque_to_pressure(base, state)\n if flx_p_1 == 0 and flx_p_0 == 0:\n dTdfP = 0\n else:\n dTdfP = (step) / (flx_p_1 - flx_p_0)\n\n deP = extp - ext_p_1\n deT = dTdeP * deP\n dfP = flxp - flx_p_1\n dfT = dTdfP * dfP\n \n eT0 = 0.5\n eT1 = eT0 + deT\n fT0 = 0.5\n fT1 = fT0 + dfT\n\n return eT1 - fT1\n\n def mass_model(self, theta):\n '''\n Mass Model\n ma -> I theta_ddot\n\n Implements: mass at a rigid point half of link length\n Complications:\n - [ ] Uniform mass distribution on the link\n '''\n M = self.LINK_MASS\n R = self.LINK_LENGTH / 2\n return M * (R**2)\n\n def vel_effects(self, theta, theta_dot):\n '''\n Damping/Velocity based effects on the system\n Complications:\n - [ ] Small damping from flexing of actuators based on change in length\n - [ ] Estimated Hysterisis effect of filling or empty actuators applying\n a torque opposite the motion\n '''\n return 0.1 * theta_dot\n\n def conservative_effects(self, theta):\n '''\n Conservative Forces on the system, converted to torques (for now)\n Complications:\n - [x] Gravity from link mass\n /////////////\n (o)\n .\\\n . \\\n . m\n . :\\\n . : \\\n . v\n - [x] Gravity from robot/Suppoting Normal force from ground at end\n /////////////\n (o)\n .\\\n . \\ ^\n . \\ :\n . \\:\n . 0\n . \n '''\n g = 9.81\n M_l = self.LINK_MASS\n F_g = M_l * g\n R_g = self.LINK_LENGTH / 2\n\n # this assumes that the robot mass is solely balanced on top of the joint\n M_r = self.ROBOT_MASS\n F_r = M_r * g\n R_n = self.LINK_LENGTH\n\n try:\n link_gravity = F_g * R_g * math.sin(theta)\n except ValueError:\n print(type(theta))\n print(theta)\n raise\n normal_force = - F_r * R_n * math.sin(theta)\n # TODO(buckbaskin): remove this\n normal_force = 0\n\n return link_gravity + normal_force\n\n def pressure_model(self, des_pressure, current_pressure, time_step):\n '''\n For now, set the pressure to the desired pressure\n In the future, use airflow model to restrict maximum pressure change\n Complications:\n - [x] Set pressure to desired pressure\n - [x] Bang-bang control\n - [x] Set maximum pressure change per time step\n - [ ] Develop airflow model to more accurately limit pressure changes \n (pressure differential, airflow limits)\n '''\n # As implemented, controller either doesn't change if close or moves to the\n # near side of the bang-bang window (close enough). This ignores details \n # of filling rate and pressure differential from the air supply to the\n # actuator.\n if not self.bang_bang and not self.limit_pressure:\n return des_pressure\n if not self.bang_bang:\n return np.clip(des_pressure,\n current_pressure - self.PRESSURE_RATE_MAX,\n current_pressure + self.PRESSURE_RATE_MAX)\n if (float(abs(des_pressure - current_pressure)) < \n float(self.PRESSURE_RESOLUTION)):\n return current_pressure\n elif des_pressure > current_pressure:\n if not self.limit_pressure:\n return des_pressure - self.PRESSURE_RESOLUTION\n return np.min([des_pressure - self.PRESSURE_RESOLUTION,\n current_pressure + self.PRESSURE_RATE_MAX * time_step])\n else: # des_pressure < current_pressure\n if not self.limit_pressure:\n return des_pressure + self.PRESSURE_RESOLUTION\n return np.max([des_pressure + self.PRESSURE_RESOLUTION,\n current_pressure - self.PRESSURE_RATE_MAX * time_step])\n\n def motion_evolution(self, state, time_step, control, control_stiffness):\n '''\n M * ddot theta + C * dot theta + N * theta = torque\n ddot theta = 1 / M * (torque - C * dot theta - N) \n '''\n ext_pres = state[3]\n flx_pres = state[4]\n\n des_ext_pres, des_flx_pres, intended_torque = control\n\n ext_pres = self.pressure_model(des_ext_pres, ext_pres, time_step)\n flx_pres = self.pressure_model(des_flx_pres, flx_pres, time_step)\n\n Torque_net = self.pressures_to_torque(extp=ext_pres, flxp=flx_pres,\n state=state, actual_torque=None) # intended_torque)\n\n M = self.mass_model(state[0])\n C = self.vel_effects(state[0], state[1])\n N = self.conservative_effects(state[0])\n\n accel = (Torque_net - C - N) / M\n \n # accelration happens over the time step\n start_vel = state[1]\n end_vel = state[1] + accel * time_step\n avg_vel = state[1] + accel * time_step / 2\n\n start_theta = state[0]\n end_theta = state[0] + avg_vel * time_step\n\n if end_theta > self.JOINT_LIMIT_MAX:\n end_theta = self.JOINT_LIMIT_MAX\n end_vel = 0\n accel = 0\n if end_theta < self.JOINT_LIMIT_MIN:\n end_theta = self.JOINT_LIMIT_MIN\n end_vel = 0\n accel = 0\n\n state = np.array([end_theta, end_vel, accel, ext_pres, flx_pres]).flatten()\n\n return state\n\n def timeline(self):\n return np.arange(self.TIME_START, self.TIME_END, self.TIME_RESOLUTION)\n\n def simulate(self, controller, state_start, desired_state):\n time = self.timeline()\n control_resolution = 1.0 / self.CONTROL_RATE\n steps_to_next_ctrl = int(np.ceil(control_resolution / self.TIME_RESOLUTION))\n last_control_time = -9001\n\n full_state = np.ones((time.shape[0], state_start.shape[0]))\n full_state[0,:] = state_start\n for i in range(full_state.shape[0] - 1):\n if i % 10 == 0:\n print('...calculating step % 6d / %d' % (i, full_state.shape[0] - 1,))\n this_time = time[i]\n control_should_update = (this_time - last_control_time) > control_resolution\n if control_should_update:\n last_control_time = this_time\n self.last_control = controller.control(\n state=full_state[i,:],\n desired_states=desired_state[i:i+steps_to_next_ctrl,:],\n times=time[i:i+steps_to_next_ctrl])\n new_state = self.motion_evolution(\n state=full_state[i,:],\n time_step=self.TIME_RESOLUTION,\n control=self.last_control,\n control_stiffness=controller.antagonistic_stiffness)\n full_state[i+1,:] = new_state\n\n return full_state\n\nclass Controller(object):\n def __init__(self, control_rate, stiffness, **kwargs):\n # TODO(buckbaskin): this assumes perfect matching parameters for motion model\n self.control_rate = control_rate\n self.sim = Simulator()\n ## \"Static\" Stiffness ##\n # Increasing the stiffness increases the range around 0 where the complete\n # desired torque works. On the other hand, decreasing the stiffness increases\n # the range of total torques that are output before the desired torque\n # saturates.\n self.antagonistic_stiffness = stiffness\n\n ## \"Dynamic\" Stiffness ##\n # Together K_p, K_v constitute \"Dynamic\" Stiffness\n # Not quite sure how to align static holding mode with dyanmic mode right now.\n self.K_p = 8\n self.K_v = 1\n\n for arg, val in kwargs.items():\n if hasattr(self, arg):\n setattr(self, arg, val)\n\n def control(self, state, desired_states, times):\n return EXT_PRESSURE, FLX_PRESSURE, 1\n\nif __name__ == '__main__':\n\n ### Set up time ###\n S = Simulator(bang_bang=False, limit_pressure=False)\n\n pos = np.arange(S.JOINT_LIMIT_MIN, S.JOINT_LIMIT_MAX, 0.01)\n for flxp in [0, 100, 200, 300, 400]:\n torques = np.zeros(pos.shape)\n for index, position in enumerate(pos):\n state = np.array([position, 0, 0])\n extp = 500\n torque = S.pressures_to_torque(extp, flxp, state)\n torques[index] = torque\n plt.plot(pos, torques, label='%d kPa' % (flxp,))\n plt.title('Position v Torque, Ext. Pressure at %d kPa' % (extp,))\n plt.ylabel('Torque (Nm)')\n plt.xlabel('Position (rad)')\n plt.legend()\n plt.tight_layout()\n\n plt.savefig('Pos_v_Torque2.png')\n plt.show()\n 1/0\n\n time = S.timeline()\n\n MAX_AMPLITUDE = S.MAX_AMPLITUDE\n\n state_start = np.array([\n S.JOINT_LIMIT_MAX, # position\n 0, # vel\n 0, # accel\n 0, # ext pressure\n 0,]) # flx pressure\n\n ### Set up desired state ###\n # the desired state velocity and acceleration are positive here\n desired_state = np.zeros((time.shape[0], state_start.shape[0],))\n\n # Try following a sin curve\n period = 10\n adjust = (pi * 2) / period \n desired_state[:, 0] = MAX_AMPLITUDE * np.sin(time * adjust)\n desired_state[:, 1] = (MAX_AMPLITUDE * adjust) * np.cos(time * adjust)\n\n plot_position = True\n\n if plot_position:\n fig = plt.figure()\n ax_pos = fig.add_subplot(1, 1, 1)\n ax_pos.set_title('Position (e: %.1f, f: %.1f)' % (EXT_PRESSURE, FLX_PRESSURE,))\n ax_pos.set_ylabel('Position (% of circle)')\n ax_pos.set_xlabel('Time (sec)')\n # ax_pos.plot(time, desired_state[:,0])\n\n print('calculating...')\n for stiffness in [0.1,]:\n # print('stiffness: %.2f' % (stiffness,))\n C = Controller(control_rate=S.CONTROL_RATE, stiffness=stiffness)\n \n full_state = S.simulate(controller=C, state_start=state_start, desired_state=desired_state)\n\n if plot_position:\n ax_pos.plot(time, full_state[:,0])\n if plot_position:\n print('show for the dough')\n plt.show()\n print('all done')\n","sub_path":"stability/constant_pressure.py","file_name":"constant_pressure.py","file_ext":"py","file_size_in_byte":16965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"536808091","text":"from connaisseur.validators.notaryv1.notaryv1_validator import NotaryV1Validator\nfrom connaisseur.validators.notaryv2.notaryv2_validator import NotaryV2Validator\nfrom connaisseur.validators.cosign.cosign_validator import CosignValidator\nfrom connaisseur.validators.static.static_validator import StaticValidator\nfrom connaisseur.exceptions import NoSuchClassError\n\n\nclass Validator:\n class_map = {\n \"notaryv1\": NotaryV1Validator,\n \"notaryv2\": NotaryV2Validator,\n \"cosign\": CosignValidator,\n \"static\": StaticValidator,\n }\n\n def __new__(cls, type: str, **kwargs):\n try:\n return cls.class_map[type](**kwargs)\n except KeyError:\n msg = f\"{type} is not a supported validator.\"\n raise NoSuchClassError(message=msg)\n","sub_path":"connaisseur/validators/validator.py","file_name":"validator.py","file_ext":"py","file_size_in_byte":792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"308658250","text":"import cv2\nimport numpy as np\n\n# Webカメラから入力を開始\n# 撮影のためのobject生成\ncap = cv2.VideoCapture(0)\nwhile True:\n # カメラの画像を読み込む　_:T/f読み込めたか、frame:画像\n _, frame = cap.read()\n # 画像を縮小表示する\n frame = cv2.resize(frame, (500,300))\n # hsv変換\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV_FULL)\n # 画像numpy配列[行,列,色(HSV)]\n h = hsv[:,:,0] # 色相\n s = hsv[:,:,1] # 彩度\n v = hsv[:,:,2] # 明度\n # 赤色っぽい色のみ抽出\n # uint8だから０整数　# hの型に合わせる\n # 要素０の配列生成 numpy.zeros(shape, dtype = float, order = ‘C’)\n img = np.zeros(h.shape, dtype = np.uint8)\n # 赤っぽい色に白(255)代入\n img[((h < 50) | (h > 200)) & (s > 100)] = 255\n # ウィンドウに画像を出力する\n # imshow('画像ファイル名',画像変数名)\n cv2.imshow('Red Camera', img)\n # ESCかEnterキーが押されたらループ抜ける\n # cv2.waitKey(t):tミリ秒キーボード入力を受け付ける\n k = cv2.waitKey(1) #1msec確認\n # 27:esc,13:enter\n if k == 27 or k == 13: break\n\ncap.release() #カメラ解放\ncv2.destoryAllWindows() #ウィンドウ破棄\n","sub_path":"ch3.5/red_camera_hsv.py","file_name":"red_camera_hsv.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"459154861","text":"from persona import Persona\nfrom archiviatore import Archiviatore\nimport random\n\nclass ListaPersone(object):\n\t\"\"\"gestisce una lista di oggetti 'persona'\"\"\"\n\n\tdef __init__(self):\n\t\tself.lista = []\n\t\tself.npersone = 0\n\t\tself.archivio = Archiviatore()\n\n\tdef __str__(self):\n\t\ttxt = \"\"\n\t\tfor p in self.lista:\n\t\t\ttxt += str(p) + \"\\n\"\n\t\treturn txt\n\n\tdef Nuovo(self, nome, cognome):\n\t\tp = Persona(nome, cognome)\n\t\tself.lista.append(p)\n\t\tself.npersone += 1\n\n\tdef Aggiungi(self):\n\t\tprint(\"\\nAggiungi nuove persone (scrivi x per finire)\\n\")\n\t\tcontinua = True\n\t\twhile continua:\n\t\t\tnome = input(\"Nome: \")\n\t\t\tif nome == \"x\":\n\t\t\t\tcontinua = False\n\t\t\telse:\n\t\t\t\tcognome = input(\"Cognome: \")\n\t\t\t\tif cognome == \"x\":\n\t\t\t\t\tcontinua = False\n\t\t\t\telse:\n\t\t\t\t\tself.Nuovo(nome, cognome)\n\t\t\t\t\tprint(\"\")\n\t\t\t\n\tdef Rimuovi(self, num):\n\t\tself.lista.pop(num)\n\t\tself.npersone -= 1\n\n\tdef Interroga(self, materia, quanti):\n\t\tinterrogabili = self.archivio.Carica(materia, True)\n\t\tif len(interrogabili) == 0:\n\t\t\tinterrogabili += self.lista\n\t\trimasti = len(interrogabili)\n\t\tif rimasti < quanti:\n\t\t\tprint(\"Sono rimaste solo {0} persone da interrogare\\n\".format(rimasti))\n\t\t\tquanti = rimasti\n\t\tfor i in range(quanti):\n\t\t\tinterrogato = random.choice(interrogabili)\n\t\t\tprint(\"Interrogato: \" + interrogato.getNomeCognome() + \"\\n\")\n\t\t\tinterrogabili.remove(interrogato)\n\t\tself.archivio.SalvaConNome(interrogabili, materia)\n\n\tdef Carica(self, file):\n\t\tself.lista = self.archivio.Carica(file)\n\t\tself.npersone = len(self.lista)\n\n\tdef Salva(self):\n\t\tself.archivio.Salva(self.lista)\n\t\t\n\tdef Ordina(self):\n\t\tself.lista = sorted(self.lista)\n\n\tdef getListaDaStampare(self):\n\t\tlista = []\n\t\tlista.append([\"Nome\", \"Cognome\"])\n\t\tfor elem in self.lista:\n\t\t\tlista.append([elem.nome, elem.cognome])\n\t\treturn lista","sub_path":"lista_persone.py","file_name":"lista_persone.py","file_ext":"py","file_size_in_byte":1752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"6932948","text":"import random\nfrom functools import reduce\nfrom itertools import chain\nfrom math import ceil\nfrom operator import add\nfrom typing import Any, Iterable, List, Optional, TypeVar\n\nfrom lhotse.audio import Recording, RecordingSet\nfrom lhotse.cut import Cut, CutSet, MixedCut\nfrom lhotse.features import Features, FeatureSet\nfrom lhotse.supervision import SupervisionSegment, SupervisionSet\nfrom lhotse.utils import Pathlike, load_yaml\n\nManifestItem = TypeVar('ManifestItem', Recording, SupervisionSegment, Features, Cut, MixedCut)\nManifest = TypeVar('Manifest', RecordingSet, SupervisionSet, FeatureSet, CutSet)\n\n\ndef split(manifest: Manifest, num_splits: int, randomize: bool = False) -> List[Manifest]:\n \"\"\"Split a manifest into `num_splits` equal parts. The element order can be randomized.\"\"\"\n num_items = len(manifest)\n if num_splits > num_items:\n raise ValueError(f\"Cannot split manifest into more chunks ({num_splits}) than its number of items {num_items}\")\n chunk_size = int(ceil(num_items / num_splits))\n split_indices = [(i * chunk_size, min(num_items, (i + 1) * chunk_size)) for i in range(num_splits)]\n\n def maybe_randomize(items: Iterable[Any]) -> List[Any]:\n items = list(items)\n if randomize:\n random.shuffle(items)\n return items\n\n if isinstance(manifest, RecordingSet):\n contents = maybe_randomize(manifest.recordings.items())\n return [RecordingSet(recordings=dict(contents[begin: end])) for begin, end in split_indices]\n\n if isinstance(manifest, SupervisionSet):\n contents = maybe_randomize(manifest.segments.items())\n return [SupervisionSet(segments=dict(contents[begin: end])) for begin, end in split_indices]\n\n if isinstance(manifest, FeatureSet):\n contents = maybe_randomize(manifest.features)\n return [FeatureSet(features=contents[begin: end]) for begin, end in split_indices]\n\n if isinstance(manifest, CutSet):\n contents = maybe_randomize(manifest.cuts.items())\n return [CutSet(cuts=dict(contents[begin: end])) for begin, end in split_indices]\n\n raise ValueError(f\"Unknown type of manifest: {type(manifest)}\")\n\n\ndef combine(*manifests: Manifest) -> Manifest:\n \"\"\"Combine multiple manifests of the same type into one.\"\"\"\n return reduce(add, manifests)\n\n\ndef to_manifest(items: Iterable[ManifestItem]) -> Optional[Manifest]:\n \"\"\"\n Take an iterable of data types in Lhotse such as Recording, SupervisonSegment or Cut, and create the manifest of the\n corresponding type. When the iterable is empty, returns None.\n \"\"\"\n items = iter(items)\n try:\n first_item = next(items)\n except StopIteration:\n return None\n items = chain([first_item], items)\n\n if isinstance(first_item, Recording):\n return RecordingSet.from_recordings(items)\n if isinstance(first_item, SupervisionSegment):\n return SupervisionSet.from_segments(items)\n if isinstance(first_item, (Cut, MixedCut)):\n return CutSet.from_cuts(items)\n if isinstance(first_item, Features):\n raise ValueError(\"FeatureSet generic construction from iterable is not possible, as the config information \"\n \"would have been lost. Call FeatureSet.from_features() directly instead.\")\n\n raise ValueError(f\"Unknown type of manifest item: {first_item}\")\n\n\ndef load_manifest(path: Pathlike) -> Manifest:\n \"\"\"Generic utility for reading an arbitrary manifest.\"\"\"\n raw_data = load_yaml(path)\n data_set = None\n for manifest_type in [RecordingSet, SupervisionSet, FeatureSet, CutSet]:\n try:\n data_set = manifest_type.from_dicts(raw_data)\n except Exception:\n pass\n if data_set is None:\n raise ValueError(f'Unknown type of manifest: {path}')\n return data_set\n","sub_path":"lhotse/manipulation.py","file_name":"manipulation.py","file_ext":"py","file_size_in_byte":3800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"6492865","text":"# F:\\itsoftware\\Anaconda\r\n# -*- coding:utf-8 -*-\r\n# Author = TJL\r\n# date:2020/3/13\r\n\r\nimport jieba.analyse\r\nimport jieba\r\nimport os\r\n\r\n# 添加专有名词，增加分词力度\r\njieba.suggest_freq('中国社科院研究生院', True)\r\njieba.suggest_freq('德国ZF集团', True)\r\njieba.suggest_freq('技术换市场', True)\r\njieba.suggest_freq('中央企业', True)\r\njieba.suggest_freq('工作会议', True)\r\njieba.suggest_freq('国资委主任', True)\r\n\r\nraw_data_path = 'data/raw_data/'\r\ncut_data_path = 'data/train_corpus/'\r\nstop_word_path = 'data/stop_words.txt' #这里只是样例，可以替换自己的\r\n\r\n\r\ndef stopwordslist(filepath):\r\n stopwords = [line.strip() for line in open(filepath, 'r').readlines()]\r\n return stopwords\r\n\r\n\r\ndef cut_word(raw_data_path, cut_data_path,stop_word_path):\r\n stopwords = stopwordslist(stop_word_path)\r\n data_file_list = os.listdir(raw_data_path)\r\n corpus = []\r\n temp = 0\r\n for file in data_file_list:\r\n with open(raw_data_path + file, 'rb') as f:\r\n print('处理第{}个原始语料文件'.format(temp + 1))\r\n temp += 1\r\n lines = f.readlines()\r\n for line in lines:\r\n line=line.decode('utf-8').strip().replace(' ','')\r\n document_cut = jieba.cut(line, cut_all=False)\r\n document_cut=[word for word in document_cut if word not in stopwords and len(word.strip())>0]\r\n # print('/'.join(document_cut))\r\n result = ' '.join(document_cut)\r\n corpus.append(result)\r\n # print(result)\r\n with open(cut_data_path + 'corpus.txt', 'w', encoding='utf-8') as f:\r\n for line in corpus:\r\n f.write(line+'\\n') # 读取的方式和写入的方式要一致\r\n\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n cut_word(raw_data_path, cut_data_path,stop_word_path)\r\n","sub_path":"Gensim Word2Vec/generate_train_corpus.py","file_name":"generate_train_corpus.py","file_ext":"py","file_size_in_byte":1857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"368608137","text":"#!/usr/bin/python3\n\nimport sys\nimport copy\nfrom random import *\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtMultimedia import *\n\nclass PyQtGame(QWidget):\n\tdef __init__(self):\n\t\tsuper(PyQtGame,self).__init__()\n\t\tself.randomInit()\n\t\tself.colors = {\n\t\t\t0:QColor(0xCDC1B4),\n\t\t\t1:QColor(0x646464),\n\t\t\t2:QColor(0xFDFDAB),\n\t\t\t4:QColor(0xF7DB6E),\n\t\t\t8:QColor(0xFAC84B),\n\t\t\t16:QColor(0xFEB42D),\n\t\t\t32:QColor(0xFF9930),\n\t\t\t64:QColor(0xFC311A),\n\t\t\t128:QColor(0xFA3619),\n\t\t\t256:QColor(0xFB0D03),\n\t\t\t512:QColor(0xE30702),\n\t\t\t1024:QColor(0xB20700),\n\t\t\t2048:QColor(0x8C0606),\n\t\t\t4096:QColor(0x740402),\n\t\t\t8192:QColor(0x630700),\n\t\t\t16384:QColor(0x560303),\n\t\t\t32768:QColor(0x240101),\n\t\t\t65536:QColor(0x030002),\n\t\t\t131072:QColor(0x000000),\n\t\t}\n\t\tself.best = 0\n\t\tself.condVictoire = False\n\t\tself.initUI()\n\n# Définition de la fenêtre\n\tdef initUI(self):\n\t\tself.setFixedSize(350,400)\n\t\tself.center()\n\t\tself.setWindowTitle(\"2048\")\n\t\tself.show()\n\n\tdef center(self):\n\t\tqr = self.frameGeometry()\n\t\tcp = QDesktopWidget().availableGeometry().center()\n\t\tqr.moveCenter(cp)\n\t\tself.move(qr.topLeft())\n\n# Initialisation de la grille\n\tdef randomInit(self) :\n\t\tself.blocs = [[0]*4 for i in range(4)]\n\t\tfor m in range(2) :\n\t\t\tself.createColor(10)\n\t\tself.score = 0\n\t\tself.overed = 0\n\t\tself.availableBlocs = range(16)\n\n# Attribution des touches\n\tdef keyPressEvent(self,e) :\n\t\tif e.key() == Qt.Key_Escape :\n\t\t\tself.resetGame()\n\t\telif e.key() == Qt.Key_Up :\n\t\t\tself.up()\n\t\telif e.key() == Qt.Key_Down :\n\t\t\tself.down()\n\t\telif e.key() == Qt.Key_Left :\n\t\t\tself.left()\n\t\telif e.key() == Qt.Key_Right :\n\t\t\tself.right()\n\t\tself.movesAvailable()\n\n\tdef up(self) :\n\t\tmoved = False\n\t\tfor i in range(1,4):\n\t\t\tfor j in range(0,4):\n\t\t\t\tif self.blocs[i][j] != 0 :\n\t\t\t\t\tk = i\n\t\t\t\t\twhile k-1 >= 0 and self.blocs[k-1][j] == 0 :\n\t\t\t\t\t\tk -= 1\n\t\t\t\t\tif k-1 >= 0 and self.blocs[k-1][j] == self.blocs[i][j] :\n\t\t\t\t\t\tself.score += self.blocs[i][j] *2\n\t\t\t\t\t\tself.blocs[k-1][j] *= 2\n\t\t\t\t\t\tself.blocs[i][j] = 0\n\t\t\t\t\t\tmoved = True\n\t\t\t\t\telif k < i :\n\t\t\t\t\t\tself.blocs[k][j] = self.blocs[i][j]\n\t\t\t\t\t\tself.blocs[i][j] = 0\n\t\t\t\t\t\tmoved = True\n\t\tif moved :\n\t\t\tif not self.condVictoire :\n\t\t\t\tQSound.play(\"sons/deplacement1.wav\")\n\t\t\tself.updateBlocs()\n\n\tdef down(self) :\n\t\tmoved = False\n\t\tfor i in range(2,-1,-1):\n\t\t\tfor j in range(0,4):\n\t\t\t\tif self.blocs[i][j] != 0 :\n\t\t\t\t\tk = i\n\t\t\t\t\twhile k+1 < 4 and self.blocs[k+1][j] == 0 :\n\t\t\t\t\t\tk += 1\n\t\t\t\t\tif k+1 < 4 and self.blocs[k+1][j] == self.blocs[i][j] :\n\t\t\t\t\t\tself.score += self.blocs[i][j] *2\n\t\t\t\t\t\tself.blocs[k+1][j] *= 2\n\t\t\t\t\t\tself.blocs[i][j] = 0\n\t\t\t\t\t\tmoved = True\n\t\t\t\t\telif k > i :\n\t\t\t\t\t\tself.blocs[k][j] = self.blocs[i][j]\n\t\t\t\t\t\tself.blocs[i][j] = 0\n\t\t\t\t\t\tmoved = True\n\t\tif moved :\n\t\t\tif not self.condVictoire :\n\t\t\t\tQSound.play(\"sons/deplacement1.wav\")\n\t\t\tself.updateBlocs()\n\n\tdef left(self) :\n\t\tmoved = False\n\t\tfor i in range(0,4) :\n\t\t\tfor j in range(0,4) :\n\t\t\t\tif self.blocs[i][j] != 0 :\n\t\t\t\t\tk = j\n\t\t\t\t\twhile k-1 >= 0 and self.blocs[i][k-1] == 0 :\n\t\t\t\t\t\tk -= 1\n\t\t\t\t\tif k-1 >= 0 and self.blocs[i][k-1] == self.blocs[i][j] :\n\t\t\t\t\t\tself.score += self.blocs[i][j] *2\n\t\t\t\t\t\tself.blocs[i][k-1] *= 2\n\t\t\t\t\t\tself.blocs[i][j] = 0\n\t\t\t\t\t\tmoved = True\n\t\t\t\t\telif k < j :\n\t\t\t\t\t\tself.blocs[i][k] = self.blocs[i][j]\n\t\t\t\t\t\tself.blocs[i][j] = 0\n\t\t\t\t\t\tmoved=True\n\t\tif moved :\n\t\t\tif not self.condVictoire :\n\t\t\t\tQSound.play(\"sons/deplacement2.wav\")\n\t\t\tself.updateBlocs()\n\n\tdef right(self) :\n\t\tmoved = False\n\t\tfor i in range(0,4) :\n\t\t\tfor j in range(2,-1,-1) :\n\t\t\t\tif self.blocs[i][j] != 0 :\n\t\t\t\t\tk = j\n\t\t\t\t\twhile k+1 < 4 and self.blocs[i][k+1] == 0 :\n\t\t\t\t\t\tk += 1\n\t\t\t\t\tif k+1 < 4 and self.blocs[i][k+1] == self.blocs[i][j] :\n\t\t\t\t\t\tself.score += self.blocs[i][j] *2\n\t\t\t\t\t\tself.blocs[i][k+1] *= 2\n\t\t\t\t\t\tself.blocs[i][j] = 0\n\t\t\t\t\t\tmoved = True\n\t\t\t\t\telif k > j :\n\t\t\t\t\t\tself.blocs[i][k] = self.blocs[i][j]\n\t\t\t\t\t\tself.blocs[i][j] = 0\n\t\t\t\t\t\tmoved = True\n\t\tif moved :\n\t\t\tif not self.condVictoire :\n\t\t\t\tQSound.play(\"sons/deplacement2.wav\")\n\t\t\tself.updateBlocs()\n\n# Mise à jour des blocs\n\tdef updateBlocs(self) :\n\t\tself.availableBlocs = []\n\t\tself.createColor(10)\n\t\tself.deleteLock(5)\n\t\tfor i in range(4) :\n\t\t\tfor j in range(4) :\n\t\t\t\tif self.blocs[i][j] ==0 :\n\t\t\t\t\tself.availableBlocs.append(i+j*4)\n\t\tself.createLock(10)\n\t\tself.update()\n\t\tself.win()\n\t\tself.best=max(self.score,self.best)\n\t\tif not self.movesAvailable():\n\t\t\tself.gameOver()\n\n# Vérification de possibilité de jeu\n\tdef movesAvailable(self):\n\t\tif len(self.availableBlocs) != 0 :\n\t\t\treturn True\n\t\tfor i in range(4) :\n\t\t\tfor j in range(4) :\n\t\t\t\tif i < 3 and self.blocs[i][j] == self.blocs[i+1][j] :\n\t\t\t\t\treturn True\n\t\t\t\tif j < 3 and self.blocs[i][j] == self.blocs[i][j+1] :\n\t\t\t\t\treturn True\t\n\t\treturn False\n\n# Victoire\n\tdef win(self) :\n\t\tfor i in range(4) :\n\t\t\tfor j in range(4) :\n\t\t\t\tif self.blocs[i][j] == 2048 :\n\t\t\t\t\tif not self.condVictoire :\n\t\t\t\t\t\tself.condVictoire = True\n\t\t\t\t\t\tQSound.play(\"sons/victoire.wav\")\n\t\t\t\t\t\tif QMessageBox.question(self,'Message',\"
    Congratulation !
    Would you continue ?
    \",QMessageBox.Yes|QMessageBox.No,QMessageBox.Yes)==QMessageBox.Yes:\n\t\t\t\t\t\t\tself.update()\n\t\t\t\t\t\t\tself.victoire = QSound(\"sons/apres2048.wav\")\n\t\t\t\t\t\t\tself.victoire.play()\n\t\t\t\t\t\t\tself.victoire.setLoops(QSound.Infinite)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tself.close()\n\t\t\t\t\t\treturn 1\n\t\treturn 0\n\n# Défaite\n\tdef gameOver(self) :\n\t\tif QMessageBox.question(self,'Message',\"
    Game Over
    Play again ?
    \",QMessageBox.Yes|QMessageBox.No,QMessageBox.Yes)==QMessageBox.Yes:\n\t\t\tself.resetGame()\n\t\telse:\n\t\t\tself.close()\n\n# Définition de la position d'un nouveau bloc\n\tdef createColor(self,num) :\n\t\twhile 1 :\n\t\t\ti = randint(0,3)\n\t\t\tj = randint(0,3)\n\t\t\tif self.blocs[i][j] == 0 :\n\t\t\t\tself.blocs[i][j] = self.randomColor(num)\n\t\t\t\tbreak\n\n# Apparition d'un nouveau bloc\n\tdef randomColor(self,taux) :\n\t\ti = randint(0,taux)\n\t\tif (i < taux) :\n\t\t\treturn 2\n\t\telse :\n\t\t\treturn 4\n\n# Définition de la position d'un bloc noir\n\tdef createLock(self,num):\n\t\texisteDeja = False\n\t\tfor i in range(4) :\n\t\t\tfor j in range(4) :\n\t\t\t\tif self.blocs[i][j] == 1 :\n\t\t\t\t\texisteDeja = True\n\t\t\t\t\tbreak\n\t\t\tif existeDeja == True :\n\t\t\t\tbreak\n\t\tif existeDeja == False :\n\n\t\t\ttest= []\n\t\t\tfor i in range(4) :\n\t\t\t\tfor j in range(4) :\n\t\t\t\t\tif self.blocs[i][j] ==0 :\n\t\t\t\t\t\ttest.append(i*4+j)\n\t\t\tif len(test)!=0:\n\t\t\t\ttmp = test[randint(0,len(test)-1)]\n\t\t\t\ti = tmp//4\n\t\t\t\tj = tmp%4\n\n\t\t\t\tself.blocs[i][j] = self.randomLock(num)\n\n\n# Apparition d'un bloc noir\n\tdef randomLock(self,taux) :\n\t\ti = randint(0,taux)\n\t\tif (i == taux) :\n\t\t\treturn 1\n\t\telse :\n\t\t\treturn 0\n\n# Définition de la position d'un bloc noir existant\n\tdef deleteLock(self,num) :\n\t\tfor i in range(4) :\n\t\t\tfor j in range(4) :\n\t\t\t\tif self.blocs[i][j] == 1 :\n\t\t\t\t\tself.blocs[i][j] = self.randomDelLock(num)\n\n# Disparition d'un bloc noir existant\n\tdef randomDelLock(self,taux) :\n\t\ti = randint(0,taux)\n\t\tif (i == taux) :\n\t\t\treturn 0\n\t\telse :\n\t\t\treturn 1\n\n# Position du pointeur lors du dernier clic\n\tdef mousePressEvent(self,e) :\n\t\tself.lastPoint=e.pos()\n\n# Bouton reset\n\tdef mouseReleaseEvent(self,e) :\n\t\tself.resetRect = QRect(240,15,80,60)\n\t\tif self.resetRect.contains(e.pos().x(),e.pos().y()) and self.resetRect.contains(self.lastPoint.x(),self.lastPoint.y()):\n\t\t\tif QMessageBox.question(self,'Message',\"
    Are you sure ?
    You can also press Escape to reset directly
    \",QMessageBox.Ok|QMessageBox.Cancel,QMessageBox.Cancel)==QMessageBox.Ok:\n\t\t\t\tself.resetGame()\n\n# Reset\n\tdef resetGame(self) :\n\t\tself.randomInit()\n\t\tself.update()\n\n# Affichage fixe\n\tdef paintEvent(self,e) :\n\t\tpainter = QPainter(self)\n\t\tpainter.setPen(Qt.NoPen)\n\t\tpainter.setBrush(QBrush(QColor(0xbbada0))) # Couleur fenêtre\n\t\tpainter.drawRect(self.rect())\n\t\tpainter.setBrush(QBrush(QColor(0x776e65))) # Couleur boutons score / reset\n\t\tpainter.drawRoundedRect(QRect(20,15,80,60),5,5)\n\t\tpainter.setFont(QFont(\"Century Gothic\",12))\n\t\tpainter.setPen(QColor(0xffffff)) # Couleur police texte \"score\"\n\t\tpainter.drawText(QRectF(QRect(20,20,80,60)),\"SCORE\",QTextOption(Qt.AlignHCenter))\n\t\tpainter.setFont(QFont(\"Century Gothic\",18))\n\t\tpainter.setPen(QColor(0xffffff)) # Couleur police nombre score\n\t\tpainter.drawText(QRectF(QRect(20,15,80,55)),str(self.score),QTextOption(Qt.AlignHCenter|Qt.AlignBottom))\n\t\tpainter.setPen(Qt.NoPen)\n\t\tpainter.drawRoundedRect(QRect(130,15,80,60),5,5)\n\t\tpainter.setFont(QFont(\"Century Gothic\",12))\n\t\tpainter.setPen(QColor(0xffffff)) # Couleur police texte \"best\"\n\t\tpainter.drawText(QRectF(QRect(130,20,80,60)),\"BEST\",QTextOption(Qt.AlignHCenter))\n\t\tpainter.setFont(QFont(\"Century Gothic\",18))\n\t\tpainter.setPen(QColor(0xffffff)) # Couleur police nombre best\n\t\tpainter.drawText(QRectF(QRect(130,15,80,55)),str(self.best),QTextOption(Qt.AlignHCenter|Qt.AlignBottom))\n\t\tpainter.setPen(Qt.NoPen)\n\t\tpainter.drawRoundedRect(QRect(240,15,80,60),5,5)\n\t\tpainter.setFont(QFont(\"Century Gothic\",16))\n\t\tpainter.setPen(QColor(0xffffff)) # Couleur police texte reset\n\t\tpainter.drawText(QRectF(QRect(240,15,80,60)),\"RESET\",QTextOption(Qt.AlignHCenter|Qt.AlignVCenter))\n\t\tpainter.setPen(Qt.NoPen)\n\t\tself.drawRectangles(painter)\n\n# Affichage mobile\n\tdef drawRectangles(self,painter):\n\t\tfor i in range(4):\n\t\t\tfor j in range(4):\n\t\t\t\tpainter.setFont(QFont(\"Century Gothic\",14,10))\n\t\t\t\tpainter.setBrush(self.colors[self.blocs[i][j]])\n\t\t\t\tpainter.drawRoundedRect(QRect(20+j*80,90+i*80,60,60),10,10)\n\t\t\t\tif self.blocs[i][j] != 0:\n\t\t\t\t\tif self.blocs[i][j]<15:\n\t\t\t\t\t\tpainter.setPen(QColor(0x646464)) # Couleur police chiffre bloc < 16\n\t\t\t\t\telse :\n\t\t\t\t\t\tpainter.setPen(QColor(0xffffff)) # Couleur police chiffre bloc >= 16\n\t\t\t\t\tpainter.drawText(QRectF(QRect(20+j*80,90+i*80,60,60)),str(self.blocs[i][j]),QTextOption(Qt.AlignHCenter|Qt.AlignVCenter))\n\t\t\t\t\tpainter.setPen(Qt.NoPen)\n\nif __name__ == '__main__':\n\tapp = QApplication(sys.argv)\n\tgame = PyQtGame()\napp.exec_()\n","sub_path":"2048/2048var1.py","file_name":"2048var1.py","file_ext":"py","file_size_in_byte":9764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"167616702","text":"import tensorflow as tf\nfrom test_runner import *\nimport os\nimport shutil\nfrom test_utils import *\n\n\nclass TfliteTestRunner(TestRunner):\n def __init__(self, case_name, overwrite_configs: str = None):\n super().__init__(case_name, overwrite_configs)\n self.model_type = \"tflite\"\n\n def from_tensorflow(self, module):\n # export model\n tf.saved_model.save(module, self.case_dir)\n converter = tf.lite.TFLiteConverter.from_saved_model(self.case_dir)\n\n # convert model\n tflite_model = converter.convert()\n model_file = os.path.join(self.case_dir, 'test.tflite')\n with open(model_file, 'wb') as f:\n f.write(tflite_model)\n\n return model_file\n\n def from_keras(self, keras_model):\n converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)\n tflite_model = converter.convert()\n model_file = os.path.join(self.case_dir, 'test.tflite')\n with open(model_file, 'wb') as f:\n f.write(tflite_model)\n\n return model_file\n\n def run(self, model_file):\n if model_file.startswith('examples'):\n model_file = os.path.join(os.path.dirname(__file__), '..', model_file)\n if self.case_dir != os.path.dirname(model_file):\n shutil.copy(model_file, self.case_dir)\n model_file = os.path.join(\n self.case_dir, os.path.basename(model_file))\n super().run(model_file)\n\n def parse_model(self, model_path: str):\n interp = tf.lite.Interpreter(model_path=model_path)\n\n def translate_shape(shape, default_shape):\n return [(i if i > 0 else d) for i, d in zip(shape, default_shape)]\n\n for item in interp.get_input_details():\n input_dict = {}\n input_dict['index'] = item['index']\n input_dict['name'] = item['name']\n input_dict['dtype'] = item['dtype']\n if item['shape_signature'].size == 0:\n shape = item['shape']\n else:\n shape = item['shape_signature']\n # todo: tflite not support set shape var for model with dynamic shape\n # don't have model with shape var to debug this feature\n if len(shape) <= 4:\n input_dict['model_shape'] = translate_shape(shape, self.default_shape)\n else:\n if -1 in shape:\n raise \"tflite test_runner not supported dynamic shape which rank > 4\"\n input_dict['model_shape'] = shape\n self.inputs.append(input_dict)\n self.calibs.append(copy.deepcopy(input_dict))\n # self.dump_range_data.append(copy.deepcopy(input_dict))\n\n for item in interp.get_output_details():\n output_dict = {}\n output_dict['index'] = item['index']\n output_dict['name'] = item['name']\n output_dict['dtype'] = item['dtype']\n output_dict['model_shape'] = item['shape']\n self.outputs.append(output_dict)\n\n def cpu_infer(self, model_file: bytes):\n interp = tf.lite.Interpreter(model_path=model_file)\n interp.allocate_tensors()\n for idx, value in enumerate(self.inputs):\n new_value = self.transform_input(\n self.data_pre_process(value['data']), \"float32\", \"CPU\")[0]\n interp.set_tensor(value[\"index\"], new_value)\n if self.cfg['compile_opt']['preprocess'] and not test_utils.in_ci():\n dump_bin_file(os.path.join(self.case_dir, f'frame_input_{idx}.bin'), new_value)\n dump_txt_file(os.path.join(self.case_dir, f'frame_input_{idx}.txt'), new_value)\n\n interp.invoke()\n\n i = 0\n results = []\n for output in self.outputs:\n data = interp.get_tensor(output['index'])\n results.append(data)\n if not test_utils.in_ci():\n dump_bin_file(os.path.join(self.case_dir, f'cpu_result_{i}.bin'), data)\n dump_txt_file(os.path.join(self.case_dir, f'cpu_result_{i}.txt'), data)\n i += 1\n\n return results\n\n def import_model(self, compiler, model_content, import_options):\n compiler.import_tflite(model_content, import_options)\n","sub_path":"tests/tflite_test_runner.py","file_name":"tflite_test_runner.py","file_ext":"py","file_size_in_byte":4219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"66534505","text":"import requests\nimport urllib.parse\nfrom decimal import Decimal\n\n\n\n\ndef coordinates_calculation(number, street, postal_code, city):\n API_key = 'AIzaSyDM17QITeync0gIHsGgyqG_IxLH-7JSHo0'\n main_api = \"https://maps.googleapis.com/maps/api/geocode/json?\"\n address =number+' ' +street + ' ' + postal_code + ' ' + city \n url = main_api + urllib.parse.urlencode({'address':address})+ '&key='+API_key\n json_data = requests.get(url).json()\n lat= Decimal(json_data['results'][0]['geometry']['location']['lat'])\n lng= Decimal(json_data['results'][0]['geometry']['location']['lng'])\n return lat, lng","sub_path":"projects/extras/coordinates.py","file_name":"coordinates.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"221905841","text":"from WebTechnology_Course.forms import UserCreationForm\r\n\r\n\r\ndef breadcrumbs(request):\r\n parts = ['/'] + ' '.join(request.path.split('/')).split()\r\n if parts.__len__() == 1:\r\n return {\r\n \"breadcrumbs\": f\"\"\"
  • /
  • \"\"\"\r\n }\r\n\r\n li = \"\"\r\n url_acc = \"/\"\r\n for i in range(parts.__len__() - 1):\r\n if i > 0:\r\n url_acc += parts[i] + \"/\"\r\n li += f\"\"\"
  • {parts[i]}
    • \"\"\"\r\n url_acc += parts[parts.__len__() - 1]\r\n li += f\"\"\"
  • {parts[parts.__len__() - 1]}
    • \"\"\"\r\n li += \"\"\"
  • \"\"\"\r\n return {\r\n \"breadcrumbs\": li\r\n }\r\n\r\ndef registration_form(request):\r\n return {\r\n 'registration_form': UserCreationForm()\r\n }","sub_path":"WebTechnology_Course/core/context_processors.py","file_name":"context_processors.py","file_ext":"py","file_size_in_byte":860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"408496037","text":"\"\"\"\nA Pythagorean triplet is a set of three natural numbers, a < b < c, for which,\n\na2 + b2 = c2\nFor example, 3**2 + 4**2 = 9 + 16 = 25 = 5**2.\n\nThere exists exactly one Pythagorean triplet for which a + b + c = sum.\nFind the product abc.\n\"\"\"\n# this seems clunky, but it took less than a second to run so whatever\n\nimport math\n\ndef pythag_triplet(sum):\n for a in range(1, sum):\n for b in range(a, sum):\n c = math.sqrt(a**2 + b**2)\n if a + b + c == sum:\n return a * b * c\n\nprint(pythag_triplet(12))\nprint(pythag_triplet(1000)) \n","sub_path":"09_special_pythag_triplet.py","file_name":"09_special_pythag_triplet.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"204119231","text":"#! /usr/bin/env python\n\nimport subprocess, os\n\ngatk = \"java -jar /data/home/zhq/program/gatk/gatk-3.8/GenomeAnalysisTK.jar\"\nrefG = \"/data/home/zhq/workstation/hg19_broad_resource/Homo_sapiens_assembly19.fasta\"\n\ndef selectsample(samplenamelist, inputvcf, outputvcf):\n com = f\"{gatk} -T SelectVariants -R {refG} -V {inputvcf} -o {outputvcf} -env -ef \"\n for i in samplenamelist:\n com += f\"-sn {i} \"\n subprocess.run(com, shell=True)\n\ndef getsampleslist(*directors):\n samplenamelist = []\n for i in directors:\n samplenamelist.extend(j for j in os.listdir(i) if os.path.isdir(f\"{i}{j}\"))\n return samplenamelist\n\nif __name__ == \"__main__\":\n bp12samplename = getsampleslist(\"/data/home/zhq/workstation/bipolar_disorder/bp12/\")\n bp10samplename = getsampleslist(\"/data/home/zhq/workstation/bipolar_disorder/bp10/\")\n selectsample(bp12samplename, \"recalibrated_variants.vcf\", \"bp12.vcf\")\n selectsample(bp10samplename, \"recalibrated_variants.vcf\", \"bp10.vcf\")\n \n","sub_path":"gatk_select_samples.py","file_name":"gatk_select_samples.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"561195399","text":"# -*- coding: utf-8 -*-\n# -----------------------------------------------------------------------------\n# arclytics_sim\n# models.py\n#\n# Attributions:\n# [1]\n# -----------------------------------------------------------------------------\n__author__ = ['David Matthews <@tree1004>', 'Dinol Shrestha <@dinolsth>']\n__license__ = 'MIT'\n__version__ = '2.0.0'\n__status__ = 'production'\n__date__ = '2019.07.03'\n\"\"\"models.py: \n\nThis module defines the Object Document Model and schemas for the Arclytics\nSim database using MongoDB. Here we define the `mongoengine.Document` and \n`mongoengine.EmbeddedDocument` models for the Arclytics SimCCT API \nmicroservice.\n\"\"\"\n\nfrom datetime import datetime\nfrom typing import Tuple, Union\n\nfrom bson import ObjectId\nfrom flask import current_app, json\nfrom mongoengine import (\n BooleanField, CASCADE, DateTimeField, DictField, Document, EmailField,\n EmbeddedDocument, EmbeddedDocumentField, EmbeddedDocumentListField,\n FloatField, IntField, ObjectIdField, ReferenceField, StringField,\n LongField, PointField, ValidationError, queryset_manager\n)\n\nfrom arc_logging import AppLogger\nfrom sim_api.extensions import bcrypt\nfrom sim_api.extensions.utilities import (\n DuplicateElementError, ElementInvalid, ElementSymbolInvalid, JSONEncoder,\n MissingElementError, PasswordValidationError, PeriodicTable\n)\n\nlogger = AppLogger(__name__)\n\n\n# ========== # FIELD CUSTOM VALIDATION # ========== #\ndef validate_comp_elements(alloy_comp: list) -> Tuple[bool, list]:\n \"\"\"We validate the alloy has all the elements that will be needed by the\n simulation algorithms using a hashed dictionary as it is much faster.\n\n Args:\n alloy_comp: a list of Alloy composition objects (i.e.\n {\"symbol\": \"C\", \"weight\": 1.0})\n\n Returns:\n A tuple response whether the validation succeeded and the missing\n elements if it did not.\n \"\"\"\n valid_elements = {\n 'C': False,\n 'Mn': False,\n 'Ni': False,\n 'Cr': False,\n 'Mo': False,\n 'Si': False,\n 'Co': False,\n 'W': False,\n 'As': False,\n 'Fe': False\n }\n\n for el in alloy_comp:\n if el['symbol'] in valid_elements.keys():\n valid_elements[el['symbol']] = True\n\n # all() returns True if all values in the dict are True\n # If it does not pass, we build up a message and respond.\n if not all(el is True for el in valid_elements.values()):\n # We build up a list of missing elements for the response.\n missing_elem = []\n for k, v in valid_elements.items():\n if not v:\n missing_elem.append(k)\n # The validation has failed so we return False and the missing elements\n return False, missing_elem\n # The validation has succeeded\n return True, []\n\n\ndef not_negative(val):\n try:\n if float(val) < 0.0:\n raise ValidationError('Cannot be a negative number.')\n except ValueError:\n raise ValidationError('Value Error.')\n except TypeError:\n raise ValidationError('Type Error.')\n\n\ndef greater_than_zero(val):\n try:\n if float(val) < 0.0 or float(val) == 0:\n raise ValidationError('Must be more than 0.0.')\n except ValueError:\n raise ValidationError('Value Error.')\n except TypeError:\n raise ValidationError('Type Error.')\n\n\ndef not_over_100(val):\n try:\n if float(val) > 100.0:\n raise ValidationError('Must be less than 100.0.')\n except ValueError:\n raise ValidationError('Value Error.')\n except TypeError:\n raise ValidationError('Type Error.')\n\n\ndef within_percentage_bounds(val):\n try:\n if float(val) > 100.0:\n raise ValidationError('Must be less than 100.0.')\n if float(val) < 0.0:\n raise ValidationError('Must be more than 0.0.')\n except ValueError:\n raise ValidationError('Value Error.')\n except TypeError:\n raise ValidationError('Type Error.')\n\n\ndef validate_no_duplicate_elements(alloy_comp: list\n ) -> Tuple[bool, Union[set, None]]:\n elements = []\n for e in alloy_comp:\n elements.append(e['symbol'])\n duplicates = set([x for x in elements if elements.count(x) > 1])\n if len(duplicates) > 0:\n return False, duplicates\n return True, None\n\n\n# ========== # EMBEDDED DOCUMENTS MODELS SCHEMA # ========== #\nclass UserProfile(EmbeddedDocument):\n # Decision:\n # Not adding profile photos as not required however, we can easily do so\n # by adding the following dependencies. Additionally, you must define a\n # Cloud CDN to ensure the photos are not stored in this server as per\n # best-practices and industry standards.\n # profile_photo = ImageField(\n # required=False,\n # size=(800, 600, True),\n # thumbnail_size=(300, 300, True),\n # help_text='Please provide a photo of 800x600 pixels only.'\n # )\n aim = StringField(\n help_text='What sentence best describes you?',\n required=True,\n default=None,\n null=True\n )\n highest_education = StringField(\n help_text='What is the highest level of education have you studied?',\n required=True,\n default=None,\n null=True\n )\n sci_tech_exp = StringField(\n help_text='What is your experience with scientific software?',\n required=True,\n default=None,\n null=True\n )\n phase_transform_exp = StringField(\n help_text=(\n 'What is your experience with solid-state phase '\n 'transformation?'\n ),\n required=True,\n default=None,\n null=True\n )\n\n def to_dict(self) -> dict:\n \"\"\"\n Simple EmbeddedDocument.UserProfile helper method to get a Python dict\n back.\n \"\"\"\n return {\n 'aim': self.aim,\n 'highest_education': self.highest_education,\n 'sci_tech_exp': self.sci_tech_exp,\n 'phase_transform_exp': self.phase_transform_exp\n }\n\n\nclass AdminProfile(EmbeddedDocument):\n position = StringField(max_length=255, required=True)\n mobile_number = StringField(max_length=11, min_length=10)\n verified = BooleanField(default=False)\n promoted_by = ObjectIdField()\n sub_to_feedback = BooleanField(default=False)\n\n def to_dict(self) -> dict:\n \"\"\"\n Simple EmbeddedDocument.AdminProfile helper method to get a\n Python dict back.\n \"\"\"\n return {\n 'position': self.position,\n 'mobile_number': self.mobile_number,\n 'verified': self.verified\n }\n\n\nclass SimulationResults(EmbeddedDocument):\n # Using DictField() because it requires no validation on the internal\n # nesting but we don't really need to validate this data.\n\n # Comments are just to describe the schema of the data.\n # Both of these will have the following:\n # ferrite_nucleation: {\"time\": [], \"temp\": []}\n # ferrite_completion: {\"time\": [], \"temp\": []}\n # pearlite_nucleation: {\"time\": [], \"temp\": []}\n # pearlite_completion: {\"time\": [], \"temp\": []}\n # bainite_nucleation: {\"time\": [], \"temp\": []}\n # bainite_completion: {\"time\": [], \"temp\": []}\n # martensite: {\"time\": [], \"temp\": []}\n TTT = DictField()\n CCT = DictField()\n # This will have the following:\n # user_cooling_curve: {\"time\": [], \"temp\": []}\n # user_phase_fraction_data: {\n # \"austenite\": [], \"ferrite\": [], \"pearlite\": [],\n # \"bainite\": [], \"martensite\": []\n # }\n # slider_time_field: float\n # slider_temp_field: float\n # slider_max: int\n USER = DictField()\n\n def to_dict(self):\n return {'TTT': self.TTT, 'CCT': self.CCT, 'USER': self.USER}\n\n\nclass Configuration(EmbeddedDocument):\n is_valid = BooleanField(default=False, required=True, null=False)\n method = StringField(\n null=False, required=True, choices=('Li98', 'Kirkaldy83')\n )\n grain_size = FloatField(null=False, required=True, validation=not_negative)\n nucleation_start = FloatField(\n null=False, required=True, validation=within_percentage_bounds\n )\n nucleation_finish = FloatField(\n null=False, required=True, validation=within_percentage_bounds\n )\n auto_calculate_ms = BooleanField(default=True, null=False, required=True)\n auto_calculate_bs = BooleanField(default=True, null=False, required=True)\n auto_calculate_ae = BooleanField(default=True, null=False, required=True)\n ms_temp = FloatField(\n default=0.0, null=False, required=True, validation=not_negative\n )\n ms_rate_param = FloatField(\n default=0.0, null=False, required=True, validation=not_negative\n )\n bs_temp = FloatField(\n default=0.0, null=False, required=True, validation=not_negative\n )\n ae1_temp = FloatField(\n default=0.0, null=False, required=True, validation=not_negative\n )\n ae3_temp = FloatField(\n default=0.0, null=False, required=True, validation=not_negative\n )\n start_temp = FloatField(\n default=900, null=False, required=True, validation=not_negative\n )\n cct_cooling_rate = FloatField(\n default=10, null=False, required=True, validation=not_negative\n )\n\n def to_dict(self) -> dict:\n \"\"\"\n Simple EmbeddedDocument.Configuration helper method to get a Python dict\n \"\"\"\n return {\n 'is_valid': self.is_valid,\n 'method': self.method,\n 'grain_size': self.grain_size,\n 'nucleation_start': self.nucleation_start,\n 'nucleation_finish': self.nucleation_finish,\n 'auto_calculate_ms': self.auto_calculate_ms,\n 'auto_calculate_bs': self.auto_calculate_bs,\n 'auto_calculate_ae': self.auto_calculate_ae,\n 'ms_temp': self.ms_temp,\n 'ms_rate_param': self.ms_rate_param,\n 'bs_temp': self.bs_temp,\n 'ae1_temp': self.ae1_temp,\n 'ae3_temp': self.ae3_temp,\n 'start_temp': self.start_temp,\n 'cct_cooling_rate': self.cct_cooling_rate\n }\n\n # noinspection PyMethodParameters\n @queryset_manager\n def as_dict(cls, queryset) -> list:\n \"\"\"Adding an additional QuerySet context method to return a list of\n `sim_api.models.Configuration` Documents instead of a QuerySet.\n\n Usage:\n config_list = Configuration.as_dict()\n\n Args:\n queryset: the queryset that must is accepted as part of the Mongo\n BSON parameter.\n\n Returns:\n A list with every Configuration Document object converted to dict.\n \"\"\"\n return [obj.to_dict() for obj in queryset]\n\n def __str__(self):\n return self.to_json()\n\n\nclass Element(EmbeddedDocument):\n symbol = StringField(max_length=2, required=True)\n weight = FloatField(required=True, validation=not_negative)\n\n def to_dict(self):\n return {'symbol': self.symbol, 'weight': self.weight}\n\n def clean(self):\n \"\"\"\n Ensure that the `symbol` field must conform to a proper periodic\n table element symbol and ensure they are both required.\n \"\"\"\n # These ensure they are not missing.\n if not self.symbol:\n msg = 'Field is required: [\"Element.symbol\"]'\n raise ElementInvalid(message=msg)\n\n if not self.weight == 0.0:\n if not self.weight:\n msg = 'Field is required: [\"Element.weight\"]'\n raise ElementInvalid(message=msg)\n\n # Make sure they are a valid Element symbol as per the `PeriodicTable`\n try:\n valid_symbol = PeriodicTable[self.symbol].name\n except KeyError:\n raise ElementSymbolInvalid()\n self.symbol = valid_symbol\n\n def __str__(self):\n return self.to_json()\n\n\nclass Alloy(EmbeddedDocument):\n oid = ObjectIdField(default=lambda: ObjectId(), primary_key=True)\n name = StringField()\n compositions = EmbeddedDocumentListField(Element)\n\n def to_dict(self):\n comp = [obj.to_dict() for obj in self.compositions]\n return {'_id': str(self.oid), 'name': self.name, 'compositions': comp}\n\n def clean(self):\n # comps = [el for el in self.compositions]\n valid, missing = validate_comp_elements(self.compositions)\n if not valid:\n raise MissingElementError(f'Missing elements {missing}')\n\n no_duplicates, duplicate = validate_no_duplicate_elements(\n self.compositions\n )\n if not no_duplicates:\n raise DuplicateElementError(f'Duplicate element {duplicate}')\n\n def __str__(self):\n return self.to_json()\n\n\nclass AlloyType(EmbeddedDocument):\n parent = EmbeddedDocumentField(\n document_type=Alloy, default=None, null=True\n )\n weld = EmbeddedDocumentField(document_type=Alloy, default=None, null=True)\n mix = EmbeddedDocumentField(document_type=Alloy, default=None, null=True)\n\n def to_dict(self):\n data = {'parent': None, 'weld': None, 'mix': None}\n\n if self.parent is not None:\n data['parent'] = self.parent.to_dict()\n if self.weld is not None:\n data['weld'] = self.weld.to_dict()\n if self.mix is not None:\n data['mix'] = self.mix.to_dict()\n\n return data\n\n\nclass AlloyStore(EmbeddedDocument):\n alloy_option = StringField(required=True, choices=('single', 'mix'))\n alloys = EmbeddedDocumentField(document_type=AlloyType, required=True)\n\n def to_dict(self):\n return {\n 'alloy_option': self.alloy_option,\n 'alloys': self.alloys.to_dict()\n }\n\n def __str__(self):\n return self.to_json()\n\n\nclass Rating(EmbeddedDocument):\n rating = IntField(min_value=1, max_value=5, required=True)\n created_date = DateTimeField(default=datetime.utcnow(), required=True)\n\n def to_dict(self):\n return {'rating': self.rating, 'created_date': str(self.created_date)}\n\n\nclass LoginData(EmbeddedDocument):\n created_datetime = DateTimeField(default=datetime.utcnow(), required=True)\n state = StringField()\n country = StringField()\n continent = StringField()\n accuracy_radius = IntField()\n geo_point = PointField()\n timezone = StringField()\n ip_address = StringField()\n\n def to_dict(self):\n return {\n 'created_datetime': str(self.created_datetime.isoformat()),\n 'state': self.state,\n 'country': self.country,\n 'continent': self.continent,\n 'accuracy_radius': self.accuracy_radius,\n 'geo_point': self.geo_point,\n 'timezone': self.timezone,\n 'ip_address': self.ip_address\n }\n\n\n# ========== # DOCUMENTS MODELS SCHEMA # ========== #\nclass User(Document):\n # The following fields describe the attributes of a user\n email = EmailField(required=True, unique=True)\n password = StringField(\n default=None, max_length=64, null=False, min_length=6\n )\n first_name = StringField(required=True, max_length=255)\n last_name = StringField(required=True, max_length=255)\n profile = EmbeddedDocumentField(document_type=UserProfile, default=None)\n admin_profile = EmbeddedDocumentField(\n document_type=AdminProfile, default=None\n )\n\n # The following fields describe the simulation properties saved to a users\n # Document for later retrieval\n # Note: It is necessary to use `sim_api.schemas.ConfigurationSchema`,\n # `sim_api.schemas.AlloySchema` and\n # `sim_api.schemas.SimulationResultsSchema` to validate these before\n # dumping to the database if we want to ensure validity.\n last_configuration = DictField(default=None)\n last_alloy_store = DictField(default=None)\n last_simulation_results = DictField(default=None)\n last_simulation_invalid_fields = DictField(default=None)\n\n # Store the number of simulations the user has run\n simulations_count = LongField(default=0)\n\n # Store alloys for the user\n saved_alloys = EmbeddedDocumentListField(document_type=Alloy)\n\n # Some rather useful metadata information that's not core to the\n # definition of a user\n active = BooleanField(default=True)\n is_admin = BooleanField(default=False, db_field='admin')\n disable_admin = BooleanField(default=False)\n verified = BooleanField(default=False)\n # Make sure when converting these that it follows ISO8601 format as\n # defined in settings.DATETIME_FMT\n created = DateTimeField(\n default=datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ'), null=False\n )\n last_updated = DateTimeField(default=None, null=False)\n last_login = DateTimeField()\n\n ratings = EmbeddedDocumentListField(document_type=Rating)\n login_data = EmbeddedDocumentListField(document_type=LoginData)\n\n # Define the collection and indexing for this document\n meta = {\n 'collection':\n 'users',\n 'indexes': [\n # This create text indexes for advanced text search\n {\n 'fields': ['$first_name', '$last_name', '$email'],\n # For a text index, the weight of an indexed field denotes\n # the significance of the field relative to the other indexed\n # fields in terms of the text search score.\n # 2 times (i.e. 10:1) the impact as a term match in the\n # last_name and email fields\n # 10:9 impact as a term match in the last_name:first_name\n 'weights': {\n 'last_name': 4,\n 'first_name': 2,\n 'email': 5\n }\n }\n ]\n }\n\n def set_password(self, raw_password: str) -> None:\n \"\"\"\n Helper utility method to save an encrypted password using the\n Bcrypt Flask extension.\n \"\"\"\n self.password = bcrypt.generate_password_hash(\n password=raw_password,\n rounds=current_app.config.get('BCRYPT_LOG_ROUNDS')\n ).decode()\n\n def to_dict(self) -> dict:\n \"\"\"Simple Document.User helper method to get a Python dict back.\"\"\"\n last_login = None\n last_updated = None\n if self.last_login is not None:\n last_login = self.last_login.isoformat()\n if self.last_updated is not None:\n last_updated = self.last_updated.isoformat()\n\n # We set a default profile that we always send to the client\n profile = {\n 'aim': None,\n 'highest_education': None,\n 'sci_tech_exp': None,\n 'phase_transform_exp': None\n }\n if self.profile is not None:\n profile = self.profile.to_dict()\n\n user = {\n 'email': self.email,\n 'first_name': self.first_name,\n 'last_name': self.last_name,\n 'active': self.active,\n 'admin': self.is_admin,\n 'verified': self.verified,\n 'last_updated': str(last_updated),\n 'created': str(self.created.isoformat()),\n 'last_login': str(last_login),\n 'profile': profile\n }\n\n if self.admin_profile is not None:\n user['admin_profile'] = self.admin_profile.to_dict()\n\n return user\n\n def to_json(self, *args, **kwargs):\n \"\"\"\n Override the default method to customise the way a JSON format is\n transformed.\n \"\"\"\n return json.dumps(self.to_dict(), cls=JSONEncoder)\n\n # MongoEngine allows you to create custom cleaning rules for your documents\n # when calling save(). By providing a custom clean() method you can do\n # any pre validation / data cleaning. This might be useful if you want to\n # ensure a default value based on other document values.\n def clean(self):\n \"\"\"\n Ensures a multitude of business logic is checked before we save the\n User to the database. These include the following:\n - Password must be set by calling `new_user.set_password(raw_pw)`\n - Every time a User object is saved the `User.last_updated` datetime\n is set to the current `utcnow()` datetime.\n - Update the `User.is_admin` boolean based on other criteria.\n - By default, if there is not `User.ratings` or `User.login_data`\n we make a Python list as the default.\n \"\"\"\n if self.password is None:\n raise PasswordValidationError()\n\n if self.last_updated is None:\n self.last_updated = self.created\n else:\n self.last_updated = datetime.utcnow()\n\n self.is_admin = (\n not self.disable_admin and self.admin_profile is not None\n )\n\n if not self.ratings:\n self.ratings = []\n\n if not self.login_data:\n self.login_data = []\n\n # noinspection PyMethodParameters\n @queryset_manager\n def as_dict(cls, queryset) -> list:\n \"\"\"Adding an additional QuerySet context method to return a list of\n `sim_api.models.Users` Documents instead of a QuerySet.\n\n Usage:\n users_list = User.as_dict()\n\n Args:\n queryset: the queryset that must is accepted as part of the Mongo\n BSON parameter.\n\n Returns:\n A list with every Users Document object converted to dict.\n \"\"\"\n return [obj.to_dict() for obj in queryset]\n\n def __str__(self):\n return self.to_json()\n\n\nclass SavedSimulation(Document):\n user = ReferenceField(User, reverse_delete_rule=CASCADE)\n configurations = EmbeddedDocumentField(\n document_type=Configuration, required=True, null=False\n )\n alloy_store = EmbeddedDocumentField(\n document_type=AlloyStore, required=True, null=False\n )\n simulation_results = EmbeddedDocumentField(\n document_type=SimulationResults, required=True, null=False\n )\n created = DateTimeField(default=datetime.utcnow(), null=False)\n\n meta = {'collection': 'saved_simulations'}\n\n def to_dict(self):\n return {\n '_id': str(self.id),\n 'configurations': self.configurations.to_dict(),\n 'alloy_store': self.alloy_store.to_dict(),\n 'created': str(self.created.isoformat()),\n 'simulation_results': self.simulation_results.to_dict()\n }\n\n def __str__(self):\n return self.to_json()\n\n\nclass SharedSimulation(Document):\n owner_email = EmailField(required=True)\n created_date = DateTimeField(default=datetime.utcnow(), required=True)\n configuration = EmbeddedDocumentField(\n document_type=Configuration, required=True\n )\n alloy_store = EmbeddedDocumentField(\n document_type=AlloyStore, required=True\n )\n simulation_results = EmbeddedDocumentField(\n document_type=SimulationResults, required=True, null=False\n )\n\n meta = {'collection': 'shared_simulations'}\n\n def to_dict(self):\n return {\n 'owner_email': self.owner_email,\n 'created_date': str(self.created_date),\n 'configurations': self.configuration.to_dict(),\n 'alloy_store': self.alloy_store.to_dict(),\n 'simulation_results': self.simulation_results.to_dict()\n }\n\n\nclass Feedback(Document):\n user = ReferenceField(User, reverse_delete_rule=CASCADE)\n category = StringField(required=True)\n rating = IntField(min_value=1, max_value=5, default=None)\n comment = StringField(required=True)\n created_date = DateTimeField(\n default=datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ'),\n required=True\n )\n\n meta = {\n 'collection':\n 'feedback',\n 'indexes': [\n # This create text indexes for advanced text search\n {\n 'fields': ['$category', '$comment'],\n # For a text index, the weight of an indexed field denotes\n # the significance of the field relative to the other indexed\n # fields in terms of the text search score.\n # 5:1 the impact as a term match in the category vs comments\n 'weights': {\n 'category': 3,\n 'comment': 1\n }\n }\n ]\n }\n\n def to_dict(self):\n return {\n 'user_email': self.user.email,\n 'category': self.category,\n 'rating': self.rating,\n 'comment': self.comment,\n 'created_date': str(self.created_date)\n }\n","sub_path":"services/simcct/sim_api/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":24501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"133568133","text":"from regression.compressed_sensing import *\nfrom regression.cross_validation import *\nimport numpy\nfrom os.path import join\nimport time\nimport pylab\nfrom matplotlib import cm, rc\nrc( 'text', usetex = True )\n\n\n#base_dir = '/home/jdjakem/software/heat/trunk/examples/data/'\nbase_dir = 'data/'\nX = numpy.loadtxt(join(base_dir, 'diabetes_data.csv.gz'))\ny = numpy.loadtxt(join(base_dir, 'diabetes_target.csv.gz'))\n\nrng = numpy.random.RandomState(42)\nX = numpy.c_[X, rng.randn(X.shape[0], 14)] # add some bad features\n\n# normalize data as done by Lars to allow for comparison\nX /= numpy.sqrt(numpy.sum(X ** 2, axis=0))\n\nlars_solver = LARSSolver( residual_tolerance = 1e-12, \n max_iterations = int32_max,\n fit_intercept = True )\ncv_iterator= KFoldCrossValidationIterator( num_folds = 20 )\n#cv_iterator = LeaveOneOutCrossValidationIterator()\n\nt1 = time.time()\nCV = PathSearchCrossValidation( cv_iterator, lars_solver )\n\n# Compute paths\nprint(\"Computing regularization path using the LARS\")\nfrom time import clock\nt0 = clock()\nCV.run( X, y, num_processors = 1 )\nt_lasso_lars_cv = time.time() - t1\n\n# Display results\ncv_res_tols = CV.cv_res_tols\nbest_cv_res_tol = CV.best_res_tol\nvalid_res_norms = CV.valid_res_norms\n\npylab.figure()\npylab.plot(cv_res_tols, valid_res_norms, ':')\npylab.plot(cv_res_tols, valid_res_norms.mean(axis=-1), 'k',\n label='Average across the folds', linewidth=2)\npylab.axvline(best_cv_res_tol, linestyle='--', color='k', label='Single CV fold')\npylab.legend()\n\npylab.xlabel(r'residual size')\npylab.ylabel('Mean square error')\npylab.title('Mean square error on each fold: Lars (train time: %.2fs)'\n % t_lasso_lars_cv)\npylab.axis('tight')\npylab.show()\n","sub_path":"examples/lasso_cross_validation_example.py","file_name":"lasso_cross_validation_example.py","file_ext":"py","file_size_in_byte":1737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"636295952","text":"import numpy as np\nimport cv2\nimport os\nimport time\nimport datetime\nimport argparse\n\n# construct the argument parser and parse the arguments\nap = argparse.ArgumentParser()\nap.add_argument(\"-o\", \"--output\", type=str, default=\"barcodes.csv\",\n\thelp=\"path to output CSV file containing barcodes\")\nargs = vars(ap.parse_args())\n\ncam = cv2.VideoCapture(0)\nfaceDetect=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\nrec=cv2.face.LBPHFaceRecognizer_create()\nrec.read(\"recognizer/training_data.yml\")\ni = 0\nfourcc = cv2.VideoWriter_fourcc(*'XVID')\nout = cv2.VideoWriter('output'+str(i)+'.avi', fourcc, 10.0, (640,480))\n\ncsv = open(args[\"output\"], \"w\")\nfound = set()\nwhile True:\n i = 0\n ret, img = cam.read()\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n faces=faceDetect.detectMultiScale(gray, 1.3, 5)\n for (x,y,w,h) in faces:\n cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n id,conf=rec.predict(gray[y:y+h,x:x+w])\n if(id==1):\n text=\"ILHAM\"\n else:\n text=\"UNKNOWN\"\n if text not in found:\n csv.write(\"{},{}\\n\".format(datetime.datetime.now(),text))\n csv.flush()\n found.add(text)\n cv2.putText(img, text, (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 2.0, (0, 255, 0),10)\n if os.path.exists(\"output\"+str(i)+\".avi\"):\n i += 1\n out.write(img)\n cv2.imshow('frame',img)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\ncam.release()\nout.release()\ncv2.destroyAllWindows()\n","sub_path":"detector_excel.py","file_name":"detector_excel.py","file_ext":"py","file_size_in_byte":1501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"298708565","text":"\n# Export functions\n\nfrom reports import *\n\n\ndef write(line):\n f.write(\"\\n\" + str(line) + \"\\n\")\n\nwith open(\"exported_stat.txt\", \"w\") as f:\n write(\"What is the title of the most played game?\")\n write(str(get_most_played(\"game_stat.txt\")))\n write(\"How many copies have been sold total?\")\n write(str(sum_sold(\"game_stat.txt\")))\n write(\"What is the average selling?\")\n write(str(get_selling_avg(\"game_stat.txt\")))\n write(\"How many characters long is the longest title?\")\n write(str(count_longest_title(\"game_stat.txt\")))\n write(\"What is the average of the release dates?\")\n write(str(get_date_avg(\"game_stat.txt\")))\n write(\"What properties does the game?\")\n write(str(get_game(\"game_stat.txt\", \"Minecraft\")))\n f.write(\"\\n\\n\")\n f.write(\"Bonus questions!\")\n f.write(\"\\n\\n\")\n write(\"How many games are there grouped by genre?\\n\")\n write(str(count_grouped_by_genre(\"game_stat.txt\")))\n write(\"What is the date ordered list of the games? \\n\")\n a = [str(element) for element in get_date_ordered(\"game_stat.txt\")[:5]]\n b = [str(element) for element in get_date_ordered(\"game_stat.txt\")[5:10]]\n c = [str(element) for element in get_date_ordered(\"game_stat.txt\")[10:15]]\n d = [str(element) for element in get_date_ordered(\"game_stat.txt\")[15:20]]\n e = [str(element) for element in get_date_ordered(\"game_stat.txt\")[20:25]]\n f.write(', '.join(a) + \"\\n\")\n f.write(', '.join(b) + \"\\n\")\n f.write(', '.join(c) + \"\\n\")\n f.write(', '.join(d) + \"\\n\")\n f.write(', '.join(e) + \"\\n\")\nprint(\"\\n\")\nprint(\"Exported to \\\"exported_stat.txt\\\"\")\n","sub_path":"part2/export.py","file_name":"export.py","file_ext":"py","file_size_in_byte":1601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"48584852","text":"import pandas as pd\r\nimport numpy as np\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn import preprocessing\r\nimport matplotlib.pyplot as plt\r\ndef sigmoid(z):\r\n return 1 / (1 + np.exp(-z))\r\n \r\ndef linreg(x,y,x_val,y_val,lr,epoch):\r\n j=0\r\n x= np.array(x)\r\n x_val= np.array(x_val)\r\n y=np.asarray(y)\r\n y=np.resize(74,1)\r\n y_val=np.resize(74,1)\r\n w= np.array([[0.1],\r\n [0.2],\r\n [0.3],\r\n [0.4]])\r\n ct=[]\r\n b=1\r\n m=75\r\n \r\n for i in range(1,epoch+1):\r\n xtrained=forwardprop(w,x,b)\r\n Traincost=cost_func(xtrained,y)\r\n dw,db=back_prop(x,y,xtrained)\r\n w,b=update(w,b,dw,db,lr)\r\n if i%10==0:\r\n ct.append(Traincost)\r\n TrainMAE=(1/m)*(np.sum(np.abs(xtrained-y)))\r\n #validataion\r\n xtest=forwardprop(w,x_val,b)\r\n Testcost=cost_func(xtest,y_val)\r\n TestMAE=(1/m)*(np.sum(np.abs(xtest-y_val)))\r\n if i%10==0:\r\n print('epoch no '+str(i)+' testcost '+str(Testcost)+' traincost '+str(Traincost)+' testMAE '+str(TestMAE)+' trainMAE '+str(TrainMAE))\r\n\r\n plt.plot(ct)\r\n plt.xlabel('iteration')\r\n plt.ylabel('Training cost')\r\n plt.title('learning rate'+str(lr))\r\n plt.show()\r\n\r\n\r\n\r\ndef forwardprop(w,x,b):\r\n\r\n h=sigmoid(b+x.dot(w))\r\n return h\r\n \r\ndef cost_func(h,y):\r\n m=75\r\n J=(1/(2*m))*np.sum(np.square(h-y))\r\n return J\r\ndef back_prop(x,y,h):\r\n m=75\r\n dh=(1/m)*(h-y)\r\n k=x.transpose()\r\n dw=np.dot(k,dh)\r\n db=np.sum(dh)\r\n return dw,db\r\ndef update(w,b,dw,db,lr):\r\n w=w-lr*dw\r\n b=b-lr*db\r\n return w,b \r\n\r\ndef model(location):\r\n lr=[0.00075,0.00009,0.0001,0.0001,0.0004,0.0008]\r\n for i in lr:\r\n epoch=100\r\n iris = pd.read_csv(location)\r\n print(\"Dataframeupd : \")\r\n print(iris)\r\n Y=iris.iloc[:,-1]\r\n X=iris.iloc[:,0:4]\r\n label_encoder = preprocessing.LabelEncoder() \r\n Y= label_encoder.fit_transform(Y) \r\n X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.5,train_size=0.5,random_state=123)\r\n linreg(X_train,Y_train,X_test,Y_test,i,epoch)\r\n\r\nmodel('iris.data')","sub_path":"python/project/LR.py","file_name":"LR.py","file_ext":"py","file_size_in_byte":2199,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"289046813","text":"import turtle, tkinter\n\ndef apply_rules(letter):\n \"\"\"Apply rules to an individual letter, and return the result.\"\"\"\n # Rules\n rules = {'F': 'FF', 'X':'--FXF++FXF++FXF--'}\n \n for i in rules:\n if letter == rules[i]:\n # print(i)\n new_string = rules[i].values([])\n # print(new_string)\n # no rules apply so keep the character\n else:\n new_string = letter\n # print(new_string)\n return new_string\n\n# for _ in range(generation):\n# string = ''.join(rules[letter] if letter in rules else letter)\n# for letter in string)\n \n\n\n \n \n\ndef process_string(original_string):\n \"\"\"Apply rules to a string, one letter at a time, and return the result.\"\"\"\n tranformed_string = \"\"\n for letter in original_string:\n tranformed_string = tranformed_string + apply_rules(letter)\n\n return tranformed_string\n\ndef create_l_system(number_of_iterations, axiom):\n \"\"\"Begin with an axiom, and apply rules to the original axiom string number_of_iterations times, then return the result.\"\"\"\n start_string = axiom\n for counter in range(number_of_iterations):\n end_string = process_string(start_string)\n start_string = end_string\n\n return end_string\n\ndef draw_l_system(some_turtle, instructions, angle, distance):\n \"\"\"Draw with some_turtle, interpreting each letter in the instructions passed in.\"\"\"\n for task in instructions:\n if task == 'F':\n # 'F': lambda:turte.forward\n some_turtle.forward(distance)\n elif task == 'B':\n some_turtle.backward(distance)\n elif task == '+':\n some_turtle.right(angle)\n elif task == '-':\n some_turtle.left(angle)\n\n############################################################################\n\n# create the string of turtle instructions\ninstruction_string = create_l_system(10, \"FXF--FF--FF\")\nprint(instruction_string)\n\n# setup for drawing\nwindow = turtle.Screen()\nwindow.setup(1920,1080,0,0)\nwindow.tracer(5)\njill = turtle.Turtle()\n\n\njill.speed(0)\n\n# using screen.tracer() speeds up your drawing (by skipping some frames when drawing)\n#window.tracer(10)\n\n# move turtle to left side of screen\njill.up()\njill.back(200)\njill.down()\n\n# draw the picture, using angle 60 and segment length 5\ndraw_l_system(jill, instruction_string, 60, 20)\n\ntkinter.mainloop()","sub_path":"week18/tortuga.py","file_name":"tortuga.py","file_ext":"py","file_size_in_byte":2428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"240245119","text":"import pandas as pd\nimport psycopg2\nfrom psycopg2 import sql\ntry:\n get_ipython().system('ln -s /var/run/postgresql/.s.PGSQL.5432 /tmp/.s.PGSQL.5432')\nexcept:\n pass\n\nconn = psycopg2.connect(database=\"datamyneUSexports\", user=\"ram22\")\ncur = conn.cursor()\n\ndef createSql(incoming = '', out = '', prefix=''):\n\n infile = open(incoming, 'r')\n varNames = infile.readline()\n infile.close()\n\n # get rid of \\n at end of line and text delimeter, then create list\n varNames = varNames[:-2].replace('\"', '').split(',')\n varNames = [prefix + i for i in varNames]\n\n cur.execute(sql.SQL(\"DROP TABLE IF EXISTS {0};\").format(sql.Identifier(out)))\n\n i = 0\n for field in varNames:\n if i == 0:\n cur.execute(sql.SQL(\"CREATE TABLE {0} ({1} varchar);\").format(sql.Identifier(out), sql.Identifier(field)))\n i += 1\n else:\n cur.execute(sql.SQL(\"ALTER TABLE {0} ADD {1} varchar;\").format(sql.Identifier(out), sql.Identifier(field)))\n\n infile = open(incoming, 'r') \n cur.copy_expert(sql.SQL(\"COPY {0} FROM STDIN WITH CSV HEADER\").format(sql.Identifier(out)), infile)\n infile.close()\n \n conn.commit()\n\n\nnameFile = '../datamyneUSexports/meshintel-datamyne_export_bol_data.csv'\noutSQL = 'billoflading'\ncreateSql(nameFile, outSQL)\nnameFile = '../../datamyneDelivery20170728/Datamyne_Delivery_7_28_2017/Data/meshintel-datamyne_d_b_family_tree.csv'\n\noutSQL = 'family_tree'\ncreateSql(nameFile, outSQL)\n\nnameFile = '../datamyneUSexports/meshintel-datamyne_export_bol_d_b_profiles.csv'\noutSQL = 'bol_pull_db_profiles'\nprefix = 'shipper_d_b_'\ncreateSql(nameFile, outSQL, prefix)\n\nnameFile = '/home/ram22/Dropbox/dataProjects/valueChains/meshintel/ozniras/datamyneDelivery20170728/Datamyne_Delivery_7_28_2017/Data/meshintel-datamyne_d_b_all_companies.csv'\noutSQL = 'db_direct_companies'\ncreateSql(nameFile, outSQL)\n\nconn.close()\n\n","sub_path":"datamyneExport20170825/01_programs/createSQLtable.py","file_name":"createSQLtable.py","file_ext":"py","file_size_in_byte":1885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"629293120","text":"import re, argparse\nfrom igf_data.illumina.samplesheet import SampleSheet\n\nparser=argparse.ArgumentParser()\nparser.add_argument('-i','--samplesheet_file', required=True, help='Illumina format samplesheet file')\nparser.add_argument('-r','--revcomp_index', default=False, action='store_true', help='Reverse complement index2 column, default: false')\nparser.add_argument('-o','--output_file', required=True, help='Reformatted samplesheet file')\nargs=parser.parse_args()\n\nsamplesheet_file=args.samplesheet_file\nrevcomp_index=args.revcomp_index\noutput_file=args.output_file\n\nsamplesheet_data=SampleSheet(infile=samplesheet_file)\nplatform_name=samplesheet_data.get_platform_name()\n\nplatform_pattern=re.compile('^HISEQ',re.IGNORECASE)\n\nif (re.search(platform_pattern, platform_name) and revcomp_index):\n samplesheet_data.get_reverse_complement_index()\n\nsamplesheet_data.print_sampleSheet(outfile=output_file)\n\n","sub_path":"scripts/SampleSheet/reformatSampleSheet.py","file_name":"reformatSampleSheet.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"647341094","text":"\nimport sys\ndef match(f,s):\n if f == '(' and s == ')' or f == ')' and s == '(': return True\n elif f == '[' and s == ']' or f == ']' and s == '[': return True\n elif f == '{' and s == '}' or f == '}' and s == '{': return True\n else: return False\nline = sys.stdin.readline()\n\nchars = [line[0]]\n\nfor i in range(1,len(line)):\n if len(chars) > 0:\n if match(line[i],chars[len(chars)-1]): chars.pop()\n else: chars.append(line[i])\n else: chars.append(line[i])\n \nif len(chars) == 0:\n sys.stdout.write('True')\nelse: sys.stdout.write('False')\n","sub_path":"balanced-delimiters.py","file_name":"balanced-delimiters.py","file_ext":"py","file_size_in_byte":578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"635284565","text":"N = 5\narr = [1,2,3,4,5]\n#각 집합 요소가, 부분집합에 포함되는지 표시하는 배열\nselected = [0] * N\n#idx : 현재 포함여부를 결정하려고 하는 인덱스 번호 \n#N : 전체 집합의 요소 개수\ndef power_set(idx,N):\n # 모든 인덱스에 대해서 요소의 부분집합 포함여부를 결정\n if idx == N:\n # print(selected)\n for i in range(N):\n if selected[i]:\n print(arr[i],end=\" \")\n print()\n return\n #아직 결정 덜했다!\n #내가 할 수 있는 모든 경우의 수를 모두 수행하기\n #idx에 해당하는 요소가 부분집합에 포함 될거냐/ 말거냐를 결정\n selected[idx] = 1\n power_set(idx+1,N)\n selected[idx] = 0\n power_set(idx+1,N)\npower_set(0,N)\n \n ","sub_path":"SWEA/문제/power_set.py","file_name":"power_set.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"6536411","text":"\"\"\"\nThis python script can be used to compute features from a set of UDMs from the ATLAS probes\n\n__author__ = crauterb\n\n\"\"\"\nimport base64\nimport datetime\nfrom dns.message import from_wire\nfrom dns import flags as dns_flags\nfrom dns import rcode\nimport pandas as pd\nfrom ripe.atlas.cousteau import (\n Probe,\n AtlasResultsRequest)\n\n\"\"\"\nPlease note, that all the features I computed here are chosen by me. The practical necessity has not been proven yet.\n\"\"\"\n\n# Predefined API-Key for creating a measurement.\nATLAS_API_KEY = \"PUT_YOUR_KEY_HERE\"\n\n# Read the measurement IDs from a data frame.\nmeasurements = pd.read_csv(\"results/20160217_results_long_run.csv\", sep=\";\")\n# open the according log-file, that corresponds to the measurement.\nwith open(\"results/2016-02-17.nii.log\") as f:\n log_content = f.readlines()\n# There are certain requests, that come from DNS resolvers, but do not belong to a certain measurement\n# disclude those\nreqs = [line for line in log_content if \"(nii.dnslab.jp)\" in line]\nlog_content = [line for line in log_content if line not in reqs]\n# Define feature vector\nres = pd.DataFrame(columns=('probe_id', # The ID of the probe used in the query\n 'timestamp_UNIX', # The timestamp of the measurement in UNIX-format\n 'timestamp_HR', # The timestamp of the measurement in human readable\n 'measurement_id', # The ID corresponding to a measurement\n 'query_argument', # The domain name that is to be resolved, i.e. like aaa.nii.dnslab.jp\n 'probe_global_ip', # The IP address of the probe in the packets\n 'probe_private_ip', # The IP address of the probe as listed in the ATLAS database\n 'probe_location', # The location of the probe by: \"CountryCode: LAT,LONG\n 'query_flags_at_probe', # Flags set in the answer arrived at the probe\n 'query_flags_at_server', # Flags set in the query that arrives at the server\n 'query_dest_on_probe', # The dest_address of the query that is send from the probe\n 'src_ip_at_server', # The source_address of the query that arrives at the server\n 'return_code', # The return code of the answer\n 'avrg_rt_time', # The avarege rrt for the packet as stated in the answer.\n 'auth', # The authentication name in the answer\n 'auth_source', # The authentication source @TODO: Clarify this!\n 'probe_tags' # The tags that the probe has. Listed last for convenience.\n ))\n# count the errors you encounter plz.\nerror_count = 0\n# start by iterating over all measurement IDs\nfor index, row in measurements.iterrows():\n print(\"Looking up \", int(row['measurement_id']))\n kwargs = {\n \"msm_id\": int(row['measurement_id']),\n \"probe_ids\": int(row['probe_ID']),\n \"key\": ATLAS_API_KEY,\n }\n is_success, results = AtlasResultsRequest(**kwargs).create()\n if len(results) > 1:\n raise Exception(\"Strange. The length of the result is not 1 but \", len(results))\n if len(results) == 0:\n error_count += 1\n print(\"No results for this measurement...\")\n # @TODO: Determine the cause for this error.\n continue\n if int(results[0]['prb_id']) != int(row['probe_ID']):\n # just for convenience\n raise Exception(\"IDs do not match\")\n if is_success:\n print(\"Got it. Now working with: \", int(row['probe_ID']))\n ans = results[0]\n print(\"Looking for \", str(row['query_argument']))\n server_srcs = list(set([str(line.split(\" \")[3]) for line in log_content if str(row['query_argument']) in line]))\n server_flags = list(\n set([str(line.split(\" \")[-2]) for line in log_content if str(row['query_argument']) in line]))\n\n probe_id = int(ans['prb_id'])\n probe = Probe(id=int(ans['prb_id']))\n\n number_of_results = len(ans['resultset'])\n\n successful = True\n tmp_res = []\n probe_flags = []\n src_adresses_for_probe = []\n dest_adresses_for_probe = []\n rt_count = 0.0\n rt = 0.0\n ret_code = \"\"\n auth = \"None\"\n auth_source = \"None\"\n for answer in ans['resultset']:\n\n src_adresses_for_probe += [str(answer['src_addr'])]\n dest_adresses_for_probe += [str(answer['dst_addr'])]\n\n if \"error\" in answer:\n ret_code = \"ERROR\"\n auth = \"ERROR\"\n\n probe_flags += \"None\"\n else:\n msg = from_wire(base64.b64decode(answer['result']['abuf']), ignore_trailing=True)\n ret_code = rcode.to_text(msg.rcode())\n rt += float(answer['result']['rt'])\n rt_count += 1\n probe_flags += list(set(probe_flags + dns_flags.to_text(msg.flags).split(\" \")))\n if msg.authority:\n auth = msg.authority[0].name\n auth_source = str(msg.authority[0][0].mname.to_text()) + \">\" + (\n msg.authority[0][0].rname.to_text()) + \">\" + str(msg.authority[0][0].serial)\n\n x = 1.0\n if rt_count == 0:\n rt_count += 1\n res = res.append({\n 'probe_id': int(ans['prb_id']), # Derive the ID from the answer.\n # Already checked for equality in ids.\n 'timestamp_UNIX': int(ans['timestamp']), # Get UNIX timestamp from measurment\n 'timestamp_HR': datetime.datetime.fromtimestamp(int(ans['timestamp'])), # do Human Readable\n 'measurement_id': int(row['measurement_id']),\n 'query_argument': str(row['query_argument']),\n 'probe_global_ip': \",\".join(list(set(src_adresses_for_probe))),\n 'probe_private_ip': str(probe.address_v4), # Get the private IP of the Probe\n 'probe_location': probe.country_code + \": \" + str(probe.geometry['coordinates'][0]) + \",\" + str(\n probe.geometry['coordinates'][1]), # Compute location. No magic.\n 'query_flags_at_probe': \",\".join(list(set(probe_flags))),\n 'query_flags_at_server': server_flags,\n 'query_dest_on_probe': \",\".join(list(set(dest_adresses_for_probe))),\n 'src_ip_at_server': server_srcs,\n 'return_code': ret_code,\n 'avrg_rt_time': rt / float(rt_count),\n 'auth': auth,\n 'auth_source': auth_source,\n 'probe_tags': \",\".join([a['name'].replace(\" \", \"_\") for a in probe.tags])}, ignore_index=True)\n else:\n print(\"Error when downloading for probe: \", int(row['probe_ID']))\nprint(\"There were overall \", error_count, \" errors with downloading\")\nres.sort_values(['probe_id', 'timestamp_UNIX'], inplace=True, ascending=[True, True])\n# stupid pandas converts back to double. Avoid that.\nres[['probe_id', 'timestamp_UNIX', 'measurement_id']] = res[['probe_id', 'timestamp_UNIX', 'measurement_id']].astype(\n int)\nres.to_csv(\"results/20160217_overall_results.csv\", sep=\";\", index=False)\n","sub_path":"compute_dns_features.py","file_name":"compute_dns_features.py","file_ext":"py","file_size_in_byte":7247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"40394308","text":"'''\r\n\r\nDefiniamo adiacenti di un pixel p di un immagine i pixel adiacenti a p in orizzontale o in verticale.\r\nSe un pixel e' sul bordo dell'immagine il suo vicinato non comprende i pixel non contenuti nell'immagine.\r\nIl pixel dell'immagine con coordinate(x,y) ha dunque come adiacenti i pixel \r\ncon coordinate (x-1,y),(x+1,y),(x,y-1),(x,y+1) appartenenti all'immagine. \r\n \r\nDefiniamo connessi due pixel se e' possibile dall'uno raggiungere l'altro spostandosi solo su \r\npixel adiacenti e dello stesso colore (ovviamente perche' cio' sia possobile e' necessario \r\nche i due pixel abbiano lo stesso colore).\r\n\r\nPer caricare e salvare immagini PNG usate le funzioni load e save che abbiamo preparato nel modulo immagini.py .\r\n\r\nScrivere una funzione ricolora(fname, lista, fnameout) che presi:\r\n- il percorso di un file che contiene un'immagine in formato PNG\r\n- una lista di quadruple del tipo (x,y,c1,c2) dove x e y sono coordinate di un pixel dell'immagine e c1 e c2 due triple colore RGB\r\n- il percorso di un file (fnameout) da creare\r\nlegge l'immagine in fname, esegue un'operazione di ricolorazione di alcuni pixel dell'immagine e \r\nregistra l'immagine ricolorata nel file fnameout.\r\n\r\nL'operazione di ricolorazione e' la seguente. Per ciascuna delle quadruple (x,y,c1,c2) della lista (nell'ordine), \r\n- tutti i pixel connessi al pixel di coordinate (x,y) nell'immagine vanno ricolorati col colore c1, \r\n- tutti i pixel del perimetro (che si trovano sul 'bordo') della zona che si e' appena colorata devono essere ricolorati col colore c2.\r\nIl perimetro della zona colorata è l'insieme dei pixel che non hanno tutti e 4 i vicini che fanno parte della zona ricolorata \r\n(ovvero almeno uno è di un colore diverso da quello che si sta ricolorando oppure almeno uno non esiste perchè sarebbe fuori dall'immagine)\r\n\r\nSi consideri ad esempio l'immagine 'I1.png', l'invocazione di ricolora('I1.png',[(10,10,(255,0,0), (0,0,255))],’OUT1.png')\r\nprodurra' l'immagine 'OUT1.png' identica all'immagine di partenza se non per il fatto che,\r\n tutti i pixel adiacenti al pixel di coordinate (10,10) (e di colore verde), verranno ricolorati \r\n di rosso ((255,0,0)), mentre i pixel sul bordo della zona inizialmente verde vengono ricolorati di blu.\r\n\r\nPer ciascuna area ricolorata bisogna inoltre calcolare area interna e perimetro, che sono definite come segue:\r\n- l'area interna e' il numero di pixel ricolorati con il colore c1\r\n- il perimetro è il numero di pixel ricolorati con il colore c2\r\n\r\nLa funzone deve tornare la lista di coppie (area interna, perimetro) nello stesso ordine in cui sono state colorate le aree.\r\n \r\nPer altri esempi vedere il file grade03.txt \r\n'''\r\n\r\nfrom immagini import *\r\n\r\nrosso = (255, 0, 0)\r\nblu = ( 0, 0, 255)\r\nverde = ( 0, 255, 0)\r\nnero = ( 0, 0, 0)\r\nbianco= (255, 255, 255)\r\ngiallo= (255, 255, 0)\r\ncyan = ( 0, 255, 255)\r\nmagenta= (255, 0, 255)\r\n\r\ndef ricolora(fname, lista, fnameout):\r\n '''Implementare qui la funzione'''\r\n lsImmagine=load(fname)\r\n x=len(lsImmagine[0])\r\n y=len(lsImmagine)\r\n \r\n lsRisultato=[]\r\n \r\n for i in range(len(lista)):\r\n CoordIniz=(lista[i][0],lista[i][1])\r\n #strProvenienzaIniz='o'\r\n \r\n ColoreIniz=(lsImmagine[CoordIniz[1]][CoordIniz[0]])\r\n Colore=lista[i][2]\r\n ColorePerimetro=lista[i][3]\r\n \r\n #print(CoordIniz,ColoreIniz,Colore,ColorePerimetro)\r\n \r\n lsArea=[]\r\n lsPerimetro=[]\r\n lsRicoloraArea=[]\r\n lsRicoloraPerimetro=[]\r\n\r\n lsPixel=[(CoordIniz)]#,strProvenienzaIniz)]\r\n \r\n #print(lsPixel)\r\n \r\n while len(lsPixel)>0:\r\n lsPixel=AnalisiIntorno(lsImmagine,x,y,lsPixel,lsArea,lsPerimetro,ColoreIniz,Colore,ColorePerimetro)\r\n \r\n lsRisultato+=[(len(lsArea),len(lsPerimetro))]\r\n lsRicoloraArea+=lsArea\r\n lsRicoloraPerimetro+=lsPerimetro\r\n \r\n for pixel in range(len(lsRicoloraArea)):\r\n j=lsRicoloraArea[pixel][1]\r\n i=lsRicoloraArea[pixel][0]\r\n lsImmagine[j][i]=Colore\r\n \r\n for pixel in range(len(lsRicoloraPerimetro)):\r\n j=lsRicoloraPerimetro[pixel][1]\r\n i=lsRicoloraPerimetro[pixel][0]\r\n lsImmagine[j][i]=ColorePerimetro\r\n\r\n save(lsImmagine,fnameout)\r\n \r\n return lsRisultato#, lsImmagine[1][0:10],(0,0) in lsPerimetro,ColorePerimetro\r\n\r\n#def Colora(Colore):\r\n# dzColori={}\r\n# dzColori['rosso'] = (255, 0, 0)\r\n# dzColori['blu'] = ( 0, 0, 255)\r\n# dzColori['verde'] = ( 0, 255, 0)\r\n# dzColori['nero'] = ( 0, 0, 0)\r\n# dzColori['bianco']= (255, 255, 255)\r\n# dzColori['giallo']= (255, 255, 0)\r\n# dzColori['cyan'] = ( 0, 255, 255)\r\n# dzColori['magenta']= (255, 0, 255)\r\n# return dzColori[str(Colore)]\r\n\r\n#def Step(strProvenienza):\r\n# dzProvenienza={}\r\n# dzProvenienza['o']=[((1,0),'dx'),((0,1),'giu'),((-1,0),'sx'),((0,-1),'su')]#provengo dall'origine, controllo tutte le direzioni\r\n# dzProvenienza['dx']=[((1,0),'dx'),((0,1),'giu'),((0,-1),'su')]#provengo da destra, controllo le direzioni tranne sx\r\n# dzProvenienza['giu']=[((1,0),'dx'),((-1,0),'sx'),((0,-1),'su')]#provengo da giu, controllo le direzioni tranne giu\r\n# dzProvenienza['sx']=[((0,1),'giu'),((-1,0),'sx'),((0,-1),'su')]#provengo da sx, controllo le direzioni tranne sx\r\n# dzProvenienza['su']=[((1,0),'dx'),((0,1),'giu'),((-1,0),'sx')]#provengo da su, controllo le direzioni tranne su\r\n# return dzProvenienza[strProvenienza]\r\n\r\ndef StessoColore(lsImmagine,x,y,Coordinate,lsNuoviPixel,ColoreIniz,Colore):\r\n# lsStep=Step(strProvenienza)\r\n# lsStep=[((1,0),'dx'),((0,1),'giu'),((-1,0),'sx'),((0,-1),'su')]\r\n lsStep=[(1,0),(0,1),(-1,0),(0,-1)] #(step c, step r)\r\n #lsStessoColore=[]\r\n TotStessoColore=0\r\n i,j=Coordinate\r\n for step in range(len(lsStep)):\r\n #print(lsStep[pixel][1])\r\n c,r=lsStep[step]\r\n if j+r==y or j+r<0 or i+c==x or i+c<0:\r\n pass\r\n elif lsImmagine[j+r][i+c]==ColoreIniz:\r\n TotStessoColore+=1\r\n lsNuoviPixel+=[(i+c,j+r)] #,lsStep[step][1])]\r\n #elif lsImmagine[j+r][i+c]==Colore:\r\n # TotStessoColore+=1\r\n# if TotStessoColore==len(lsStep):\r\n# #lsNuoviPixel+=lsStessoColore\r\n# return True\r\n# else:\r\n# return False\r\n return TotStessoColore==len(lsStep)\r\n\r\ndef Bordo(lsImmagine,x,y,Coordinate):\r\n i,j=Coordinate \r\n return i==x-1 or i==0 or j==y-1 or j==0\r\n\r\ndef AnalisiIntorno(lsImmagine,x,y,lsPixel,lsArea,lsPerimetro,ColoreIniz,Colore,ColorePerimetro):\r\n lsNuoviPixel=[]\r\n for pixel in range(len(lsPixel)):\r\n i,j=lsPixel[pixel]\r\n# strProvenienza=lsPixel[pixel][1]\r\n if (not (i,j) in lsArea) and (not (i,j) in lsPerimetro) and lsImmagine[j][i]==ColoreIniz:\r\n #if Bordo(lsImmagine,x,y,(i,j)):\r\n #coloro il pixel con il colore perimetro\r\n #lsImmagine[j][j]=ColorePerimetro\r\n #aggiungo le coordinate a lista perimetro\r\n #lsPerimetro+=[(i,j)]\r\n #lsNuoviPixel+=[(lsPixel[pixel])]\r\n if StessoColore(lsImmagine,x,y,(i,j),lsNuoviPixel,ColoreIniz,Colore):\r\n #coloro il pixel con il colore area\r\n #lsImmagine[j][j]=Colore\r\n #aggiungo le coordinate a lista area\r\n lsArea+=[(i,j)]\r\n else:\r\n #coloro il pixel con il colore perimetro\r\n #lsImmagine[j][j]=ColorePerimetro\r\n #aggiungo le coordinate a lista perimetro\r\n lsPerimetro+=[(i,j)]\r\n lsPixel=lsNuoviPixel\r\n return lsPixel","sub_path":"students/792515/homework03/program03.py","file_name":"program03.py","file_ext":"py","file_size_in_byte":7686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"256250155","text":"import os\nimport pickle\nimport tarfile\nimport urllib.request\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn import preprocessing\n\n\nclass Batch:\n def __init__(self, images, one_hot_labels, numeric_labels, class_names):\n self.size = images.shape[1]\n self.images = images\n self.one_hot_labels = one_hot_labels\n self.numeric_labels = numeric_labels\n self.class_names = class_names\n\n def description(self, name: str = 'Dataset') -> str:\n res = '{} (total images {})\\n'.format(name, self.size)\n for l in range(len(self.class_names)):\n res += '- {:.2%} {}\\n'.format(\n (self.numeric_labels == l).sum() / self.size,\n self.class_names[l])\n return res\n\n def subset(self, new_size):\n return Batch(\n images=self.images[:, :new_size],\n one_hot_labels=self.one_hot_labels[:, :new_size],\n numeric_labels=self.numeric_labels[:new_size],\n class_names=self.class_names\n )\n\n def mean(self):\n return self.images.mean(axis=1)\n\n\nclass CIFAR10:\n output_size = 10\n input_size = 32 * 32 * 3\n\n def __init__(self):\n self.download_dataset()\n self.labels = self.load_labels()\n self.loaded_batches = {}\n self.label_encoder = preprocessing.LabelBinarizer()\n self.label_encoder.fit([x for x in range(self.output_size)])\n\n @staticmethod\n def load_labels():\n with open('cifar-10-batches-py/batches.meta', 'rb') as f:\n data = pickle.load(f, encoding='bytes')\n return [x.decode('ascii') for x in data[b'label_names']]\n\n @staticmethod\n def download_dataset():\n if not os.path.isdir('cifar-10-batches-py'):\n file, _ = urllib.request.urlretrieve(\n \"https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\",\n \"temp.tar.gz\")\n with tarfile.open(file, \"r:gz\") as tar:\n tar.extractall()\n os.remove(file)\n with open('.gitignore', 'a+') as out:\n out.write('cifar-10-batches-py\\n')\n\n def get_named_batch(self, batch_name) -> Batch:\n if batch_name not in self.loaded_batches:\n with open('cifar-10-batches-py/' + batch_name, 'rb') as f:\n data = pickle.load(f, encoding='bytes')\n\n self.loaded_batches[batch_name] = Batch(\n images=np.divide(data[b'data'], 255).T,\n one_hot_labels=self.label_encoder.transform(data[b'labels']).T,\n numeric_labels=data[b'labels'],\n class_names=self.labels\n )\n return self.loaded_batches[batch_name]\n\n def get_named_batches(self, *args) -> Batch:\n batches = [self.get_named_batch(name) for name in args]\n return Batch(\n images=np.hstack([b.images for b in batches]),\n one_hot_labels=np.hstack([b.one_hot_labels for b in batches]),\n numeric_labels=np.hstack([b.numeric_labels for b in batches]),\n class_names=self.labels\n )\n\n def show_image(self, img, label: int = None, interpolation='gaussian'):\n squared_image = np.rot90(np.reshape(img, (32, 32, 3), order='F'), k=3)\n plt.imshow(squared_image, interpolation=interpolation)\n plt.axis('off')\n plt.title(self.labels[label] if label is not None else '')\n\n\nif __name__ == '__main__':\n cifar = CIFAR10()\n training = cifar.get_named_batches('data_batch_1', 'data_batch_2', 'data_batch_3', 'data_batch_4')\n validation = cifar.get_named_batches('data_batch_5')\n test = cifar.get_named_batches('test_batch')\n\n print(training.description('Training'))\n print(validation.description('Validation'))\n print(test.description('Test'))\n\n for plot_i, img_i in enumerate(np.random.choice(training.size, 15, replace=False)):\n plt.subplot(3, 5, plot_i + 1)\n cifar.show_image(training.images[:, img_i], training.numeric_labels[img_i])\n\n plt.show()\n","sub_path":"lab2/datasets.py","file_name":"datasets.py","file_ext":"py","file_size_in_byte":4009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"127530410","text":"import socket\n\n\ndef main():\n # 1.创建tcp套接字,socket.AF_INET表示ipv4,socket.SOCK_STREAM表示tcp\n tcp_server_socket = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n # 2.绑定ip和端口号\n server_addr = (\"10.200.202.22\", 8081)\n tcp_server_socket.bind(server_addr)\n # 3.开启监听\n tcp_server_socket.listen(128)\n print(\"服务器%s已经开启了，等待连接中...\" %(server_addr,))\n # 4.循环等待客户端连接\n while True:\n new_client_socket, client_addr = tcp_server_socket.accept()\n\n print(\"%s连接上了\" % (client_addr,))\n\n # 5.循环接收连接上的socket信息，如果receive_data为null则说明tcp客户端断开\n while True:\n receive_data = new_client_socket.recv(1024)\n if receive_data and receive_data.decode(\"GBK\") != \"exit\": # 如果receive_data不为null,并且receive_data不等于“exit”,就给tcp客户端回消息\n print(\"%s发来消息:\\n%s\" % (client_addr, receive_data.decode(\"GBK\")))\n new_client_socket.send((\"hello~%s\" % receive_data.decode(\"GBK\")).encode(\"utf-8\"))\n else:\n print(\"%s断开连接了,服务完毕!\" % (client_addr,))\n # 服务端在单线程里同时只能为一个tcp连接服务，连接时处于阻塞状态，其他的tcp连接进不来\n # 为一个客户端服务完毕了，这里主动关闭连接，解阻塞\n new_client_socket.close()\n break\n\n # 6.关闭服务端套接字\n tcp_server_socket.close()\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"Python进阶/01-网络编程/05-socket-tcp-server.py","file_name":"05-socket-tcp-server.py","file_ext":"py","file_size_in_byte":1620,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"542774091","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\nimport pandas as pd\nimport os, os.path\nimport wget\nimport numpy as np\n\n\n# ### Enter the path where the data files are stored\n\n# In[ ]:\n\n\nDIR = input(\"path:\")\nn=len([name for name in os.listdir(DIR) if os.path.isfile(os.path.join(DIR, name))])\nn\n\n\n# In[ ]:\n\n\nfrom os.path import isfile, join\nfrom os import listdir\nonlyfiles = [f for f in listdir(DIR) if isfile(join(DIR, f))]\nonlyfiles\n\n\n# In[ ]:\n\n\nc=[]\nfor i in range(0,n):\n c1=os.path.splitext(onlyfiles[i])[0]\n c.append(c1)\n\n\n# In[ ]:\n\n\nfor i in range(0,n):\n if os.path.isfile(DIR):\n directory = c[i]\n parent_dir=DIR\n path = os.path.join(parent_dir, directory)\n os.mkdir(path)\n print(\"Directory '%s' created\" %directory)\n \n else:\n print('Folder exist')\n \n\n\n# In[ ]:\n\n\nonlyfiles\n\n\n# In[ ]:\n\n\nexcel_files = []\ncsv_files=[]\n\nfor i in range(0,len(onlyfiles)):\n filename1 = onlyfiles[i]\n if \".xlsx\" in filename1:\n excel_files.append(filename1) \n else:\n csv_files.append(filename1)\n\n\n# ### Working with a .xlsx files \n\n# In[ ]:\n\n\nexcel_files\n\n\n# In[ ]:\n\n\npath = input('Path: + /filename.xlsx')\nd1=pd.read_excel(path)\nd1=d1.drop(['Docket No','Damage Description','Engineer Name'],axis=1)\n\n\n# In[ ]:\n\n\nnew_df = d1['Picture 1']\nnew_df=new_df.append([d1['Picture 2'],d1['Picture 3'],d1['Picture 4'],d1['Picture 5'],d1['Picture 6'],d1['Picture 7'],d1['Picture 8']],ignore_index=True)\nnew_df\n\n\n# In[ ]:\n\n\nnew_df=pd.DataFrame(new_df)\nnew_df.replace(np.nan,0,inplace=True)\na_series = (new_df != 0).any(axis=1)\nnew_df1 = new_df.loc[a_series]\nnew_df1\n\n\n# In[ ]:\n\n\nnew_df1=pd.DataFrame(new_df1)\nnew_df1\n\n\n# In[ ]:\n\n\nfolder=os.listdir(DIR)\nfolder\n\nonlyfolders = [f for f in listdir(DIR) if os.path.isdir(join(DIR, f))]\nonlyfolders[0]\n\n\n# In[ ]:\n\n\nab=new_df1[0].to_list()\nab\nn=len(ab)\n\n\n# In[ ]:\n\n\npath = input('Give a path of directory who have same name as file:')\nfor i in range(0,n):\n img1 = ab[i]\n img=wget.download(img1,path)\n print(img)\n\n\n# In[ ]:\n\n\nnew_dir=path\nprint(len([name for name in os.listdir(new_dir) if os.path.isfile(os.path.join(new_dir, name))]))\n\n\n# ### Working with .csv files\n\n# In[ ]:\n\n\ncsv_files\n\n\n# In[ ]:\n\n\npath = input('Path: + /filename.xlsx')\nd2=pd.read_csv(path)\nd2=d2.drop(['Docket No','Damage Description','Engineer Name'],axis=1)\n\n\n# In[ ]:\n\n\nnew_df_csv = d2['Picture 1']\nnew_df_csv=new_df_csv.append([d2['Picture 2'],d2['Picture 3'],d2['Picture 4'],d2['Picture 5'],d2['Picture 6'],d2['Picture 7'],d2['Picture 8']],ignore_index=True)\nnew_df_csv\n\n\n# In[ ]:\n\n\nnew_df_csv=pd.DataFrame(new_df_csv)\nnew_df_csv.replace(np.nan,0,inplace=True)\na_series = (new_df_csv != 0).any(axis=1)\nnew_df_csv1 = new_df_csv.loc[a_series]\nnew_df_csv1\n\n\n# In[ ]:\n\n\nnew_df_csv1=pd.DataFrame(new_df_csv1)\nnew_df_csv1\n\n\n# In[ ]:\n\n\nfolder=os.listdir(DIR)\nfolder\n\nonlyfolders = [f for f in listdir(DIR) if os.path.isdir(join(DIR, f))]\nonlyfolders\n\n\n# In[ ]:\n\n\nab=new_df_csv1[0].to_list()\nab\nn=len(ab)\nn\n\n\n# In[ ]:\n\n\npath = input('Give a path of directory who have same name as file:')\nfor i in range(0,n):\n img1 = ab[i]\n img=wget.download(img1,path)\n print(img) \n\n\n# In[ ]:\n\n\nprint(len([name for name in os.listdir(new_dir) if os.path.isfile(os.path.join(new_dir, name))]))\n\n","sub_path":"Bridgestone_Image_Data_Analysis.py","file_name":"Bridgestone_Image_Data_Analysis.py","file_ext":"py","file_size_in_byte":3283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"188175194","text":"#!/usr/bin/env python\n\n\"\"\"\nFor your homework this week, you'll be creating a wsgi application of\nyour own.\nYou'll create an online calculator that can perform several operations.\nYou'll need to support:\n * Addition\n * Subtractions\n * Multiplication\n * Division\nYour users should be able to send appropriate requests and get back\nproper responses. For example, if I open a browser to your wsgi\napplication at `http://localhost:8090/multiple/3/5' then the response\nbody in my browser should be `15`.\nConsider the following URL/Response body pairs as tests:\n```\n http://localhost:8090/multiply/3/5 => 15\n http://localhost:8090/add/23/42 => 65\n http://localhost:8090/subtract/23/42 => -19\n http://localhost:8090/divide/22/11 => 2\n http://localhost:8090/ => Here's how to use this page...\n```\nTo submit your homework:\n * Fork this repository (Session03).\n * Edit this file to meet the homework requirements.\n * Your script should be runnable using `$ python calculator.py`\n * When the script is running, I should be able to view your\n application in my browser.\n * I should also be able to see a home page (http://localhost:8080/)\n that explains how to perform calculations.\n * Commit and push your changes to your fork.\n * Submit a link to your Session03 fork repository!\n\n\"\"\"\n\nhtml_text = \"\"\"\n\nWSGI Calulator \n\n\n

    {print_op_a} {print_op_sign} {print_op_b}\n= {print_result}

    \n
    \n

    Try links below

    \n

    add/2/10

    \n

    divide/4/0

    \n

    subtract/1300/30

    \n

    multiply/4/7

    \n

    divide/42/6

    \n

    subtract/12/0

    \n
    \n

    Path Info: {print_path} Entries: {print_no_entries}

    \n

    Operation: {print_op} First Op: {print_op_a}\nSecond Op: {print_op_b}

    \n\n\"\"\"\n\n\ndef app(environ, start_response):\n import pprint\n pprint.pprint(environ)\n headers = [(\"Content-type\", \"text/html\")]\n try:\n path = environ.get('PATH_INFO', None)\n if path is None:\n raise NameError\n\n args = resolve_path(path)\n\n if len(args) != 3:\n op_sign = \"f\"\n else:\n pass\n\n ops = [\"multiply\", \"divide\", \"add\", \"subtract\"]\n\n if args[0].strip() in ops:\n oper = args[0].strip()\n else:\n oper = \"failed\"\n op_sign = \"f\"\n\n try:\n op_a = int(args[1])\n except:\n op_a = \"error\"\n\n try:\n op_b = int(args[2])\n except:\n op_b = \"error\"\n\n if oper == \"multiply\":\n result = op_a * op_b\n op_sign = \"*\"\n elif oper == \"divide\":\n if op_b == 0:\n result = \"can't divide by zero\"\n op_sign = \"/\"\n else:\n result = op_a / op_b\n op_sign = \"/\"\n elif oper == \"add\":\n result = op_a + op_b\n op_sign = \"+\"\n elif oper == \"subtract\":\n result = op_a - op_b\n op_sign = \"-\"\n elif oper == \"failed\":\n result = \"error - please try again\"\n op_sign = \"f\"\n else:\n result = \"failed\"\n\n body = html_text.format(\n print_path=path,\n print_no_entries=len(args),\n print_op=oper,\n print_op_sign=op_sign,\n print_op_a=op_a,\n print_op_b=op_b,\n print_result=result\n )\n status = \"200 OK\"\n\n except NameError:\n status = \"404 Not Found\"\n body = \"

    Not Found

    \"\n\n except ValueError:\n status = \"400 Bad Request\"\n body = \"

    Bad Request

    \"\n\n except Exception:\n status = \"500 Internal Server Error\"\n body = \"

    Internal Server Error, check the code!

    \"\n finally:\n headers.append(('Content-length', str(len(body))))\n start_response(status, headers)\n return [body.encode('utf8')]\n\n\ndef resolve_path(path):\n args = path.strip(\"/\").split(\"/\")\n return args\n\n\nif __name__ == '__main__':\n from wsgiref.simple_server import make_server\n srv = make_server('localhost', 8090, app)\n srv.serve_forever()\n","sub_path":"calculator.py","file_name":"calculator.py","file_ext":"py","file_size_in_byte":4433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"471930660","text":"from custom.math import eulers_totient\nfrom collections import defaultdict\n\nbest_n = 0\nmin_ratio = 100\n\ndef same_permutations(a, b):\n d = defaultdict(int)\n for x in a:\n d[x] += 1\n for x in b:\n d[x] -= 1\n return not any(d.values())\n\nfor n in range(6636841, int(1e7), 2): # It must be an odd number (from previous tests)\n eul = int(eulers_totient(n))\n meep = str(n)\n merp = str(eul)\n if len(meep) == len(merp):\n if sorted(meep) == sorted(merp):\n ratio = n / eul\n if n / eul < min_ratio:\n min_ratio = ratio\n best_n = n\n print(\"NEW:\", n)\n\nprint(\"Final: \", best_n)\n# Should find no more after 8319823\n","sub_path":"Problems 051 - 100/Problem 070.py","file_name":"Problem 070.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"597013246","text":"from threading import Thread\nfrom functools import partial\nfrom random import SystemRandom\nfrom time import sleep\n\nshould_wait = partial(SystemRandom().getrandbits)\n\n\nclass Int:\n a = 0\n\n def slow_assignment(self, value):\n if should_wait:\n sleep(1)\n\n self.a = value\n\n\na = Int()\nthreads = [\n Thread(target=a.slow_assignment, args=[1]),\n Thread(target=a.slow_assignment, args=[2]),\n]\n\nfor thread in threads:\n thread.start()\n\nfor thread in threads:\n thread.join()\n\nprint(a.a)\n","sub_path":"python/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"319917569","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 ('history', '0008_remove_history_date_strung'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='StringHistory',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, serialize=False, auto_created=True)),\n ('name', models.CharField(max_length=50)),\n ('gauge', models.CharField(max_length=3, choices=[('15', '15'), ('15L', '15L'), ('16', '16'), ('16L', '16L'), ('17', '17'), ('17L', '17L'), ('18', '18'), ('18L', '18L'), ('19', '19'), ('19L', '19L')])),\n ('price_per_racket', models.DecimalField(null=True, max_digits=5, decimal_places=2)),\n ],\n options={\n 'abstract': False,\n },\n ),\n migrations.AlterField(\n model_name='stringjob',\n name='string',\n field=models.ForeignKey(to='history.StringHistory'),\n ),\n ]\n","sub_path":"stringtracker/history/migrations/0009_auto_20160318_0308.py","file_name":"0009_auto_20160318_0308.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"} +{"seq_id":"289616119","text":"# Definition for a binary tree node.\r\nclass TreeNode:\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:\r\n def buildTree(self, preorder, inorder):\r\n \"\"\"\r\n :type preorder: List[int]\r\n :type inorder: List[int]\r\n :rtype: TreeNode\r\n \"\"\"\r\n def helper(node, _preorder, _inorder):\r\n if not (_preorder or _inorder):\r\n return\r\n\r\n # node_val = _preorder[0]\r\n node_loc = _inorder.index(node.val)\r\n\r\n # node.val = node_val\r\n\r\n left_pre = _preorder[1:node_loc+1]\r\n right_pre = _preorder[node_loc+1:]\r\n\r\n left_in = _inorder[:node_loc]\r\n right_in = _inorder[node_loc+1:]\r\n\r\n if left_pre:\r\n node.left = TreeNode(left_pre[0])\r\n helper(node.left, left_pre, left_in)\r\n if right_pre:\r\n node.right = TreeNode(right_pre[0])\r\n helper(node.right, right_pre, right_in)\r\n\r\n root = TreeNode(preorder[0])\r\n helper(root, preorder, inorder)\r\n return root\r\n\r\n# class Solution:\r\n# def buildTree(self, preorder, inorder):\r\n# \"\"\"\r\n# :type preorder: List[int]\r\n# :type inorder: List[int]\r\n# :rtype: TreeNode\r\n# \"\"\"\r\n# if not preorder:\r\n# return None\r\n \r\n# i = 1\r\n# j = 0\r\n# root = TreeNode(preorder[0])\r\n# s = [root]\r\n# n = len(preorder)\r\n# while i < n:\r\n# # check if top has more left nodes\r\n# if inorder[j] != s[-1].val:\r\n# node = TreeNode(preorder[i])\r\n# s[-1].left = node\r\n# s.append(node)\r\n# i += 1\r\n# else:\r\n# # while s and preorder[i] != inorder[j]:\r\n# while s and s[-1].val == inorder[j]:\r\n# parent = s.pop()\r\n# j += 1\r\n \r\n# node = TreeNode(preorder[i])\r\n# parent.right = node\r\n# s.append(node)\r\n# i += 1\r\n \r\n# return root","sub_path":"leetcode/python/constructBTFromPreInorderTraversal.py","file_name":"constructBTFromPreInorderTraversal.py","file_ext":"py","file_size_in_byte":2202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"98"}