diff --git "a/4016.jsonl" "b/4016.jsonl" new file mode 100644--- /dev/null +++ "b/4016.jsonl" @@ -0,0 +1,648 @@ +{"seq_id":"483666777","text":"\ndef read_subtree(strs):\n children = int(strs[0])\n meta = int(strs[1])\n node = {'children': []}\n length=2\n for i in range(0, children):\n llength, leaf = read_subtree(strs[length:len(strs) - meta])\n node['children'].append(leaf)\n length += llength\n node['metadata'] = strs[length: length + meta]\n length += meta\n return length, node\n\n\ndef sumup_meta(root):\n if root is None:\n return 0\n elif len(root['children']) == 0:\n return sum([int(i) for i in root['metadata']])\n else:\n metasum = 0\n for m in root['metadata']:\n meta = int(m)\n if 0 < meta <= len(root['children']):\n child = root['children'][meta-1]\n metasum += sumup_meta(child)\n return metasum\n\n\nwith open(\"../resources/task15.txt\") as f:\n content = f.readlines()\n tree = {}\n\n for line in content:\n strs = line.split(' ')\n length, tree = read_subtree(strs)\n\n print(sumup_meta(tree))\n","sub_path":"out/production/adventofcode2018/task16.py","file_name":"task16.py","file_ext":"py","file_size_in_byte":1004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"255992358","text":"#!/usr/bin/env python3\n\nimport socket, configparser, sys\nfrom time import time\n\nfrom bot import Bot\n\n\ndef main():\n last_check = int(time())\n check_interval = 60\n\n config = configparser.ConfigParser()\n config.read('config.ini')\n\n admins = config['IRC']['admins'].split(' ')\n impbot = Bot(config)\n\n s = socket.socket()\n impbot.connect_to_server(s)\n\n for CHAN in admins:\n impbot.connect_to_channel(CHAN)\n\n readbuffer = ''\n\n while True:\n if (int(time()) - last_check) >= check_interval:\n last_check = int(time())\n impbot.update_status()\n\n response = s.recv(1024).decode('utf-8')\n readbuffer = readbuffer + response\n temp = readbuffer.split('\\r\\n')\n readbuffer = temp.pop()\n\n for line in temp:\n backlog = len(temp) - 1\n print(backlog, end='')\n\n impbot.recieved_message(line)\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"603712295","text":"# coding: utf-8\n# 2019/11/26 @ tongshiwei\n\nfrom EduSim.Envs.KS import KS as BaseKS\n\ngraph_edges = [\n (0, 1),\n (0, 2),\n (1, 3),\n (2, 4),\n (2, 8),\n (3, 4),\n (4, 8),\n (5, 4),\n (5, 9),\n (6, 7),\n (7, 8),\n (8, 9),\n]\n\n\nclass KS(BaseKS):\n pass\n\n\ndef get_knowledge_structure():\n ks = KS()\n ks.add_edges_from(graph_edges)\n return ks\n","sub_path":"EduSim/Envs/KSS/KS.py","file_name":"KS.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"250050380","text":"import os\n\nfrom esass_utils import *\n\n\"\"\"\nRun all steps of the eSASS analysis in required order.\n\"\"\"\nsubdir = './'\nevt_dir = \"%s/events\" % subdir\noutfile = \"products/\"\noutfile_suffix = \"post\"\nsuffix_srctool = \"_001_t%s\" % version\n\nproduct_dir = \"%s/%s\" % (subdir, outfile)\nsrctool_dir = \"%s/srctool_products\" % product_dir\nmake_dir(product_dir)\nmake_dir(srctool_dir)\n\n# Choose energy bands for analysis\nemin_ev = [100, 500, 2000, 5000]\nemax_ev = [500, 2000, 5000, 10000]\nemin_kev = [0.1, 0.5, 2.0, 5.0]\nemax_kev = [0.5, 2.0, 5.0, 10.0]\njoined_e_min_ev = ' '.join(emin_ev)\njoined_e_max_ev = ' '.join(emax_ev)\ne_band = [\"1\", \"2\", \"3\", \"4\"]\nccd = [1, 2, 3, 4, 5, 6, 7]\n\n# Subselect eSASS tasks to perform\neband_selected = [0, 1, 2, 3]\ndo_evtool = True\ndo_expmap = True\ndo_ermask = True\ndo_erbox_local = True\ndo_erbackmap = True\ndo_erbox_m = True\ndo_ermldet = True\ndo_catprep = True\ndo_srctool = False\n\n\n\"\"\"\nImage each of the four energy bands.\nConstruct exposure map for each CCD event list +\na merged exposure map.\n\"\"\"\nfor ii in range(len(eband_selected)):\n index = eband_selected[ii]\n outfile_evtool.append(\"%s02%s_EventList_%s.fits\" % (os.path.join(subdir, outfile), eband[index], outfile_suffix))\n srcmaps.append(\"%s02%s_SourceMap_%s\" % (os.path.join(subdir, outfile), eband[index], outfile_suffix))\n\n\n infile_expmap.append(outfile_evtool[ii])\n\n for jj in range(len(ccd)): # if making exp map for each ccd\n expmaps.append(\n \"%s%d2%s_ExposureMap_%s\" % (os.path.join(subdir, outfile), ccd[jj], eband[index], outfile_suffix))\n expmap_all.append(\"%s02%s_ExposureMap_%s\" % (os.path.join(subdir, outfile), eband[index], outfile_suffix))\n emin.append(\"%f\" % (emin_kev[index]))\n emax.append(\"%f\" % (emax_kev[index]))\n emin_ev_str.append(\"%ld\" % (emin_ev[index]))\n emax_ev_str.append(\"%ld\" % (emax_ev[index]))\n\n print('expmap_all ', (\" \").join(expmap_all))\n print((\" \").join(emin))\n\nif do_expmap == True:\n for kk in range(len(eband)):\n # expmap\n print(cmd)\n subprocess.check_call(cmd)\n print('final test')\n\n# ------------------------------------------------------------------------------\n\"\"\"\nDetection mask.\n\"\"\"\ndetmask = \"%s020_DetectionMask_%s\" % (os.path.join(subdir, outfile), outfile_suffix)\ncmd = [\"ermask\",\n \"expimage=%s\" % (expmap_all[0]),\n # use the first exposure maps calculated for that skyfield, independent of the energy band\n \"detmask=%s\" % (detmask),\n \"threshold1=0.2\",\n \"threshold2=0.9\", # 1.0\n \"regionfile_flag=no\"\n ]\nif (do_ermask == True):\n if (os.path.isfile(detmask) == True):\n os.remove(detmask)\n print(cmd)\n subprocess.check_call(cmd)\n\nboxlist_l = \"%s020_BoxDetSourceListL_%s\" % (os.path.join(subdir, outfile), outfile_suffix)\n\ncmd = [\"erbox\",\n \"images=%s\" % ((\" \").join(outfile_evtool)),\n \"boxlist=%s\" % (boxlist_l),\n \"expimages=%s\" % ((\" \").join(expmap_all)),\n \"detmasks=%s\" % (detmask),\n \"emin=%s\" % ((\" \").join(emin_ev_str)),\n \"emax=%s\" % ((\" \").join(emax_ev_str)),\n \"hrdef=\",\n \"ecf=1.0 1.0 1.0 1.0\",\n \"nruns=3\",\n \"likemin=6.0\",\n \"boxsize=4\",\n \"compress_flag=N\",\n \"bkgima_flag=N\",\n \"expima_flag=Y\",\n \"detmask_flag=Y\"\n ]\n\nerbox\nif (do_erbox_local == True):\n if (os.path.isfile(boxlist_l) == True):\n os.remove(boxlist_l)\n print(cmd)\n subprocess.check_call(cmd)\n\n# ------------------------------------------------------------------\n\nfor ii in range(len(eband_selected)):\n index = eband_selected[ii]\n cheesemask.append(\"%s02%s_CheeseMask_%s\" % (os.path.join(subdir, outfile), eband[index], outfile_suffix))\n bkgimage.append(\"%s02%s_BackgrImage_%s\" % (os.path.join(subdir, outfile), eband[index], outfile_suffix))\n\n cmd = [\"erbackmap\",\n \"image=%s\" % (outfile_evtool[ii]),\n \"expimage=%s\" % (expmap_all[ii]),\n \"boxlist=%s\" % (boxlist_l),\n \"detmask=%s\" % (detmask),\n \"cheesemask=%s\" % (cheesemask[ii]),\n \"bkgimage=%s\" % (bkgimage[ii]),\n \"idband=%s\" % (eband_selected[ii]),\n \"scut=0.001\",\n \"mlmin=6\", # GL: 0\n \"maxcut=0.5\",\n \"fitmethod=smooth\",\n \"nsplinenodes=36\",\n \"degree=2\",\n \"smoothflag=yes\",\n \"smoothval=15.\",\n \"snr=40.0\",\n \"excesssigma=10000.\",\n \"nfitrun=1\",\n \"cheesemaskflag=Y\"\n ]\n if (do_erbackmap == True):\n if (os.path.isfile(cheesemask[ii]) == True):\n os.remove(cheesemask[ii])\n if (os.path.isfile(bkgimage[ii]) == True):\n os.remove(bkgimage[ii])\n print(cmd)\n subprocess.check_call(cmd)\n\n# --------------------------------------------------------------------------\n\nboxlist_m = \"%s020_BoxDetSourceListM_%s\" % (os.path.join(subdir, outfile), outfile_suffix)\ncmd = [\"erbox\",\n \"images=%s\" % ((\" \").join(outfile_evtool)),\n \"boxlist=%s\" % (boxlist_m),\n \"expimages=%s\" % ((\" \").join(expmap_all)),\n \"detmasks=%s\" % (detmask),\n \"bkgimages=%s\" % ((\" \").join(bkgimage)),\n \"emin=%s\" % ((\" \").join(emin_ev_str)),\n \"emax=%s\" % ((\" \").join(emax_ev_str)),\n \"hrdef=\",\n \"ecf=1.0 1.0 1.0 1.0\",\n \"nruns=3\",\n \"likemin=6.\", # GL: 4\n \"boxsize=4\",\n \"compress_flag=N\",\n \"bkgima_flag=Y\",\n \"expima_flag=Y\",\n \"detmask_flag=Y\"\n ]\nif (do_erbox_m == True):\n if (os.path.isfile(boxlist_m) == True):\n os.remove(boxlist_m)\n print(cmd)\n subprocess.check_call(cmd)\n\n# ----------------------------------------------------------------------------\n\n\n# ----------------------------------------------------------------\n# Define inputs\ninfile = \"%s/merged_agn.fits\" % evt_dir # merged calibrated event file\n\n# Define products of eSASS pipeline\nevt_img_files = [os.path.join(outdir, f\"{outprefix}02{bname}_EvtImg.fits\") for bname in bandname]\njoined_imgs = ' '.join(evt_img_files)\n\nsrc_map_files = [os.path.join(outdir, f\"{outprefix}02{bname}_SrcMap.fits\") for bname in bandname]\njoined_src_maps = ' '.join(src_map_files)\n\nexp_map_files = [os.path.join(outdir, f\"{outprefix}02{bname}_ExpMap.fits\") for bname in bandname]\njoined_exp_maps = ' '.join(exp_map_files)\n\nbkg_map_files = [os.path.join(outdir, f\"{outprefix}02{bname}_BkgImg.fits\") for bname in bandname]\njoined_bkg_maps = ' '.join(bkg_map_files)\n\nche_msk_files = [os.path.join(outdir, f\"{outprefix}02{bname}_CheMsk.fits\") for bname in bandname]\n\ndet_mask = os.path.join(outdir, f\"{outprefix}020_DetMsk.fits\")\n\nbox_cat_1 = os.path.join(outdir, f\"{outprefix}020_BoxCa1.fits\")\n\nbox_cat_2 = os.path.join(outdir, f\"{outprefix}020_BoxCa2.fits\")\n\nml_cat = os.path.join(outdir, f\"{outprefix}020_MLCat.fits\")\n\nsrc_cat = os.path.join(outdir, f\"{outprefix}020_SrcCat.fits\")\n\n\n\nif __name__ == '__main__':\n for i in range(len(e_band)):\n evtool(do_evtool, infile, evt_img_files[i], emin_kev[i], emax_kev[i])\n\n expmap(do_expmap, infile, evt_img_files[0],\n\n ermldet(do_ermldet, ml_cat, box_cat_2, joined_imgs, joined_exp_maps,\n det_mask, joined_bkg_maps, joined_e_min_ev, joined_e_max_ev, joined_src_maps))\n catprep(do_catprep, ml_cat, src_cat)\n srctool_lc(do_srctool, infile, os.path.join(subdir, srctool_dir, outfile), suffix_srctool, src_catalogue)\n","sub_path":"src/data/tmp/run_esass_test.py","file_name":"run_esass_test.py","file_ext":"py","file_size_in_byte":7346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"542593730","text":"# coding: utf-8\nimport itertools\n\nimport pandas as pd\n\n\ndef dimension_levels(dimension_key, dimension):\n if 'display_field' in dimension:\n return [dimension_key, dimension['display_field']]\n return [dimension_key]\n\n\ndef wrap_list(value):\n return value if isinstance(value, (tuple, list)) else [value]\n\n\ndef slice_first(item):\n if isinstance(item, (tuple, list)):\n return item[0]\n return item\n\n\ndef correct_dimension_level_order(dataframe, display_schema):\n if isinstance(dataframe.index, pd.MultiIndex):\n dimension_orders = [order\n for key, dimension in display_schema['dimensions'].items()\n for order in dimension_levels(key, dimension)]\n\n dataframe = dataframe.reorder_levels(dataframe.index.names.index(level)\n for level in dimension_orders)\n\n metrics = list(display_schema['metrics'])\n if display_schema.get('references'):\n references = [''] + list(display_schema['references'])\n return dataframe[list(itertools.product(references, metrics))]\n\n return dataframe[metrics]\n\n\ndef filter_duplicates(iterable):\n filtered_list, seen = [], set()\n for item in iterable:\n key = slice_first(item)\n\n if key in seen:\n continue\n\n seen.add(key)\n filtered_list.append(item)\n\n return filtered_list\n","sub_path":"fireant/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"209589193","text":"\"\"\"This demo colors a given mesh entities such that entities with the\nsame color are not neighbors. 'Neighbors' can be in the sense of shared\nvertices, edges or facets, or a user-provided tuple defintion\"\"\"\n\n# Copyright (C) 2010-2011 Garth N. Wells\n#\n# This file is part of DOLFIN.\n#\n# DOLFIN is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Lesser 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# DOLFIN 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 Lesser General Public License for more details.\n#\n# You should have received a copy of the GNU Lesser General Public License\n# along with DOLFIN. If not, see .\n#\n# Modified by Anders Logg, 2010.\n#\n# First added: 2010-11-16\n# Last changed: 2010-11-17\n\nfrom dolfin import *\n\n# Create mesh\nmesh = UnitCube(24, 24, 24)\n\n# Compute vertex-based coloring\ncolors = mesh.color(\"vertex\")\nplot(colors, title=\"Vertex-based cell coloring\", interactive=True)\n\n# Compute edge-based coloring\ncolors = mesh.color(\"edge\")\nplot(colors, title=\"Edge-based cell coloring\", interactive=True)\n\n# Compute facet-based coloring\ncolors = mesh.color(\"facet\")\nplot(colors, title=\"Facet-based cell coloring\", interactive=True)\n\n# Compute facet-based coloring with distance 2\ndim = mesh.topology().dim()\ncoloring_type = (dim, dim - 1, dim, dim - 1, dim)\ncolors = mesh.color(coloring_type)\nplot(colors, title=\"Facet-based cell coloring with distance 2\", interactive=True)\n","sub_path":"docs/dolfin/1.0.beta/python/source/demo/undocumented/coloring/python/demo_coloring.py","file_name":"demo_coloring.py","file_ext":"py","file_size_in_byte":1699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"477618432","text":"#!/usr/bin/env python\nimport rospy\nimport termios, tty, os, time, sys, math\n#import mobile_base_driver.msg\n\nfrom std_msgs.msg import String\nfrom mobile_base_driver.msg import ChestLeds\nfrom mobile_base_driver.msg import Led\n\ndef getch():\n fd = sys.stdin.fileno()\n old_settings = termios.tcgetattr(fd)\n try:\n tty.setraw(sys.stdin.fileno())\n ch = sys.stdin.read(1)\n\n finally:\n termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)\n return ch\n\ndef rainbow(frequency1, frequency2, frequency3, phase1, phase2, phase3):\n pub = rospy.Publisher('/mobile_base/commands/chest_leds', ChestLeds, queue_size = 10)\n rospy.init_node('kuri_light')\n rate = rospy.Rate(100)\n l = ChestLeds()\n\n print(\"kuri rainbow working\")\n center = 128;\n width = 127;\n length = 50;\n\n for i in range(length):\n for p in range(len(l.leds)):\n l.leds[p].red = math.sin(frequency1*i + phase1) * width + center;\n l.leds[p].green = math.sin(frequency2*i + phase2) * width + center;\n l.leds[p].blue = math.sin(frequency3*i + phase3) * width + center;\n pub.publish(l)\n time.sleep(0.1)\n\ndef run():\n pub = rospy.Publisher('/mobile_base/commands/chest_leds', ChestLeds, queue_size = 10)\n rospy.init_node('kuri_light')\n rate = rospy.Rate(100)\n l = ChestLeds()\n\n print(\"Light Program Started\")\n for i in range(len(l.leds)):\n l.leds[i].red = 255\n l.leds[i].green = 255\n l.leds[i].blue = 255\n button_delay = 0.2\n\n while not rospy.is_shutdown():\n try:\n letter = getch()\n if (letter == \"r\"):\n print(\"___you pressed red\")\n for i in range(len(l.leds)):\n l.leds[i].red = 255\n l.leds[i].green = 0\n l.leds[i].blue = 0\n #print( l.leds[i])\n\n time.sleep(button_delay)\n if (letter == \"g\"):\n print(\"___you pressed green___\")\n for i in range(len(l.leds)):\n l.leds[i].red = 0\n l.leds[i].green = 255\n l.leds[i].blue = 0\n #print( l.leds[i])\n\n time.sleep(button_delay)\n if (letter == \"b\"):\n print(\"___you pressed blue___\")\n for i in range(len(l.leds)):\n l.leds[i].red = 0\n l.leds[i].green = 0\n l.leds[i].blue = 255\n #print( l.leds[i])\n\n time.sleep(button_delay)\n\n if(letter == \"q\"):\n rainbow(.3, .3, .3, 0, 2, 4)\n\n\n if (letter == \"c\"):\n print(\"Teleop ended.\")\n exit(0)\n\n \n pub.publish(l)\n rate.sleep()\n except:\n pass\n\n\nif __name__ == '__main__':\n run()","sub_path":"src/kuri_mi/src/kuri_light.py","file_name":"kuri_light.py","file_ext":"py","file_size_in_byte":2914,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"261693518","text":"# coding:utf-8\nfrom collections import Counter\n\nR, C, K = map(int, input().split())\nN = int(input())\nr = [0]*R\nc = [0]*C\nstep = []\nfor _ in range(N):\n x, y = map(int, input().split())\n r[x-1] += 1\n c[y-1] += 1\n step.append((x-1, y-1))\n\nans = 0\n\nrnum = Counter(r)\ncnum = Counter(c)\nfor k, v in rnum.items():\n # try,exceptがなくてもAC\n try:\n ans += v*cnum[K-k]\n except Exception:\n continue\n\nfor (x, y) in step:\n if r[x]+c[y]==K: # 二重カウントが起きてansにカウントされてる\n ans -= 1\n if r[x]+c[y]==K+1: # 二重カウントが起きてansから漏れた\n ans += 1\n\nprint(ans)\n","sub_path":"atcoder/ABC/023/023c.py","file_name":"023c.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"193230560","text":"#!/usr/bin/env python\n\nimport os\nimport pprint\nimport re\nimport sys\n\nsys.path.insert(0, \"%s/work/mutant/ec2-tools/lib/util\" % os.path.expanduser(\"~\"))\nimport Cons\nimport Util\n\nimport Conf\nimport CpuAvg\nimport DstatLog\nimport ProcMemLog\nimport YcsbLog\n\nsys.path.insert(0, \"%s/RocksdbLog\" % os.path.dirname(__file__))\nimport RocksdbLog\n\nsys.path.insert(0, \"%s/CompareTwo\" % os.path.dirname(__file__))\nimport CompareCpu\nimport CompareMem\n\n\n# (# of the total size) of SSTables that are\n# compacted\n# compaction-migrated (show the efficacy of the integration)\n# migrated\n#\n# Actual storage cost paid\n# How well does Mutant meet the target cost SLOs\n\ndef main(argv):\n Util.MkDirs(Conf.GetOutDir())\n PlotCseVsAll()\n\n # TODO: clean up\n #PlotTimeVsAllMetrics()\n #PlotCompareTwo()\n\n\ndef PlotCseVsAll():\n # Cost SLO epsilon vs all metrics\n (fn_cse_vs_all, linear_reg_params) = RocksdbLog.GetFnCostSloEpsilonVsMetrics()\n #Cons.P(linear_reg_params)\n\n fn_out = \"%s/cost-slo-epsilon-vs-metrics.pdf\" % Conf.GetOutDir()\n\n with Cons.MT(\"Plotting cost SLO epsilon vs metrics ...\"):\n env = os.environ.copy()\n env[\"FN_CSE_VS_ALL\"] = fn_cse_vs_all\n env[\"LINEAR_REG_PARAMS\"] = linear_reg_params\n env[\"FN_OUT\"] = fn_out\n Util.RunSubp(\"gnuplot %s/cost-slo-epsilon-vs-metrics.gnuplot\" % os.path.dirname(__file__), env=env)\n Cons.P(\"Created %s %d\" % (fn_out, os.path.getsize(fn_out)))\n\n \ndef PlotTimeVsAllMetrics():\n dn_base = Conf.GetDir(\"dn_base\")\n for i in range(2):\n fn_ycsb = \"%s/%s\" % (dn_base, Conf.Get(i))\n _PlotTimeVsAllMetrics(fn_ycsb)\n\n\n# Not parallizable cause DstatLog is not. Not checked with RocksdbLog.\ndef _PlotTimeVsAllMetrics(fn_ycsb_log):\n # 171121-194901/ycsb/171122-010708.903-d\n mo = re.match(r\"(?P.+)/(?P\\d\\d\\d\\d\\d\\d-\\d\\d\\d\\d\\d\\d)/ycsb/(?P\\d\\d\\d\\d\\d\\d-\\d\\d\\d\\d\\d\\d\\.\\d\\d\\d).+\", fn_ycsb_log)\n dn_log = mo.group(\"dn_log\")\n job_id = mo.group(\"job_id\")\n exp_dt = mo.group(\"exp_dt\")\n #Cons.P(dn_log)\n #Cons.P(job_id)\n #Cons.P(exp_dt)\n\n fn_out = \"%s/time-vs-all-metrics-%s.pdf\" % (Conf.GetOutDir(), exp_dt)\n if os.path.exists(fn_out):\n Cons.P(\"%s %d already exists.\" % (fn_out, os.path.getsize(fn_out)))\n return\n\n (fn_ycsb, time_max, params1) = YcsbLog.GenDataMetricsByTime(fn_ycsb_log, exp_dt)\n #Cons.P(\"%s\\n%s\\n%s\" % (fn_ycsb, time_max, params1))\n #time_max = \"00:30:00\"\n\n params_formatted = fn_ycsb_log + \"\\n\" + pprint.pformat(params1[0]) + \"\\n\" + pprint.pformat(params1[1])\n # No idea how to put spaces for the indentations. It used to work.\n # Neither replace(\" \", \"\\ \") or replace(\" \", \"\\\\ \") worked when a line starts with spaces followed by digits or [.\n # work when it is followed by u. I guess regular characters.\n params_formatted = params_formatted.replace(\"_\", \"\\\\\\\\_\").replace(\"\\n\", \"\\\\n\").replace(\"{\", \"\\{\").replace(\"}\", \"\\}\")\n #Cons.P(params_formatted)\n\n dn_log_job = \"%s/%s\" % (dn_log, job_id)\n\n (fn_dstat, num_stgdevs) = DstatLog.GetPlotFn1(dn_log_job, exp_dt)\n fn_rocksdb = RocksdbLog.GetFnTimeVsMetrics(fn_ycsb_log)\n\n fn_cpu_avg = CpuAvg.GetFnForPlot(fn_ycsb_log)\n fn_mem_usage = ProcMemLog.GetFnForPlot(dn_log, job_id, exp_dt)\n\n with Cons.MT(\"Plotting ...\"):\n env = os.environ.copy()\n env[\"PARAMS\"] = params_formatted\n env[\"NUM_STGDEVS\"] = str(num_stgdevs)\n env[\"TIME_MAX\"] = str(time_max)\n env[\"IN_FN_DSTAT\"] = fn_dstat\n env[\"IN_FN_YCSB\"] = fn_ycsb\n env[\"IN_FN_ROCKSDB\"] = fn_rocksdb\n env[\"IN_FN_CPU_AVG\"] = fn_cpu_avg\n env[\"IN_FN_MEM\"] = fn_mem_usage\n env[\"OUT_FN\"] = fn_out\n Util.RunSubp(\"gnuplot %s/time-vs-all-metrics.gnuplot\" % os.path.dirname(__file__), env=env)\n Cons.P(\"Created %s %d\" % (fn_out, os.path.getsize(fn_out)))\n\n\ndef PlotCompareTwo():\n (fns_rocksdb, fn_sst_creation_stat) = RocksdbLog.GenDataFilesForGnuplot()\n #fn_cpu_stat_by_time = CompareCpu.GetHourlyFn()\n fn_cpu_1min_avg = CompareCpu.Get1minAvgFn()\n fn_mem_stat_by_time = CompareMem.GetHourlyFn()\n fn_mem_1min_avg = CompareMem.Get1minAvgFn()\n #time_max = \"09:00:00\"\n #time_max = \"08:00:00\"\n time_max = \"07:50:00\"\n\n exp_dts = []\n for i in range(2):\n mo = re.match(r\".+/(?P\\d\\d\\d\\d\\d\\d-\\d\\d\\d\\d\\d\\d\\.\\d\\d\\d)-d\", Conf.Get(i))\n exp_dts.append(mo.group(\"exp_dt\"))\n fn_out = \"%s/mutant-overhead-%s.pdf\" % (Conf.GetOutDir(), \"-\".join(exp_dts))\n\n with Cons.MT(\"Plotting ...\"):\n env = os.environ.copy()\n env[\"TIME_MAX\"] = str(time_max)\n #env[\"CPU_STAT\"] = fn_cpu_stat_by_time\n env[\"FN_CPU_1MIN_AVG\"] = fn_cpu_1min_avg\n #env[\"MEM_STAT\"] = fn_mem_stat_by_time\n env[\"FN_MEM_1MIN_AVG\"] = fn_mem_1min_avg\n env[\"ROCKSDB0\"] = fns_rocksdb[0]\n env[\"ROCKSDB1\"] = fns_rocksdb[1]\n env[\"OUT_FN\"] = fn_out\n Util.RunSubp(\"gnuplot %s/compare-two-exps.gnuplot\" % os.path.dirname(__file__), env=env)\n Cons.P(\"Created %s %d\" % (fn_out, os.path.getsize(fn_out)))\n\n\nif __name__ == \"__main__\":\n sys.exit(main(sys.argv))\n","sub_path":"rocksdb/eval/mutant-overhead/cost-slo-epsilons-vs-metrics/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":4883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"562063959","text":"import pyautogui\nimport time\n\n# 초반 에러를 없애기 위해 screenshot 메서드를 이용해 스크린샷을 찍어 five.png를 찍어옵니다\n# im3 = pyautogui.screenshot('potion.png', region=(622, 183, (674-622), (235-183)))\n# print(im3)\n# locateOnScreen() 에 그림('five.PNG')를 설정해주면,\n# 아래와 같이 화면 상에서 일치하는 영역을 찾아서\n# 왼쪽 위의 위치와 영역의 가로, 세로 크기를 튜플의 형태((left, top, width, height))로 출력합니다.\n# 해당 영역을 찾지 못하면 None을 반환합니다.\n# potion_sig = pyautogui.locateOnScreen('potion.png')\n# print(five_btn)\n# print(potion_sig)\n# 테스트\n\nwhile True:\n time.sleep(1)\n potion_sig = pyautogui.locateOnScreen('potion.png', grayscale=True, confidence=.6)\n if potion_sig:\n pyautogui.press('o')\n break\n\n","sub_path":"macro0_1_a/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":851,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"85138984","text":"'''\nInput: an array of space separated numbers\nOutput: sorted array of numbers\n'''\n\nfrom sys import argv\n\n\ndef main(array):\n N = len(array)\n # From N - 1 to 1 (highest index to second lowest)\n # In first loop, shrink the working array from the right side. Since each iteration\n # will push the largest element to the right side, we shrink the array since that\n # element is in its final position.\n for i in range(N - 1, 0, -1):\n # From 0 to i - 1 (lowest index to second highest index up to position i)\n # i - 1 b/c we want to be able to reference the current element and the next\n # element (which isn't possible if we go up to i)\n for j in range(0, i):\n print(i, j, N)\n if (array[j] > array[j + 1]):\n array[j], array[j + 1] = array[j + 1], array[j]\n print(array)\n\n\narray = list(map(int, argv[1:]))\nmain(array)\n","sub_path":"bubble-sort/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"454456193","text":"def wordcount(str): \n text=str.split()\n count=dict()\n for i in text:\n if i in count:\n count[i]=count[i]+1\n else :\n count[i]=1\n return count\n \nstr1=input(\"Enter The String: \")\nprint(wordcount(str1))\n","sub_path":"1.2.1.wordcount.py","file_name":"1.2.1.wordcount.py","file_ext":"py","file_size_in_byte":233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"607033632","text":"\"\"\"Create carssparepart Table\n\nRevision ID: c7ef87ce5116\nRevises: 6a422b8dbe6d\nCreate Date: 2020-11-19 20:46:35.101249\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'c7ef87ce5116'\ndown_revision = '6a422b8dbe6d'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n op.create_table(\n 'carspareparts',\n sa.Column('CarsId', sa.Integer, sa.ForeignKey('cars.CarsId'), primary_key=True),\n sa.Column('SparepartId', sa.Integer, sa.ForeignKey('spareparts.SparepartId'), primary_key=True),\n )\n\n\ndef downgrade():\n op.drop_table('carspareparts')","sub_path":"App/DB/migrations/versions/c7ef87ce5116_create_carsspareparts_table.py","file_name":"c7ef87ce5116_create_carsspareparts_table.py","file_ext":"py","file_size_in_byte":626,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"149579273","text":"import json\n\ndef show_menu(): \n print(\"1. Ask question\")\n print(\"2. Add a question\")\n print(\"3. Print highscores\")\n print(\"4. Exit\")\n \n return input(\"Enter choice: \")\n\ndef ask_questions():\n score = 0\n n_answers_right = 0\n total_questions = 0\n highscore = {}\n highscore[\"name\"] = input(\"Enter player name: \")\n \n with open(\"questions.txt\", \"r\") as f: \n lines = f.read().split(\"\\n\")\n lines = lines[:-1]\n \n \n for line in lines:\n total_questions += 1\n qa = line.split(\"|\")\n answer = input(qa[0] + \" \")\n if qa[1].lower() == answer.lower(): \n print(\"right answer\")\n score+=2\n n_answers_right += 1\n print(\"score: {0}\".format(score))\n else: \n print(\"wrong answer! The correct answer is: {0}\".format(qa[1]))\n score-=1\n print(\"score: {0}\".format(score))\n \n highscore[\"score\"] = score\n with open('highscores.txt', 'a') as outfile: \n j = json.dumps(highscore) + \"\\n\"\n outfile.write(j) \n \n print(\"you got {0} answer right out of {1}. Your score is {2}\".format(n_answers_right, total_questions, score)) \n \ndef add_question():\n question = input(\"Enter a question: \")\n answer = input(\"Okay, then tell me: \" + question.lower() +\": \")\n text = question + \"|\" + answer + \"\\n\" \n \n with open(\"questions.txt\", \"a\") as f:\n f.write(text)\n\ndef show_highscores():\n with open(\"highscores.txt\") as f: \n scores = f.read()\n print(scores)\n\ndef main():\n while True: \n choice = show_menu()\n \n if choice == \"1\": \n ask_questions()\n \n if choice == \"2\":\n add_question()\n \n if choice == \"3\": \n show_highscores()\n \n if choice == \"4\": \n break \nmain()\n","sub_path":"quiz.py","file_name":"quiz.py","file_ext":"py","file_size_in_byte":1869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"622385174","text":"import tensorflow as tf\nfrom sklearn.metrics import confusion_matrix\nimport numpy as np\n\nfrom tools.preprocessing import preprocess_images, preprocess_single_image\nfrom tools.kfold import KFold_cross_validation_split\nfrom tools.extraction_and_metrics import extract_features, compute_confusion_matrix\n\nfrom .network import Net\n\nimport torchvision.models as models\nimport torch\n\nimport os\nimport cv2\n\n# Feature composer training\ndef train_feature_composer(\n composed_dataset_path: str,\n epochs: int,\n batch_size: int,\n num_classes: int,\n folds: int,\n lr:float,\n cuda: bool,\n ckpt_dir: str\n):\n \"\"\"\n Feature extractor training.\n\n params:\n composed_dataset_path\n epochs\n batch_size\n num_classes\n folds: Number of folds for KFold cross validation \n lr: Learning rate\n cuda: Whether to use GPU or not\n ckpt_dir: Model's location\n \"\"\"\n\n # Preprocess images, returning the classes, features and labels\n class_names, x, y = preprocess_images(\n dataset_path=composed_dataset_path, \n width=224, \n height=224, \n num_classes=num_classes, \n framework=\"torch\", \n imagenet=True\n )\n\n # Split data\n X_train, X_test, Y_train, Y_test = KFold_cross_validation_split(\n features=x, \n labels=y, \n n_splits=folds\n )\n\n # Normalize\n X_train /= 255\n X_test /= 255\n\n # Instantiate model\n net = Net(\n models.vgg16(pretrained=True),\n num_classes=num_classes,\n lr=lr,\n cuda=cuda,\n mode=\"feature_composer\",\n ckpt_dir=ckpt_dir,\n labels=class_names\n )\n\n # Train model\n net.fit(\n X_train,\n Y_train,\n X_test,\n Y_test,\n epochs,\n batch_size,\n resume=False\n )\n\n # Confusion matrix\n compute_confusion_matrix(\n y_true=Y_test, \n y_pred=net.infer(X_test), \n framework=\"torch\", \n mode=\"feature_composer\", \n num_classes = num_classes // 2\n )\n\n# Inference\ndef infer(\n ckpt_dir: str, \n ckpt_name: str, \n input_image: str\n) -> dict:\n \"\"\"\n Main inference method.\n\n params:\n ckpt_dir: Saved model's directory\n ckpt_name: Saved model's name\n input_image: Image path\n\n returns:\n Dictionary containing the predictions with their levels of confidence.\n E.g.: {\n COVID19_1:0.10\n COVID19_2:0.15\n ...\n }\n \"\"\"\n ckpt_path = os.path.join(ckpt_dir, ckpt_name)\n num_classes = torch.load(ckpt_path, map_location=lambda storage, loc: storage)[\"num_classes\"]\n \n # Instantiate model\n net = Net(\n models.vgg16(pretrained=True),\n num_classes=num_classes,\n mode=\"feature_composer\",\n ckpt_dir=ckpt_dir\n )\n \n # Load model\n net.load_model_for_inference(os.path.join(ckpt_dir, ckpt_name))\n \n # Check if inputed file is an image.\n assert input_image.lower().endswith(\"png\") or input_image.lower().endswith(\"jpg\") or input_image.lower().endswith(\"jpeg\")\n\n # Preprocess\n img = preprocess_single_image(\n img=input_image, \n width=224, \n height=224, \n imagenet=True, \n framework=\"torch\"\n )\n\n # Return prediction\n return net.infer(img, ckpt_path = os.path.join(ckpt_dir, ckpt_name), use_labels=True)\n","sub_path":"src/frameworks/detrac_torch/feature_composer.py","file_name":"feature_composer.py","file_ext":"py","file_size_in_byte":3481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"130881285","text":"import pdb\n\ndef confusion_matrix(x, y, prediction):\n true_positive = 0\n true_negative = 0\n false_positive = 0\n false_negative = 0\n for i in range(0, len(x)):\n p = 1 if prediction[i] > 0.5 else 0\n if p == 1 and y[i] == 1:\n true_positive += 1\n if p == 0 and y[i] == 0:\n true_negative += 1\n if p == 1 and y[i] == 0:\n false_positive += 1\n if p == 0 and y[i] == 1:\n false_negative += 1\n return (true_positive, false_positive, false_negative, true_negative)\n\ndef precision_recall(x, y, prediction):\n (true_positive, false_positive, false_negative, true_negative) = confusion_matrix(x, y, prediction)\n precision = float(true_positive)/(true_positive + false_positive)\n recall = float(true_positive)/(true_positive + false_negative)\n return (precision, recall)\n","sub_path":"model_analysis.py","file_name":"model_analysis.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"40745719","text":"# !/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2017/10/24 16:16\n# @Author : abc\nimport os\nimport datetime\nimport jieba\nimport math\nimport time\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nfrom utils.sdata_helper import batch_iter\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom sklearn.model_selection import train_test_split\n\n\n# 构建模型\nclass TextCNN(object):\n def __init__(self, env_conf, model_json):\n self._env_conf = env_conf\n self._model_json = model_json\n # 参数转内部\n self.max_features = self._model_json[\"max_features\"]\n self.maxlen = self._model_json[\"maxlen\"]\n self.embedding_size = self._model_json[\"embedding_size\"]\n self.batch_size = self._model_json[\"batch_size\"]\n self.num_epochs = self._model_json[\"num_epochs\"]\n self.max_learning_rate = self._model_json[\"max_learning_rate\"]\n self.min_learning_rate = self._model_json[\"min_learning_rate\"]\n self.decay_coefficient = self._model_json[\"decay_coefficient\"]\n self.dropout_keep_prob_outnn = self._model_json[\"dropout_keep_prob\"]\n self.evaluate_every = self._model_json[\"evaluate_every\"]\n self.early_stop = self._model_json[\"early_stop\"]\n self.save_step = self._model_json[\"save_step\"]\n self.num_filters = self._model_json[\"num_filters\"]\n self.filter_sizes = self._model_json[\"filter_sizes\"]\n self.l2_reg_lambda = self._model_json[\"l2_reg_lambda\"]\n self.model_name = self._model_json[\"model_name\"]\n self.default_g = None\n\n def build(self):\n with tf.Graph().as_default():\n l2_loss = tf.constant(0.0)\n self.default_g = l2_loss.graph\n # 1. 输入层\n with tf.name_scope('input'):\n # sequence_length 即 训练文本的单行长度\n self.input_x = tf.placeholder(tf.int32, [None, self.length_y], name='input_x')\n self.input_y_l = tf.placeholder(tf.float32, [None, self.label_length_l], name='input_y_l')\n self.input_y_r = tf.placeholder(tf.float32, [None, self.label_length_r], name='input_y_r')\n self.input_y_m = tf.placeholder(tf.float32, [None, self.label_length_m], name='input_y_m')\n self.dropout_keep_prob = tf.placeholder(tf.float32, name='dropout_keep_prob')\n self.learning_rate = tf.placeholder(tf.float32, [], name='learning_rate')\n\n # 2. embedding层\n with tf.name_scope('embedding'):\n # vocab_size 即 max_features\n self.W = tf.Variable(tf.random_uniform([self.max_features, self.embedding_size], -1.0, 1.0),\n name='W', trainable=True)\n # [batch_size,sequence_length,embedding_size]\n self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)\n # [batch_size,sequence_length,embedding_size,1]\n # 为了将其应用于conv2d，故需要维度类似于图片，即[batch_size,height,width,channels]\n # 最后的维度1就是channels\n self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)\n\n pooled_outputs = []\n # 3. 卷积和池化层(包含len(filter_sizes)个)\n for i, filter_size in enumerate(self.filter_sizes):\n with tf.name_scope('conv-maxpool-%s' % filter_size):\n # [filter_height,filter_width,filter,in_channels,out_channels]\n filter_shape = [filter_size, self.embedding_size, 1, self.num_filters]\n W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name=\"W\")\n b = tf.Variable(tf.constant(0.1, shape=[self.num_filters]), name=\"b\")\n # [batch_size,sequence_length-filter_size+1,1,num_filters]\n conv = tf.nn.conv2d(\n self.embedded_chars_expanded,\n W,\n strides=[1, 1, 1, 1],\n padding=\"VALID\",\n name=\"conv\")\n # [batch_size,sequence_length-filter_size+1,1,num_filters]\n h = tf.nn.relu(tf.nn.bias_add(conv, b), name=\"relu\")\n # [batch_size,1,1,num_filters]\n # sequence_length 即 self.length_y\n pooled = tf.nn.max_pool(\n h,\n ksize=[1, self.length_y - filter_size + 1, 1, 1],\n strides=[1, 1, 1, 1],\n padding='VALID',\n name=\"pool\")\n pooled_outputs.append(pooled)\n\n # 4. 合并所有pool的输出\n num_filters_total = self.num_filters * len(self.filter_sizes)\n # [batch_size,1,1,num_filter*len(filter_sizes)]\n self.h_pool = tf.concat(pooled_outputs, len(self.filter_sizes))\n # [bathc_size, num_filter*len(filter_sizes)]\n self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])\n\n # 5. Dropout\n with tf.name_scope(\"dropout\"):\n self.h_drop = tf.nn.dropout(self.h_pool_flat, self.dropout_keep_prob)\n\n # 6. 输出分类\n with tf.name_scope(\"output\"):\n W = tf.get_variable(\n \"W\",\n shape=[num_filters_total, self.label_length_l],\n initializer=tf.contrib.layers.xavier_initializer())\n b = tf.Variable(tf.constant(0.1, shape=[self.label_length_l]), name=\"b\")\n l2_loss += tf.nn.l2_loss(W)\n l2_loss += tf.nn.l2_loss(b)\n self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name=\"scores\")\n self.predictions = tf.argmax(self.scores, 1, name=\"predictions\")\n\n # 7. 计算loss\n with tf.name_scope(\"loss\"):\n # loss\n losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels=self.input_y_l)\n # 正则化后的loss\n self.loss = tf.reduce_mean(losses) + self.l2_reg_lambda * l2_loss\n tf.summary.scalar('loss', self.loss)\n\n # 8. Accuracy\n with tf.name_scope(\"accuracy\"):\n correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y_l, 1))\n self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, \"float\"), name=\"accuracy\")\n tf.summary.scalar('accuracy', self.accuracy)\n\n # self.lr = tf.Variable(self.learning_rate, name=\"lr\")\n # # self.lr = tf.add(self.learning_rate, tf.constant(0.0), name='lr')\n # tf.summary.scalar('lr', self.lr)\n\n def fit(self, x_train, x_dev, y_train_m, y_dev_m, y_train_r, y_dev_r, y_train_l, y_dev_l):\n bpath = os.path.join(\"..\", \"data\")\n if self.model_name is None:\n self.model_name = time.strftime(\"%Y%m%d%H%M%S\", time.localtime())\n self.model_dir = os.path.join(bpath, \"thinking2\", \"models\", self.model_name)\n if not os.path.exists(self.model_dir):\n os.makedirs(self.model_dir)\n model_name = os.path.join(self.model_dir, \"{}.ckpt\".format(self.model_name))\n log_dir = os.path.join(bpath, \"thinking2\", \"logs\", self.model_name)\n best_loss = 1e9\n best_counter = 0\n # 模型训练\n with self.default_g.as_default():\n # 1. 模型会话\n session_conf = tf.ConfigProto(\n gpu_options=tf.GPUOptions(allow_growth=True),\n allow_soft_placement=True, # 如果指定的设备不存在，允许tf自动分配设备\n log_device_placement=False) # 不打印设备分配日志\n # sess = tf.Session(config=session_conf) # 使用session_conf对session进行配置\n saver = tf.train.Saver(tf.global_variables(), max_to_keep=self.early_stop)\n with tf.Session(config=session_conf) as sess:\n # 2. 用于统计全局的step\n print(\"start\".center(20, \" \").center(80, \"*\"))\n global_step = tf.Variable(0, name=\"global_step\", trainable=False)\n optimizer = tf.train.AdamOptimizer(self.learning_rate)\n tvars = tf.trainable_variables() # 返回需要训练的variable\n # tf.gradients(nn.loss, tvars)，计算loss对tvars的梯度\n grads, _ = tf.clip_by_global_norm(tf.gradients(self.loss, tvars), 5) # 为了防止梯度爆炸，对梯度进行控制\n grads_and_vars = tuple(zip(grads, tvars))\n # train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_steps)\n train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step) # 自动更新global_step\n\n merged = tf.summary.merge_all()\n train_writer = tf.summary.FileWriter(log_dir, sess.graph) # 保存位置\n sess.run(tf.global_variables_initializer())\n batches = batch_iter(np.hstack((x_train, y_train_l, y_train_m, y_train_r)), self.batch_size,\n self.num_epochs)\n decay_speed = self.decay_coefficient * len(y_train_l) / self.batch_size\n counter = 0 # 用于记录当前的batch数\n for batch in batches:\n learning_rate = self.min_learning_rate + (\n self.max_learning_rate - self.min_learning_rate) * math.exp(\n -counter / decay_speed)\n counter += 1\n x_batch = batch[:, :self.length_y]\n y_batch_l = batch[:, self.length_y:self.length_y + self.label_length_l]\n y_batch_m = batch[:,\n self.length_y + self.label_length_l:self.length_y + self.label_length_l + self.label_length_m]\n y_batch_r = batch[:, self.length_y + self.label_length_l + self.label_length_m:]\n # 训练\n feed_dict = {self.input_x: x_batch,\n self.input_y_l: y_batch_l,\n self.input_y_m: y_batch_m,\n self.input_y_r: y_batch_r,\n self.dropout_keep_prob: self.dropout_keep_prob_outnn,\n self.learning_rate: learning_rate}\n _, step, loss, accuracy = sess.run([train_op, global_step, self.loss, self.accuracy], feed_dict)\n current_step = tf.train.global_step(sess, global_step)\n # Evaluate\n if current_step % self.evaluate_every == 0:\n print(\"Evaluation:\")\n feed_dict = {\n self.input_x: x_dev,\n self.input_y_l: y_dev_l,\n self.input_y_m: y_dev_m,\n self.input_y_r: y_dev_r,\n self.dropout_keep_prob: 1.0\n }\n summary, step, loss, accuracy = sess.run(\n [merged, global_step, self.loss, self.accuracy], feed_dict)\n train_writer.add_summary(summary, step)\n time_str = datetime.datetime.now().isoformat()\n print(\"{}: step {}, loss {:g}, acc {:g}\".format(time_str, step, loss, accuracy))\n # print(\"\")\n # 每10步保存一次参数\n if step % self.save_step == 0:\n print(\"保存模型：\", saver.save(sess, model_name, step))\n if best_loss > loss:\n best_loss = loss\n best_counter = 0\n else:\n best_counter += 1\n if self.early_stop < best_counter:\n print(\"保存模型：\", saver.save(sess, model_name, step))\n print(\"training finished!\")\n return 0\n print(\"best_loss: %s, best_counter: %s\" % (best_loss, best_counter))\n\n def predict(self, x_test):\n # 模型训练\n with self.default_g.as_default():\n # 1. 模型会话\n session_conf = tf.ConfigProto(\n gpu_options=tf.GPUOptions(allow_growth=True),\n allow_soft_placement=True, # 如果指定的设备不存在，允许tf自动分配设备\n log_device_placement=False) # 不打印设备分配日志\n # sess = tf.Session(config=session_conf) # 使用session_conf对session进行配置\n with tf.Session(config=session_conf) as sess:\n # predict test set\n all_predictions = []\n test_batches = batch_iter(x_test, self.batch_size, num_epochs=1, shuffle=False)\n for batch in test_batches:\n feed_dict = {\n self.input_x: batch,\n self.dropout_keep_prob: 1.0\n }\n predictions = sess.run([self.predictions], feed_dict)[0]\n all_predictions.extend(list(predictions))\n\n def load_mode(self, modelname):\n pass\n # headname = os.path.join(self.model_dir,self.model_name)\n # saver = tf.train.import_meta_graph('{}.meta'.format(headname))\n # saver.restore(tf.get_default_session(), 'save/filename.ckpt-16000')\n #\n # saver = tf.train.Saver()\n # with tf.Session() as sess:\n # latest_ckpt = tf.train.latest_checkpoint(self.model_dir)\n # if latest_ckpt:\n # \"\"\"Load model from a checkpoint.\"\"\"\n # try:\n # saver.restore(sess, latest_ckpt)\n # except tf.errors.NotFoundError as e:\n # print(\"Can't load checkpoint\")\n\n def data4train(self):\n # 1. 读取数据源\n self._get_standard_data()\n # 2. 清洗数据\n self._data_clean()\n # 3 数据转token\n self._data_tokenize()\n # 4 标签处理\n self._data_label()\n # 5. 数据切分\n self._data_split()\n return self.x_test, self.x_train, self.x_dev, self.y_train_m, self.y_dev_m, self.y_train_r, self.y_dev_r, self.y_train_l, self.y_dev_l\n\n def _get_standard_data(self):\n # 内测版读文件，生产调用接口\n if os.getenv('prtest') is None:\n pass\n else:\n # 1.1 数据读取\n bpath = os.path.join(\"..\", \"data\")\n # train_file = os.path.join(bpath, \"thinking2\", \"question_obj.csv\")\n # test_file = os.path.join(bpath, \"thinking2\", \"predict_obj.csv\")\n train_file = os.path.join(bpath, \"thinking2\", \"train_compare_origin.csv\")\n test_file = os.path.join(bpath, \"thinking2\", \"train_compare_label.csv\")\n dict_file = os.path.join(bpath, \"thinking2\", \"review_obj.csv\")\n self.train_data = pd.read_csv(train_file, header=0, delimiter=\",\")\n self.test_data = pd.read_csv(test_file, header=0, delimiter=\",\")\n dict_pd = pd.read_csv(dict_file, header=0, delimiter=\",\")\n self.dict_points = {dict_pd.loc[i1, \"_id\"]: dict_pd.loc[i1, \"name\"] for i1 in dict_pd.index}\n self.label_list = [i1 for i1 in self.dict_points]\n self.label_fullname_list = [i1 for i1 in self.dict_points.values()]\n self.label_length_r = self.label_length_m = len(self.label_list)\n\n def _data_clean(self):\n if os.getenv('prtest') is None:\n self.test_data.rename(columns={\"description\": \"text\"}, inplace=True)\n self.train_data['mainReviewPoints'] = data_mongo_clean(self.train_data['mainReviewPoints'])\n self.train_data['reviewPoints'] = data_mongo_clean(self.train_data['reviewPoints'])\n else:\n pass\n\n def pree(strdata):\n return \" \".join(jieba.cut(strdata))\n\n self.train_data[\"text\"] = self.train_data[\"text\"].map(pree)\n self.test_data[\"text\"] = self.test_data[\"text\"].map(pree)\n\n def _data_tokenize(self):\n # 建立tokenizer\n tokenizer = Tokenizer(num_words=self.max_features, lower=True)\n tokenizer.fit_on_texts(list(self.train_data['text'].values) + list(self.test_data['text'].values))\n # word_index = tokenizer.word_index\n x_train = tokenizer.texts_to_sequences(list(self.train_data['text'].values))\n self.x_train = pad_sequences(x_train, maxlen=self.maxlen) # padding\n x_test = tokenizer.texts_to_sequences(list(self.test_data['text'].values))\n self.x_test = pad_sequences(x_test, maxlen=self.maxlen) # padding\n # 长度定义\n self.length_x = self.train_data.shape[0]\n self.length_y = self.maxlen\n\n def _data_label(self):\n # y_train = to_categorical(list(train['sentiment'])) # one-hot\n self.train_data = pd.get_dummies(self.train_data, columns=['level'])\n self.list_classes = [i1 for i1 in self.train_data.columns if i1.startswith(\"level_\")]\n self.label_length_l = len(self.list_classes)\n self.y_train_l = self.train_data[self.list_classes].values\n self.y_train_r = np.zeros((self.length_x, self.label_length_r), dtype=int)\n self.y_train_m = np.zeros((self.length_x, self.label_length_m), dtype=int)\n for i1 in range(self.length_x):\n try:\n for i2 in self.train_data.loc[i1, \"mainReviewPoints\"].split(\",\"):\n # y_train_r[i1, label_list.index(i2)] = 1\n self.y_train_m[i1, self.label_fullname_list.index(i2)] = 1\n except Exception as e:\n pass\n try:\n for i2 in self.train_data.loc[i1, \"reviewPoints\"].split(\",\"):\n # y_train_r[i1, label_list.index(i2)] = 1\n self.y_train_r[i1, self.label_fullname_list.index(i2)] = 1\n except Exception as e:\n pass\n\n def _data_split(self):\n # 1.6 划分训练和验证集\n # x_train, x_dev, y_train, y_dev = train_test_split(x_train, y_train, test_size=0.3, random_state=0)\n self.x_train, self.x_dev, self.y_train_m, self.y_dev_m, self.y_train_r, self.y_dev_r, \\\n self.y_train_l, self.y_dev_l = train_test_split(self.x_train, self.y_train_m, self.y_train_r, self.y_train_l,\n test_size=0.2, random_state=0)\n\n\ndef data_mongo_clean(pdser):\n # 清洗 csv 列表\n tmplist = []\n for id1, i1 in enumerate(pdser):\n tmplist.append(\",\".join(i1.lstrip(\"[\").rstrip(\"]\").strip(\" \").strip(\"'\").split(\"', '\")))\n return np.array(tmplist)\n\n\nif __name__ == '__main__':\n pass\n","sub_path":"thinking/models/model_cnn_l.py","file_name":"model_cnn_l.py","file_ext":"py","file_size_in_byte":19072,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"426937069","text":"class Node(object):\n def _init_(self, val, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\n def _repr_(self):\n return 'Node({})'.format(self.val)\nBT = Node(1)\nBT.left = Node(3)\nBT.left.left = Node(6)\nBT.right = Node(4)\nBT.right.left = Node(7)\nBT.right.right = Node(8)\n\ndef serialize_bt(bt):\n components = []\n\n def incorporate(bt, components):\n components.append(str(bt.val))\n if bt.left:\n components.append('L')\n incorporate(bt.left, components)\n if bt.right:\n components.append('R')\n incorporate(bt.right, components)\n components.append('U')\n return ''.join(components)\n\n incorporate(bt, components)\n components.pop()\n return ''.join(components)\n\n\ndef deserialize(string):\n chars = ''\n nodes = []\n next_child = None\n for i, char in enumerate(string):\n if char not in ('L', 'R', 'U'):\n chars += char\n else:\n if not nodes:\n nodes.append(Node(int(chars)))\n elif next_child == 'left':\n nodes[-1].left = Node(int(chars))\n nodes.append(nodes[-1].left)\n elif next_child == 'right':\n nodes[-1].right = Node(int(chars))\n nodes.append(nodes[-1].right)\n elif next_child == 'up':\n nodes.pop()\n if char == 'L':\n next_child = 'left'\n elif char == 'R':\n next_child = 'right'\n elif char == 'U':\n next_child = 'up'\n chars = ''\n return nodes[0]\n\n\ndef deserialize_recursive(string):\n list_ = []\n chars = ''\n for char in string:\n if char in ('L', 'R', 'U'):\n if chars:\n list_.append(int(chars))\n chars = ''\n list_.append(char)\n else:\n chars += char\n list_.reverse()\n\n nodes = [Node(list_.pop())] \n\n def rebuild():\n val = list_.pop()\n if val == 'L':\n nodes[-1].left = Node(list_.pop())\n nodes.append(nodes[-1].left)\n if val == 'R':\n nodes[-1].right = Node(list_.pop())\n nodes.append(nodes[-1].right)\n if val == 'U':\n nodes.pop()\n if list_:\n rebuild()\n\n rebuild()\n return nodes[0]\n","sub_path":"char_ser_deser.py","file_name":"char_ser_deser.py","file_ext":"py","file_size_in_byte":2385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"388564992","text":"from django.db import models\nfrom django.dispatch import receiver\nfrom django.contrib.auth.models import User\nfrom django.db.models.signals import post_save\n\nfrom source.models import Document\n\n_UserProfiles = (\n (1, 'Annotation Verifier'),\n (0, 'Annotation Engineer'),\n (2, 'Super Admin'),\n (3, 'Annotation Trainee'),\n)\n\n\nclass Profile(models.Model):\n \"\"\"\n User profile type i.e annotator, verifier\n \"\"\"\n user = models.OneToOneField(User, on_delete=models.CASCADE)\n type = models.IntegerField(blank=False, default=0, choices=_UserProfiles)\n email_confirmed = models.BooleanField(default=False)\n assigned_document = models.ForeignKey(Document, blank=False, null=True, on_delete=models.CASCADE)\n multi_image_pref = models.IntegerField(blank=True, null=True, default=25)\n\n @receiver(post_save, sender=User)\n def update_user_profile(sender, instance, created, **kwargs):\n if created:\n Profile.objects.create(user=instance)\n instance.profile.save()\n\n def __str__(self):\n return self.user.username\n\n class Meta:\n verbose_name_plural = \"Profiles\"\n","sub_path":"core/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"609765294","text":"print('--- CALCULO PERIMETRO/AREA ---')\nlado = float(input('Digite o lado do retangulo: '))\naltura = float(input('Digite a altura do retangulo'))\narea = lado*altura\nperimetro = 2*lado+2*altura\n\nprint('O retangulo de l = {} e h = {} possui area {} e perimetro {}'.format(lado,altura,area,perimetro))\n\n\n\n","sub_path":"PythonBasico/lista01exercicio.py","file_name":"lista01exercicio.py","file_ext":"py","file_size_in_byte":302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"390040870","text":"from math import *\nimport proteus.MeshTools\nfrom proteus import Domain\nfrom proteus.default_n import * \nfrom proteus.Profiling import logEvent\n \n# Discretization -- input options \nRefinement = 20#45min on a single core for spaceOrder=1, useHex=False\n#Refinement = 40#45min on a single core for spaceOrder=1, useHex=False\ngenMesh=True\nmovingDomain=False\napplyRedistancing=True\nuseOldPETSc=False\nuseSuperlu=False#True\n#timeDiscretization='be'#'vbdf'#'be','flcbdf'\ntimeDiscretization='vbdf'#'vbdf'#'be','flcbdf'\nspaceOrder = 1\nuseHex = True#False\nuseRBLES = 0.0\nuseMetrics = 1.0\napplyCorrection=True\nuseVF = 1.0\nuseOnlyVF = False\nuseRANS = 0 # 0 -- None\n # 1 -- K-Epsilon\n # 2 -- K-Omega\n \n# Discretization \nnd = 2\nif spaceOrder == 1:\n hFactor=1.0\n if useHex:\n basis=C0_AffineLinearOnCubeWithNodalBasis\n elementQuadrature = CubeGaussQuadrature(nd,2)\n elementBoundaryQuadrature = CubeGaussQuadrature(nd-1,2) \t \n else:\n basis=C0_AffineLinearOnSimplexWithNodalBasis\n elementQuadrature = SimplexGaussQuadrature(nd,3)\n elementBoundaryQuadrature = SimplexGaussQuadrature(nd-1,3) \t \n \n# Domain and mesh\n#L = (0.584,0.350)\nL = (1.0,1.0)\nhe = L[0]/float(4*Refinement-1)\n#he*=0.5\n#he*=0.5\n#he*=0.5\n#he*=0.5\nweak_bc_penalty_constant = 100.0\nnLevels = 1\nparallelPartitioningType = proteus.MeshTools.MeshParallelPartitioningTypes.element\n#parallelPartitioningType = proteus.MeshTools.MeshParallelPartitioningTypes.node\nnLayersOfOverlapForParallel = 0\n\n\nstructured=True#False\nboundaries=['bottom','right','top','left']\nboundaryTags=dict([(key,i+1) for (i,key) in enumerate(boundaries)])\nnnx=4*Refinement+1\nnny=4*Refinement+1\n#hex=True \nquad=True\n#triangleFlag=1\ndomain = Domain.RectangularDomain(L)\ndomain.MeshOptions.setParallelPartitioningType('element')\n \n\n\n#logEvent(\"\"\"Mesh generated using: tetgen -%s %s\"\"\" % (triangleOptions,domain.polyfile+\".poly\"))\n# Time stepping\nT=10.0\ndt_fixed = 0.25#5.0\ndt_init = min(0.1*dt_fixed,0.1*he)\nrunCFL=0.9\nnDTout = int(round(T/dt_fixed))\n\n# Numerical parameters\nns_forceStrongDirichlet = True\nif useMetrics:\n ns_shockCapturingFactor = 0.25\n ns_lag_shockCapturing = True\n ns_lag_subgridError = True\n ls_shockCapturingFactor = 0.25\n ls_lag_shockCapturing = True\n ls_sc_uref = 1.0\n ls_sc_beta = 1.0\n vof_shockCapturingFactor = 0.25\n vof_lag_shockCapturing = True\n vof_sc_uref = 1.0\n vof_sc_beta = 1.0\n rd_shockCapturingFactor = 0.25\n rd_lag_shockCapturing = False\n epsFact_density = 3.0\n epsFact_viscosity = epsFact_curvature = epsFact_vof = epsFact_consrv_heaviside = epsFact_consrv_dirac = epsFact_density\n epsFact_redistance = 0.33\n epsFact_consrv_diffusion = 0.1\n redist_Newton = True\n kappa_shockCapturingFactor = 0.25\n kappa_lag_shockCapturing = True#False\n kappa_sc_uref = 1.0\n kappa_sc_beta = 1.0\n dissipation_shockCapturingFactor = 0.25\n dissipation_lag_shockCapturing = True#False\n dissipation_sc_uref = 1.0\n dissipation_sc_beta = 1.0\nelse:\n ns_shockCapturingFactor = 0.9\n ns_lag_shockCapturing = True\n ns_lag_subgridError = True\n ls_shockCapturingFactor = 0.9\n ls_lag_shockCapturing = True\n ls_sc_uref = 1.0\n ls_sc_beta = 1.0\n vof_shockCapturingFactor = 0.9\n vof_lag_shockCapturing = True\n vof_sc_uref = 1.0\n vof_sc_beta = 1.0\n rd_shockCapturingFactor = 0.9\n rd_lag_shockCapturing = False\n epsFact_density = 1.5\n epsFact_viscosity = epsFact_curvature = epsFact_vof = epsFact_consrv_heaviside = epsFact_consrv_dirac = epsFact_density\n epsFact_redistance = 0.33\n epsFact_consrv_diffusion = 1.0\n redist_Newton = False\n kappa_shockCapturingFactor = 0.9\n kappa_lag_shockCapturing = True#False\n kappa_sc_uref = 1.0\n kappa_sc_beta = 1.0\n dissipation_shockCapturingFactor = 0.9\n dissipation_lag_shockCapturing = True#False\n dissipation_sc_uref = 1.0\n dissipation_sc_beta = 1.0\n\nns_nl_atol_res = max(1.0e-8,0.001*he**2)\nvof_nl_atol_res = max(1.0e-8,0.001*he**2)\nls_nl_atol_res = max(1.0e-8,0.001*he**2)\nrd_nl_atol_res = max(1.0e-8,0.005*he)\nmcorr_nl_atol_res = max(1.0e-8,0.001*he**2)\nkappa_nl_atol_res = max(1.0e-8,0.001*he**2)\ndissipation_nl_atol_res = max(1.0e-8,0.001*he**2)\n\n#turbulence\nns_closure=0 #1-classic smagorinsky, 2-dynamic smagorinsky, 3 -- k-epsilon, 4 -- k-omega\nif useRANS == 1:\n ns_closure = 3\nelif useRANS == 2:\n ns_closure == 4\n# Water\n#rho_0 = 1000.0*1.01#998.2*1.01\nrho_0 = 1000.0*1.01\n#nu_0 = 1.0#Re 10 1.004e-6\n#nu_0 = 0.01#Re 1000 1.004e-6\n#nu_0 = 0.001 #Re 10000\n#nu_0 = 0.0001 #Re 100000\nnu_0 = 0.00001 #Re=1e6\n\n# Air\nrho_1 = 1000.0#998.2\nnu_1 = nu_0#1.004e-6\n\n# Surface tension\nsigma_01 = 0.0\n\n# Gravity\ng = [0.0,-9.8]\n#g = [0.0,0.0]\n\n# Initial condition\nwaterLine_x = L[0]*2.0\n#waterLine_z = L[1]*3.0/4.0#0.292\nwaterLine_z = L[1]*0.5#0.292\n\nimport math\ndef signedDistance(x):\n phi_x = x[0]-waterLine_x\n phi_z = x[1]-waterLine_z#*(1+0.1*math.sin(2*np.pi/L[0]*x[0]))\n if phi_x < 0.0:\n if phi_z < 0.0:\n return max(phi_x,phi_z)\n else:\n return phi_z\n else:\n if phi_z < 0.0:\n return phi_x\n else:\n return sqrt(phi_x**2 + phi_z**2)\n\neps=1.0e-8\ndef getPDBC(x,tag):\n if x[0] < eps or x[0] > L[0] - eps:\n return np.array([0.0,round(x[1],5),0.0])\n","sub_path":"periodicCouette/dambreak.py","file_name":"dambreak.py","file_ext":"py","file_size_in_byte":5385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"537894473","text":"import numpy as np\n\nfrom bambi.families.multivariate import Categorical, Multinomial\nfrom bambi.families.univariate import Bernoulli\nfrom bambi.utils import extract_argument_names, extra_namespace\n\n\nclass ResponseTerm:\n \"\"\"Representation of a single response model term.\n\n Parameters\n ----------\n term : formulae.ResponseMatrix\n An object describing the response of the model,\n as returned by ``formulae.design_matrices().response``\n spec : bambi.Model\n The model where this response term is used.\n \"\"\"\n\n def __init__(self, term, spec):\n self.name = term.name\n self.categorical = term.kind == \"categoric\"\n self.reference = None\n self.levels = None # Not None for categorical variables\n self.binary = None # Not None for categorical variables (either True or False)\n self.success = None # Not None for binary variables (either True or False)\n self.alias = None\n self.data = None\n\n if self.categorical:\n if term.levels is None:\n self.binary = True\n else:\n self.levels = term.levels\n self.binary = len(term.levels) == 2\n\n if self.binary:\n self.success = get_success_level(term.term.term)\n if term.design_matrix.ndim == 1:\n self.data = term.design_matrix\n else:\n idx = self.levels.index(self.success)\n self.data = term.design_matrix[:, idx]\n # Applies to the categorical family\n else:\n self.reference = get_reference_level(term.term.term)\n self.data = np.nonzero(term.design_matrix)[1]\n elif isinstance(spec.family, Bernoulli):\n # We've already checked the values are all 0 and 1\n self.success = 1\n self.data = term.design_matrix\n else:\n self.data = term.design_matrix\n\n # We use pymc coords when the response is multi-categorical.\n # These help to give the appropriate shape to coefficients and make the resulting\n # InferenceData object much cleaner\n self.coords = {}\n if isinstance(spec.family, Categorical):\n name = self.name + \"_dim\"\n self.coords[name] = [level for level in term.levels if level != self.reference]\n elif isinstance(spec.family, Multinomial):\n name = self.name + \"_dim\"\n labels = extract_argument_names(self.name, list(extra_namespace))\n if labels:\n self.levels = labels\n else:\n self.levels = [str(level) for level in range(self.data.shape[1])]\n labels = self.levels[1:]\n self.coords[name] = labels\n # TBD: Continue here when we add general multivariate responses.\n\n def set_alias(self, value):\n self.alias = value\n\n def __str__(self):\n args = [\n f\"name: {self.name}\",\n f\"shape: {self.data.squeeze().shape}\",\n ]\n\n if self.alias:\n args[0] = f\"{args[0]} (alias: {self.alias})\"\n\n if self.categorical:\n args += [f\"levels: {self.levels}\"]\n if self.binary:\n args += [f\"success: {self.success}\"]\n else:\n args += [f\"reference: {self.reference}\"]\n\n return f\"{self.__class__.__name__}({', '.join(args)})\"\n\n def __repr__(self):\n return self.__str__()\n\n\nclass Term:\n \"\"\"Representation of a single (common) model term.\n\n Parameters\n ----------\n name: str\n Name of the term.\n term_dict: dict\n A dictionary describing the components of the term. These can be found in\n ``formulae.design_matrices().common.terms_info``\n data: ndarray\n The term values.\n prior: Prior\n A specification of the prior(s) to use. An instance of class ``priors.Prior``.\n \"\"\"\n\n group_specific = False\n\n def __init__(self, name, term, data, prior=None):\n self.name = name\n self.data = data\n self.prior = prior\n self.kind = term.kind\n self.levels = term.labels\n self.categorical = False\n self.term = term\n self.alias = None\n\n # If the term has one component, it's categorical if the component is categorical.\n # If the term has more than one component (i.e. it is an interaction), it's categorical if\n # at least one of the components is categorical.\n if self.kind == \"interaction\":\n if any(component.kind == \"categoric\" for component in term.components):\n self.categorical = True\n else:\n self.categorical = self.kind == \"categoric\"\n\n # Flag constant terms\n if self.categorical and len(term.levels) == 1 and (data == data[0]).all():\n raise ValueError(f\"The term '{name}' has only 1 category!\")\n\n if not self.categorical and self.kind != \"intercept\" and np.all(data == data[0]):\n raise ValueError(f\"The term '{name}' is constant!\")\n\n # Flag cell-means terms (i.e., full-rank coding), which receive special priors\n # To flag intercepts we use `self.kind`\n self.is_cell_means = self.categorical and (self.data.sum(1) == 1).all()\n\n # Obtain pymc coordinates, only for categorical components of a term.\n # A categorical component can have up to two coordinates if it is including with both\n # reduced and full rank encodings.\n self.coords = {}\n if self.categorical:\n name = self.name + \"_dim\"\n self.coords[name] = term.levels\n elif self.data.ndim > 1 and self.data.shape[1] > 1:\n name = self.name + \"_dim\"\n self.coords[name] = np.arange(self.data.shape[1])\n\n def set_alias(self, value):\n self.alias = value\n\n def __str__(self):\n args = [\n f\"name: {self.name}\",\n f\"prior: {self.prior}\",\n f\"kind: {self.kind}\",\n f\"shape: {self.data.squeeze().shape}\",\n f\"categorical: {self.categorical}\",\n ]\n\n if self.alias:\n args[0] = f\"{args[0]} (alias: {self.alias})\"\n\n if self.categorical:\n args += [f\"levels: {self.levels}\"]\n\n return f\"{self.__class__.__name__}({', '.join(args)})\"\n\n def __repr__(self):\n return self.__str__()\n\n\nclass GroupSpecificTerm:\n # pylint: disable=too-many-instance-attributes\n \"\"\"Representation of a single (group specific) model term.\n\n Parameters\n ----------\n name: str\n Name of the term.\n term: dict\n A dictionary describing the components of the term. These can be found in\n ``formulae.design_matrices().group.terms_info``\n data: (DataFrame, Series, ndarray)\n The term values.\n prior: Prior\n A specification of the prior(s) to use. An instance of class ``priors.Prior``.\n \"\"\"\n\n group_specific = True\n\n def __init__(self, name, term, data, prior=None):\n self.categorical = False\n self.alias = None\n self.hyperprior_alias = {}\n\n self.name = name\n self.data = data\n self.prior = prior\n self.kind = term.kind\n self.groups = term.groups\n self.levels = term.labels\n self.grouper = term.factor.data\n self.predictor = term.expr.data\n self.group_index = self.invert_dummies(self.grouper)\n self.term = term\n\n # Determine if the expression is categorical\n if self.kind == \"interaction\":\n if any(component.kind == \"categoric\" for component in term.expr.components):\n self.categorical = True\n else:\n self.categorical = self.kind == \"categoric\"\n\n # Determine if the term represents cell-means encoding.\n self.is_cell_means = self.categorical and (self.data.sum(1) == 1).all()\n\n # Used in pymc model coords to label coordinates appropriately\n self.coords = {}\n\n # Group is always a coordinate added to the model.\n expr, factor = self.name.split(\"|\")\n self.coords[factor + \"__factor_dim\"] = self.groups\n\n if self.categorical:\n name = expr + \"__expr_dim\"\n self.coords[name] = term.expr.levels\n\n def invert_dummies(self, dummies):\n \"\"\"\n For the sake of computational efficiency (i.e., to avoid lots of large matrix\n multiplications in the backend), invert the dummy-coding process and represent full-rank\n dummies as a vector of indices into the coefficients.\n \"\"\"\n vec = np.zeros(len(dummies), dtype=int)\n for i in range(1, dummies.shape[1]):\n vec[dummies[:, i] == 1] = i\n return vec\n\n def set_alias(self, value):\n self.alias = value\n\n def set_hyperprior_alias(self, name, value):\n self.hyperprior_alias.update({name: value})\n\n def __str__(self):\n args = [\n f\"name: {self.name}\",\n f\"prior: {self.prior}\",\n f\"groups: {self.groups}\",\n f\"type: {self.kind}\",\n f\"shape: {self.data.squeeze().shape}\",\n f\"categorical: {self.categorical}\",\n ]\n\n if self.alias:\n args[0] = f\"{args[0]} (alias: {self.alias})\"\n\n if self.categorical:\n args += [f\"levels: {self.levels}\"]\n\n return f\"{self.__class__.__name__}({', '.join(args)})\"\n\n def __repr__(self):\n return self.__str__()\n\n\n# pylint: disable = protected-access\ndef get_reference_level(term):\n if term.kind != \"categoric\":\n return None\n\n if term.levels is None:\n return None\n\n levels = term.levels\n intermediate_data = term.components[0]._intermediate_data\n if hasattr(intermediate_data, \"_contrast\"):\n return intermediate_data._contrast.reference\n\n return levels[0]\n\n\n# pylint: disable = protected-access\ndef get_success_level(term):\n if term.kind != \"categoric\":\n return None\n\n if term.levels is None:\n return term.components[0].reference\n\n levels = term.levels\n intermediate_data = term.components[0]._intermediate_data\n if hasattr(intermediate_data, \"_contrast\"):\n return intermediate_data._contrast.reference\n\n return levels[0]\n","sub_path":"bambi/terms.py","file_name":"terms.py","file_ext":"py","file_size_in_byte":10233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"35930607","text":"from fastapi.testclient import TestClient\nfrom .utils import app, headers, hcv_coal_first_sample, ebola_first_sample\nfrom beastiary import crud\nfrom beastiary.schemas import TraceCreate, sample\nfrom beastiary.db.session import SessionLocal\nfrom beastiary.db.init_db import init_db\nfrom beastiary.api.core import get_headers\n\ndb = SessionLocal()\ninit_db(db)\n\nclient = TestClient(app)\n\npath = \"tests/data/hcv_coal.log\"\nlast_byte, headers_line = get_headers(path=path)\n\ntrace = crud.trace.create(\n db,\n obj_in=TraceCreate(path=path),\n headers_line=headers_line,\n last_byte=last_byte,\n)\n\npath = \"tests/data/prior.ebola.log\"\nlast_byte, headers_line = get_headers(path=path)\n\nna_trace = crud.trace.create(\n db,\n obj_in=TraceCreate(path=path),\n headers_line=headers_line,\n last_byte=last_byte,\n)\n\n\ndef test_read_no_trace_id() -> None:\n response = client.get(\"/api/samples/\", headers=headers)\n assert response.status_code == 422\n assert response.json() == {\n \"detail\": [\n {\n \"loc\": [\"query\", \"trace_id\"],\n \"msg\": \"field required\",\n \"type\": \"value_error.missing\",\n }\n ]\n }\n\n\ndef test_no_trace() -> None:\n response = client.get(\"/api/samples/?trace_id=100\", headers=headers)\n assert response.status_code == 404\n\n\ndef test_get_sample() -> None:\n response = client.get(\"/api/samples/?trace_id=1\", headers=headers)\n assert response.status_code == 200\n json = response.json()\n assert json[0][\"trace_id\"] == trace.id\n assert json[0][\"data\"] == hcv_coal_first_sample\n\n\ndef test_get_sample_limit() -> None:\n response = client.get(\"/api/samples/?trace_id=1&limit=1\", headers=headers)\n assert response.status_code == 200\n json = response.json()\n assert len(json) == 1\n response = client.get(\"/api/samples/?trace_id=1&limit=1000000\", headers=headers)\n assert response.status_code == 200\n json = response.json()\n assert len(json) == 1001\n\n\ndef test_get_samples_with_missing_values() -> None:\n response = client.get(\"/api/samples/?trace_id=2\", headers=headers)\n assert response.status_code == 200\n json = response.json()\n assert json[0][\"trace_id\"] == na_trace.id\n assert json[0][\"data\"] == ebola_first_sample\n","sub_path":"backend/tests/test_api/test_sample.py","file_name":"test_sample.py","file_ext":"py","file_size_in_byte":2265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"525088979","text":"__author__ = 'fernando'\n\n\ndef islice(iterable, start, end=None):\n iterable = iter(iterable if iterable else [])\n if end is not None:\n end = start - end + 1\n for item in iterable:\n start -= 1\n if start >= 0:\n continue\n yield item\n if start < end:\n break\n else:\n for item in iterable:\n start -= 1\n if start >= 0:\n continue\n yield item","sub_path":"app/support/islice.py","file_name":"islice.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"25297785","text":"import sys\nfrom cx_Freeze import setup, Executable\n\nbase = None\nif sys.platform == \"win32\":\n\tbase = \"Win32GUI\"\n\nsetup(\tname=\"Gist Uploader\",\n\t\tversion='1.0',\n\t\tdescription=\"Uploads text to Gist\",\n\t\tauthor=\"Roy Portas\",\n\t\texecutables= {Executable(\"gist_uploader.py\", base=base)})","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":278,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"289534355","text":"from setuptools import setup, find_packages\n\n\nNAME = 'torchutils'\nVERSION = '0.0.1'\nAUTHOR = 'Joseph Nagel'\nEMAIL = 'JosephBNagel@gmail.com'\nURL = 'https://github.com/joseph-nagel/torchutils'\nLICENSE = 'MIT'\nDESCRIPTION = 'Keras-like convenience for PyTorch'\n\n\ntry:\n with open('README.md', 'r') as f:\n long_description = f.read()\nexcept FileNotFoundError:\n long_description = DESCRIPTION\n\n\nsetup(\n name = NAME,\n version = VERSION,\n author = AUTHOR,\n author_email = EMAIL,\n url = URL,\n license = LICENSE,\n description = DESCRIPTION,\n long_description = long_description,\n long_description_content_type = 'text/markdown',\n packages = find_packages(),\n install_requires = [\n 'numpy',\n 'torch',\n 'torchvision'\n ],\n python_requires = '>=3.6'\n)\n\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":813,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"127039346","text":"def plotFeature():\n\n dataSet = np.genfromtxt(os.path.join(logDirName,featureCSVFile),delimiter=';',skiprows=1,missing_values='NA')\n groupColorDict = {1:'red',2:'blue',3:'green'}\n\n #read first line from file\n with open(os.path.join(logDirName,featureCSVFile), 'r') as f:\n featuresLine = f.readline().strip()\n\n featuresList = featuresLine.split(';')\n if plotFeatureOverTime not in featuresList:\n print ('%s is not present in the file'%plotFeatureOverTime)\n return\n else:\n indexFeature = [i for i,x in enumerate(featuresList) if x == plotFeatureOverTime]\n indexTime = [i for i,x in enumerate(featuresList) if x == 'windowIndex']\n speakerPairIndex = [i for i,x in enumerate(featuresList) if x == 'speakerPair']\n groupIdIndex = [i for i,x in enumerate(featuresList) if x == 'groupID']\n\n valuesToPlot = []\n allFeatureValues = {key: [] for key in groupColorDict.keys()}\n worstPeformingGroup = mpatches.Patch(color='red', label='Group 1')\n mediumPeformingGroup = mpatches.Patch(color='green', label='Group 2')\n bestPeformingGroup = mpatches.Patch(color='blue', label='Group 3')\n #plt.figure()\n\n for i in range(dataSet.shape[0]):\n if i>0 and dataSet[i][speakerPairIndex[0]] != dataSet[i-1][speakerPairIndex[0]]:\n sortedTimeIndexFeatureValue = np.asarray(sorted(valuesToPlot, key=lambda tup: tup[0]))\n plt.plot(sortedTimeIndexFeatureValue[:, 0], sortedTimeIndexFeatureValue[:, 1],\\\n color=groupColorDict[dataSet[i-1][groupIdIndex[0]]])\n valuesToPlot = []\n valuesToPlot.append((dataSet[i][indexTime[0]],dataSet[i][indexFeature[0]]))\n allFeatureValues[int(dataSet[i][groupIdIndex[0]])].append((dataSet[i][indexTime[0]],dataSet[i][indexFeature[0]]))\n\n # for the last pair speakers\n #sortedTimeIndexFeatureValue = np.asarray(sorted(valuesToPlot, key=lambda tup: tup[0]))\n # plt.plot(sortedTimeIndexFeatureValue[:, 0], sortedTimeIndexFeatureValue[:, 1], \\\n # color=groupColorDict[dataSet[i-1][groupIdIndex[0]]])\n # plt.ylabel(plotFeatureOverTime)\n # plt.xlabel('Time (number of windows)')\n # plt.title('%s per Window'%plotFeatureOverTime)\n # plt.legend([mediumPeformingGroup.get_label(),bestPeformingGroup.get_label(),worstPeformingGroup.get_label()])\n # plt.show()\n\n # compute correlation\n for j in allFeatureValues:\n wroteCorrToFile = False\n groupFeatureValues = np.asarray(allFeatureValues[j])\n corr,p_value = pearsonr(groupFeatureValues[:,0],groupFeatureValues[:,1])\n for fileName in os.listdir(os.path.join(logDirName)):\n if fileName.find('correlationsWithTime.%d.txt'%j) > -1:\n with open(os.path.join(logDirName,fileName), 'a') as outfile:\n outfile.write('%s person correlation with time = %.3f (%.3f)\\n'%(plotFeatureOverTime,corr,p_value))\n wroteCorrToFile = True\n break\n\n if not wroteCorrToFile:\n correlationFileName = open(os.path.join(logDirName,'correlationsWithTime.%s.txt'%j),'w')\n correlationFileName.write('%s person correlation with time = %.3f (%.3f)\\n'%(plotFeatureOverTime,corr,p_value))\n","sub_path":"src/utils/plot_utils.py","file_name":"plot_utils.py","file_ext":"py","file_size_in_byte":3239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"387293118","text":"circuit_control_label = [\n 'MIC_RST', 'INT_CHK', 'INT_SVC_END',\n\n \"PC_S0\", \"PC_OUT\", \"PC_INC\",\n \"PAR_IN\", \"FLASH_OUT\",\n \"DPTR_IN\",\"DPTR_OUT\",\n\n \"ADT_L8E\",\"ADT_H8E\",\"DTOALU\",\n \"ALUADDRTE\",\"ALUTOADDR\",\n\n \"ALU_OUT\",\"ALU_EXTOUT\", \"ALU_M\", \"ALU_S0\", \"ALU_S1\", \"ALU_S2\", \"ALU_S3\", \"ALU_CN+0_S0\",\n \"ALU_A_S0\",\"ALU_A_S1\",\"ALU_A_S2\",\n \"ALU_B_S0\",\"ALU_B_S1\",\"BOP_CLR_CY\",\n\n \n\n \"RAM_IN\",\"RAM_OUT\",\"RAM_LATCH_OUT\",\n \"RAR_IN\",\"PC_S1\",\"RAR_ADDRS0\",\"RAR_ADDRS1\",\n \"ACC_IN\",\"ACC_OUT\",\n \"B_IN\",\"B_OUT\",\n \"SP_IN\",\"SP_OUT\",\n \"TMP_S0\",'TMP_S1',\"TMP_OUT\",\"TMP_SIS0\",\"TMP_SIS1\",\n \"BOP_IN\",\"BOP_OUT\", \"TMP_BOP_BS0\",\"TMP_BOP_BS1\",\"TMP_BOP_BS2\",\"TMP_BOP_ADDS0\",\"TMP_BOP_ADDS1\",\n \"TMP_DA_OUT\",\"TMP_BITADDR_OUT\",\n \"IR_IN\", \"IR_OUT\",\n\n \"PSW_FLAG_IN\",\"PSW_USER_IN\",\"PSW_OUT\",\"PSW_BUS/FLAG\",\"DEBUG_HALT\",\"PC_S2\",\"ALU_CN+0_S1\",\"IC_END\",\"INT_ADDR_OUT\"\n ]\nassert( len(set(circuit_control_label)) == len(circuit_control_label))\n\ncontrol_function = {\n \"ALU_ADD\" : ('ALU_S3','ALU_S0','ALU_CN+0_S0','ALU_OUT'),\n \"ALU_ADDC\": ('ALU_S3','ALU_S0','ALU_OUT',\"ALU_CN+0_S1\",\"ALU_CN+0_S0\"),\n \"ALU_SUB\" : ('ALU_S2','ALU_S1','ALU_OUT'),\n \"ALU_SUBB\": ('ALU_S2','ALU_S1',\"ALU_CN+0_S1\",'ALU_OUT'),\n \"ALU_AND\" : ('ALU_M','ALU_S3','ALU_S1','ALU_S0','ALU_OUT'),\n \"ALU_OR\" : ('ALU_M','ALU_S3','ALU_S2','ALU_S1','ALU_OUT'),\n \"ALU_XOR\" : ('ALU_M','ALU_S2','ALU_S1','ALU_OUT'),\n \"ALU_NOT\" : ('ALU_M','ALU_OUT'),\n \"ALU_DEC\" : ('ALU_CN+0_S0','ALU_S3','ALU_S2','ALU_S1','ALU_S0','ALU_OUT'),\n \"ALU_INC\" :('ALU_OUT',),\n \"ALU_A_IN\" : ('ALU_A_S0','ALU_A_S1'),\n 'ALU_A_L8IN':('ALU_A_S0',),\n 'ALU_A_H8IN':('ALU_A_S1',),\n \"ALU_A_CNIN\" : ('ALU_A_S2',),\n \"ALU_A_BITIN\" :('ALU_A_S2','ALU_A_S0'),\n \"ALU_B_IN\" :('ALU_B_S0','ALU_B_S1'),\n \"ALU_B_SHIFT_LEFT\":('ALU_B_S0',),\n \"ALU_B_SHIFT_RIGHT\":('ALU_B_S1',),\n \"RAR_IDXR0-7\":(\"RAR_ADDRS0\",),\n \"RAR_IDXR0-1\":(\"RAR_ADDRS0\",\"RAR_ADDRS1\"),\n \"PSW_LOAD_BUS\":('PSW_FLAG_IN','PSW_USER_IN'),\n \"PSW_LOAD_ALUFLAG\":('PSW_FLAG_IN',\"PSW_BUS/FLAG\"),\n \"TMP_RR_SHIFT\":('TMP_S1', 'TMP_SIS0', 'TMP_SIS1'),\n \"TMP_RL_SHIFT\":('TMP_S0', 'TMP_SIS0', 'TMP_SIS1'),\n 'TMP_SHIFT_RIGHT':('TMP_S1',),\n 'TMP_SHIFT_CN':('TMP_S0','TMP_SIS0'),\n \"TMP_IN\":('TMP_S0','TMP_S1'),\n \"TMP_BOP_IDX_0_IN\":(\"BOP_IN\",\"TMP_BOP_ADDS0\"),\n \"TMP_BOP_IDX_OV_IN\":(\"BOP_IN\",\"TMP_BOP_ADDS1\"),\n \"TMP_BOP_IDX_CY_IN\":(\"BOP_IN\",\"TMP_BOP_ADDS0\",\"TMP_BOP_ADDS1\"),\n \"TMP_BOP_CPL\":('TMP_BOP_BS0',),\n \"TMP_BOP_TMPN\":('TMP_BOP_BS1',),\n \"TMP_BOP_CLR\":('TMP_BOP_BS0','TMP_BOP_BS1'),\n \"TMP_BOP_LOAD_TMP_ZF\":('TMP_BOP_BS2',),\n \"TMP_BOP_LOAD_DA_CF\":('TMP_BOP_BS0','TMP_BOP_BS2'),\n \"TMP_BOP_OR_TMPN\":('TMP_BOP_BS1','TMP_BOP_BS2'),\n \"TMP_BOP_AND_TMPN\":('TMP_BOP_BS0','TMP_BOP_BS1','TMP_BOP_BS2'),\n \"NEXT_BYTE\":('PC_OUT','PC_INC','PAR_IN'),\n \"ALU_A_DBUS_L8IN\":('DTOALU','ADT_L8E','ALU_A_S0','ALU_A_S1'),\n \"ALU_A_DBUS_H8IN\":('DTOALU','ADT_H8E','ALU_A_S0','ALU_A_S1'),\n \"ALU_B_DBUS_L8IN\":('DTOALU','ADT_L8E','ALU_B_S0','ALU_B_S1'),\n \"ALU_B_DBUS_H8IN\":('DTOALU','ADT_H8E','ALU_B_S0','ALU_B_S1'),\n \"ALU_A_OUT\":('ALU_OUT','ALU_CN+0_S0'),\n \"RAR-@RI\":(\"RAR_ADDRS0\",\"RAR_ADDRS1\",'RAM_OUT','RAR_IN'),\n \"RAR-RI_IN\":(\"RAR_ADDRS0\",'RAM_IN'),\n \"RAR-RI_OUT\":(\"RAR_ADDRS0\",'RAM_OUT'),\n \"ALUEXT_ADDR11\":(\"ALU_S1\", \"ALU_S2\", \"ALU_S3\",\"ALU_EXTOUT\"),\n \"ALUEXT_XCHD\":(\"ALU_S0\", \"ALU_S2\", \"ALU_S3\",\"ALU_EXTOUT\"),\n \"ALUEXT_TWO_CMP\":(\"ALU_S0\", \"ALU_S1\", \"ALU_S3\",\"ALU_EXTOUT\"),\n \"ALUEXT_SWAP\":(\"ALU_S0\", \"ALU_S1\", \"ALU_S2\",\"ALU_EXTOUT\"),\n \"PC_IN\":(\"PC_S0\",),\n \"PC_ZFIN\":(\"PC_S1\",),\n \"PC_/ZFIN\":(\"PC_S1\",\"PC_S0\"),\n \"PC_BITQIN\":(\"PC_S2\",),\n \"PC_/BITQIN\":(\"PC_S2\",\"PC_S0\"),\n \"PC_CyIN\":(\"PC_S2\",\"PC_S1\"),\n \"PC_/CyIN\":(\"PC_S2\",\"PC_S1\",\"PC_S0\"),\n}\n\n \n\nif __name__ == '__main__':\n print(len(circuit_control_label))","sub_path":"script/instructions/controlSingal.py","file_name":"controlSingal.py","file_ext":"py","file_size_in_byte":3827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"196940193","text":"import torch as t\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch import optim\nimport torchvision as tv\nimport torchvision.transforms as transforms\nfrom torchvision.transforms import ToPILImage\nimport torchsnooper\n\ntransforms = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))]) #定义数据下载后处理方式\ntrainset = tv.datasets.CIFAR10(root='/home/cy/data/',train=True,download=True,transform=transforms)\ntestset = tv.datasets.CIFAR10('/home/cy/data/',train=False,download=True,transform=transforms)\ntrainloader = t.utils.data.DataLoader(trainset,batch_size = 4,shuffle = True,num_workers = 2)\ntestloader = t.utils.data.DataLoader(testset,batch_size=4,shuffle=False,num_workers=2)\n\n\nclasses = ('plane','car','bird','cat','deer','dog','from','horse','ship','truck')\n(data,label)=trainset[100]\nprint(classes[label])\nToPILImage()((data+1)/2).resize((100,100))\ndataiter = iter(trainloader)\nimages,labels = dataiter.next()\nprint(''.join('%10s' % classes[labels[j]] for j in range(4))) #join不一定要是序列list，generator也可,generator是个函数！\nToPILImage()(tv.utils.make_grid((images+1)/2)).resize((80*4,80))\n\n\nclass Net1(nn.Module):\n def __init__(self):\n super(Net1, self).__init__()\n self.conv1 = nn.Conv2d(3, 6, 5)\n self.conv2 = nn.Conv2d(6, 16, 5)\n self.fc1 = nn.Linear(16 * 5 * 5, 120)\n self.fc2 = nn.Linear(120, 84)\n self.fc3 = nn.Linear(84, 10)\n def forward(self, x):\n x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n x = F.max_pool2d(F.relu(self.conv2(x)), 2)\n x = x.view(x.size()[0], -1)\n x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n x = self.fc3(x)\n return x\nclass Net2(nn.Module):\n def __init__(self):\n super(Net2,self).__init__()\n self.features = nn.Sequential(\n nn.Conv2d(3,6,5),\n nn.ReLU(),\n nn.MaxPool2d(2,2),\n nn.Conv2d(6,16,5),\n nn.ReLU(),\n nn.MaxPool2d(2,2)\n )\n self.classifier = nn.Sequential(\n nn.Linear(16*5*5,120),\n nn.ReLU(),\n nn.Linear(120,84),\n nn.ReLU(),\n nn.Linear(84,10)\n )\n def forward(self,x):\n x = self.features(x)\n x = x.view(-1,16*5*5)\n x = self.classifier(x)\n return x\nnet = Net1()\nprint(net)\n\n#Todo:此处要���两次运行！\ncriterion = nn.CrossEntropyLoss()\n#optimizer = optim.SGD(net.parameters(),lr=0.001,momentum=0.9)\noptimizer = optim.Adam(net.parameters())\nfor epoch in range(10):\n running_loss = 0.0\n for i, data in enumerate(trainloader, 0): # 适用于没有len的情况，如迭代器，生成器。 从0开始，每个trainloader的对象x[i]会变成（i,x[i]）\n inputs, labels = data\n print(data)\n optimizer.zero_grad()\n outputs = net(inputs)\n loss = criterion(outputs, labels)\n loss.backward(create_graph=True)\n optimizer.step()\n\n running_loss += loss.item()\n if i % 2000 == 1999:\n print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000))\n running_loss = 0.0\nprint('Finished Training')\n\ndataiter = iter(testloader)\nimages,labels = dataiter.next()\nprint('实际的label:',''.join('%08s' % classes[labels[j]] for j in range(4)))\n#ToPILImage()(tv.utils.make_grid(images/2-0.5)).resize((400,100))\n\noutputs = net(images)\n_,predicted = t.max(outputs.data,1)\nprint('预测结果:',''.join('%5s' % classes[predicted[j]] for j in range(4)))\n\ncorrect = 0\ntotal = 0\nfor data in testloader:\n images,labels = data\n outputs = net(images)\n _,predicted = t.max(outputs.data,1)\n total += labels.size(0)\n correct += (predicted == labels).sum()\nprint('10000张测试集中的准确率为：%d %%' % (100*correct/total))\n\n","sub_path":"Pytorch学习/pytorch7.py","file_name":"pytorch7.py","file_ext":"py","file_size_in_byte":3850,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"597651102","text":"import re\r\nimport datetime\r\nimport telegram\r\nimport telebotapi\r\nimport pdfplumber\r\nimport requests\r\nimport telebot\r\nfrom telegram.ext import *\r\nimport Constants as keys\r\nap_url=''\r\n\r\n\r\ndef start_command(update,context):\r\n update.message.reply_text('Benvenuto nel TutorBot, creato da Sergio Boffi, per iniziare ad utilizzare il Bot, scrivere \"/\" ed in seguito il nome della provincia di cui si vuole avere informazioni riguardo ai tutor, ti verranno inviate i nomi delle posizioni dei tutor')\r\ndef Abruzzo_command(update, context):\r\n ap_url = 'https://www.poliziadistato.it/statics/33/abruzzo.pdf'\r\n PDF(update, ap_url)\r\ndef Basilicata_command(update, context):\r\n ap_url = 'https://www.poliziadistato.it/statics/27/basilicata.pdf'\r\n PDF(update, ap_url)\r\ndef Calabria_command(update, context):\r\n ap_url = 'https://www.poliziadistato.it/statics/14/calabria.pdf'\r\n PDF(update, ap_url)\r\ndef Campania_command(update, context):\r\n ap_url = 'https://www.poliziadistato.it/statics/35/campania.pdf'\r\n PDF(update, ap_url)\r\ndef Emilia_command(update, context):\r\n ap_url = 'https://www.poliziadistato.it/statics/13/emilia.pdf'\r\n PDF(update, ap_url)\r\ndef Friuli_command(update, context):\r\n ap_url = 'https://www.poliziadistato.it/statics/39/friuli.pdf'\r\n PDF(update, ap_url)\r\ndef Lazio_command(update, context):\r\n ap_url = 'https://www.poliziadistato.it/statics/15/lazio.pdf'\r\n PDF(update, ap_url)\r\ndef Liguria_command(update, context):\r\n ap_url = 'https://www.poliziadistato.it/statics/31/liguria.pdf'\r\n PDF(update, ap_url)\r\ndef Lombardia_command(update, context):\r\n ap_url = 'https://www.poliziadistato.it/statics/49/lombardia.pdf'\r\n PDF(update, ap_url)\r\ndef Marche_command(update, context):\r\n ap_url = 'https://www.poliziadistato.it/statics/43/marche.pdf'\r\n PDF(update, ap_url)\r\ndef Molise_command(update, context):\r\n ap_url = 'https://www.poliziadistato.it/statics/40/molise.pdf'\r\n PDF(update, ap_url)\r\ndef Piemonte_command(update, context):\r\n ap_url = 'https://www.poliziadistato.it/statics/46/piemonte.pdf'\r\n PDF(update, ap_url)\r\ndef Puglia_command(update, context):\r\n ap_url = 'https://www.poliziadistato.it/statics/40/puglia.pdf'\r\n PDF(update, ap_url)\r\ndef Sardegna_command(update, context):\r\n ap_url = 'https://www.poliziadistato.it/statics/41/sardegna.pdf'\r\n PDF(update, ap_url)\r\ndef Sicilia_command(update, context):\r\n ap_url = \"https://www.poliziadistato.it/statics/30/sicilia.pdf\"\r\n PDF(update, ap_url)\r\ndef Toscana_command(update, context):\r\n ap_url = 'https://www.poliziadistato.it/statics/06/toscana.pdf'\r\n PDF(update, ap_url)\r\ndef Trentino_command(update, context):\r\n ap_url = 'https://www.poliziadistato.it/statics/30/trentino.pdf'\r\n PDF(update, ap_url)\r\ndef Umbria_command(update, context):\r\n ap_url = 'https://www.poliziadistato.it/statics/24/umbria.pdf'\r\n PDF(update, ap_url)\r\ndef ValleDAosta_command(update, context):\r\n ap_url = 'https://www.poliziadistato.it/statics/32/valle_d_aosta.pdf'\r\n PDF(update, ap_url)\r\ndef Veneto_command(update, context):\r\n ap_url = 'https://www.poliziadistato.it/statics/25/veneto.pdf'\r\n PDF(update, ap_url)\r\ndef PDF(update, ap_url):\r\n now = datetime.date.today()\r\n today = now.strftime(\"%d/%m/%Y\")\r\n\r\n\r\n def download_file(url):\r\n local_filename = url.split('/')[-1]\r\n\r\n with requests.get(url) as r:\r\n with open(local_filename, 'wb') as f:\r\n f.write(r.content)\r\n\r\n return local_filename\r\n counter=0\r\n stampa = False\r\n ap = download_file(ap_url)\r\n with pdfplumber.open(ap) as pdf:\r\n page = pdf.pages[0]\r\n text = page.extract_text()\r\n new_vend_re = re.compile(today)\r\n for line in text.split('\\n'):\r\n for i in range (6):\r\n notnow = now + datetime.timedelta(days=i)\r\n someday = notnow.strftime(\"%d/%m/%Y\")\r\n if line ==someday:\r\n stampa = False\r\n if line == today:\r\n stampa = True\r\n if new_vend_re.match(line) or stampa == True:\r\n update.message.reply_text(line)\r\n counter=counter+1\r\n if counter==0:\r\n update.message.reply_text(\"Oggi in questa regione non sono presenti tutor accesi\")\r\n\r\ndef error(update, context):\r\n print(f\"Update {update} caused error {context.error}\")\r\n\r\ndef main():\r\n updater=Updater(keys.API_KEY, use_context=True)\r\n dp=updater.dispatcher\r\n dp.add_handler(CommandHandler(\"start\", start_command))\r\n dp.add_handler(CommandHandler(\"Lombardia\", Lombardia_command))\r\n dp.add_handler(CommandHandler(\"ValleDAosta\", ValleDAosta_command))\r\n dp.add_handler(CommandHandler(\"Piemonte\", Piemonte_command))\r\n dp.add_handler(CommandHandler(\"Trentino\", Trentino_command))\r\n dp.add_handler(CommandHandler(\"Veneto\", Veneto_command))\r\n dp.add_handler(CommandHandler(\"Friuli\", Friuli_command))\r\n dp.add_handler(CommandHandler(\"Liguria\", Liguria_command))\r\n dp.add_handler(CommandHandler(\"Emilia\", Emilia_command))\r\n dp.add_handler(CommandHandler(\"Toscana\", Toscana_command))\r\n dp.add_handler(CommandHandler(\"Marche\", Marche_command))\r\n dp.add_handler(CommandHandler(\"Umbria\", Umbria_command))\r\n dp.add_handler(CommandHandler(\"Sardegna\", Sardegna_command))\r\n dp.add_handler(CommandHandler(\"Campania\", Campania_command))\r\n dp.add_handler(CommandHandler(\"Basilicata\", Basilicata_command))\r\n dp.add_handler(CommandHandler(\"Molise\", Molise_command))\r\n dp.add_handler(CommandHandler(\"Abruzzo\", Abruzzo_command))\r\n dp.add_handler(CommandHandler(\"Lazio\", Lazio_command))\r\n dp.add_handler(CommandHandler(\"Puglia\", Puglia_command))\r\n dp.add_handler(CommandHandler(\"Sicilia\", Sicilia_command))\r\n dp.add_handler(CommandHandler(\"Calabria\", Calabria_command))\r\n dp.add_error_handler(error)\r\n updater.start_polling()\r\n updater.idle()\r\n\r\n\r\nmain()\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"32107503","text":"import s01_reading as s1 ,s02_vectorization as s2 ,s03_DataPrepration as s3,s04_Modeling as s4 ,s05_Training as s5 ,conf as c\r\nimport tensorflow as tf ,time\r\n\r\nmodel = s4.build_model(\r\n vocab_size = len(s1.vocab),\r\n embedding_dim=s4.embedding_dim,\r\n rnn_units=s4.rnn_units,\r\n batch_size=s3.BATCH_SIZE)\r\n\r\n# Training step\r\nEPOCHS = 1\r\noptimizer = tf.train.AdamOptimizer()\r\n\r\nfor epoch in range(EPOCHS):\r\n start = time.time()\r\n\r\n # initializing the hidden state at the start of every epoch\r\n # initially hidden is None\r\n hidden = model.reset_states()\r\n\r\n for (batch_n, (inp, target)) in enumerate(s3.dataset):\r\n with tf.GradientTape() as tape:\r\n # feeding the hidden state back into the model\r\n # This is the interesting step\r\n predictions = model(inp)\r\n loss = tf.losses.sparse_softmax_cross_entropy(target, predictions)\r\n\r\n grads = tape.gradient(loss, model.trainable_variables)\r\n optimizer.apply_gradients(zip(grads, model.trainable_variables))\r\n\r\n if batch_n % 100 == 0:\r\n template = 'Epoch {} Batch {} Loss {:.4f}'\r\n print(template.format(epoch+1, batch_n, loss))\r\n\r\n # saving (checkpoint) the model every 5 epochs\r\n if (epoch + 1) % 5 == 0:\r\n model.save_weights(s5.checkpoint_prefix.format(epoch=epoch))\r\n\r\n print ('Epoch {} Loss {:.4f}'.format(epoch+1, loss))\r\n print ('Time taken for 1 epoch {} sec\\n'.format(time.time() - start))\r\n\r\nmodel.save_weights(s5.checkpoint_prefix.format(epoch=epoch))","sub_path":"Text Generation by Shekespeare/s07_Customization.py","file_name":"s07_Customization.py","file_ext":"py","file_size_in_byte":1551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"262052885","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Aug 4 20:08:12 2018\n\n@author: hs_pk\n\"\"\"\n\n\"\"\"Just a playground for testing different numpy functions\"\"\"\n\nimport numpy as np\n\nn = 1000\nnp.random.seed(42) #to keep the same permutaion every time\nshuffled_indices = np.random.permutation(n)\n\ntest_set_size = int(n * 0.2)\n\ntest_indices = shuffled_indices[:test_set_size]","sub_path":"playground.py","file_name":"playground.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"368429830","text":"from behave import given, when, then, step\nimport requests\n\napi_url= None\n\n#given\n@given('I set the API generation')\ndef step_impl(context):\n global api_url\n api_url = 'https://pokeapi.co/api/v2/generation/' \n\n#when\n@when('I set the GET with the id - \"{id}\"')\ndef step_impl(context, id):\n global api_url\n api_url = api_url+id+\"/\"\n\n#then\n@then('I receive GET response \"{code}\"')\ndef step_impl(context, code):\n response = requests.get(api_url)\n status_code = response.status_code\n test_code = int(code)\n assert status_code == test_code\n\n@then('I receive response body generation informations name-\"{name}\", region-\"{region}\"')\ndef step_impl(context, name, region):\n response = requests.get(api_url)\n if response.status_code == 200: \n context.response_body = response.json()\n test_data = context.response_body['name'] == name and context.response_body['main_region']['name'] == region\n assert test_data is True\n else:\n return False\n\n@then('I receive response body has the language-\"{language}\" the name is \"{name}\"')\ndef step_impl(context, name, language):\n response = requests.get(api_url)\n response_body = response.text\n if name in response_body and language in response_body: \n return True\n else:\n return False\n\n@then('I receive response pokemon-\"{pokemon}\" pertence to generation-\"{generation}\"')\ndef step_impl(context, pokemon, generation):\n response = requests.get(api_url)\n response_body = response.text\n if pokemon in response_body and generation in response_body: \n return True\n else:\n return False","sub_path":"features/steps/poke_api_generations_steps.py","file_name":"poke_api_generations_steps.py","file_ext":"py","file_size_in_byte":1623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"632292780","text":"#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nThis script allows tracking from a trained Transformer model.\n (original version)\n\"\"\"\nimport argparse\nimport logging\nimport math\n\nimport dipy.core.geometry as gm\nfrom dipy.io.utils import is_header_compatible\nimport h5py\nimport nibabel as nib\nimport torch\n\nfrom scilpy.io.utils import (add_sphere_arg,\n assert_inputs_exist, assert_outputs_exist,\n verify_compression_th)\nfrom scilpy.tracking.utils import (add_seeding_options,\n verify_streamline_length_options,\n verify_seed_options, add_out_options)\n\nfrom dwi_ml.data.dataset.utils import add_dataset_args\nfrom dwi_ml.experiment_utils.prints import format_dict_to_str, add_logging_arg\nfrom dwi_ml.experiment_utils.timer import Timer\nfrom dwi_ml.models.projects.transforming_tractography import OriginalTransformerModel\nfrom dwi_ml.tracking.projects.transformer_propagator import \\\n TransformerPropagator\nfrom dwi_ml.tracking.tracker import DWIMLTracker\nfrom dwi_ml.tracking.utils import (add_mandatory_options_tracking,\n add_tracking_options,\n prepare_seed_generator,\n prepare_tracking_mask,\n prepare_dataset_one_subj,\n prepare_step_size_vox, track_and_save)\n\n\ndef build_argparser():\n p = argparse.ArgumentParser(\n formatter_class=argparse.RawTextHelpFormatter,\n description=__doc__)\n\n add_mandatory_options_tracking(p)\n\n track_g = add_tracking_options(p)\n # Sphere used if the direction_getter key is the sphere-classification.\n add_sphere_arg(track_g, symmetric_only=False)\n\n add_dataset_args(p)\n\n # As in scilpy:\n add_seeding_options(p)\n add_out_options(p)\n\n add_logging_arg(p)\n\n return p\n\n\ndef prepare_tracker(parser, args, device,\n min_nbr_pts, max_nbr_pts, max_invalid_dirs):\n hdf_handle = h5py.File(args.hdf5_file, 'r')\n\n sub_logger_level = args.logging.upper()\n if sub_logger_level == 'DEBUG':\n # make them info max\n sub_logger_level = 'INFO'\n\n with Timer(\"\\n\\nLoading data and preparing tracker...\",\n newline=True, color='green'):\n logging.info(\"Loading seeding mask + preparing seed generator.\")\n seed_generator, nbr_seeds, seeding_mask_header = \\\n prepare_seed_generator(parser, args, hdf_handle)\n\n logging.info(\"Loading tracking mask.\")\n tracking_mask, ref = prepare_tracking_mask(args, hdf_handle)\n\n # Comparing tracking and seeding masks\n is_header_compatible(ref, seeding_mask_header)\n res = seeding_mask_header['pixdim'][0:3]\n\n logging.info(\"Loading subject's data.\")\n subset, subj_idx = prepare_dataset_one_subj(args)\n\n logging.info(\"Loading model.\")\n model = OriginalTransformerModel.load_params_and_state(\n args.experiment_path + '/best_model', log_level=sub_logger_level)\n logging.info(\"* Formatted model: \" +\n format_dict_to_str(model.params_for_json_prints))\n\n logging.debug(\"Instantiating propagator.\")\n theta = gm.math.radians(args.theta)\n step_size_vox, normalize_directions = prepare_step_size_vox(\n args.step_size, res)\n propagator = TransformerPropagator(\n dataset=subset, subj_idx=subj_idx, model=model,\n input_volume_group=args.input_group, step_size=step_size_vox,\n algo=args.algo, theta=theta, device=device)\n\n logging.debug(\"Instantiating tracker.\")\n tracker = DWIMLTracker(\n propagator, tracking_mask, seed_generator, nbr_seeds, min_nbr_pts,\n max_nbr_pts, max_invalid_dirs, args.compress, args.nbr_processes,\n args.save_seeds, args.rng_seed, args.track_forward_only,\n use_gpu=args.use_gpu,\n simultanenous_tracking=args.simultaneous_tracking,\n log_level=args.logging)\n\n return tracker, ref\n\n\ndef main():\n parser = build_argparser()\n args = parser.parse_args()\n\n # Setting root logger to high level to max info, not debug, prints way too\n # much stuff. (but we can set our tracker's logger to debug)\n root_level = args.logging\n if root_level == logging.DEBUG:\n root_level = logging.INFO\n logging.basicConfig(level=root_level)\n\n # ----- Checks\n if not nib.streamlines.is_supported(args.out_tractogram):\n parser.error('Invalid output streamline file format (must be trk or '\n 'tck): {0}'.format(args.out_tractogram))\n\n assert_inputs_exist(parser, args.hdf5_file)\n assert_outputs_exist(parser, args, args.out_tractogram)\n\n verify_streamline_length_options(parser, args)\n verify_compression_th(args.compress)\n verify_seed_options(parser, args)\n\n # ----- Prepare values\n max_nbr_pts = int(args.max_length / args.step_size)\n min_nbr_pts = int(args.min_length / args.step_size)\n max_invalid_dirs = int(math.ceil(args.max_invalid_len / args.step_size))\n\n device = torch.device('cpu')\n if args.use_gpu:\n if args.nbr_processes > 1:\n logging.warning(\"Number of processes was set to {} but you \"\n \"are using GPU. Parameter ignored.\"\n .format(args.nbr_processes))\n if torch.cuda.is_available():\n device = torch.device('cuda')\n\n tracker, ref = prepare_tracker(parser, args, device,\n min_nbr_pts, max_nbr_pts, max_invalid_dirs)\n\n # ----- Track\n track_and_save(tracker, args, ref)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"scripts_python/tto_track_from_model.py","file_name":"tto_track_from_model.py","file_ext":"py","file_size_in_byte":5751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"444900117","text":"# written: Aakash Bangalore Satish @ NHERI SimCenter, UC Berkeley\n\nimport json\nimport os\nimport sys\nimport time\n\nfrom uqRunner import UqRunner\n\n\nclass HeirBayesRunner(UqRunner):\n def __init__(self) -> None:\n super().__init__()\n self.n_samples = 0\n self.n_burn_in = 0\n self.tuning_interval = 0\n\n def storeUQData(self, uqData):\n for val in uqData[\"Parameters\"]:\n if val[\"name\"] == \"File To Run\":\n self.file_to_run = val[\"value\"]\n elif val[\"name\"] == \"# Samples\":\n self.n_samples = int(val[\"value\"])\n elif val[\"name\"] == \"# Burn-in\":\n self.n_burn_in = int(val[\"value\"])\n elif val[\"name\"] == \"Tuning Interval\":\n self.tuning_interval = int(val[\"value\"])\n\n def performHeirBayesSampling(self):\n self.dir_name = os.path.dirname(self.file_to_run)\n sys.path.append(self.dir_name)\n from main_real_data_34 import HeirBayesSampler as heir_code\n\n self.heir_code = heir_code()\n\n self.trace, self.time_taken, self.inf_object = self.heir_code.perform_sampling(\n n_samples=self.n_samples,\n n_burn_in=self.n_burn_in,\n tuning_interval=self.tuning_interval,\n )\n\n def saveResultsToPklFile(self):\n self.saved_pickle_filename = self.heir_code.save_results(\n self.trace, self.time_taken, self.inf_object, prefix=\"realdata_filtered\"\n )\n\n def createHeadingStringsList(self):\n self.params = [\"fy\", \"E\", \"b\", \"cR1\", \"cR2\", \"a1\", \"a3\"]\n self.num_params = len(self.params)\n self.num_coupons = 34\n\n # self.heading_list = [\"interface\"]\n self.heading_list = [\"Sample#\", \"interface\"]\n for i in range(self.num_coupons):\n for j in range(self.num_params):\n self.heading_list.append(\n \"\".join([\"Coupon_\", str(i + 1), \"_\", self.params[j]])\n )\n\n for row in range(self.num_params):\n for col in range(row + 1):\n self.heading_list.append(\"\".join([\"Cov_\", str(row + 1), str(col + 1)]))\n\n for par in self.params:\n self.heading_list.append(\"\".join([\"Mean_\", par]))\n\n for sig in range(self.num_coupons):\n self.heading_list.append(\"\".join([\"ErrorVariance_\", str(sig + 1)]))\n\n def makeHeadingRow(self, separator=\", \"):\n self.headingRow = separator.join([item for item in self.heading_list])\n\n def makeOneRowString(self, sample_num, sample, separator=\", \"):\n initial_string = separator.join([str(sample_num), \"1\"])\n coupon_string = separator.join(\n [\n str(sample[i][j])\n for i in range(self.num_coupons)\n for j in range(self.num_params)\n ]\n )\n cov_string = separator.join(\n [\n str(sample[self.num_coupons][row][col])\n for row in range(self.num_params)\n for col in range(row + 1)\n ]\n )\n mean_string = separator.join(\n [\n str(sample[self.num_coupons + 1][par_num])\n for par_num in range(self.num_params)\n ]\n )\n error_string = separator.join(\n [str(sample[-1][coupon_num]) for coupon_num in range(self.num_coupons)]\n )\n row_string = separator.join(\n [initial_string, coupon_string, cov_string, mean_string, error_string]\n )\n return row_string\n\n def makeTabularResultsFile(self, save_file_name=\"dakotaTab.out\"):\n self.createHeadingStringsList()\n self.makeHeadingRow(separator=\" \")\n\n cwd = os.getcwd()\n save_file_dir = os.path.dirname(cwd)\n save_file_full_path = os.path.join(save_file_dir, save_file_name)\n with open(save_file_full_path, \"w\") as f:\n f.write(self.headingRow)\n f.write(\"\\n\")\n for sample_num, sample in enumerate(self.trace):\n row = self.makeOneRowString(\n sample_num=sample_num, sample=sample, separator=\" \"\n )\n f.write(row)\n f.write(\"\\n\")\n\n def makePlots(self):\n from temp_postprocess_real_data import make_plots\n\n make_plots(self.saved_pickle_filename)\n\n def startTimer(self):\n self.startingTime = time.time()\n\n def computeTimeElapsed(self):\n self.timeElapsed = time.time() - self.startingTime\n\n def printTimeElapsed(self):\n self.computeTimeElapsed()\n print(\"Time elapsed: {:0.2f} minutes\".format(self.timeElapsed / 60))\n\n def startSectionTimer(self):\n self.sectionStartingTime = time.time()\n\n def resetSectionTimer(self):\n self.startSectionTimer()\n\n def computeSectionTimeElapsed(self):\n self.sectionTimeElapsed = time.time() - self.sectionStartingTime\n\n def printSectionTimeElapsed(self):\n self.computeSectionTimeElapsed()\n print(\"Time elapsed: {:0.2f} minutes\".format(self.sectionTimeElapsed / 60))\n\n @staticmethod\n def printEndMessages():\n print(\"Heirarchical Bayesian estimation done!\")\n\n def runUQ(\n self,\n uqData,\n simulationData,\n randomVarsData,\n demandParams,\n workingDir,\n runType,\n localAppDir,\n remoteAppDir,\n ):\n \"\"\"\n This function configures and runs hierarchical Bayesian estimation based on the\n input UQ configuration, simulation configuration, random variables,\n and requested demand parameters\n\n Input:\n uqData: JsonObject that UQ options as input into the quoFEM GUI\n simulationData: JsonObject that contains information on the analysis package to run and its\n configuration as input in the quoFEM GUI\n randomVarsData: JsonObject that specifies the input random variables, their distributions,\n and associated parameters as input in the quoFEM GUI\n demandParams: JsonObject that specifies the demand parameters as input in the quoFEM GUI\n workingDir: Directory in which to run simulations and store temporary results\n runType: Specifies whether computations are being run locally or on an HPC cluster\n localAppDir: Directory containing apps for local run\n remoteAppDir: Directory containing apps for remote run\n \"\"\"\n self.startTimer()\n self.storeUQData(uqData=uqData)\n os.chdir(workingDir)\n self.performHeirBayesSampling()\n self.saveResultsToPklFile()\n self.makeTabularResultsFile()\n self.makePlots()\n self.printTimeElapsed()\n self.printEndMessages()\n\n\nclass testRunUQ:\n def __init__(self, json_file_path_string) -> None:\n self.json_file_path_string = json_file_path_string\n self.getUQData()\n self.createRunner()\n self.runTest()\n\n def getUQData(self):\n with open(os.path.abspath(self.json_file_path_string), \"r\") as f:\n input_data = json.load(f)\n\n self.uqData = input_data[\"UQ_Method\"]\n self.simulationData = input_data[\"fem\"]\n self.randomVarsData = input_data[\"randomVariables\"]\n self.demandParams = input_data[\"EDP\"]\n self.localAppDir = input_data[\"localAppDir\"]\n self.remoteAppDir = input_data[\"remoteAppDir\"]\n self.workingDir = input_data[\"workingDir\"]\n self.workingDir = os.path.join(self.workingDir, \"tmp.SimCenter\", \"templateDir\")\n self.runType = \"runningLocal\"\n\n def createRunner(self):\n self.runner = HeirBayesRunner()\n\n def runTest(self):\n self.runner.runUQ(\n uqData=self.uqData,\n simulationData=self.simulationData,\n randomVarsData=self.randomVarsData,\n demandParams=self.demandParams,\n workingDir=self.workingDir,\n runType=self.runType,\n localAppDir=self.localAppDir,\n remoteAppDir=self.remoteAppDir,\n )\n\n\ndef main():\n filename = \"/Users/aakash/Desktop/SimCenter/Joel/heirarchical-refactor/dakota.json\"\n testRunUQ(filename)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"backend/modules/performUQ/other/HeirBayesRunner.py","file_name":"HeirBayesRunner.py","file_ext":"py","file_size_in_byte":8198,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"133549166","text":"from typing import Dict, Iterator, Set, Tuple\nfrom json import dumps, load\nfrom conproknow.identity.context import Context\n\n\nclass Lattice(object):\n def __init__(self, resource: str):\n self.dict: Dict[str, Set[Context]] = dict()\n self.counter = 0\n self.resource = resource\n\n def __repr__(self):\n return str(self.__dict__)\n\n def to_json(self):\n dict_tmp = dict()\n for lvl in self.dict:\n dict_tmp[lvl] = list()\n for c in self.dict[lvl]:\n dict_tmp[lvl].append(c.to_json())\n # if c.parent_ids is None:\n # parent_ids = list()\n # else:\n # parent_ids = list(c.parent_ids)\n # {\n # \"id\": c.id,\n # \"properties\": list(c.properties),\n # \"instances\": list(c.instances),\n # \"parent_ids\": list(parent_ids)\n # })\n return dict_tmp\n\n @staticmethod\n def load_from_file(resource: str, json_file_path: str):\n lattice = Lattice(resource)\n with open(json_file_path, mode=\"r\", encoding=\"utf-8\") as json_file:\n data = load(json_file)\n for lvl in data:\n lattice.dict[lvl] = set()\n for c in data[lvl]:\n r = c[\"resource\"] if \"resource\" in c else resource\n context = Context(r, int(c[\"id\"]), set(c[\"parent_ids\"]), set(\n c[\"properties\"]), set(c[\"instances\"]))\n if len(context.parent_ids) == 0:\n context.parent_ids = set()\n lattice.dict[lvl].add(context)\n return lattice\n\n def get_max_lvl(self) -> int:\n if not bool(self.dict):\n return 0\n return max([int(k) for k in self.dict.keys()])\n\n def add(self, context: Context, level: int) -> None:\n key = str(level)\n if key in self.dict:\n self.dict[key].add(context)\n else:\n self.dict[key] = {context}\n\n def get_contexts(self, level: int) -> Iterator[Context]:\n if str(level) in self.dict:\n for context in self.dict[str(level)]:\n yield context\n\n def get_all_contexts(self) -> Iterator[Context]:\n for level in self.dict:\n for context in self.dict[level]:\n yield context\n\n def count_contexts(self, level: int = None) -> int:\n if level is None:\n return sum(1 for _ in self.get_all_contexts())\n return sum(1 for _ in self.get_contexts(level))\n\n def build_context(self, parent_ids: Set[int], properties: Set[str], instances: Set[str]) -> Context:\n context = Context(self.resource, self.counter,\n parent_ids, properties, instances)\n self.counter += 1\n return context\n\n def build_lattice(self, level: int = 2, output: bool = False):\n if output:\n print(f\"level {level}\")\n if level <= 1: # this case should never happend, since by default level=2, then it is recursively called and increased\n return self\n nb_elements_at_level_1 = self.count_contexts(1)\n if nb_elements_at_level_1 <= 1: # There is only one element at level one, thus the lattice is composed of only one node\n return self\n super_contexts = list(self.get_contexts(level - 1))\n visited_nodes: Set[tuple] = set()\n for i in range(len(super_contexts)):\n c_1: Context = super_contexts[i]\n for j in range(i + 1, len(super_contexts)):\n c_2: Context = super_contexts[j]\n props_union = c_1.properties.union(c_2.properties)\n key = tuple(sorted(list(props_union)))\n if len(props_union) == level and key not in visited_nodes:\n visited_nodes.add(key)\n intersection = c_1.instances.intersection(c_2.instances)\n if bool(intersection):\n context = self.build_context(\n {c_1.id, c_2.id}, props_union, intersection)\n context.propagables = c_1.propagables.intersection(\n c_2.propagables)\n self.add(context, level)\n if self.get_max_lvl() == level:\n return self.build_lattice(level + 1)\n else:\n return self\n\n def save_to_file(self, output_json_file_path: str) -> None:\n print(\"saving lattice\")\n with open(output_json_file_path, encoding=\"utf-8\", mode=\"w\") as f:\n json = dumps(self.to_json(), sort_keys=True, indent=4)\n f.write(json)\n print(f\"lattice saved in {output_json_file_path}\")\n","sub_path":"conproknow/identity/lattice.py","file_name":"lattice.py","file_ext":"py","file_size_in_byte":4755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"10433662","text":"import socket\n\nfrom common_ports import ports_and_services\n\n\ndef is_ip(address):\n return not address.split('.')[-1].isalpha()\n\n\ndef to_verbose_results(ip, domain, open_ports):\n verbose_results = []\n\n verbose_results_header = 'Open ports for '\n\n # If target is IP address, try to get domain name from IP\n if not domain:\n try:\n domain = socket.gethostbyaddr(ip)[0]\n except socket.error:\n domain = None\n\n if domain:\n verbose_results_header += '%s (%s)' % (domain, ip)\n else:\n verbose_results_header += ip\n\n verbose_results_header += '\\nPORT SERVICE'\n verbose_results.append(verbose_results_header)\n\n # Use the dictionary 'ports_and_services' to get service name\n # for each port\n for port in open_ports:\n if port in ports_and_services:\n service = ports_and_services[port]\n else:\n service = ''\n\n # Convert port from integer to string and\n # right pad the string with spaces to make its length 4\n port_str = str(port).ljust(4, ' ')\n verbose_results.append('%s %s' % (port_str, service))\n\n return verbose_results\n\n\ndef get_open_ports(target, port_range, verbose=False):\n ip = None\n domain = None\n\n # Ensure IP address is valid\n if is_ip(target):\n try:\n socket.inet_aton(target)\n ip = target\n except:\n return 'Error: Invalid IP address'\n # Ensure domain name is valid\n else:\n try:\n ip = socket.gethostbyname(target)\n domain = target\n except socket.error:\n return 'Error: Invalid hostname'\n\n first_port = port_range[0]\n last_port = port_range[1]\n open_ports = []\n\n # Get all open ports in the given range.\n for port in range(first_port, last_port + 1):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.settimeout(5)\n if not s.connect_ex((ip, port)):\n open_ports.append(port)\n s.close()\n\n if not verbose:\n return open_ports\n\n verbose_results = to_verbose_results(ip, domain, open_ports)\n\n return '\\n'.join(verbose_results)\n","sub_path":"port_scanner.py","file_name":"port_scanner.py","file_ext":"py","file_size_in_byte":2166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"333418079","text":"# SQL\r\nimport sqlite3\r\n\r\ndef connect(dbname):\r\n conn = sqlite3.connect(dbname) # to connect with database or create file, if not exist # returns connection object\r\n\r\n # create table\r\n conn.execute(\" CREATE TABLE IF NOT EXISTS PRODUCTS (NAME TEXT, PRICE INT, REVIEW TEXT, SCREEN_SIZE INT, PROCESSOR TEXT, RAM TEXT, ROM TEXT)\")\r\n\r\n print(\"Table created successfully!!\")\r\n\r\n conn.close()\r\n\r\ndef insert_into_table(dbname,values):\r\n conn = sqlite3.connect(dbname)\r\n\r\n print(\"Inserted values in table: \" + str(values)) # print values to be inserted\r\n # inserting data\r\n insert_data = \"INSERT INTO PRODUCTS (NAME, PRICE, REVIEW, SCREEN_SIZE, PROCESSOR, RAM, ROM) VALUES (?,?,?,?,?,?,?)\"\r\n conn.execute(insert_data, values)\r\n\r\n conn.commit() # commit the changes\r\n conn.close() \r\n\r\n\r\ndef get_product_info(dbname):\r\n conn = sqlite3.connect(dbname) # to connect with database or create file, if not exist # returns connection object\r\n\r\n # select query\r\n cur = conn.cursor() # object\r\n cur.execute(\"SELECT * FROM PRODUCTS\")\r\n\r\n table_data = cur.fetchall()\r\n\r\n for record in table_data:\r\n print(record)\r\n\r\n conn.close() \r\n\t\r\n","sub_path":"Web_Scraper/Connect.py","file_name":"Connect.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"594556275","text":"#%%\n# https://icc-aria.ir/courses/%D8%B1%D8%A7%D8%A8%D8%B7-%DA%AF%D8%B1%D8%A7%D9%81%DB%8C%DA%A9%DB%8C-tkinter-%D9%BE%D8%A7%DB%8C%D8%AA%D9%88%D9%86/episode/radio-button\nfrom tkinter import *\n\nwindow = Tk()\nwindow.geometry('300x300')\n\n\n\nwindow.mainloop()\n","sub_path":"06-radio button.py","file_name":"06-radio button.py","file_ext":"py","file_size_in_byte":253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"538900279","text":"# coding=utf-8\nfrom __future__ import unicode_literals\nfrom django.conf.urls import patterns, include, url\nfrom video.views_dir import youtube_view, youtube_subscription_view, \\\n youku_view, \\\n subtitle_view, video\n\n__author__ = 'GoTop'\n\nfrom django.conf.urls import patterns, url\nfrom . import views\n\nurlpatterns = [\n ##########################################################################\n # YouTube\n ##########################################################################\n # http://127.0.0.1:8000/video/search?q=gta&max_results=10\n url(r'search/(?P\\w+)/(?P\\d+)$', views.search_view,\n name='search'),\n\n #####################\n # YouTube 获取视频信息\n #####################\n # http://127.0.0.1:8000/video/get_my_subscription\n url(r'get_my_subscription$',\n youtube_subscription_view.get_my_subscription_view,\n name='my_subscription'),\n\n # http://127.0.0.1:8000/video/my_homepage_subscription/50\n url(r'my_homepage_subscription/(?P\\d+)$',\n views.my_homepage_subscription_view,\n name='my_youtube_homepage'),\n\n # http://127.0.0.1:8000/video/my_watchlater_lists/1\n url(r'my_watchlater_lists/(?P\\d+)$',\n views.my_watchlater_lists_view,\n name='my_watchlater_lists'),\n\n # http://127.0.0.1:8000/video/get_subscription_update_video/50\n # 1 如果没登陆django admin就访问这个页面，会被转到\n # http://127.0.0.1:8000/accounts/login/?next=/oauth2/authenticate\n # 提示Page not found (404)\n # 2 如果没访问 127.0.0.1:8000/oauth2/authenticate 进行认证就直接访问该页面，\n # 会提示 int 错误\n url(r'get_subscription_update_video/(?P\\d+)$',\n youtube_view.get_subscription_update_video_view,\n name='my_youtube_homepage'),\n\n # 下载num个已对标题进行翻译的youtube视频\n # http://127.0.0.1:8000/video/download_youtube_video/1\n url(r'download_multi_youtube_video/(?P\\d+)$',\n youtube_view.download_multi_youtube_video_view),\n\n # http://127.0.0.1:8000/video/download_single_youtube_video/_9coAtC2PZI\n # 因为youtube的video id 里可能含有 - 号，所以这样要用 . 来 代替 \\w\n url(r'download_single_youtube_video/(?P.+)$',\n youtube_view.download_single_youtube_video_view,\n name='download_single_youtube_video'),\n\n # http://127.0.0.1:8000/video/auto_youtube_download/1\n url(r'auto_youtube_download/(?P\\d+)$',\n youtube_view.auto_youtube_download_view),\n\n # http://127.0.0.1:8000/video/download_upload_video/cJ5uaUTnMps\n url(r'download_subtitle/(?P.+)$',\n youtube_view.download_subtitle_view, name='download_subtitle'),\n\n # http://127.0.0.1:8000/video/get_multi_youtube_video_info\n url(r'get_multi_youtube_video_info$',\n youtube_view.get_multi_youtube_video_info_view,\n name='get_multi_youtube_video_info'),\n\n ###########################################################################\n # 字幕\n ###########################################################################\n # http://127.0.0.1:8000/video/merge_subtitle/_9coAtC2PZI\n url(r'merge_subtitle/(?P.+)/$', subtitle_view.merge_subtitle_view,\n name='merge_subtitle'),\n\n # http://127.0.0.1:8000/video/merge_sub_edit_style/_9coAtC2PZI\n url(r'merge_sub_edit_style/(?P.+)/$',\n subtitle_view.merge_sub_edit_style_view,\n name='merge_sub_edit_style'),\n\n # http://127.0.0.1:8000/video/merge_subtitle_to_video/_9coAtC2PZI/zh-Hans_en\n url(r'^merge_subtitle_to_video/(?P.{11})/('\n r'?P(en|zh-Hans|zh-Hans_en))$',\n subtitle_view.merge_subtitle_to_video_view,\n name='merge_subtitle_to_video'),\n\n ########################################################################\n # 优酷\n #########################################################################\n # http://127.0.0.1:8000/video/youku_upload/1\n url(r'youku_upload/(?P.+)/$', youku_view.youku_upload_view,\n name='youku_upload'),\n\n # http://127.0.0.1:8000/video/youku_upload/1\n url(r'delete_youku_video/(?P.+)/$',\n youku_view.delete_youku_video_view,\n name='delete_youku_video'),\n\n # http://127.0.0.1:8000/video/get_youku_video/XMTQyOTQ3NzgyOA==\n # 因为优酷的video id 里可能含有 = 号，所以这样要用 . 来 代替 \\w\n url(r'get_youku_video_info/(?P.+)$',\n youku_view.get_youku_video_info_view, name='get_youku_video_info'),\n\n # http://127.0.0.1:8000/video/get_my_playlists\n url(r'get_my_playlists$', youku_view.get_my_playlists_view),\n\n # http://127.0.0.1:8000/video/set_youku_playlist/XMTQyOTQ3NzgyOA==\n url(r'set_youku_playlist/(?P.+)$',\n youku_view.set_youku_playlist_view),\n\n # http://127.0.0.1:8000/video/update_youku_info/\n url(r'update_youku_online_info/(?P.+)$',\n youku_view.update_youku_online_info_view,\n name='update_youku_online_info'),\n\n # http://127.0.0.1:8000/video/auto_set_youku_category\n url(r'auto_set_youku_category', youku_view.auto_set_youku_category_view),\n\n # http://127.0.0.1:8000/video/auto_youku_upload/1\n url(r'auto_youku_upload/(?P\\d+)$', youku_view.auto_youku_upload_view),\n\n #########################################################################\n # 综合操作\n #########################################################################\n # http://127.0.0.1:8000/video/auto_youku_upload/1\n url(r'auto_youku_upload/(?P\\d+)$', youku_view.auto_youku_upload_view),\n\n # http://127.0.0.1:8000/video/download_upload_video/cJ5uaUTnMps\n url(r'download_upload_video/(?P.+)$',\n video.download_upload_video_view, name='download_upload_video'),\n]\n","sub_path":"video/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":5864,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"185854316","text":"import json\nimport urllib2\nfrom homemonitoring.setup.json_parse import JsonConfig\nfrom homemonitoring.client.client import ClientParameters\nfrom homemonitoring.setup.ssh_apis import Login\n\nclass ClientRestURL():\n def __init__(self, jsonconfig, node = \"cert\"):\n self.jsonconfig = jsonconfig\n self.myx_id = jsonconfig[\"client_conf\"][\"Myxid\"]\n self.resturl = jsonconfig[\"debug_url\"]\n self.node = node\n\n def load_client_debugconfig(self, cli_obj):\n \"\"\"\n Description : Accessing the debug URL and getting the latest Telo & OR info\n http://dtool.cn.ooma.com:8080/fsTeloWebControl/v1/myx_001861223A7A/status\n {\n \"online\": true,\n \"sw_version\": \"179239\",\n \"device_type\": \"boyle\",\n \"usb_bluetooth\": false,\n \"usb_wireless\": false,\n \"openremote_status\": \"running\",\n \"openremote_version\": \"179812\"\n }\n \"\"\"\n if self.node == \"cert\":\n client_rest_url = self.resturl + self.myx_id + \"/status\"\n my_response = urllib2.urlopen(client_rest_url)\n json_response = json.load(my_response)\n #Appending the json response dictionary with controller info\n cli_obj.controller_info.update(json_response)\n return cli_obj","sub_path":"ooma-automation/ooma/homemonitoring/client/rest_client.py","file_name":"rest_client.py","file_ext":"py","file_size_in_byte":1358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"416036158","text":"import pandas as pd\nimport warnings\nimport numpy as np\nimport tensorflow as tf\nfrom sklearn.metrics import r2_score\nwarnings.filterwarnings(\"ignore\")\n#database\nimport matplotlib.pyplot as plt\nfrom keras import backend as K\ndef r2_score(y_true, y_pred):\n SS_res = K.sum(K.square(y_true - y_pred))\n SS_tot = K.sum(K.square(y_true - K.mean(y_true)))\n return ( 1 - SS_res/(SS_tot + K.epsilon()) )\n\nMainDatabase = pd.read_excel(\"Database.xlsx\").iloc[:1500]\ny = MainDatabase.iloc[ : , 5:6].values\nfrom sklearn.preprocessing import MinMaxScaler\nsc =MinMaxScaler(feature_range=(0,1))\n\ntraining_set_scaled = sc.fit_transform(y)\n# print(training_set_scaled)\n\nx_train = []\ny_train = []\n\n#timestamp 60 means I will create 60 features.\n\nfor i in range(60, len(training_set_scaled)):\n x_train.append(training_set_scaled[i-60:i,0])\n y_train.append(training_set_scaled[i,0])\n\nx_train, y_train = np.array(x_train),np.array(y_train)\n\nx_train = np.reshape(x_train,(x_train.shape[0], x_train.shape[1],1))\n# print(x_train)\n# #input shape must be (batch_size, timesteps, input_dim)\n#\nfrom keras.models import Sequential\nfrom keras.layers import Dense , LSTM, Dropout\nmodel = Sequential()\nmodel.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1],1)))\nmodel.add(Dropout(0.2))\nmodel.add(LSTM(units=50,return_sequences=True))\nmodel.add(Dropout(0.2))\nmodel.add(LSTM(units=50,return_sequences=True))\nmodel.add(Dropout(0.2))\nmodel.add(LSTM(units=50))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(units=1))\nmodel.compile(optimizer='adam', loss = 'mean_squared_error',metrics=[r2_score])\n\nhistory = model.fit(x_train,y_train,epochs=10,batch_size=16)\n\n\nhistory_dict = history.history\nprint(history_dict.keys())\n\n\nloss_values = history_dict['loss']\nepochs = range(1,len(loss_values)+1)\nplt.plot(epochs, loss_values, label = \"loss\")\nplt.xlabel('Epochs')\nplt.ylabel('Scores')\nplt.legend()\nplt.show()\n\nR2Socre = history_dict['r2_score']\nepochs = range(1,len(R2Socre)+1)\nplt.plot(epochs, R2Socre,label = 'R2 score')\nplt.xlabel('Epochs')\nplt.ylabel('Scores')\nplt.legend()\nplt.show()","sub_path":"Accuracy/LSTM.py","file_name":"LSTM.py","file_ext":"py","file_size_in_byte":2076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"447906641","text":"from typing import Union, Any\n\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import cross_val_score\nfrom genetic_algorithm.utils.pipeline_maker import PipelineMaker\n\nclass ModelScorer:\n '''\n Class to evaluate model accuracy given data and evaluation criteria\n -----\n \n params\n estimator -- scikit-learn estimator or pipeline\n X -- input data\n y -- target data\n scoring -- metric to evaluate model accuracy\n crossValidator -- scikit-learn cross validation scheme\n errorScore -- how to score CV folds that encounter errors\n \n public methods\n scoreModel -- Evaluate model accuracy, given data and evaluation criteria\n \n public attributes\n X -- feature data\n y -- target data\n evalMetric -- evaluation metric for scoring\n crossValidator -- cross-validation strategy for scoring\n '''\n \n def __init__(\n self,\n X:Union[pd.DataFrame, np.array],\n y:[pd.Series, np.array],\n evalMetric:str,\n crossValidator:Any, # could be any number of classes from sklearn.model_selection\n errorScore:Union[float, int, str]=np.nan\n ):\n self.X = X\n self.y = y\n self.evalMetric = evalMetric\n self.crossValidator = crossValidator\n self.errorScore = errorScore\n \n def scoreModel(\n self, pipeline:Pipeline, aggregator:str='mean'\n ) -> float:\n '''\n score model using scikit-learn's cross_val_score\n -----\n \n params\n pipeline -- scikit-learn pipeline to score\n aggregator -- how to extract single metric from array of CV fold scores\n \n returns\n modelScore -- model score given data, eval metric, and cross-validator\n '''\n crossValScores = cross_val_score(\n estimator=pipeline, X=self.X, y=self.y, scoring=self.evalMetric,\n cv=self.crossValidator, error_score=self.errorScore\n )\n if aggregator == 'mean':\n modelScore = self._getMeanCrossValScore(crossValScores)\n return modelScore\n \n def _getMeanCrossValScore(self, crossValScores:np.array) -> float:\n meanCrossValScore = (\n crossValScores.mean() \n if not np.isnan(crossValScores).all() else np.NINF\n )\n return meanCrossValScore\n ","sub_path":"genetic_algorithm/utils/model_scorer.py","file_name":"model_scorer.py","file_ext":"py","file_size_in_byte":2431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"239056986","text":"from flask import Flask,request,render_template\r\nfrom fscraper import find_productF\r\nfrom ascraper import find_productA\r\n\r\napp = Flask(__name__)\r\n@app.route('/', methods=['GET','POST'])\r\ndef product():\r\n search = ''\r\n dic1 = []\r\n dic2 = []\r\n if request.method == 'POST' and 'pro' in request.form:\r\n search = request.form.get('pro')\r\n dic1=find_productF(search)\r\n dic2=find_productA(search)\r\n return render_template(\"index.html\",\r\n dic1=dic1,dic2=dic2)\r\napp.run()","sub_path":"flask_app.py","file_name":"flask_app.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"106481133","text":"from pdf2image import convert_from_path\nimport os\nimport cv2\nimport numpy as np\nfrom skimage import io\nimport math\n\n\ndef get_document_bounds(image):\n \"\"\"\n Get horizontal bounds of the health book pages on pdf page\n :param image: pdf page\n :return: left bound, right bound\n \"\"\"\n rows = []\n x1, x2 = 0, 0\n size, _ = image.shape\n c = 0.035\n threshold = size * c\n\n for row in image:\n row_sum = int(sum(row) / 255)\n rows.append(row_sum)\n\n for i in range(len(rows)):\n if rows[i] > threshold:\n x1 = i\n break\n\n for i in range(len(rows)):\n if rows[-(i + 1)] > threshold:\n x2 = len(rows) - 1 - i\n break\n\n return x1, x2\n\n\ndef get_document_bounds(image):\n \"\"\"\n Get horizontal bounds of the health book pages on pdf page\n :param image: pdf page\n :return: left bound, right bound\n \"\"\"\n rows = []\n x1, x2 = 0, 0\n size, _ = image.shape\n c = 0.035\n threshold = size * c\n\n for row in image:\n row_sum = int(sum(row) / 255)\n rows.append(row_sum)\n\n for i in range(len(rows)):\n if rows[i] > threshold:\n x1 = i\n break\n\n for i in range(len(rows)):\n if rows[-(i + 1)] > threshold:\n x2 = len(rows) - 1 - i\n break\n\n return x1, x2\n\n\ndef get_image_pages(image, inverted=False):\n \"\"\"\n Get all health book pages presented in the pdf page\n :param image: pdf page\n :param inverted: boolean that represents page orientation\n :return: health book page(s)\n \"\"\"\n # Place page horizontally\n height, width = image.shape[:2]\n if height > width:\n image = cv2.rotate(image, cv2.ROTATE_90_COUNTERCLOCKWISE)\n\n if inverted:\n image = cv2.rotate(image, cv2.ROTATE_180)\n\n original = image.copy()\n scale = 2\n new_height = round(height / scale)\n new_width = round(width / scale)\n\n image = cv2.resize(image, (new_width, new_height))\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n canny = cv2.Canny(gray, 100, 80)\n cannyt = np.transpose(canny)\n\n y1, y2 = tuple([scale * x for x in get_document_bounds(canny)])\n x1, x2 = tuple([scale * x for x in get_document_bounds(cannyt)])\n w1 = x2 - x1\n middle = x1 + round(w1 / 2)\n\n height, width, _ = original.shape\n original_area = height * height\n document_area = abs(y1 - y2) * abs(x1 - x2)\n\n pages = []\n\n if document_area > (original_area * 0.6):\n left_page = original[y1:y2, x1:middle]\n right_page = original[y1:y2, middle:x2]\n pages.append(left_page)\n pages.append(right_page)\n else:\n page = original[y1:y2, x1:x2]\n pages.append(page)\n\n return pages\n\ndef rotate_image(image, center, angle):\n \"\"\"Rotate image.\n\n :param image: input image\n :param center: rotation center\n :param angle: angle of rotation\n :return: rotated image\n \"\"\"\n rot_mat = cv2.getRotationMatrix2D(center.to_tuple(), angle, 1.0)\n result = cv2.warpAffine(image, rot_mat, image.shape[1::-1], flags=cv2.INTER_LINEAR)\n\n return result\n\ndef fix_axis_rotation(image, xx, yy):\n \"\"\"Fix page rotation based on axis.\n\n :param image: input image\n :param xx: x-axis\n :param yy: y-axis\n :return: image with fixed rotation\n \"\"\"\n # yy axis\n yy_dx = yy.delta_x()\n yy_dy = yy.delta_y()\n\n if yy_dx == 0 or yy_dy == 0:\n yy_angle = 0\n else:\n yy_angle = math.degrees(math.atan(yy_dx / yy_dy))\n\n # xx axis\n xx_dx = xx.delta_x()\n xx_dy = xx.delta_y()\n\n if xx_dy == 0 or xx_dx == 0:\n xx_angle = 0\n else:\n xx_angle = math.degrees(math.atan(xx_dy / xx_dx))\n\n rotation = (xx_angle + yy_angle) / 2\n\n if rotation != 0.0:\n image = rotate_image(image, xx.point1, rotation)\n xx.rotate(rotation)\n yy.rotate(rotation + 90)\n\n return image, xx, yy\n\n\ndef get_predefined_axis_values(page, gender):\n \"\"\"Get predefined axis values.\n\n :param page: page number\n :param gender: child gender\n :return: values\n \"\"\"\n values = {\n 'GIRL': {\n 4: (17, 0, 0, 24),\n 5: (100, 40, 0, 24),\n 6: (90, 5, 2, 20),\n 7: (180, 75, 2, 20),\n 8: (55, 30, 0, 36),\n 9: (34, 12, 2, 20)\n },\n 'BOY': {\n 4: (17, 0, 0, 24),\n 5: (100, 40, 0, 24),\n 6: (105, 5, 2, 20),\n 7: (195, 75, 2, 20),\n 8: (55, 30, 0, 36),\n 9: (36, 12, 2, 20)\n }\n }\n\n return values[gender][page]\ndef get_white_mask(image):\n \"\"\"Get the pixels in the color range of whites.\n\n :param image: input image\n :return: result from the mask applied to the image\n \"\"\"\n lower = np.array([225, 225, 225], np.uint8)\n upper = np.array([255, 255, 255], np.uint8)\n\n return cv2.inRange(image, lower, upper)\n\ndef get_content_boundaries(image):\n \"\"\"Isolates the important content of the page.\n\n :param image: input image (page)\n :return: page content\n \"\"\"\n original = image.copy()\n\n # Scaling down the image to optimize the edge detection\n scale = 5\n height, width = tuple([round(x / scale) for x in image.shape[:2]])\n image = cv2.resize(image, (width, height))\n\n # Remove white background\n white_mask = get_white_mask(image)\n image = cv2.bitwise_not(image, image, white_mask)\n\n # Convert to grayscale image and apply gaussian blur\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n blur = cv2.GaussianBlur(gray, (5, 5), 0)\n\n # Edge detection\n canny = cv2.Canny(blur, 30, 10)\n\n # Apply closing and dilation to improve table/graphic borders' connectivity\n broken_line_h = np.array([[0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [1, 0, 0, 0, 1],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0]], dtype=np.uint8)\n\n # Apply closing and dilation to improve table/graphic borders' connectivity\n broken_line_v = np.array([[0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0]], dtype=np.uint8)\n\n closing = cv2.morphologyEx(canny, cv2.MORPH_CLOSE, broken_line_h)\n closing = cv2.morphologyEx(closing, cv2.MORPH_CLOSE, broken_line_v)\n dilation = cv2.dilate(closing, (5, 5), iterations=5)\n closing = cv2.morphologyEx(dilation, cv2.MORPH_CLOSE, broken_line_h)\n\n # Find contours\n contours, _hierarchy = cv2.findContours(closing, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n\n # Establish limits for the tables/graphics area. Between 40% and 60% of the page area\n height, width, _ = image.shape\n area = width * height\n lower_area = area * 0.4\n upper_area = area * 0.7\n\n # Find the best table/graph boundaries\n max_brightness = 0\n brightest_rectangle = 0, 0, width, height\n for cnt in contours:\n rect = cv2.boundingRect(cnt)\n x, y, w, h = rect\n if lower_area < w * h < upper_area:\n mask = np.zeros(image.shape, np.uint8)\n mask[y:y + h, x:x + w] = image[y:y + h, x:x + w]\n brightness = np.sum(mask)\n if brightness > max_brightness:\n brightest_rectangle = rect\n max_brightness = brightness\n\n # Upscale the measures to the original image\n x, y, w, h = tuple([x * scale for x in brightest_rectangle])\n\n # Cut the content from the original image\n image = original[y:y + h, x:x + w]\n\n return image\n\ndef fix_page_rotation(image, gender):\n \"\"\"Fix page rotation based on page lines.\n\n :param image: input image (page)\n :param gender: child gender\n :return: page with fixed rotation\n \"\"\"\n if gender == 'BOY':\n mask = get_blue_mask(image)\n else:\n mask = get_pink_mask(image)\n\n height, width = mask.shape\n skell = np.zeros([height, width], dtype=np.uint8)\n kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))\n\n while np.count_nonzero(mask) != 0:\n eroded = cv2.erode(mask, kernel)\n temp = cv2.dilate(eroded, kernel)\n temp = cv2.subtract(mask, temp)\n skell = cv2.bitwise_or(skell, temp)\n mask = eroded.copy()\n\n edges = cv2.Canny(skell, 50, 150)\n lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 40, minLineLength=50, maxLineGap=30)\n\n sum_angle = 0\n sum_len = 0\n\n for line in lines:\n for x1, y1, x2, y2 in line:\n dx = x1 - x2\n if dx != 0:\n dy = y1 - y2\n m = dy / dx\n angle = math.degrees(math.atan(m))\n else:\n angle = 90.00\n\n if abs(angle) > 45.00:\n angle -= 90.00\n\n if abs(angle) < 5.0:\n distance = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)\n sum_len += distance\n sum_angle += (angle * distance)\n\n if sum_len == 0:\n sum_len = 1\n\n rot_angle = (sum_angle / sum_len)\n\n image_center = tuple(np.array(image.shape[1::-1]) / 2)\n rot_mat = cv2.getRotationMatrix2D(image_center, rot_angle, 1.0)\n result = cv2.warpAffine(image, rot_mat, image.shape[1::-1], flags=cv2.INTER_LINEAR)\n\n return result\n\n\ndef get_blue_mask(img):\n \"\"\"Get the pixels in the color range of blues.\n\n :param image: input image\n :return: result from the mask applied to the image\n \"\"\"\n\n hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n lower_blue = np.array([95, 40, 50], np.uint8)\n upper_blue = np.array([125, 255, 255], np.uint8)\n\n return cv2.inRange(hsv_img, lower_blue, upper_blue)\n\ndef blue_pixels_count(image):\n \"\"\"Count of pixels in the color range of blues.\n\n :param image: input image\n :return: blue pixels count\n \"\"\"\n blue_mask = get_blue_mask(image)\n\n return np.count_nonzero(blue_mask)\n\ndef get_pink_mask(img):\n \"\"\"Get the pixels in the color range of pinks.\n\n :param image: input image\n :return: result from the mask applied to the image\n \"\"\"\n\n hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n lower_pink = np.array([137, 20, 30], np.uint8)\n upper_pink = np.array([167, 255, 255], np.uint8)\n\n return cv2.inRange(hsv_img, lower_pink, upper_pink)\n\ndef get_gender(image):\n \"\"\"Get the child gender based on health book color.\n\n :param image: input image\n :return: child gender\n \"\"\"\n img = cv2.imread(image)\n blue = blue_pixels_count(img)\n pink = pink_pixels_count(img)\n\n if blue >= pink:\n return 'BOY'\n else:\n return 'GIRL'\n\ndef pink_pixels_count(image):\n \"\"\"Count of pixels in the color range of pinks.\n\n :param image: input image\n :return: pink pixels count\n \"\"\"\n pink_mask = get_pink_mask(image)\n\n return np.count_nonzero(pink_mask)\n\ndef getBoyCount(genders):\n boy =0;\n for string in genders:\n if string == \"BOY\":\n boy = boy + 1\n return boy\n\ndef getGirlCount(genders):\n girl=0\n for string in genders:\n if string == \"GIRL\":\n girl = girl + 1\n return girl\n\n\ndef pagesplitting(currentFolder):\n livros = os.listdir('.')\n for livro in livros:\n pages = convert_from_path(currentFolder + '/' + livro + '/' + livro + '.pdf', poppler_path='C:/Program Files (x86)/poppler-0.68.0/bin')\n x=0\n os.chdir(currentFolder + '/' + livro)\n if len(pages) == 4:\n for page in pages:\n x=x+1\n if x==1:\n page.save('TABELA' + '.png', 'PNG')\n else:\n page.save('GRAFICO ' + str(x-1) + '.png', 'PNG')\n if len(pages) == 5:\n for page in pages:\n x=x+1\n if x==1:\n print(\"page 1 removed from: \" + livro)\n else:\n if x==2:\n page.save('TABELA' + '.png', 'PNG')\n else:\n page.save('GRAFICO ' + str(x-2) + '.png', 'PNG')\n pathPDF = os.getcwd()\n os.remove(livro + '.pdf')\n os.chdir('..')\n\n\ndef gender(path):\n os.chdir(path)\n pages = os.listdir('.')\n\n i=0\n genderTotal = []\n\n for page in pages:\n #image = cv2.imread(page)\n gender = get_gender(page)\n\n genderTotal.append(gender)\n i=i+1\n\n xy = getBoyCount(genderTotal)\n xx = getGirlCount(genderTotal)\n\n if xy > xx:\n os.chdir('..')\n return 'M'\n else:\n os.chdir('..')\n return 'F'\n","sub_path":"preproc.py","file_name":"preproc.py","file_ext":"py","file_size_in_byte":12514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"571140774","text":"import time\nimport torch\nimport numpy as np\nimport helper\nfrom torch.functional import F\nfrom hvplot import hvPlot\nfrom torch import nn\nfrom torch import optim\nfrom intro import view_classify\nimport matplotlib.pyplot as plt\nfrom torchvision import datasets, transforms\n\n\n# now we'll make a bigger structure for computer vision\n\n# define a transform to normalize the data\ntransform = transform = transforms.Compose(\n [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]\n)\n\n# download and load the training data\ntrainset = datasets.MNIST(\"MNIST_data/\", download=True, train=True, transform=transform)\ntrainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n\ndataiter = iter(trainloader)\nimages, labels = dataiter.next()\n\nmodel = torch.nn.Sequential(\n nn.Linear(784, 128),\n nn.ReLU(), # activation function\n nn.Linear(128, 64),\n nn.ReLU(),\n nn.Linear(64, 10),\n nn.LogSoftmax(dim=1),\n)\n\ncriterion = nn.NLLLoss()\n\n\n# Clear the gradients\noptimizer = optim.SGD(model.parameters(), lr=0.003)\n\nstart = time.time()\nepochs = 5\nfor e in range(epochs):\n running_loss = 0\n for images, labels in trainloader:\n\n # Flatten Images\n images = images.view(images.shape[0], -1)\n optimizer.zero_grad()\n\n output = model.forward(images)\n loss = criterion(output, labels)\n loss.backward()\n optimizer.step()\n\n running_loss += loss.item()\n else:\n print(\n \"Training loss in epoch {} is {}:\".format(\n e, running_loss / len(trainloader)\n )\n )\n\nprint(\"Time taken for {} epochs is {}:\".format(epochs, time.time() - start))\n\nimages, labels = next(iter(trainloader))\nimg = images[0].view(1, 784)\n\nwith torch.no_grad():\n logps = model.forward(img)\nps = F.softmax(logps, dim=1)\n\n\nprint(ps)\nview_classify(img.view(1, 28, 28), ps)\nplt.show()\n","sub_path":"PyTorch/Udacity/network_mnist.py","file_name":"network_mnist.py","file_ext":"py","file_size_in_byte":1878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"484127048","text":"def swap(L, left, right):\n # L[left], L[right] = L[right], L[left]\n item = L[left]\n L[left] = L[right]\n L[right] = item\n\n\ndef shakersort(L, left, right):\n k = right\n while left < right:\n for j in range(right, left, -1): # od prawej\n if L[j-1] > L[j]:\n swap(L, j-1, j)\n k = j\n left = k\n for j in range(left, right): # od lewej\n if L[j] > L[j+1]:\n swap(L, j, j+1)\n k = j\n right = k\n","sub_path":"Python (Algorithms and Data Structures with Python)/Zestaw11/shakesort.py","file_name":"shakesort.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"187582432","text":"#Look for #IMPLEMENT tags in this file. These tags indicate what has\n#to be implemented to complete the Sokoban warehouse domain.\n\n# You may add only standard python imports---i.e., ones that are automatically\n# available on TEACH.CS\n# You may not remove any imports.\n# You may not import or otherwise source any of your own files\n\n#import os for time functions\nfrom search import * #for search engines\nfrom sokoban import SokobanState, Direction, PROBLEMS, sokoban_goal_state #for Sokoban specific classes and problems\n\nfrom itertools import *\n\n#SOKOBAN HEURISTICS\ndef heur_displaced(state):\n '''trivial admissible sokoban heuristic'''\n '''INPUT: a sokoban state'''\n '''OUTPUT: a numeric value that serves as an estimate of the distance of the state to the goal.''' \n count = 0\n for box in state.boxes:\n if box not in state.storage:\n count += 1\n return count\n\ndef heur_manhattan_distance(state):\n '''admissible sokoban heuristic: manhattan distance'''\n '''INPUT: a sokoban state'''\n '''OUTPUT: a numeric value that serves as an estimate of the distance of the state to the goal.''' \n #We want an admissible heuristic, which is an optimistic heuristic. \n #It must always underestimate the cost to get from the current state to the goal.\n #The sum Manhattan distance of the boxes to their closest storage spaces is such a heuristic. \n #When calculating distances, assume there are no obstacles on the grid and that several boxes can fit in one storage bin.\n #You should implement this heuristic function exactly, even if it is tempting to improve it.\n #Your function should return a numeric value; this is the estimate of the distance to the goal.\n \n sum = 0\n \n for box in state.boxes:\n #print(box)\n distance = state.width + state.height\n #print(distance)\n for storage in state.storage:\n #print(storage)\n dx = abs(box[0] - storage[0])\n dy = abs(box[1] - storage[1])\n #print(x)\n #print(y)\n if (dx + dy) < distance:\n distance = dx + dy\n sum += distance\n return sum\n\ndef heur_alternate(state):\n#IMPLEMENT\n '''a better sokoban heuristic'''\n '''INPUT: a sokoban state'''\n '''OUTPUT: a numeric value that serves as an estimate of the distance of the state to the goal.''' \n #heur_min_moves has flaws. \n #Write a heuristic function that improves upon heur_manhattan_distance to estimate distance between the current state and the goal.\n #Your function should return a numeric value for the estimate of the distance to the goal.\n sum = state.width + state.height\n perm = permutations(range(len(state.boxes)))\n box=[]\n storage=[]\n for b in state.boxes:\n box.append(b)\n for s in state.storage:\n storage.append(s)\n for tup in perm:\n distance = 0\n for i in range(len(state.boxes)):\n dx = abs(box[i][0] - storage[(tup[i])][0])\n dy = abs(box[i][1] - storage[(tup[i])][1])\n distance += (dx + dy)\n if distance < sum:\n sum = distance\n return sum\n \n \n\n\ndef fval_function(sN, weight):\n#IMPLEMENT\n \"\"\"\n Provide a custom formula for f-value computation for Anytime Weighted A star.\n Returns the fval of the state contained in the sNode.\n\n @param sNode sN: A search node (containing a SokobanState)\n @param float weight: Weight given by Anytime Weighted A star\n @rtype: float\n \"\"\"\n # f = (1−w)∗g(node)+w∗h(node)\n return (1 - weight) * sN.gval + weight * sN.hval\n \n \n #Many searches will explore nodes (or states) that are ordered by their f-value.\n #For UCS, the fvalue is the same as the gval of the state. For best-first search, the fvalue is the hval of the state.\n #You can use this function to create an alternate f-value for states; this must be a function of the state and the weight.\n #The function must return a numeric f-value.\n #The value will determine your state's position on the Frontier list during a 'custom' search.\n #You must initialize your search engine object as a 'custom' search engine if you supply a custom fval function.\n return 0\n\ndef weighted_astar(initail_state, timebound = 10):\n#IMPLEMENT\n '''Provides an implementation of weighted a-star, as described in the HW1 handout'''\n '''INPUT: a sokoban state that represents the start state and a timebound (number of seconds)'''\n '''OUTPUT: A goal state (if a goal is found), else False''' \n weight = 1\n \n return False\n\nif __name__ == \"__main__\":\n #TEST CODE\n solved = 0; unsolved = []; counter = 0; percent = 0; timebound = 2; #2 second time limit for each problem\n print(\"*************************************\") \n print(\"Running A-star\") \n\n for i in range(0,40): #note that there are 40 problems in the set that has been provided. We just run through 10 here for illustration.\n\n print(\"*************************************\") \n print(\"PROBLEM {}\".format(i))\n \n s0 = PROBLEMS[i] #Problems will get harder as i gets bigger\n\n se = SearchEngine('astar', 'full')\n final = se.search(s0, sokoban_goal_state, heur_displaced, timebound)\n\n if final:\n final.print_path()\n solved += 1\n else:\n unsolved.append(i) \n counter += 1\n\n if counter > 0: \n percent = (solved/counter)*100\n\n print(\"*************************************\") \n print(\"{} of {} problems ({} %) solved in less than {} seconds.\".format(solved, counter, percent, timebound)) \n print(\"Problems that remain unsolved in the set are Problems: {}\".format(unsolved)) \n print(\"*************************************\") \n\n solved = 0; unsolved = []; counter = 0; percent = 0; timebound = 8; #8 second time limit \n print(\"Running Anytime Weighted A-star\") \n\n for i in range(0,40):\n print(\"*************************************\") \n print(\"PROBLEM {}\".format(i))\n\n s0 = PROBLEMS[i] #Problems get harder as i gets bigger\n final = weighted_astar(s0, timebound)\n\n if final:\n final.print_path() \n solved += 1 \n else:\n unsolved.append(i)\n counter += 1 \n\n if counter > 0: \n percent = (solved/counter)*100 \n \n print(\"*************************************\") \n print(\"{} of {} problems ({} %) solved in less than {} seconds.\".format(solved, counter, percent, timebound)) \n print(\"Problems that remain unsolved in the set are Problems: {}\".format(unsolved)) \n print(\"*************************************\") \n\n\n","sub_path":"solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":6491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"576787227","text":"import unittest\n\n\nclass Solution(object):\n def __init__(self):\n self.s = None\n self.n = 0\n self.combination = []\n self.result = []\n\n def partition(self, s):\n \"\"\"\n :type s: str\n :rtype: List[List[str]]\n \"\"\"\n self.s = s\n self.n = len(s)\n\n self._partition(0, 0)\n\n return self.result\n\n def _partition(self, lo, hi):\n if hi == self.n:\n if lo == hi:\n self.result.append(list(self.combination))\n else:\n i = lo\n j = hi\n while i < j:\n if self.s[i] != self.s[j]:\n break\n i += 1\n j -= 1\n else:\n self.combination.append(self.s[lo:hi + 1])\n self._partition(hi + 1, hi + 1)\n self.combination.pop()\n\n self._partition(lo, hi + 1)\n\n\nclass Test(unittest.TestCase):\n def test(self):\n self._test(\"aab\", [\n [\"a\", \"a\", \"b\"],\n [\"aa\", \"b\"],\n ])\n\n self._test(\"aba\", [\n [\"a\", \"b\", \"a\"],\n [\"aba\"],\n ])\n\n self._test(\"abba\", [\n [\"a\", \"b\", \"b\", \"a\"],\n [\"a\", \"bb\", \"a\"],\n [\"abba\"],\n ])\n\n self._test(\"abbab\", [])\n\n def _test(self, s, expected):\n actual = Solution().partition(s)\n self.assertItemsEqual(actual, expected)\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"p131_recursive_backtrack.py","file_name":"p131_recursive_backtrack.py","file_ext":"py","file_size_in_byte":1480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"474047223","text":"# Copyright (c) 2016. Mount Sinai School of Medicine\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\nfrom __future__ import (\n print_function,\n division,\n absolute_import,\n)\nimport collections\nfrom copy import copy\n\nCOMMON_AMINO_ACIDS = collections.OrderedDict(sorted({\n \"A\": \"Alanine\",\n \"R\": \"Arginine\",\n \"N\": \"Asparagine\",\n \"D\": \"Aspartic Acid\",\n \"C\": \"Cysteine\",\n \"E\": \"Glutamic Acid\",\n \"Q\": \"Glutamine\",\n \"G\": \"Glycine\",\n \"H\": \"Histidine\",\n \"I\": \"Isoleucine\",\n \"L\": \"Leucine\",\n \"K\": \"Lysine\",\n \"M\": \"Methionine\",\n \"F\": \"Phenylalanine\",\n \"P\": \"Proline\",\n \"S\": \"Serine\",\n \"T\": \"Threonine\",\n \"W\": \"Tryptophan\",\n \"Y\": \"Tyrosine\",\n \"V\": \"Valine\",\n}.items()))\nCOMMON_AMINO_ACIDS_WITH_UNKNOWN = copy(COMMON_AMINO_ACIDS)\nCOMMON_AMINO_ACIDS_WITH_UNKNOWN[\"X\"] = \"Unknown\"\n\nAMINO_ACID_INDEX = dict(\n (letter, i) for (i, letter) in enumerate(COMMON_AMINO_ACIDS_WITH_UNKNOWN))\n","sub_path":"mhcflurry/amino_acid.py","file_name":"amino_acid.py","file_ext":"py","file_size_in_byte":1439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"590253083","text":"import datetime\nfrom os import path\nfrom sqlalchemy import func\nfrom flask import render_template, Blueprint,redirect, url_for\n\nfrom server.models import db,Log,User\nfrom server.forms import LogForm\n\nuser_blueprint = Blueprint(\n 'user',\n __name__,\n template_folder='../templates/user',\n url_prefix='/user'\n)\n\n\n@user_blueprint.route('/')\ndef user(username):\n user = User.query.filter_by(username = username).first_or_404()\n\n return render_template(\n 'user.html',\n user = user\n )\n\n@user_blueprint.route('/feedback')\ndef feedback():\n form = LogForm()\n return render_template(\n 'feedback.html',\n form=form\n )\n","sub_path":"server/controllers/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":704,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"183675285","text":"from django.http import HttpResponse\nfrom django.shortcuts import render, redirect\nimport random\nfrom intelligence.models import Storyinfo, Paragraph\n\n\ndef read(request):\n stories = Storyinfo.objects.filter(ended=True)\n if stories.count() == 0:\n return HttpResponse(\"Пока нет рассказов\")\n story = random.choice(stories)\n texts = Paragraph.objects.filter(storyinfo=story)\n\n return render(request, 'Readstory.html', {'story': story, 'texts': texts})\n\n\ndef write(request):\n if request.method == 'GET':\n stories = Storyinfo.objects.filter(ended=False)\n if stories.count() == 0:\n return HttpResponse(\"Пока нет рассказов\")\n story = random.choice(stories)\n texts = Paragraph.objects.filter(storyinfo=story)\n id = story.id\n\n return render(request, 'storiesfile.html', {'story': story, 'id': id, 'texts': texts})\n elif request.method == 'POST':\n id = request.POST['id']\n story = Storyinfo.objects.get(pk=id)\n texts = Paragraph.objects.filter(storyinfo=story)\n newtext = request.POST['Prodolzhenie']\n if request.POST['Sohranit'] == 'Сохранить':\n paragraph = Paragraph()\n paragraph.username = request.POST['username']\n paragraph.text = newtext\n paragraph.storyinfo = story\n paragraph.save()\n return render(request, 'storiesfile.html', {'story': story, 'texts': texts})\n\n elif request.POST['Novaya'] == 'Novaya':\n\n return render(request, 'Newstory.html')\n\n else:\n return HttpResponse(\"Пока ничего нет, но скоро будет.\")\n\n\ndef new(request):\n if request.method == 'GET':\n return render(request, 'Newstory.html')\n if request.method == 'POST':\n if request.POST['Добавить'] == 'Добавить':\n story = Storyinfo()\n paragraph = Paragraph()\n story.storyname = request.POST['addname']\n paragraph.text = request.POST['newtext']\n story.ended = False\n paragraph.username = request.POST['username']\n story.save()\n paragraph.storyinfo_id = story.id\n paragraph.save()\n return render(request, 'Storylist.html')\n\n\ndef list(request):\n stories = Storyinfo.objects.filter(ended=True)\n if request.method == 'GET':\n if stories.count() == 0:\n return HttpResponse(\"Пока нет рассказов\")\n sname = Storyinfo.objects()\n","sub_path":"intelligence/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"126308133","text":"#ejercicio1\r\na=12\r\nb=13\r\nc=10\r\npromedio=(a+b+c)/3\r\nprint(f\"tu promedio de practicas es: {promedio:.2f}\")\r\n#ejercicio2\r\nd=10\r\ne=11\r\nf=12\r\ng=13\r\nprom=(d+e+f+g-min(d,e,f,g))/3\r\nprint(f\"tu otro promedio de practicas es: {prom:.2f}\")\r\n#ejercio3\r\nprecio=int(input(\"Ingrese el precio del articulo:\"))\r\nprecio2=precio*0.18\r\nprint(f\"El igv es: {precio2:.2f}\")\r\n#ejercicio4\r\nsubtot=int(input(f\"Ingrese el subtotal:\"))\r\ntotal=subtot*0.82\r\nprint(f\"El monto total es:{total:.2f}\")\r\n#ejercicio5\r\npromedi=3.9\r\nif 0<=promedi and promedi<3:\r\n print(\"En observación\")\r\nif 3<=promedi and promedi<4.5:\r\n print(\"Bueno\")\r\nif 4.5<=promedi and promedi<=5:\r\n print(\"Sobresaliente\")\r\n#ejercicio6\r\nA=\"Computo\"\r\nB=\"TV\"\r\nC=\"Celulares\"\r\nD=\"Entretenimiento\"\r\nE=\"Electrohogar\"\r\nF=\"Infantil\"\r\nprint(F)\r\n#ejercicio10\r\naño=int(input(\"Ingrese el año:\"))\r\nX=año%4\r\nY=año%100\r\nif X==0 and Y!=0:\r\n print(\"Año bisiesto\")\r\nelse:\r\n print(\"Año no bisiesto\")\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"Ejerciosbic.py","file_name":"Ejerciosbic.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"427648793","text":"class ListNode(object):\n\tdef __init__(self, x):\n\t\tself.val = x\n\t\tself.next = None\n\nclass Solution(object):\n\tdef swapParis(self, head):\n\t\tprev = ListNode(-1)\n\t\tprev.next = head\n\t\ttemp = prev\n\t\twhile temp.next and temp.next.next:\n\t\t\tnode1 = temp.next\n\t\t\tnode2 = temp.next.next\n\t\t\ttemp.next = node2\n\t\t\tnode1.next = node2.next\n\t\t\tnode2.next = node1\n\t\t\ttemp = temp.next.next\n\t\treturn prev.next\n\t\t\n","sub_path":"Part2/Swap_Node_In_Paris.py","file_name":"Swap_Node_In_Paris.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"575706578","text":"\"\"\" Importando Tkinter \"\"\"\r\nfrom tkinter import *\r\nfrom tkinter import messagebox \r\nfrom tkinter import filedialog\r\nimport time\r\nimport win32com.client\r\n\r\nspeaker = win32com.client.Dispatch(\"SAPI.SpVoice\")\r\nultimoPilaY = 295\r\n\r\n\"\"\"Colores utilizados:\r\n \r\n 1. Hexadecimal: 2a1a5e <::> RGB = #422694\r\n 2. Hexadecimal: f45905 <::>\r\n 3. Hexadecimal: fb9224 <::> rgb(251, 146, 36)\r\n 4. Hexadecimal: fbe555 <::>\r\n 5. Hexadecimal: dfddc7 <::>\r\n 6. Hexadecimal: 16ed48 (Verde)\r\n 7. Hexadecimal: cb3234 (Rojo)\r\n\r\n El orden de los colores va desde el más oscuro hasta el más claro\"\"\"\r\n\r\nroot = Tk()\r\nroot.title(\"Palindromo Con Pila PRO\")\r\nroot.resizable(False,False)\r\nroot.config(background='#fb9224')\r\nmiFrame = Frame(root, width=720, height=500)\r\nmiFrame.pack()\r\n\r\n\"\"\"----------------- Todos los Canvas ----------------------------\"\"\"\r\nEstado1 = Canvas(root, width=120, height=130)\r\nEstado2 = Canvas(root, width=120, height=130)\r\nEstado3 = Canvas(root, width=120, height=120)\r\nFlecha1 = Canvas(root, width=90, height=10)\r\nFlechaInicial = Canvas(root, width=60, height=10)\r\nFlecha2 = Canvas(root, width=90, height=10)\r\nPilaLineas = Canvas(root,bg=\"#2a1a5e\", width=40, height=290)\r\nA_Numeral_p1 = Canvas(root,width=80,height=25)\r\nB_Numeral_p1 = Canvas(root, width=80,height=25)\r\nrec = Canvas(root, width=30, height=30)\r\nAA = Canvas(root, width=80,height=25)\r\nAB = Canvas(root,width=80,height=25)\r\nBA = Canvas(root, width=80,height=25)\r\nBB = Canvas(root, width=80,height=25)\r\nC_Numeral = Canvas(root, width=80,height=25)\r\nC_B = Canvas(root, width=80,height=25)\r\nC_A = Canvas(root, width=80,height=25)\r\nB_LAMBDA = Canvas(root, width=80,height=25)\r\nA_LAMBDA = Canvas(root, width=80,height=25)\r\nLAMBDA_Numeral = Canvas(root, width=80,height=25)\r\n\"\"\"----------------- Fin de Todos los Canvas ----------------------\"\"\"\r\n\r\n\"\"\"--------------- Inicio del Menú -------------------------------\"\"\"\r\n\r\ndef infoAdicional():\r\n messagebox.showinfo(\"Informacion de Aplicacion\", \r\n \"Si necesitas ayuda, debes ir a un Psicologo o a un Psiquiatra todo depende de ti y tus necesidades\")\r\ndef avisoLicencia():\r\n messagebox.showwarning(\"Tacaño\", \"No compraste la licencia, la pirateaste\")\r\ndef salirDeLaAplicacion():\r\n valorSalir=messagebox.askquestion(\"¿Desea salir de la Aplicación?\")\r\n if valorSalir==\"yes\":\r\n root.destroy()\r\n\r\nbarraMenu = Menu(root)\r\nroot.config(menu=barraMenu, width=350, height=300)\r\n\r\narchivoMenu=Menu(barraMenu, tearoff=0)\r\narchivoMenu.add_command(label=\"Nuevo\")\r\narchivoMenu.add_command(label=\"Abrir archivo\")\r\narchivoMenu.add_command(label=\"Guardar\")\r\narchivoMenu.add_command(label=\"Guardar como...\")\r\narchivoMenu.add_separator()\r\narchivoMenu.add_command(label=\"Cerrar\")\r\narchivoMenu.add_command(label=\"Salir\", command=lambda:salirDeLaAplicacion())\r\n\r\narchivoEdicion=Menu(barraMenu, tearoff=0)\r\narchivoEdicion.add_command(label=\"Copiar\")\r\narchivoEdicion.add_command(label=\"Cortar\")\r\narchivoEdicion.add_command(label=\"Pegar\")\r\n\r\narchivoHerramientas=Menu(barraMenu)\r\n\r\narchivoAyuda=Menu(barraMenu, tearoff=0)\r\narchivoAyuda.add_command(label=\"Ayuda extra...\", command=lambda:infoAdicional())\r\narchivoAyuda.add_command(label=\"Licencia\", command=lambda:avisoLicencia())\r\narchivoAyuda.add_command(label=\"Acerca de...\")\r\n\r\nbarraMenu.add_cascade(label=\"Archivo\", menu=archivoMenu)\r\nbarraMenu.add_cascade(label=\"Edicion\", menu=archivoEdicion)\r\nbarraMenu.add_cascade(label=\"Herramientas\", menu=archivoHerramientas)\r\nbarraMenu.add_cascade(label=\"Ayuda\", menu=archivoAyuda)\r\n\r\n\"\"\"--------------- Fin del Menú -------------------------------\"\"\"\r\n\"\"\"--------------- Inicio de Clase de la Pila -------------------------------\"\"\"\r\nclass Pila(object):\r\n def __init__(self):\r\n self.items=[\"#\"]\r\n def apilar(self,dato):\r\n self.items.append(dato)\r\n def desapilar(self):\r\n return self.items.pop()\r\n\"\"\"--------------- Fin de Clase de la Pila -------------------------------\"\"\"\r\n\"\"\"--------------- Inicio de la Clase del Automata -------------------------------\"\"\"\r\n\r\ndef VerificarPalindromo(tiempo):\r\n ultimoPilaY = 295\r\n all_canvas()\r\n FlechaInicial.place(x=11,y=255)\r\n FlechaInicial.create_line(0,7,70,7, width=3.0, fill=\"#2a1a5e\")\r\n FlechaInicial.create_line(50,2,60,7, fill=\"#2a1a5e\", width=3.0)\r\n FlechaInicial.create_line(60,7,50,12, fill=\"#2a1a5e\", width=3.0)\r\n FlechaInicial.update()\r\n estadosCanvas()\r\n pintarElementosPila(ultimoPilaY, \"#\")\r\n ultimoPilaY -= 25\r\n \r\n pilaPalindromo = Palindro.get()\r\n Palindromo=Pila()\r\n estado = 1\r\n color=1\r\n entrada = 1\r\n for i in range(0,len(pilaPalindromo)):\r\n all_canvas()\r\n estadosCanvas()\r\n transicion=pilaPalindromo[i]\r\n print(transicion)\r\n topePila=Palindromo.desapilar()\r\n if estado == 1:\r\n Estado1.create_oval(25,35,100,110, fill=\"#2a1a5e\")\r\n Estado1.create_text(63,75,text=\"P\", fill=\"#f45905\", font=(\"\",24))\r\n Estado1.create_line(32,10,32,50, fill=\"#f45905\", width=3.0)\r\n Estado1.create_line(32,10,93,10, fill=\"#f45905\", width=3.0)\r\n Estado1.create_line(93,10,93,50, fill=\"#f45905\", width=3.0)\r\n Estado1.create_line(88,37,93,50, fill=\"#f45905\", width=3.0)\r\n Estado1.create_line(98,37,93,50, fill=\"#f45905\", width=3.0)\r\n if transicion==\"a\" and topePila==\"#\":\r\n Palindromo.apilar(\"#\")\r\n Palindromo.apilar(\"a\")\r\n A_Numeral_p1.place(x=73,y=160)\r\n A_Numeral_p1.create_text(40,15,text=\"a, #/#a\", fill=\"#f45905\",font=(\"\",16))\r\n A_Numeral_p1.update()\r\n pintarElementosPila(ultimoPilaY, \"a\")\r\n ultimoPilaY -= 25\r\n elif transicion==\"b\" and topePila==\"#\":\r\n Palindromo.apilar(\"#\")\r\n Palindromo.apilar(\"b\")\r\n B_Numeral_p1.place(x=73,y=139)\r\n B_Numeral_p1.create_text(40,15,text=\"b, #/#b\", fill=\"#f45905\",font=(\"\",16))\r\n B_Numeral_p1.update()\r\n pintarElementosPila(ultimoPilaY, \"b\")\r\n ultimoPilaY -= 25\r\n elif transicion==\"a\" and topePila==\"a\":\r\n Palindromo.apilar(\"a\")\r\n Palindromo.apilar(\"a\")\r\n AA.place(x=73,y=118)\r\n AA.create_text(40,15,text=\"a, a/aa\", fill=\"#f45905\",font=(\"\",16))\r\n AA.update()\r\n pintarElementosPila(ultimoPilaY, \"a\")\r\n ultimoPilaY -= 25\r\n elif transicion==\"b\" and topePila==\"a\":\r\n Palindromo.apilar(\"a\")\r\n Palindromo.apilar(\"b\")\r\n AB.place(x=73,y=97)\r\n AB.create_text(40,15,text=\"b, a/ab\", fill=\"#f45905\",font=(\"\",16))\r\n AB.update()\r\n pintarElementosPila(ultimoPilaY, \"b\")\r\n ultimoPilaY -= 25\r\n elif transicion==\"a\" and topePila==\"b\":\r\n Palindromo.apilar(\"b\")\r\n Palindromo.apilar(\"a\")\r\n BA.place(x=73,y=76)\r\n BA.create_text(40,15,text=\"a, b/ba\", fill=\"#f45905\",font=(\"\",16))\r\n BA.update()\r\n pintarElementosPila(ultimoPilaY, \"a\")\r\n ultimoPilaY -= 25\r\n elif transicion==\"b\" and topePila==\"b\":\r\n Palindromo.apilar(\"b\")\r\n Palindromo.apilar(\"b\")\r\n BB.place(x=73,y=55)\r\n BB.create_text(40,15,text=\"b, b/bb\", fill=\"#f45905\",font=(\"\",16))\r\n BB.update()\r\n pintarElementosPila(ultimoPilaY, \"b\")\r\n ultimoPilaY -= 25\r\n elif transicion==\"c\" and topePila==\"#\":\r\n Flecha1.place(x=151,y=255)\r\n Flecha1.create_line(0,7,100,7, width=3.0, fill=\"#f45905\")\r\n Flecha1.create_line(80,2,90,7, fill=\"#f45905\", width=3.0)\r\n Flecha1.create_line(90,7,80,12, fill=\"#f45905\", width=3.0)\r\n Flecha1.update()\r\n Palindromo.apilar(\"#\")\r\n C_Numeral.place(x=158,y=220)\r\n C_Numeral.create_text(40,15,text=\"c, #/#\", fill=\"#f45905\",font=(\"\",16))\r\n C_Numeral.update()\r\n estado = 2\r\n elif transicion==\"c\" and topePila==\"b\":\r\n Flecha1.place(x=151,y=255)\r\n Flecha1.create_line(0,7,100,7, width=3.0, fill=\"#f45905\")\r\n Flecha1.create_line(80,2,90,7, fill=\"#f45905\", width=3.0)\r\n Flecha1.create_line(90,7,80,12, fill=\"#f45905\", width=3.0)\r\n Flecha1.update()\r\n Palindromo.apilar(\"b\")\r\n C_B.place(x=158,y=199)\r\n C_B.create_text(40,15,text=\"c, b/b\", fill=\"#f45905\",font=(\"\",16))\r\n C_B.update()\r\n estado = 2\r\n elif transicion==\"c\" and topePila==\"a\":\r\n Estado1.place(x=50,y=190)\r\n Estado1.create_line(32,10,32,50, fill=\"#2a1a5e\", width=3.0)\r\n Estado1.create_line(32,10,93,10, fill=\"#2a1a5e\", width=3.0)\r\n Estado1.create_line(93,10,93,50, fill=\"#2a1a5e\", width=3.0)\r\n Estado1.create_line(88,37,93,50, fill=\"#2a1a5e\", width=3.0)\r\n Estado1.create_line(98,37,93,50, fill=\"#2a1a5e\", width=3.0)\r\n Estado1.update()\r\n Flecha1.place(x=151,y=255)\r\n Flecha1.create_line(0,7,100,7, width=3.0, fill=\"#f45905\")\r\n Flecha1.create_line(80,2,90,7, fill=\"#f45905\", width=3.0)\r\n Flecha1.create_line(90,7,80,12, fill=\"#f45905\", width=3.0)\r\n Flecha1.update()\r\n Palindromo.apilar(\"a\")\r\n C_A.place(x=158,y=178)\r\n C_A.create_text(40,15,text=\"c, a/a\", fill=\"#f45905\",font=(\"\",16))\r\n C_A.update()\r\n estado = 2\r\n else:\r\n Palindromo.apilar(topePila)\r\n Estado3.create_oval(20,20,100,100, fill=\"#cb3234\")\r\n Estado3.create_oval(25,25,95,95, fill=\"#2a1a5e\")\r\n Estado3.create_text(60,62,text=\"R\", fill=\"#cb3234\", font=(\"\",24))\r\n Estado3.update()\r\n\r\n speaker.Speak(\"La Expresión No Es Válida. El Color Rojo Lo Índica\")\r\n return False\r\n elif estado == 2:\r\n if color==1:\r\n estadosCanvas()\r\n color = 2\r\n Estado2.place(x=220,y=190)\r\n Estado2.create_oval(25,35,100,110, fill=\"#2a1a5e\")\r\n Estado2.create_text(63,75,text=\"Q\", fill=\"#f45905\", font=(\"\",24))\r\n Estado2.create_line(32,10,32,50, fill=\"#f45905\", width=3.0)\r\n Estado2.create_line(32,10,93,10, fill=\"#f45905\", width=3.0)\r\n Estado2.create_line(93,10,93,50, fill=\"#f45905\", width=3.0)\r\n Estado2.create_line(88,37,93,50, fill=\"#f45905\", width=3.0)\r\n Estado2.create_line(98,37,93,50, fill=\"#f45905\", width=3.0)\r\n Estado2.update()\r\n if transicion==\"a\" and topePila==\"a\":\r\n if entrada==1:\r\n ultimoPilaY -= 25\r\n entrada=2\r\n # pass \r\n # ultimoPilaY += 25\r\n quitarElementosPila(ultimoPilaY, \"a\")\r\n ultimoPilaY += 25\r\n A_LAMBDA.place(x=243,y=139)\r\n A_LAMBDA.create_text(40,15,text=\"a, a/y\", fill=\"#f45905\",font=(\"\",16))\r\n A_LAMBDA.update()\r\n elif transicion==\"b\" and topePila==\"b\":\r\n # pass \r\n # ultimoPilaY += 25\r\n if entrada==1:\r\n ultimoPilaY -= 25\r\n entrada=2\r\n quitarElementosPila(ultimoPilaY, \"b\")\r\n ultimoPilaY += 25\r\n B_LAMBDA.place(x=243,y=160)\r\n B_LAMBDA.create_text(40,15,text=\"b, b/y\", fill=\"#f45905\",font=(\"\",16))\r\n B_LAMBDA.update()\r\n else:\r\n Palindromo.apilar(topePila)\r\n Estado3.create_oval(20,20,100,100, fill=\"#cb3234\")\r\n Estado3.create_oval(25,25,95,95, fill=\"#2a1a5e\")\r\n Estado3.create_text(60,62,text=\"R\", fill=\"#cb3234\", font=(\"\",24))\r\n Estado3.update()\r\n\r\n speaker.Speak(\"La Expresión No Es Válida. El Color Rojo Lo Índica\")\r\n return False\r\n time.sleep(tiempo)\r\n topePila=Palindromo.desapilar()\r\n if topePila==\"#\":\r\n Palindromo.apilar(\"#\")\r\n estado = 3\r\n if estado == 3:\r\n estadosCanvas()\r\n all_canvas()\r\n Estado3.create_oval(20,20,100,100, fill=\"#16ed48\")\r\n Estado3.create_oval(25,25,95,95, fill=\"#2a1a5e\")\r\n Estado3.create_text(60,62,text=\"R\", fill=\"#16ed48\", font=(\"\",24))\r\n Estado3.update()\r\n \r\n speaker.Speak(\"La Expresión Es Válida. El Color Verde Lo Índica\")\r\n return True\r\n else:\r\n Estado3.create_oval(20,20,100,100, fill=\"#cb3234\")\r\n Estado3.create_oval(25,25,95,95, fill=\"#2a1a5e\")\r\n Estado3.create_text(60,62,text=\"R\", fill=\"#cb3234\", font=(\"\",24))\r\n Estado3.update()\r\n\r\n speaker.Speak(\"La Expresión No Es Válida. El Color Rojo Lo Índica\")\r\n return False\r\n \r\n\"\"\"--------------- Fin de la Clase del Automata -------------------------------\"\"\"\r\nLabelPalindromo = Label(miFrame, text=\"Palindromo Con Pila PRO\", font=(\"Full Pack 2025\",12))\r\nLabelPalindromo.config(justify=\"center\")\r\nLabelPalindromo.place(bordermode=OUTSIDE, x=200, y=10)\r\n\r\nPalindro = StringVar()\r\n\r\nTextoPalindromo = Entry(miFrame, width=69, textvariable=Palindro)\r\nTextoPalindromo.place(bordermode=OUTSIDE, x=80, y=330)\r\nTextoPalindromo.config(bg=\"#dfddc7\")\r\n# TextoPalindromo\r\n\r\n\r\nButtonPalindromoLento = Button(miFrame, text=\"Verificar Lento\", command=lambda:VerificarPalindromo(1))\r\nButtonPalindromoLento.place(bordermode=OUTSIDE, x=130, y=360)\r\nButtonPalindromoLento.config(bg=\"#fbe555\")\r\n\r\nButtonPalindromoRapido = Button(miFrame, text=\"Verificar Rapido\", command=lambda:VerificarPalindromo(0.3))\r\nButtonPalindromoRapido.place(bordermode=OUTSIDE, x=240, y=360)\r\nButtonPalindromoRapido.config(bg=\"#fbe555\")\r\n\r\ndef estadosCanvas():\r\n Estado1.place(x=50,y=190)\r\n Estado1.create_oval(25,35,100,110, fill=\"#2a1a5e\")\r\n Estado1.create_text(63,75,text=\"P\", fill=\"#ffffff\", font=(\"\",24))\r\n Estado1.create_line(32,10,32,50, fill=\"#2a1a5e\", width=3.0)\r\n Estado1.create_line(32,10,93,10, fill=\"#2a1a5e\", width=3.0)\r\n Estado1.create_line(93,10,93,50, fill=\"#2a1a5e\", width=3.0)\r\n Estado1.create_line(88,37,93,50, fill=\"#2a1a5e\", width=3.0)\r\n Estado1.create_line(98,37,93,50, fill=\"#2a1a5e\", width=3.0)\r\n Estado1.update()\r\n \r\n Estado2.place(x=220,y=190)\r\n Estado2.create_oval(25,35,100,110, fill=\"#2a1a5e\")\r\n Estado2.create_text(63,75,text=\"Q\", fill=\"#ffffff\", font=(\"\",24))\r\n Estado2.create_line(32,10,32,50, fill=\"#2a1a5e\", width=3.0)\r\n Estado2.create_line(32,10,93,10, fill=\"#2a1a5e\", width=3.0)\r\n Estado2.create_line(93,10,93,50, fill=\"#2a1a5e\", width=3.0)\r\n Estado2.create_line(88,37,93,50, fill=\"#2a1a5e\", width=3.0)\r\n Estado2.create_line(98,37,93,50, fill=\"#2a1a5e\", width=3.0)\r\n Estado2.update()\r\n\r\n Estado3.place(x=395,y=200)\r\n Estado3.create_oval(20,20,100,100, fill=\"#dfddc7\")\r\n Estado3.create_oval(25,25,95,95, fill=\"#2a1a5e\")\r\n Estado3.create_text(60,62,text=\"R\", fill=\"#ffffff\", font=(\"\",24))\r\n Estado3.update()\r\n\r\ndef all_canvas():\r\n\r\n FlechaInicial.place(x=11,y=255)\r\n FlechaInicial.create_line(0,7,70,7, width=3.0, fill=\"#2a1a5e\")\r\n FlechaInicial.create_line(50,2,60,7, fill=\"#2a1a5e\", width=3.0)\r\n FlechaInicial.create_line(60,7,50,12, fill=\"#2a1a5e\", width=3.0)\r\n FlechaInicial.update()\r\n\r\n Flecha1.place(x=151,y=255)\r\n Flecha1.create_line(0,7,100,7, width=3.0, fill=\"#2a1a5e\")\r\n Flecha1.create_line(80,2,90,7, fill=\"#2a1a5e\", width=3.0)\r\n Flecha1.create_line(90,7,80,12, fill=\"#2a1a5e\", width=3.0)\r\n Flecha1.update()\r\n \r\n Flecha2.place(x=321,y=255)\r\n Flecha2.create_line(0,7,100,7, width=3.0, fill=\"#2a1a5e\")\r\n Flecha2.create_line(80,2,90,7, fill=\"#2a1a5e\", width=3.0)\r\n Flecha2.create_line(90,7,80,12, fill=\"#2a1a5e\", width=3.0)\r\n Flecha2.update()\r\n \r\n A_Numeral_p1.place(x=73,y=160)\r\n A_Numeral_p1.create_text(40,15,text=\"a, #/#a\", fill=\"#2a1a5e\",font=(\"\",16))\r\n A_Numeral_p1.update()\r\n \r\n B_Numeral_p1.place(x=73,y=139)\r\n B_Numeral_p1.create_text(40,15,text=\"b, #/#b\", fill=\"#2a1a5e\",font=(\"\",16))\r\n B_Numeral_p1.update()\r\n \r\n AA.place(x=73,y=118)\r\n AA.create_text(40,15,text=\"a, a/aa\", fill=\"#2a1a5e\",font=(\"\",16))\r\n AA.update()\r\n \r\n AB.place(x=73,y=97)\r\n AB.create_text(40,15,text=\"b, a/ab\", fill=\"#2a1a5e\",font=(\"\",16))\r\n AB.update()\r\n \r\n BA.place(x=73,y=76)\r\n BA.create_text(40,15,text=\"a, b/ba\", fill=\"#2a1a5e\",font=(\"\",16))\r\n BA.update()\r\n \r\n BB.place(x=73,y=55)\r\n BB.create_text(40,15,text=\"b, b/bb\", fill=\"#2a1a5e\",font=(\"\",16))\r\n BB.update()\r\n \r\n C_Numeral.place(x=158,y=220)\r\n C_Numeral.create_text(40,15,text=\"c, #/#\", fill=\"#2a1a5e\",font=(\"\",16))\r\n C_Numeral.update()\r\n \r\n C_B.place(x=158,y=199)\r\n C_B.create_text(40,15,text=\"c, b/b\", fill=\"#2a1a5e\",font=(\"\",16))\r\n C_B.update()\r\n\r\n C_A.place(x=158,y=178)\r\n C_A.create_text(40,15,text=\"c, a/a\", fill=\"#2a1a5e\",font=(\"\",16))\r\n C_A.update()\r\n \r\n B_LAMBDA.place(x=243,y=160)\r\n B_LAMBDA.create_text(40,15,text=\"b, b/y\", fill=\"#2a1a5e\",font=(\"\",16))\r\n B_LAMBDA.update()\r\n \r\n A_LAMBDA.place(x=243,y=139)\r\n A_LAMBDA.create_text(40,15,text=\"a, a/y\", fill=\"#2a1a5e\",font=(\"\",16))\r\n A_LAMBDA.update()\r\n \r\n LAMBDA_Numeral.place(x=323,y=220)\r\n LAMBDA_Numeral.create_text(40,15,text=\"y, #/#\", fill=\"#2a1a5e\",font=(\"\",16))\r\n LAMBDA_Numeral.update()\r\n\r\ndef pintarElementosPila(ultimoPilaY, textoApilar):\r\n ultimoPilaY -= 25\r\n\r\n PilaLineas.place(x=600,y=55)\r\n PilaLineas.create_text(22, ultimoPilaY, text=textoApilar, fill=\"#ffffff\", font=(\"\",16))\r\ndef quitarElementosPila(ultimoPilaY, textoApilar):\r\n ultimoPilaY += 25\r\n\r\n PilaLineas.place(x=600,y=55)\r\n PilaLineas.create_text(22, ultimoPilaY, text=textoApilar, fill=\"#2a1a5e\", font=(\"\",16))\r\n PilaLineas.create_text(22, ultimoPilaY, text=\"8\", fill=\"#2a1a5e\", font=(\"\",16))\r\n PilaLineas.create_text(22, ultimoPilaY, text=\"#\", fill=\"#2a1a5e\", font=(\"\",16))\r\n PilaLineas.create_text(22, ultimoPilaY, text=\"9\", fill=\"#2a1a5e\", font=(\"\",16))\r\n PilaLineas.create_text(22, ultimoPilaY, text=\"b\", fill=\"#2a1a5e\", font=(\"\",16))\r\n PilaLineas.create_text(22, ultimoPilaY, text=\"a\", fill=\"#2a1a5e\", font=(\"\",16))\r\n PilaLineas.create_text(22, ultimoPilaY, text=\"b\", fill=\"#2a1a5e\", font=(\"\",16))\r\n PilaLineas.create_text(22, ultimoPilaY, text=\"a\", fill=\"#2a1a5e\", font=(\"\",16))\r\n\r\npilaYGlobal = 295\r\npintarElementosPila(pilaYGlobal, \"#\")\r\nall_canvas()\r\nestadosCanvas()\r\nroot.mainloop()","sub_path":"palindromo 0.4.py","file_name":"palindromo 0.4.py","file_ext":"py","file_size_in_byte":18933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"30363568","text":"from RMPE.testing.python.config_reader import config_reader\nfrom RMPE.testing.python.demo import getposture\nfrom predict import feed_dir\nimport urllib\nfrom Face import faceapi\nfrom Face import bytesfaceapi\nimport os\nimport ast\nfrom PIL import Image\nfrom shutil import copyfile\n\nparam, model = config_reader()\n\n\ndef generatewardrobe(image_list, personId, personGroupId, time):\n\t### image_list must be a list of urls\n\timage_list = ast.literal_eval(str(image_list))\n\timage_dir = 'USERS/'+personId+' wardrobe'\n\tif not os.path.isdir('bottlenecks/'+image_dir):\n\t\tos.mkdir('bottlenecks/'+image_dir)\n\tif not os.path.isdir(image_dir):\n\t\tos.mkdir(image_dir)\n\n\timage_dir = image_dir+'/'+time\n\tif not os.path.isdir(image_dir):\n\t\tos.mkdir(image_dir)\n\n\tpath = 'wardrobe_candidate.jpg'\n\tfor i in range(len(image_list)):\n\t\turllib.urlretrieve(image_list[i], path)\n\t\tpic = Image.open(path)\n\t\t\n\n\t\tif hasattr(pic, '_getexif'):\n\t\t\torientation = 0x0112\n\t\t\texif = pic._getexif()\n\t\t\tif exif is not None:\n\t\t\t\torientation = exif[orientation]\n\t\t\t\trotations = {\n\t\t\t\t\t3: Image.ROTATE_180,\n\t\t\t\t\t6: Image.ROTATE_270,\n\t\t\t\t\t8: Image.ROTATE_90\n\t\t\t\t}\n\t\t\t\tpic = pic.transpose(rotations[orientation])\n\n\t\tx = (pic.size)\n\t\ta = x[0]/5\n\t\tb = x[1]/5\n\n\t\tim_resized = pic.resize((a,b), Image.ANTIALIAS)\n\t\tim_resized.save(path, \"JPEG\")\n\t\t\n\n\t\t# pic.save(path,quality=30,optimize=True)\n\t\twith open(path, 'rb') as imageFile:\n\t\t\tf = imageFile.read()\n\t\t\tb = bytearray(f)\n\n\t\tcoords = bytesfaceapi.check_photo(b, personId, personGroupId)\n\t\t#coords = faceapi.check_photo(image_list[i], personId, personGroupId)\n\t\tcoords = ast.literal_eval(str(coords))\n\t\tprint (coords)\n\t\tif coords is not None:\n\t\t\timage_path = os.path.join(image_dir, str(i)+'.jpg')\n\t\t\tcopyfile(path, image_path)\n\t\t\t# urllib.urlretrieve(image_list[i], image_path) # saves image to user's folder, to be returned later in feed_dir\n\t\t\t# pic = Image.open(image_path)\n\t\t\t# pic.save(image_path,quality=30,optimize=True)\n\t\t\tface_x = coords[1]+coords[2]/2\n\t\t\tface_y = coords[0]+coords[3]/2\n\t\t\tgetposture(image_path, param, model, face_x, face_y)\n\n\tx = feed_dir(image_dir)\n\t# print (x)\n\treturn x\n\nif __name__ == '__main__':\n\t# a = generatewardrobe(['https://jafrianews.files.wordpress.com/2012/05/russian-president-putin-with-vladimir-putin-may-7-2012.jpg', \n\t# \t\t\t\t'http://america.aljazeera.com/content/ajam/opinions/2014/3/vladimir-putin-ukrainerussiacrimeainternationallaw/jcr:content/mainpar/adaptiveimage/src.adapt.960.high.putin_ukraine_doctrine-1a.1394060261398.jpg'], \n\t# \t\t\t\t'b00c6a39-7807-4cf2-9a04-6b41f2efcf18', \n\t# \t\t\t\t'putin', '2017-04-08 20:26')\n\n\ta = generatewardrobe([\"https://firebasestorage.googleapis.com/v0/b/yuxapp-84210.appspot.com/o/1%2F513432653368.jpg?alt=media&token=81c709c2-192d-418e-8340-d879701d7ed4\"], \n\t\t\t\t\t'e49417ac-0960-4711-a6e4-be3ffaf32ab9', \n\t\t\t\t\t'xinchen', '2017-04-08 20:26')\n\n\ta = str(a)\n\tprint (a)\n\n\t# generatewardrobe([\"http://www.thewrap.com/wp-content/uploads/2015/11/Donald-Trump.jpg\"], \n\t# \t\t\t\t'b00c6a39-7807-4cf2-9a04-6b41f2efcf18', \n\t# \t\t\t\t'putin')\n","sub_path":"generatewardrobe.py","file_name":"generatewardrobe.py","file_ext":"py","file_size_in_byte":3003,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"247941851","text":"# =======================================================================================================================================================================================================\n# SUBJECT : sentiment analysis from twitter\n# OBJECT TYPE : python\n# OBJECT NAME : py_twitter_jobs\n# CREATED BY : Harold Delaney\n# CREATED ON : 20170623\n# SOURCE : twitter (tweepy python library)\n# PREPERATION : \n# FREQUENCY : STREAMIN\n# \n# REMARKS : 1) \n# 2) \n# 3)\n# =======================================================================================================================================================================================================\n# =============================================================================\n# ENVIRNMENT AND LIBRARY SETUP -- START\n# =============================================================================\nfrom tweepy import StreamListener\nimport json, time, sys\n\nclass pySListener(StreamListener):\n\n def __init__(self, api = None, fprefix = 'streamer', **g):\n self.api = api or API()\n self.counter = 0\n self.fprefix = fprefix\n '''self.output = open(fprefix + '.' \n + time.strftime('%Y%m%d-%H%M%S') + '.json', 'w')'''\n self.delout = open('delete.txt', 'a')\n self.db = g['DB']\n self.country_filter = g['CONTRY_RESTRICTIONS']\n\n def on_data(self, tweet, **g):\n print(tweet) \n # CONVERT JSON TEXT OBJECT (DATA) TO PYTHON DICTIONARY\n d = json.loads(tweet)\n #print (str(d).upper().encode('ascii', 'ignore')) # print(d['glossary']['title'])\n \n \n coords = d['coordinates']\n if d.get('place'):\n place_coords = d['place']['bounding_box']['coordinates']\n place_type = d['place']['place_type']\n place_name = d['place']['name']\n place_full_name = d['place']['full_name']\n place_country_code = d['place']['country_code']\n place_country = d['place']['country'] \n else:\n return\n \n print(coords)\n print(place_coords)\n print(place_type)\n print(place_name)\n print(place_full_name)\n print(place_country_code)\n print(place_country)\n \n try:\n # BASIC FILTERS\n if place_country_code in self.country_filter and str(d['in_reply_to_status_id']).upper().encode('ascii', 'ignore') == 'NONE' and 'retweeted_status' not in d and 'HTTP' not in str(d['text']).upper().encode('ascii', 'ignore'):\n print('keep : ' + tweet)\n #self.on_status(**d)\n return\n elif 'delete' in tweet:\n delete = json.loads(tweet)['delete']['status']\n if self.on_delete(delete['id'], delete['user_id']) is False:\n return False\n elif 'limit' in tweet:\n if self.on_limit(json.loads(tweet)['limit']['track']) is False:\n return False\n elif 'warning' in tweet:\n warning = json.loads(tweet)['warnings']\n print (warning['message'])\n return false\n except KeyError as k:\n print (\"Fail: \" + str(k))\n \n def on_status(self, **d): #status):\n # PREPARE TO WRITE TO A DATABASE OR FILE\n # GENERATE DICTIONARY PAIRS FOR TABLE CALL AND INSERTION\n tweet = dict(\n MSMT_DTE_ID = time.strftime('%Y%m%d')\n ,DATA_TYPE = 'TEMP'\n ,CNTRY_CDE = d['user']['country_code']\n ,LOCATION = d['user']['location']\n ,GEO = d['geo']\n ,COORDS = d['coords']['lon'] + ',' + d['coords']['lat'] #'d['coordinates']\n ,UTC_OFFSET = d['user']['utc_offset']\n ,TIME_ZONE = d['user']['time_zone']\n ,LANGUAGE = d['user']['lang']\n ,DESCRIPTION = d['user']['description']\n ,TEXT = str(d['text']).upper().encode('utf-8')\n ,USER_NAME = d['user']['screen_name']\n ,USER_CREATED = d['user']['created_at']\n ,RETWEET_COUNT = d['retweet_count']\n ,RETWEET_STATUS = d['retweeted']\n )\n print(tweet)\n \n self.filter_tweet(**tweet)\n \n return\n \n def on_delete(self, status_id, user_id):\n self.delout.write( str(status_id) + \"\\n\")\n return\n\n def on_limit(self, track):\n sys.stderr.write(track + \"\\n\")\n return\n\n def on_error(self, status_code):\n sys.stderr.write('Error: ' + str(status_code) + \"\\n\")\n return False\n\n def on_timeout(self):\n sys.stderr.write(\"Timeout, sleeping for 60 seconds...\\n\")\n time.sleep(60)\n return \n \n def filter_tweet(**tweet):\n \n print(tweet)\n # REMOVE TWEETS THAT DONT MATCH FURTHER BUSINESS CRITERIA\n if not tweet_matches_criteria(tweet):\n return\n # Process the remaining tweets.\n self.store_tweet(**tweet)\n \n \n def store_tweet(**tweet): \n # =============================================================================\n # WRITE RESULTS OF TWEEP EXTRACT TO LOCAL DB\n # ============================================================================= \n dbmgr = pyDB(g['DB'])\n q = r\"\"\"INSERT INTO {0} (MSMT_DTE_ID, DATA_TYPE, CNTRY_CDE, LOCATION, GEO, COORDS, UTC_OFFSET, TIME_ZONE, LANGUAGE, DESCRIPTION, TEXT, USER_NAME, USER_CREATED, RETWEET_COUNT, POLARITY, SUBJECTIVITY) \n VALUES ({1}, '{2}', '{3}', '{4}', '{5}', '{6}', '{7}', '{8}', '{9}', {10}, '{11}', '{12}', '{13}', {14})\"\"\".format(\n g['TBL_NME'] #[0]\n ,tweet['MSMT_DTE_ID'] #[1]\n ,tweet['DATA_TYPE'] #[2]\n ,tweet['CNTRY_CDE'] #[3]\n ,tweet['LOCATION'] #[4]\n ,tweet['GEO'] #[5]\n ,tweet['COORDS'] #[6]\n ,tweet['UTC_OFFSET'] #[7]\n ,tweet['TIME_ZONE'] #[8]\n ,tweet['LANGUAGE'] #[9]\n ,tweet['DESCRIPTION'] #[10] \n ,tweet['TEXT'] #[11]\n ,tweet['USER_NAME'] #[12]\n ,tweet['USER_CREATED'] #[13]\n ,tweet['RETWEET_COUNT'] #[14]\n )\n dbmgr.query(q)","sub_path":"__python/__utils/PY_UTIL_TWEEPY_SLISTENER.py","file_name":"PY_UTIL_TWEEPY_SLISTENER.py","file_ext":"py","file_size_in_byte":6312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"476144251","text":"class Scanner():\n def __init__(self, depth, range):\n self.depth = depth\n self.range = range\n self.scannerIndex = 0\n self.stepDirection = 1\n self.__lowerBound = -1\n\n def tick(self):\n newIndex = self.scannerIndex + self.stepDirection\n\n if newIndex == self.__lowerBound or newIndex == self.range:\n self.stepDirection *= -1\n newIndex = self.scannerIndex + self.stepDirection\n\n self.scannerIndex = newIndex\n\n def caughtPacket(self):\n return self.scannerIndex == 0\n \n def getSeverity(self):\n if self.caughtPacket():\n return self.range * self.depth\n else:\n return 0\n\nclass Rider():\n def __init__(self, delay, maxDepth):\n self.delay = delay\n self.depth = 0\n self.maxDepth = maxDepth\n self.wasCaught = False\n\n def completeWithoutBeingCaught(self):\n return self.depth > self.maxDepth\n\n def __repr__(self):\n return \"Rider delay: {}, depth: {}, successful: {}\".format(self.delay, self.depth, self.completeWithoutBeingCaught())\n\ndef splitDepthAndRange(rawDepthRange):\n theSplit = rawDepthRange.split(\": \")\n return (theSplit[0], theSplit[1].strip())\n\ndef createDepthToScanner(rawDepthRanges):\n depthToScanner = {}\n for rawDepthRange in rawDepthRanges:\n strDepth, strRange = splitDepthAndRange(rawDepthRange)\n depthToScanner[int(strDepth)] = Scanner(int(strDepth), int(strRange))\n\n return depthToScanner\n\ndef getRawFile():\n rawDepthRanges = []\n with open(\"input.txt\", \"r\") as depthRangeFile:\n rawDepthRanges = depthRangeFile.readlines()\n\n return rawDepthRanges\n\ndef getAllSuccessfulRiders(riders):\n return [rider for rider in riders if rider.completeWithoutBeingCaught()]\n\nrawDepthRanges = getRawFile()\ndepthToScanner = createDepthToScanner(rawDepthRanges)\nmaxDepth = max([int(depth) for depth in depthToScanner])\ntotalSeverity = 0\nfor curDepth in range(maxDepth + 1):\n\n if curDepth in depthToScanner:\n scanner = depthToScanner[curDepth]\n totalSeverity += scanner.getSeverity()\n\n for depth, scanner in depthToScanner.items():\n scanner.tick()\n\nprint(\"Total severity: {}\".format(totalSeverity))\n\ndelayTicks = 0\ndepthToScanner = createDepthToScanner(rawDepthRanges)\nmaxDepth = max([int(depth) for depth in depthToScanner])\nriders = []\n\nwhile len(getAllSuccessfulRiders(riders)) == 0:\n riders.append(Rider(delayTicks, maxDepth))\n\n for rider in riders:\n if rider.depth in depthToScanner:\n scanner = depthToScanner[rider.depth]\n rider.wasCaught = scanner.caughtPacket()\n rider.depth += 1\n\n for rider in [rider for rider in riders if rider.wasCaught]:\n riders.remove(rider)\n\n for depth, scanner in depthToScanner.items():\n scanner.tick()\n\n delayTicks += 1\n\nfor rider in getAllSuccessfulRiders(riders):\n print(rider)\n","sub_path":"day13.py","file_name":"day13.py","file_ext":"py","file_size_in_byte":2924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"550600997","text":"\nfrom pylab import *\nr=arange(1,4,0.01)\n\n\nfor i in xrange(len(r)) :\n ans=[]\n repeat=[]\n x=0.5\n s=0\n j=0\n for j in range(1000):\n s=r[i]*x*(1-x)\n repeat.append(round(x,4))\n if round(s,4) in repeat :\n ans.append(s)\n x=s\n abc=np.zeros(len(ans))\n for k in xrange(len(abc)):\n abc[k]=r[i]\n plot(abc,ans,'k.')\nshow()\n","sub_path":"student/101020021/hw2/chaos.py","file_name":"chaos.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"54041217","text":"from django.conf.urls import patterns, include, url\nfrom django.contrib import flatpages\nfrom django.contrib import admin\n\nadmin.autodiscover()\n\n \nurlpatterns = patterns('',\n # Uncomment the admin/doc line below to enable admin documentation:\n # url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n # Uncomment the next line to enable the admin:\n url(r'^admin/', include(admin.site.urls)),\n \n # ** DEVELOPMENT MODE ONLY ** DO NOT use this to serve static content in production mode\n url(r'^tiny_mce/(?P.*)$', 'django.views.static.serve',\n {'document_root': '/home/jwong/Documents/Aptana_Studio_3_Workspace/django_workspace/tinymce/js/tinymce'},),\n \n # the argument should be the path relative to the application (django_workspace/cms/)\n url(r'^jraywo/search/$', 'search.views.search', name='search'), \n \n # weblog \n url(r'^jraywo/categories/', include('coltrane.urls.categories')),\n url(r'^jraywo/links/', include('coltrane.urls.links')),\n url(r'^jraywo/tags/', include('coltrane.urls.tags')),\n url(r'^jraywo/', include('coltrane.urls.entries')),\n \n url(r'', include('django.contrib.flatpages.urls')),\n)\n\n\n\n##From ********** testsite\n#from django.conf.urls.defaults import *\n# Uncomment the next two lines to enable the admin:\n# from django.contrib import admin\n# admin.autodiscover()\n\n#urlpatterns = patterns('',\n# (r'^hello-django', 'testsite.views.hello'),\n # Example:\n # (r'^testsite/', include('testsite.foo.urls')),\n\n # Uncomment the admin/doc line below and add 'django.contrib.admindocs'\n # to INSTALLED_APPS to enable admin documentation:\n # (r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n # Uncomment the next line to enable the admin:\n # (r'^admin/', include(admin.site.urls)),\n#)","sub_path":"main/control/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"431311212","text":"#encoding=utf-8\n\nimport json\n\nfrom ycfspider.utils.ctrip_fc import CtripJs\n__author__ = 'lizhipeng'\n\nclass CtripMHotelPriceKeysMiddleware(object):\n\n def __init__(self):\n self.js = CtripJs()\n # self.key = ''\n\n def process_request(self, request, spider):\n if spider.name is 'CtripMHotelPriceSpider':\n body_json = json.loads(request.body)\n proxy = request.meta['proxy']\n hotel_id = request.meta['hotel_id']\n guid = request.meta['guid']\n check_in_date = request.meta['check_in_date'].replace('-', '')\n cookie = request.cookies\n # if self.key == '':\n self.key = self.js.ctrip_m_key(str(hotel_id), guid, check_in_date, proxy, cookie)\n body_json['Key'] = self.key\n request._set_body(json.dumps(body_json))\n\n\n","sub_path":"ycfspider/ycf/ycfspider-for-schedule/ycfspider/middleware/ctrip_m_hotel_price_keys_middleware.py","file_name":"ctrip_m_hotel_price_keys_middleware.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"515284323","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon May 4 20:52:01 2020\n\n@author: estanislau\n\"\"\"\n\n\nimport cv2\nimport glob\n\n\nnumero_pasta = 2\n\ncaminho_pasta = '/home/estanislau/Projetos/Atena/Frames/frames_video_plc_'+str(numero_pasta)+'/*.jpg'\n\nn_placa_1, c_placa_1 = \"Pare\", cv2.CascadeClassifier('/home/estanislau/Projetos/Atena/Modulo_Placas/Classificadores/cascade_pare.xml')\nn_placa_2, c_placa_2 = \"Pedestre\", cv2.CascadeClassifier('/home/estanislau/Projetos/Atena/Modulo_Placas/Classificadores/cascade_pedestre.xml')\n\nclassificadores = [(n_placa_1, c_placa_1), (n_placa_2, c_placa_2)]\n\n\ndef detecta_Placas(img, nome, classificador):\n img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n img_placa = classificador.detectMultiScale(img_gray, scaleFactor = 1.1, minNeighbors = 10, minSize=(10, 10), maxSize=(100, 100))\n \n for (x,y,w,h) in img_placa: \n cv2.rectangle(img, (x, y), (x + w, y + h), ((0, 220, 220)), 2)\n cv2.putText(img, nome, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 220, 220), 2)\n \n return img\n\n\ncont_imagem = 1000\ntry: \n for i in sorted(glob.glob(caminho_pasta)): \n imagem = cv2.imread(i)\n \n for n, c in classificadores: \n imagem_placas = detecta_Placas(imagem, n, c)\n\n cv2.imshow(\"Apresenta Imagem\", imagem_placas)\n cv2.waitKey(5)\n \n print(\"Frame: {0}\".format(cont_imagem))\n cont_imagem += 1\n \nexcept KeyboardInterrupt:\n cv2.destroyAllWindows()\n \nfinally:\n cv2.destroyAllWindows()","sub_path":"Experimentos/Placas/define_parametros.py","file_name":"define_parametros.py","file_ext":"py","file_size_in_byte":1509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"529862296","text":"# Indexes for haystack elasticsearch\nfrom django.conf import settings\nfrom haystack import indexes\nfrom wiki.models import Article\n\n\nclass ArticleIndex(indexes.SearchIndex, indexes.Indexable):\n text = indexes.CharField(document=True, use_template=True)\n modified = indexes.DateTimeField(model_attr=\"modified\")\n title = indexes.CharField(model_attr=\"current_revision__title\")\n content = indexes.CharField(model_attr=\"current_revision__content\")\n deleted = indexes.BooleanField(model_attr=\"current_revision__deleted\")\n locked = indexes.BooleanField(model_attr=\"current_revision__locked\")\n urlpath = indexes.CharField(model_attr=\"get_absolute_url\")\n id = indexes.IntegerField(model_attr=\"id\")\n other_read = indexes.BooleanField(model_attr=\"other_read\")\n group = indexes.IntegerField(null=True)\n\n def get_model(self):\n return Article\n\n def prepare_urlpath(self, obj):\n return obj.get_absolute_url()[1::]\n\n def prepare_group(self, obj):\n if obj.group is None:\n return None\n else:\n return obj.group.id\n\n def index_queryset(self, using=None):\n \"\"\"Used when the entire index for model is updated.\"\"\"\n return self.get_model().objects.filter(urlpath__site_id=settings.SITE_ID)\n","sub_path":"trojsten/search_indexes.py","file_name":"search_indexes.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"77095035","text":"import requests\nimport os\n\n\ndef reader():\n emails = open('email.csv', 'r', encoding='utf-8')\n for email in emails:\n find(email)\n #print(email)\n emails.close()\n\n\ndef find(email):\n try:\n pdl_url = \"https://api.peopledatalabs.com/v4/person\"\n\n api_key = 'f0678ea13cadea484273be0513376148ffd53294ac5222496dff2f1e769e3a4f'\n\n params = {\n \"api_key\": api_key,\n \"email\": email,\n \"required\": \"profiles.network:linkedin\"\n }\n\n response = requests.get(pdl_url, params=params).json()\n\n res = response['data']['primary']['linkedin']\n\n print(res)\n write_csv(res, email)\n\n except Exception as err:\n print('Linkedin not found - {}'.format(err))\n res = 'not found'\n write_csv(res, email)\n\n\ndef write_csv(res, email):\n try:\n # Verify if exists file\n log_file = os.path.isfile('./res.csv')\n\n if log_file:\n f = open(\"res.csv\", \"a\", encoding='utf-8') # Append in file\n f.write('{},{}'.format(res, email))\n f.close()\n else:\n f = open(\"res.csv\", \"w\", encoding='utf-8') # Write in file\n f.write('{},{}'.format(res, email))\n f.close()\n\n except Exception as err:\n print(err)\n\n\nif __name__ == \"__main__\":\n reader()\n","sub_path":"user_verify.py","file_name":"user_verify.py","file_ext":"py","file_size_in_byte":1336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"280907105","text":"from datetime import datetime\nimport mock\nfrom nose.tools import *\nfrom requests import Response, HTTPError\nfrom backdrop.collector.write import DataSet\nimport unittest\n\n\nclass TestDataSet(unittest.TestCase):\n def test_from_target(self):\n data_set = DataSet(url='foo', token='bar')\n eq_(data_set.url, 'foo')\n eq_(data_set.token, 'bar')\n\n @mock.patch('backdrop.collector.write.requests')\n def test_post_data_to_data_set(self, requests):\n data_set = DataSet('foo', 'bar')\n\n data_set.post({'key': 'value'})\n\n requests.post.assert_called_with(\n url='foo',\n headers={\n 'Authorization': 'Bearer bar',\n 'Content-type': 'application/json'\n },\n data='{\"key\": \"value\"}'\n )\n\n @mock.patch('backdrop.collector.write.requests')\n def test_post_to_data_set_serializes_datetimes(self, requests):\n data_set = DataSet(None, None)\n\n data_set.post({'key': datetime(2012, 12, 12)})\n\n requests.post.assert_called_with(\n url=mock.ANY,\n headers=mock.ANY,\n data='{\"key\": \"2012-12-12T00:00:00+00:00\"}'\n )\n\n @mock.patch('requests.post')\n def test_raises_error_on_4XX_or_5XX_responses(self, mock_post):\n data_set = DataSet(None, None)\n response = Response()\n response.status_code = 418\n mock_post.return_value = response\n self.assertRaises(HTTPError, data_set.post, {'key': 'foo'})\n","sub_path":"tests/test_write.py","file_name":"test_write.py","file_ext":"py","file_size_in_byte":1493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"564306550","text":"# coding: utf-8\n\"\"\"Setup post actions, used in main setup.py.\"\"\"\n\nimport os\nimport shutil\nimport sys\nfrom setuptools.command.install import install\nfrom stakkr import package_utils\n\n\ntry:\n import click\n\n @click.command(help=\"\"\"Initialize for the first time stakkr by copying\ntemplates and directory structure\"\"\")\n @click.option('--force', '-f', help=\"Force recreate directories structure\", is_flag=True)\n def init(force: bool):\n \"\"\"CLI Entry point, when initializing stakkr manually.\"\"\"\n config_file = package_utils.get_venv_basedir() + '/conf/compose.ini'\n if os.path.isfile(config_file) and force is False:\n click.secho('Config file (conf/compose.ini) already present. Leaving.', fg='yellow')\n return\n\n msg = \"Config (conf/compose.ini) not present, don't forget to create it\"\n click.secho(msg, fg='yellow')\n _post_install(force)\n\nexcept ImportError:\n def init():\n \"\"\"If click is not installed, display that message.\"\"\"\n print('Stakkr has not been installed yet')\n sys.exit(1)\n\n\ndef _post_install(force: bool = False):\n \"\"\"Create templates (directories and files).\"\"\"\n print('Post Installation : create templates')\n\n venv_dir = package_utils.get_venv_basedir()\n # If already installed don't do anything\n if os.path.isfile(venv_dir + '/conf/compose.ini'):\n return\n\n required_dirs = [\n 'conf/mysql-override',\n 'conf/php-fpm-override',\n 'conf/xhgui-override',\n 'data',\n 'home/www-data',\n 'home/www-data/bin',\n 'logs',\n 'plugins',\n 'services',\n 'www'\n ]\n for required_dir in required_dirs:\n _create_dir(venv_dir, required_dir, force)\n\n required_tpls = [\n '.env',\n 'bash_completion',\n 'conf/compose.ini.tpl',\n 'conf/mysql-override/mysqld.cnf',\n 'conf/php-fpm-override/example.conf',\n 'conf/php-fpm-override/README',\n 'conf/xhgui-override/config.php',\n 'home/www-data/.bashrc'\n ]\n for required_tpl in required_tpls:\n _copy_file(venv_dir, required_tpl, force)\n\n\ndef _create_dir(venv_dir: str, dir_name: str, force: bool):\n dir_name = venv_dir + '/' + dir_name.lstrip('/')\n if os.path.isdir(dir_name) and force is False:\n return\n\n if not os.path.isdir(dir_name):\n os.makedirs(dir_name)\n\n\ndef _copy_file(venv_dir: str, source_file: str, force: bool):\n full_path = package_utils.get_file('tpls', source_file)\n dest_file = venv_dir + '/' + source_file\n if os.path.isfile(dest_file) and force is False:\n print(' - {} exists, do not overwrite'.format(source_file))\n return\n\n print(' - {} written'.format(source_file))\n try:\n shutil.copy(full_path, dest_file)\n except Exception:\n msg = \"Error trying to copy {} .. check that the file is there ...\".format(full_path)\n print(msg, file=sys.stderr)\n\n\nclass StakkrPostInstall(install):\n \"\"\"Class called by the main setup.py.\"\"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\"Inherit from setup install class and ensure we are in a venv.\"\"\"\n super(StakkrPostInstall, self).__init__(*args, **kwargs)\n\n try:\n package_utils.get_venv_basedir()\n _post_install(False)\n except OSError:\n msg = 'You must run setup.py from a virtualenv if you want to have'\n msg += ' templates installed'\n print(msg)\n","sub_path":"stakkr/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":3465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"300253807","text":"'''\nCreated on Oct 27, 2015\n\n@author: siva\n'''\nimport game_config\nimport MCTS_node\nimport MCS_support\nimport MCTS_Support\nimport Support\nimport copy\nimport math\nimport time\nimport pydotplus\nimport random\nimport os, shutil\n\nrandom.seed()\n\nprint ('Hello World')\nfolder = 'MCTS_imgs/'\nfor the_file in os.listdir(folder):\n file_path = os.path.join(folder, the_file)\n try:\n if os.path.isfile(file_path):\n os.unlink(file_path)\n #elif os.path.isdir(file_path): shutil.rmtree(file_path)\n except Exception as e:\n print (e)\n\ncurr_board = MCS_support.Game_board(game_config.INIT_GAMESTATE)\nrow_idx = copy.deepcopy(curr_board.min_choice_row)\ncol_idx = copy.deepcopy(curr_board.min_choice_col)\npossible_val_parent = curr_board.board_cells[row_idx][col_idx].cell_choices\n#print(row_idx,col_idx,possible_val_parent)\ngame_root = MCTS_node.MCTSnode(None,game_config.INIT_GAMESTATE,row_idx,col_idx,0,'tree_root')\n#game_root.print_node_details()\ngame_root.node_choices.extend(possible_val_parent)\nprint('Initial Game state')\nprint (game_root.node_game_string)\nprint('***************************************************')\nstart_time = time.time()\niter_cntr = 1\nfor i in range(game_config.MAX_SIMULATIONS):\n #file_name = 'MCTS_imgs/mcts_tree_' + str(iter_cntr) + '.png'\n #file_name = ''.join(file_name)\n current_node = MCTS_Support.Tree_policy(game_root)\n return_val = MCTS_Support.Default_policy(current_node)\n reward_val = ((float)(return_val)) / ((float)(math.pow(game_config.GAME_DIMENSION,4)))\n MCTS_Support.Back_propagate(current_node, reward_val)\n if(return_val == (math.pow(game_config.GAME_DIMENSION,4))):\n current_node.node_solved_from = True\n #MCTS_Support.create_graph(game_root, file_name)\n break\n '''For creating intermmediate images'''\n #MCTS_Support.create_graph(game_root, file_name)\n '''For generating stage images\n Remove everything in between lines,in original code\n ----------------------------------------------------------------------------------'''\n '''current_node.node_children.pop()'''\n '''----------------------------------------------------------------------------------'''\n iter_cntr = iter_cntr + 1\n #print('Iteration:',(iter_cntr -1) )\n print(iter_cntr)\n ","sub_path":"MCTS_search.py","file_name":"MCTS_search.py","file_ext":"py","file_size_in_byte":2283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"652166180","text":"from __future__ import division, print_function\n\nimport os.path as osp\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom torchvision.transforms import Compose\nfrom torch_scatter import scatter_mean\nfrom torch_geometric.datasets import Cuneiform\nfrom torch_geometric.utils import DataLoader\nfrom torch_geometric.nn.modules import SplineConv\nfrom torch_geometric.transform import (RandomRotate, RandomScale,\n RandomTranslate, CartesianAdj)\n\ntransform = Compose([\n RandomRotate(0.6),\n RandomScale(1.4),\n RandomTranslate(0.1),\n CartesianAdj(),\n])\npath = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'Cuneiform')\ntrain_dataset = Cuneiform(path, transform=transform)\ntest_dataset = Cuneiform(path, transform=CartesianAdj())\n\n# Modify inputs.\n# input = train_dataset.input\n# input[:, :7] = 2 * input[:, :7] - 1\n# input = torch.cat([input, input.new(input.size(0), 1).fill_(1)], dim=1)\n# train_dataset.input = input\n# test_dataset.input = input\n\n# Create random splits.\nn = len(train_dataset)\nstep = (n + 10) // 10\nperm = torch.randperm(n)\nsplit = torch.arange(0, n + step, step).long()\n\n\nclass Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n self.conv1 = SplineConv(8, 32, dim=2, kernel_size=5)\n self.conv2 = SplineConv(32, 64, dim=2, kernel_size=5)\n self.conv3 = SplineConv(64, 64, dim=2, kernel_size=5)\n self.fc1 = nn.Linear(64, 30)\n\n def forward(self, data):\n x, edge_index, pseudo = data.input, data.index, data.weight\n x = F.elu(self.conv1(x, edge_index, pseudo))\n x = F.elu(self.conv2(x, edge_index, pseudo))\n x = F.elu(self.conv3(x, edge_index, pseudo))\n\n x = scatter_mean(Variable(data.batch.unsqueeze(1).expand_as(x)), x)\n\n x = F.dropout(x, training=self.training)\n x = self.fc1(x)\n return F.log_softmax(x, dim=1)\n\n\nmodel = Net()\nif torch.cuda.is_available():\n model = model.cuda()\n\noptimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n\n\ndef train(epoch):\n model.train()\n\n if epoch == 1:\n for param_group in optimizer.param_groups:\n param_group['lr'] = 0.01\n\n if epoch == 201:\n for param_group in optimizer.param_groups:\n param_group['lr'] = 0.001\n\n for data in train_loader:\n data = data.cuda().to_variable()\n optimizer.zero_grad()\n F.nll_loss(model(data), data.target).backward()\n optimizer.step()\n\n\ndef test(run, loader):\n model.eval()\n correct = 0\n num_examples = 0\n\n for data in loader:\n data = data.cuda().to_variable(['input', 'weight'])\n pred = model(data).data.max(1)[1]\n correct += pred.eq(data.target).sum()\n num_examples += data.target.size(0)\n\n print('Run:', run, 'Test Accuracy:', correct / num_examples)\n return correct / num_examples\n\n\n# 10-fold cross-validation.\nacc = []\nfor i in range(split.size(0) - 1):\n print('Split {}:'.format(i + 1))\n\n if i == 0:\n train_dataset.split = perm[split[i + 1]:]\n elif i == split.size(0) - 2:\n train_dataset.split = perm[:split[i]]\n else:\n train_dataset.split = torch.cat([perm[:split[i]], perm[split[i + 1]:]])\n test_dataset.split = perm[split[i]:split[i + 1]]\n train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n test_loader = DataLoader(test_dataset, batch_size=32)\n\n acc_split = []\n for run in range(1, 11):\n model.conv1.reset_parameters()\n model.conv2.reset_parameters()\n model.conv3.reset_parameters()\n model.fc1.reset_parameters()\n\n for epoch in range(1, 301):\n train(epoch)\n\n acc_split.append(test(run, test_loader))\n acc.append(acc_split)\n\nprint('Mean:', torch.Tensor(acc).mean(), 'Stddev:', torch.Tensor(acc).std())\n","sub_path":"examples/cuneiform.py","file_name":"cuneiform.py","file_ext":"py","file_size_in_byte":3871,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"373161927","text":"from helper import gen_primes\n\np = gen_primes(1000000)\n\ni = 0\nsm = 0\n\nwhile True:\n if sm + p[i] >= 1000000:\n break\n sm += p[i]\n i += 1\n\nlg = i\n\n\ns = 0\nfound = False\nwhile not found:\n print(lg)\n n = 0\n while True:\n sm = sum(p[0 + n:lg + n])\n if lg + n > len(p) or sm >= 1000000:\n break\n if sm in p:\n s = sm\n found = True\n break\n n += 1\n lg -= 1\n\nprint(s)","sub_path":"euler50.py","file_name":"euler50.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"249439450","text":"from django.db import models\nfrom django.utils import timezone\nfrom routeCalc.validators.ibge_code import validate_ibge_code\nfrom django.utils.translation import gettext_lazy as _\n\nimport requests\n\n\nclass City(models.Model):\n \"\"\"\n Model para armazenar cidades já registradas.\n Terá também como chave o CÓDIGO DO IBGE para padronizar as inserções e buscas.\n \"\"\"\n\n ibge_code = models.CharField(max_length=7, unique=True, db_index=True, validators=[validate_ibge_code])\n name = models.CharField(max_length=50)\n created = models.DateTimeField(editable=False)\n modified = models.DateTimeField(editable=False)\n\n def save(self, *args, **kwargs):\n \"\"\"\n Ao salvar garante que as datas sejam salvas corretamentes sem os bloqueios de auto_add_now\n \"\"\"\n\n if not self.id:\n self.created = timezone.now()\n\n self.modified = timezone.now()\n\n return super(City, self).save(*args, **kwargs)\n\n @staticmethod\n def add_inexisting(cities):\n \"\"\"\n Adiciona cidades que ainda não existem no nosso banco de dados\n :param cities: list\n :return: Bool\n \"\"\"\n existing_cities = City.objects.filter(ibge_code__in=cities)\n\n # Check which cities need to be inserted\n insert_cities = []\n\n for city in cities:\n ins = True\n\n for existing_city in existing_cities:\n if city == existing_city.ibge_code:\n ins = False\n\n if ins:\n insert_cities.append(city)\n\n # Prepare statement\n statement = []\n\n for city in insert_cities:\n statement.append(City(ibge_code=city, name=City.get_name_by_ibge_code(city), created=timezone.now(),\n modified=timezone.now()))\n\n if statement:\n City.objects.bulk_create(statement)\n\n return True\n\n @staticmethod\n def get_name_by_ibge_code(ibge_code):\n \"\"\"\n Faz uma requisição na API do IBGE e retorna nome da cidade\n :param ibge_code:\n :return: Str\n \"\"\"\n response = requests.get(\"https://servicodados.ibge.gov.br/api/v1/localidades/municipios/\" + ibge_code)\n\n if 200 >= response.status_code < 300:\n return response.json()['nome']\n else:\n raise Exception(_(\"Código do IBGE inválido\"))\n\n def __str__(self):\n return self.ibge_code\n\n class Meta:\n db_table = \"cities\"\n","sub_path":"routeCalc/models/City.py","file_name":"City.py","file_ext":"py","file_size_in_byte":2479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"135734311","text":"import requests\nfrom urllib.parse import quote\nimport urllib.request\nimport os\nimport sys\nimport json\n\ndef call(keyword, start):\n client_id = \"OXwS1xuacb66kX8AfAtY\"\n client_secret = \"NcNxkXMDw3\"\n encText = urllib.parse.quote(keyword)\n url = \"https://openapi.naver.com/v1/search/blog?display=100&start=\"+str(start)+\"&query=\" + encText # json 결과\n # url = \"https://openapi.naver.com/v1/search/blog.xml?query=\" + encText # xml 결과\n resuslt = requests.get(url=url,\n headers= {\"X-Naver-Client-Id\":\"OXwS1xuacb66kX8AfAtY\",\n \"X-Naver-Client-Secret\":\"NcNxkXMDw3\"})\n print(resuslt)\n return resuslt.json()\n\ndef get1000Result(keyword):\n list = []\n for num in range(0,10):\n list = list + call(keyword,num*100+1)['items']\n return list\n\n#print(len(get1000Result(\"강남역맛집\")))","sub_path":"python_crawler_example_01/com/21_naver_api_caller.py","file_name":"21_naver_api_caller.py","file_ext":"py","file_size_in_byte":838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"232074306","text":"from os.path import join\n\nfrom core.Exceptions import ConfigurationException\nfrom core.config.SimpleConfig import SimpleConfig\n\n__author__ = 'kitru'\n\nclass TranslationConfig(SimpleConfig):\n \"\"\" Reads tranlation codes for selected language. Translation file should be placed in ./resources/trans \"\"\"\n def __init__(self, lang):\n \"\"\" Reads translation file and opens new translation log \"\"\"\n SimpleConfig.__init__(self, 'translations')\n try:\n self._logger.info('Read translation codes for ' + lang)\n self._getTranslationConfig(lang)\n self._codes = self.getConfigBySection('codes')\n except Exception as ex:\n msg = ex.args + (lang,)\n raise ConfigurationException(msg, self._logger)\n\n def get(self, key):\n \"\"\" Get translation for code - key. The keys are case insensitive\n Attr:\n key - code, need to be trnaslated\n Return:\n translation or key value \"\"\"\n if key.lower() in self._codes:\n return self._codes[key.lower()]\n else:\n self._logger.warning('Missing translation code ' + key)\n self._codes[key.lower()] = key.lower()\n return key\n\n def _getTranslationConfig(self, language):\n \"\"\" Return SafeConfigParser from name.conf file \"\"\"\n self._logger.info('Read translations for ' + language)\n return self.readConfiguration(join('resources', 'trans', language))\n\n ","sub_path":"core/config/TranslationConfig.py","file_name":"TranslationConfig.py","file_ext":"py","file_size_in_byte":1475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"174193555","text":"from django.db import models\n\nclass Convocatoria(models.Model):\n\ttitulo = models.CharField( max_length=500 )\n\tfecha = models.DateField()\n\textracto = models.TextField()\n\tdescripcion = models.TextField()\n\timagen = models.ImageField( upload_to=\"uploads/%Y/%m/%d\", blank=True, null=True, default=\"uplaods/noimage.png\" )\n\n\tcreated_at = models.DateTimeField( auto_now_add=True, blank=True, null=True )\n\tmodified_at = models.DateTimeField( auto_now=True, blank=True, null=True )\n\n\tdef __unicode__(self):\n\t\treturn self.titulo\n\n\tclass Meta:\n\t\tverbose_name = \"convocatoria\"\n\t\tverbose_name_plural = \"convocatorias\"\n\t\tget_latest_by = \"created_at\"","sub_path":"convocatorias/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"298343577","text":"\n\nuser_input = input(\"Dime el número que deseas agregar a la lista: \")\n\nuser_number_list = []\n\nwhile user_input != \"FIN\":\n user_number_list.append(user_input)\n print(\"¡Número añadido correctamente!\")\n user_input = input(\"Introduce un nuevo número para añadirlo: \").upper()\n\nlist_longitude = len(user_number_list)\ninitial_index = 0\n\nprint(list_longitude)\n\n\n","sub_path":"multiplier_list.py","file_name":"multiplier_list.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"562135387","text":"#Elabore um programa que calcule o valor a ser pago por um produto, considerando o seu preço\n# normal e a condição de pagamento:\n# - À vista dinheiro /cheque: 10 % de desconto\n# - A vista no cartão: 5% de desconto\n# - Em até duas vezes no cartão: preço normal\n# - Três vezes ou mais no cartão: 20% de juros\nvalprod = float(input('Informe o valor do produto: '))\nprint('''Escolha a forma de pagamento: \n[ 1 ] A vista dinheiro / cheque 10% de desconto\n[ 2 ] A vista Cartão de credito 5% de desconto\n[ 3 ] Em até duas vezes no cartão: preço normal\n[ 4 ] Três vezes ou mais no cartão: 20% de juros\n''')\nopcao = int(input('Escolha a forma de pgamento: '))\nif opcao == 1:\n desconto = (valprod * 10)/100\n produto = valprod - desconto\n print('O valor do produto {:.2f} para pagamento {:.2f}:'.format(valprod, produto))\nelif opcao == 2:\n desconto = (valprod * 5)/100\n produto = valprod - desconto\n print('O valor do produto {:.2f} para pagamento {:.2f}:'.format(valprod, produto))\nelif opcao == 3:\n total = valprod /2\n print('O valor do produto paracelado em duas vezes é {:.2f}, parcelas de {:.2f} '.format(valprod, total))\nelif opcao == 4:\n cond = float(input('informe o numero de vezes:'))\n juros = (valprod * 20)/100\n produto = valprod + juros\n parcelas = produto /cond\n print('O valor do produto para pagamento em {} vezes é {} com parcelas de {} :'.format(cond, produto, parcelas))\nelse:\n print('Essa forma de pagamento não existe, escolha a forma de pagamento descrita:')\n\n\n\n\n\n","sub_path":"pythonCursoEmVideo/SegundoMundoFase12/ExerciciosCondicoesAninhadas/desafio44CalculaPrecoProduto.py","file_name":"desafio44CalculaPrecoProduto.py","file_ext":"py","file_size_in_byte":1539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"422227164","text":"import json, csv\ninfile = open('reviews_Electronics.txt','r')\ncount =0\nfor i in infile.readlines():\n\tcount += 1\n\t#print i \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\tjson_string = i\n\tobj = json.loads(json_string) \n\t#print obj['helpful'], obj['reviewText'], obj['overall'], obj['summary']\n\tofile = open(\"reviews_Electronics.csv\", \"a+b\")\n\tc = csv.writer(ofile)\n\tc.writerow([str(count), obj['helpful'], obj['reviewText'], obj['overall'], obj['summary']])\n\tofile.close()\n\tif count == 200000:\n\t\tbreak\n","sub_path":"code/json2csv.py","file_name":"json2csv.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"148406291","text":"from django.shortcuts import render\r\nfrom itertools import chain\r\nfrom django.http import HttpResponse, JsonResponse\r\nfrom django.core import serializers\r\nfrom .models import *\r\nimport json\r\nfrom users.models import my_stations\r\nimport users.views as uv\r\nimport users.forms as au\r\nimport weather.models as wm\r\nfrom django.core import serializers\r\n\r\n\r\n# Create your views here.\r\ndef index(request):\r\n\tform1 = au.UserForm()\r\n\tform2 = au.AuthForm()\r\n\treturn render(request, 'index.html', {\"form1\": form1, \"form2\": form2})\r\n\r\ndef BusStation(request):\r\n\tret = BusStops.objects.values('stop_name','stop_lat','stop_long').distinct()\r\n\tdata = list(ret)\r\n\tdata = json.dumps(data)\r\n\treturn HttpResponse(data)\r\n\r\ndef RouteDirection(request):\r\n\tstart_stop = request.GET.get(\"start_stop\",\"\")\r\n\tend_stop = request.GET.get(\"end_stop\",\"\")\r\n\tdate = request.GET.get(\"date\",\"\")\r\n\ttime = request.GET.get(\"time\",\"\")\r\n\tret_start_stop = BusStops.objects.values('stop_name','stop_lat','stop_long').filter(stop_name = start_stop).distinct()\r\n\tret_end_stop = BusStops.objects.values('stop_name','stop_lat','stop_long').filter(stop_name = end_stop).distinct()\r\n\tret = chain(ret_start_stop, ret_end_stop)\r\n\tdata = list(ret)\r\n\tdata = json.dumps(data)\r\n\treturn HttpResponse(data)\r\n\r\ndef GetUserStatus(request):\r\n\tif request.user.is_authenticated:\r\n\t\tres = json.dumps(\"true\")\r\n\telse:\r\n\t\tres = json.dumps(\"false\")\r\n\treturn HttpResponse(res)\r\n\r\ndef AddPlan(request):\r\n\tplan_name = request.GET.get(\"plan_name\",\"\")\r\n\tstart_stop = request.GET.get(\"start_stop\",\"\")\r\n\tend_stop = request.GET.get(\"end_stop\",\"\")\r\n\tdate = request.GET.get(\"date\",\"\")\r\n\ttime = request.GET.get(\"time\",\"\")\r\n\r\ndef AddFavoriteStop(request):\r\n\r\n\tstop = request.GET.get(\"stop_name\",\"\")\r\n\tret1 = BusStops.objects.values('stop_name','routes_serving').filter(stop_name = stop).distinct()\r\n\tret2 = NameToID.objects.values('stop_name','stop_id').filter(stop_name = stop).distinct()\r\n\tdata1 = list(ret1)\r\n\tdata2 = list(ret2)\r\n\r\n\tstop_name = data1[0]['stop_name']\r\n\tprint(stop_name)\r\n\troute_nums = data1[0]['routes_serving'].split(',')\r\n\tprint(route_nums)\r\n\tstop_id = data2[0]['stop_id']\r\n\tprint(stop_id)\r\n\tif request.user.is_authenticated:\r\n\t\tcurrent_user = request.user\r\n\t\tstop_name = data1[0]['stop_name']\r\n\t\tprint(stop_name)\r\n\t\troute_nums = data1[0]['routes_serving'].split(',')\r\n\t\tprint(route_nums)\r\n\t\tstop_id = data2[0]['stop_id']\r\n\t\tprint(stop_id)\r\n\t\tuser_fav = my_stations(stop_id=stop_id, user=current_user)\r\n\t\tuser_fav.save()\r\n\r\n\telse:\r\n\t\tprint('not logged in')\r\n\t\tpass\r\n\r\n\treturn HttpResponse(data1)\r\n\r\ndef login(response):\r\n uv.login(response)\r\n\r\ndef users(response):\r\n uv.users(response)\r\n\r\ndef extra(response):\r\n uv.extra(response)\r\n\r\n\r\ndef get_live_updates(response):\r\n\tx = wm.AA_Road_Report.objects.all()\r\n\troad_updates = serializers.serialize(\"json\", wm.AA_Road_Report.objects.all())\r\n\tprint(road_updates)\r\n\treturn HttpResponse(road_updates, \"application/json\")","sub_path":"map/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2914,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"488169963","text":"import sys\n\ndef pooling(profiles):\n return [sum(pos_vals) for pos_vals in zip(*profiles)]\n\ndef join_sorted(iter_1, iter_2, key=lambda x: x):\n iter_1 = iter(iter_1)\n iter_2 = iter(iter_2)\n try:\n obj_1 = next(iter_1)\n obj_2 = next(iter_2)\n k_1 = key(obj_1)\n k_2 = key(obj_2)\n while True:\n if k_1 == k_2:\n yield (k_1, obj_1, obj_2)\n obj_1 = next(iter_1)\n obj_2 = next(iter_2)\n k_1 = key(obj_1)\n k_2 = key(obj_2)\n elif k_1 < k_2:\n # print(f'join_sorted - skip {k_1} from first iterator', file=sys.stderr)\n obj_1 = next(iter_1)\n k_1 = key(obj_1)\n elif k_1 > k_2:\n # print(f'join_sorted - skip {k_2} from second iterator', file=sys.stderr)\n obj_2 = next(iter_2)\n k_2 = key(obj_2)\n except StopIteration:\n pass\n","sub_path":"pooling.py","file_name":"pooling.py","file_ext":"py","file_size_in_byte":958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"281657579","text":"from model_bakery import baker\n\nfrom datetime import time\n\nfrom django.contrib.auth.models import User\nfrom django.urls import reverse\nfrom django.core import management\nfrom django.test import TestCase\n\nfrom common.helpers import set_up_fb\n\nfrom timetable.models import Location, WeeklySession\n\n\nclass TestMixin(object):\n\n @classmethod\n def setUpTestData(cls):\n set_up_fb()\n cls.user = User.objects.create_user(\n username='user', email='user@test.com', password='test'\n )\n cls.staff_user = User.objects.create_user(\n username='staff_user', email='staff@test.com', password='test'\n )\n cls.staff_user.is_staff = True\n cls.staff_user.save()\n\n\nclass TimetableViewsTests(TestMixin, TestCase):\n\n @classmethod\n def setUpTestData(cls):\n super(TimetableViewsTests, cls).setUpTestData()\n cls.url = reverse('timetable:timetable')\n\n def setUp(self):\n self.full_session = baker.make(WeeklySession, full=True)\n self.spaces_session = baker.make(WeeklySession, full=False)\n\n def test_sessions_displayed(self):\n resp = self.client.get(self.url)\n self.assertEqual(resp.status_code, 200)\n self.assertEqual(\n sorted([session.id for session in resp.context_data['sessions']]),\n sorted([self.full_session.id, self.spaces_session.id])\n )\n\n # def test_toggle_spaces_button_only_shown_for_staff(self):\n # resp = self.client.get(self.url)\n # self.assertNotIn('toggle_spaces_button', resp.rendered_content)\n #\n # self.client.login(username=self.user.username, password='test')\n # resp = self.client.get(self.url)\n # self.assertNotIn('toggle_spaces_button', resp.rendered_content)\n #\n # self.client.login(username=self.staff_user.username, password='test')\n # resp = self.client.get(self.url)\n # self.assertIn('toggle_spaces_button', resp.rendered_content)\n\n def test_toggle_spaces_only_allowed_for_staff(self):\n self.assertTrue(self.full_session.full)\n toggle_url = reverse(\n 'timetable:toggle_spaces', args=[self.full_session.id]\n )\n\n resp = self.client.get(toggle_url)\n self.assertEqual(resp.status_code, 302)\n self.assertIn(\n reverse('account_login') + \"?next={}\".format(toggle_url),\n resp.url\n )\n self.full_session.refresh_from_db()\n self.assertTrue(self.full_session.full)\n\n self.client.login(username=self.user.username, password='test')\n resp = self.client.get(toggle_url)\n self.assertEqual(resp.status_code, 302)\n self.assertIn(\n reverse('permission_denied'),\n resp.url\n )\n self.full_session.refresh_from_db()\n self.assertTrue(self.full_session.full)\n\n self.client.login(username=self.staff_user.username, password='test')\n resp = self.client.get(toggle_url)\n self.assertEqual(resp.status_code, 200)\n self.full_session.refresh_from_db()\n self.assertFalse(self.full_session.full)\n\n\nclass TimeTableModelTests(TestCase):\n\n def test_weekly_session_str(self):\n wsession = baker.make(\n WeeklySession, name=\"Test\", day=WeeklySession.MON, time=time(19, 0)\n )\n self.assertEqual(\n str(wsession), \"Test - Monday 19:00\"\n )\n\n def test_location_str(self):\n location = baker.make(\n Location, short_name=\"test\", full_name=\"a test location\"\n )\n self.assertEqual(str(location), 'test')\n\n\nclass TimetableManagementTests(TestMixin, TestCase):\n\n def test_create_locations_and_weekly_sessions(self):\n self.assertFalse(Location.objects.exists())\n self.assertFalse(WeeklySession.objects.exists())\n\n management.call_command('create_locations_and_weekly_sessions')\n self.assertTrue(Location.objects.exists())\n self.assertTrue(WeeklySession.objects.exists())\n self.assertEqual(Location.objects.count(), 3)\n self.assertEqual(WeeklySession.objects.count(), 6)\n\n def test_locations_and_weekly_sessions_not_recreated(self):\n management.call_command('create_locations_and_weekly_sessions')\n self.assertTrue(Location.objects.exists())\n self.assertTrue(WeeklySession.objects.exists())\n self.assertEqual(Location.objects.count(), 3)\n self.assertEqual(WeeklySession.objects.count(), 6)\n session_ids = [sess.id for sess in WeeklySession.objects.all()]\n\n # call again; counts stay the same\n management.call_command('create_locations_and_weekly_sessions')\n self.assertTrue(Location.objects.exists())\n self.assertTrue(WeeklySession.objects.exists())\n self.assertEqual(Location.objects.count(), 3)\n self.assertEqual(WeeklySession.objects.count(), 6)\n session_ids1 = [sess.id for sess in WeeklySession.objects.all()]\n\n # sessions are the same, not created again\n self.assertEqual(sorted(session_ids), sorted(session_ids1))\n\n def test_existing_sessions_restored_to_default(self):\n management.call_command('create_locations_and_weekly_sessions')\n\n # get the Monday 7pm session\n mon_sess = WeeklySession.objects.get(\n day=WeeklySession.MON, time=time(19, 0)\n )\n self.assertEqual(mon_sess.description, '')\n mon_sess.description = 'new'\n mon_sess.save()\n\n self.assertEqual(mon_sess.description, 'new')\n management.call_command('create_locations_and_weekly_sessions')\n\n mon_sess.refresh_from_db()\n # description has been set back to default\n self.assertEqual(mon_sess.description, '')\n","sub_path":"timetable/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":5703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"374408215","text":"# -*- coding: utf-8 -*-\r\n\r\nfrom math import*\r\n\r\ntype_calcul=str(input(\"Quel calculer voulez-vous effectuer ? Appuyez sur P pour calculer le périmète d'un cercle ou sur A pour calculer le rayon d'un disque: \"))\r\nrayon=float(input(\"Saisissez la valeur rayon: \"))\r\n\r\nif rayon<0:\r\n print(\"Erreur: le rayon ne peut pas être négatif\")\r\nelse:\r\n if (type_calcul==\"p\") or (type_calcul==\"P\"):\r\n print(\"L'aire vaut\", 2*pi*rayon)\r\n elif (type_calcul==\"a\") or (type_calcul==\"A\"):\r\n print(\"L'aire vaut\", pi*(rayon**2))\r\n else: \r\n print(\"Erreur: type de calcul invalide\")","sub_path":"TP2/ex6.py","file_name":"ex6.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"63320177","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\n# Copyright 2021 A10 Networks\n# GNU General Public License v3.0+\n# (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nREQUIRED_NOT_SET = (False, \"One of ({}) must be set.\")\nREQUIRED_MUTEX = (False, \"Only one of ({}) can be set.\")\nREQUIRED_VALID = (True, \"\")\n\nDOCUMENTATION = r'''\nmodule: a10_ddos_dst_zone_port_zone_service_stats_tcp_zone_port\ndescription:\n - Statistics for the object zone-service\nauthor: A10 Networks\noptions:\n state:\n description:\n - State of the object to be created.\n choices:\n - noop\n - present\n - absent\n type: str\n required: True\n ansible_host:\n description:\n - Host for AXAPI authentication\n type: str\n required: True\n ansible_username:\n description:\n - Username for AXAPI authentication\n type: str\n required: True\n ansible_password:\n description:\n - Password for AXAPI authentication\n type: str\n required: True\n ansible_port:\n description:\n - Port for AXAPI authentication\n type: int\n required: True\n a10_device_context_id:\n description:\n - Device ID for aVCS configuration\n choices: [1-8]\n type: int\n required: False\n a10_partition:\n description:\n - Destination/target partition for object/command\n type: str\n required: False\n protocol:\n description:\n - Key to identify parent object\n type: str\n required: True\n zone_service_port_num:\n description:\n - Key to identify parent object\n type: str\n required: True\n zone_name:\n description:\n - Key to identify parent object\n type: str\n required: True\n port_num:\n description:\n - \"Port Number\"\n type: int\n required: True\n protocol:\n description:\n - \"'dns-tcp'= DNS-TCP Port; 'dns-udp'= DNS-UDP Port; 'http'= HTTP Port; 'tcp'= TCP\n Port; 'udp'= UDP Port; 'ssl-l4'= SSL-L4 Port; 'sip-udp'= SIP-UDP Port; 'sip-\n tcp'= SIP-TCP Port; 'quic'= QUIC Port;\"\n type: str\n required: True\n stats:\n description:\n - \"Field stats\"\n type: dict\n required: False\n suboptions:\n tcp_zone_port:\n description:\n - \"Field tcp_zone_port\"\n type: dict\n\n'''\n\nRETURN = r'''\nmodified_values:\n description:\n - Values modified (or potential changes if using check_mode) as a result of task operation\n returned: changed\n type: dict\naxapi_calls:\n description: Sequential list of AXAPI calls made by the task\n returned: always\n type: list\n elements: dict\n contains:\n endpoint:\n description: The AXAPI endpoint being accessed.\n type: str\n sample:\n - /axapi/v3/slb/virtual_server\n - /axapi/v3/file/ssl-cert\n http_method:\n description:\n - HTTP method being used by the primary task to interact with the AXAPI endpoint.\n type: str\n sample:\n - POST\n - GET\n request_body:\n description: Params used to query the AXAPI\n type: complex\n response_body:\n description: Response from the AXAPI\n type: complex\n'''\n\nEXAMPLES = \"\"\"\n\"\"\"\n\nimport copy\n\nfrom ansible.module_utils.basic import AnsibleModule\nfrom ansible_collections.a10.acos_axapi.plugins.module_utils import \\\n errors as a10_ex\nfrom ansible_collections.a10.acos_axapi.plugins.module_utils import \\\n wrapper as api_client\nfrom ansible_collections.a10.acos_axapi.plugins.module_utils import \\\n utils\nfrom ansible_collections.a10.acos_axapi.plugins.module_utils.client import \\\n client_factory\nfrom ansible_collections.a10.acos_axapi.plugins.module_utils.kwbl import \\\n KW_OUT, translate_blacklist as translateBlacklist\n\n# Hacky way of having access to object properties for evaluation\nAVAILABLE_PROPERTIES = [\"port_num\", \"protocol\", \"stats\", ]\n\n\ndef get_default_argspec():\n return dict(\n ansible_host=dict(type='str', required=True),\n ansible_username=dict(type='str', required=True),\n ansible_password=dict(type='str', required=True, no_log=True),\n state=dict(type='str', default=\"present\", choices=['noop', 'present', 'absent']),\n ansible_port=dict(type='int', choices=[80, 443], required=True),\n a10_partition=dict(type='str', required=False,\n ),\n a10_device_context_id=dict(type='int', choices=[1, 2, 3, 4, 5, 6, 7, 8], required=False,\n ),\n get_type=dict(type='str', choices=[\"single\", \"list\", \"oper\", \"stats\"]),\n )\n\n\ndef get_argspec():\n rv = get_default_argspec()\n rv.update({\n 'port_num': {\n 'type': 'int',\n 'required': True,\n },\n 'protocol': {\n 'type': 'str',\n 'required': True,\n 'choices': ['dns-tcp', 'dns-udp', 'http', 'tcp', 'udp', 'ssl-l4', 'sip-udp', 'sip-tcp', 'quic']\n },\n 'stats': {\n 'type': 'dict',\n 'tcp_zone_port': {\n 'type': 'dict',\n 'filter1_match': {\n 'type': 'str',\n },\n 'filter2_match': {\n 'type': 'str',\n },\n 'filter3_match': {\n 'type': 'str',\n },\n 'filter4_match': {\n 'type': 'str',\n },\n 'filter5_match': {\n 'type': 'str',\n },\n 'filter_none_match': {\n 'type': 'str',\n },\n 'port_rcvd': {\n 'type': 'str',\n },\n 'port_drop': {\n 'type': 'str',\n },\n 'port_pkt_sent': {\n 'type': 'str',\n },\n 'port_pkt_rate_exceed': {\n 'type': 'str',\n },\n 'port_kbit_rate_exceed': {\n 'type': 'str',\n },\n 'port_conn_rate_exceed': {\n 'type': 'str',\n },\n 'port_conn_limm_exceed': {\n 'type': 'str',\n },\n 'filter_auth_fail': {\n 'type': 'str',\n },\n 'syn_auth_fail': {\n 'type': 'str',\n },\n 'ack_auth_fail': {\n 'type': 'str',\n },\n 'syn_cookie_fail': {\n 'type': 'str',\n },\n 'port_bytes': {\n 'type': 'str',\n },\n 'outbound_port_bytes': {\n 'type': 'str',\n },\n 'outbound_port_rcvd': {\n 'type': 'str',\n },\n 'outbound_port_pkt_sent': {\n 'type': 'str',\n },\n 'port_bytes_sent': {\n 'type': 'str',\n },\n 'port_bytes_drop': {\n 'type': 'str',\n },\n 'port_src_bl': {\n 'type': 'str',\n },\n 'port_src_escalation': {\n 'type': 'str',\n },\n 'current_es_level': {\n 'type': 'str',\n },\n 'sess_create': {\n 'type': 'str',\n },\n 'filter_action_blacklist': {\n 'type': 'str',\n },\n 'filter_action_drop': {\n 'type': 'str',\n },\n 'filter_action_default_pass': {\n 'type': 'str',\n },\n 'filter_action_whitelist': {\n 'type': 'str',\n },\n 'exceed_drop_prate_src': {\n 'type': 'str',\n },\n 'exceed_drop_crate_src': {\n 'type': 'str',\n },\n 'exceed_drop_climit_src': {\n 'type': 'str',\n },\n 'exceed_drop_brate_src': {\n 'type': 'str',\n },\n 'outbound_port_bytes_sent': {\n 'type': 'str',\n },\n 'outbound_port_drop': {\n 'type': 'str',\n },\n 'outbound_port_bytes_drop': {\n 'type': 'str',\n },\n 'syn_auth_pass': {\n 'type': 'str',\n },\n 'exceed_drop_brate_src_pkt': {\n 'type': 'str',\n },\n 'port_kbit_rate_exceed_pkt': {\n 'type': 'str',\n },\n 'syn_cookie_sent': {\n 'type': 'str',\n },\n 'ack_retry_init': {\n 'type': 'str',\n },\n 'ack_retry_gap_drop': {\n 'type': 'str',\n },\n 'conn_prate_excd': {\n 'type': 'str',\n },\n 'out_of_seq_excd': {\n 'type': 'str',\n },\n 'retransmit_excd': {\n 'type': 'str',\n },\n 'zero_window_excd': {\n 'type': 'str',\n },\n 'syn_retry_init': {\n 'type': 'str',\n },\n 'syn_retry_gap_drop': {\n 'type': 'str',\n },\n 'ack_retry_pass': {\n 'type': 'str',\n },\n 'syn_retry_pass': {\n 'type': 'str',\n },\n 'bl': {\n 'type': 'str',\n },\n 'src_drop': {\n 'type': 'str',\n },\n 'frag_rcvd': {\n 'type': 'str',\n },\n 'frag_drop': {\n 'type': 'str',\n },\n 'sess_create_inbound': {\n 'type': 'str',\n },\n 'sess_create_outbound': {\n 'type': 'str',\n },\n 'conn_create_from_syn': {\n 'type': 'str',\n },\n 'conn_create_from_ack': {\n 'type': 'str',\n },\n 'conn_close': {\n 'type': 'str',\n },\n 'conn_close_w_rst': {\n 'type': 'str',\n },\n 'conn_close_w_fin': {\n 'type': 'str',\n },\n 'conn_close_w_idle': {\n 'type': 'str',\n },\n 'conn_close_half_open': {\n 'type': 'str',\n },\n 'sess_aged': {\n 'type': 'str',\n },\n 'syn_drop': {\n 'type': 'str',\n },\n 'auth_drop': {\n 'type': 'str',\n },\n 'auth_resp': {\n 'type': 'str',\n },\n 'unauth_drop': {\n 'type': 'str',\n },\n 'rst_cookie_fail': {\n 'type': 'str',\n },\n 'syn_retry_failed': {\n 'type': 'str',\n },\n 'filter_total_not_match': {\n 'type': 'str',\n },\n 'src_syn_auth_fail': {\n 'type': 'str',\n },\n 'src_syn_cookie_sent': {\n 'type': 'str',\n },\n 'src_syn_cookie_fail': {\n 'type': 'str',\n },\n 'src_unauth_drop': {\n 'type': 'str',\n },\n 'src_rst_cookie_fail': {\n 'type': 'str',\n },\n 'src_syn_retry_init': {\n 'type': 'str',\n },\n 'src_syn_retry_gap_drop': {\n 'type': 'str',\n },\n 'src_syn_retry_failed': {\n 'type': 'str',\n },\n 'src_ack_retry_init': {\n 'type': 'str',\n },\n 'src_ack_retry_gap_drop': {\n 'type': 'str',\n },\n 'src_ack_auth_fail': {\n 'type': 'str',\n },\n 'src_out_of_seq_excd': {\n 'type': 'str',\n },\n 'src_retransmit_excd': {\n 'type': 'str',\n },\n 'src_zero_window_excd': {\n 'type': 'str',\n },\n 'src_conn_pkt_rate_excd': {\n 'type': 'str',\n },\n 'src_filter_action_blacklist': {\n 'type': 'str',\n },\n 'src_filter_action_drop': {\n 'type': 'str',\n },\n 'src_filter_action_default_pass': {\n 'type': 'str',\n },\n 'src_filter_action_whitelist': {\n 'type': 'str',\n },\n 'tcp_rexmit_syn_limit_drop': {\n 'type': 'str',\n },\n 'tcp_rexmit_syn_limit_bl': {\n 'type': 'str',\n },\n 'conn_ofo_rate_excd': {\n 'type': 'str',\n },\n 'conn_rexmit_rate_excd': {\n 'type': 'str',\n },\n 'conn_zwindow_rate_excd': {\n 'type': 'str',\n },\n 'src_conn_ofo_rate_excd': {\n 'type': 'str',\n },\n 'src_conn_rexmit_rate_excd': {\n 'type': 'str',\n },\n 'src_conn_zwindow_rate_excd': {\n 'type': 'str',\n },\n 'ack_retry_rto_pass': {\n 'type': 'str',\n },\n 'ack_retry_rto_fail': {\n 'type': 'str',\n },\n 'ack_retry_rto_progress': {\n 'type': 'str',\n },\n 'syn_retry_rto_pass': {\n 'type': 'str',\n },\n 'syn_retry_rto_fail': {\n 'type': 'str',\n },\n 'syn_retry_rto_progress': {\n 'type': 'str',\n },\n 'src_syn_retry_rto_pass': {\n 'type': 'str',\n },\n 'src_syn_retry_rto_fail': {\n 'type': 'str',\n },\n 'src_syn_retry_rto_progress': {\n 'type': 'str',\n },\n 'src_ack_retry_rto_pass': {\n 'type': 'str',\n },\n 'src_ack_retry_rto_fail': {\n 'type': 'str',\n },\n 'src_ack_retry_rto_progress': {\n 'type': 'str',\n },\n 'wellknown_sport_drop': {\n 'type': 'str',\n },\n 'src_well_known_port': {\n 'type': 'str',\n },\n 'secondary_port_pkt_rate_exceed': {\n 'type': 'str',\n },\n 'secondary_port_kbit_rate_exceed': {\n 'type': 'str',\n },\n 'secondary_port_kbit_rate_exceed_pkt': {\n 'type': 'str',\n },\n 'secondary_port_conn_rate_exceed': {\n 'type': 'str',\n },\n 'secondary_port_conn_limm_exceed': {\n 'type': 'str',\n },\n 'src_auth_drop': {\n 'type': 'str',\n },\n 'src_frag_drop': {\n 'type': 'str',\n },\n 'no_policy_class_list_match': {\n 'type': 'str',\n },\n 'frag_timeout': {\n 'type': 'str',\n },\n 'create_conn_non_syn_dropped': {\n 'type': 'str',\n },\n 'src_create_conn_non_syn_dropped': {\n 'type': 'str',\n },\n 'port_syn_rate_exceed': {\n 'type': 'str',\n },\n 'src_syn_rate_exceed': {\n 'type': 'str',\n },\n 'pattern_recognition_proceeded': {\n 'type': 'str',\n },\n 'pattern_not_found': {\n 'type': 'str',\n },\n 'pattern_recognition_generic_error': {\n 'type': 'str',\n },\n 'pattern_filter1_match': {\n 'type': 'str',\n },\n 'pattern_filter2_match': {\n 'type': 'str',\n },\n 'pattern_filter3_match': {\n 'type': 'str',\n },\n 'pattern_filter4_match': {\n 'type': 'str',\n },\n 'pattern_filter5_match': {\n 'type': 'str',\n },\n 'pattern_filter_drop': {\n 'type': 'str',\n },\n 'src_filter1_match': {\n 'type': 'str',\n },\n 'src_filter2_match': {\n 'type': 'str',\n },\n 'src_filter3_match': {\n 'type': 'str',\n },\n 'src_filter4_match': {\n 'type': 'str',\n },\n 'src_filter5_match': {\n 'type': 'str',\n },\n 'src_filter_none_match': {\n 'type': 'str',\n },\n 'src_filter_total_not_match': {\n 'type': 'str',\n },\n 'src_filter_auth_fail': {\n 'type': 'str',\n },\n 'syn_tfo_rcv': {\n 'type': 'str',\n },\n 'ack_retry_timeout': {\n 'type': 'str',\n },\n 'ack_retry_reset': {\n 'type': 'str',\n },\n 'ack_retry_blacklist': {\n 'type': 'str',\n },\n 'src_ack_retry_timeout': {\n 'type': 'str',\n },\n 'src_ack_retry_reset': {\n 'type': 'str',\n },\n 'src_ack_retry_blacklist': {\n 'type': 'str',\n },\n 'syn_retry_timeout': {\n 'type': 'str',\n },\n 'syn_retry_reset': {\n 'type': 'str',\n },\n 'syn_retry_blacklist': {\n 'type': 'str',\n },\n 'src_syn_retry_timeout': {\n 'type': 'str',\n },\n 'src_syn_retry_reset': {\n 'type': 'str',\n },\n 'src_syn_retry_blacklist': {\n 'type': 'str',\n },\n 'sflow_internal_samples_packed': {\n 'type': 'str',\n },\n 'sflow_external_samples_packed': {\n 'type': 'str',\n },\n 'sflow_internal_packets_sent': {\n 'type': 'str',\n },\n 'sflow_external_packets_sent': {\n 'type': 'str',\n },\n 'pattern_recognition_sampling_started': {\n 'type': 'str',\n },\n 'pattern_recognition_pattern_changed': {\n 'type': 'str',\n },\n 'exceed_action_tunnel': {\n 'type': 'str',\n },\n 'dst_hw_drop': {\n 'type': 'str',\n },\n 'synack_reset_sent': {\n 'type': 'str',\n },\n 'synack_multiple_attempts_per_ip_detected': {\n 'type': 'str',\n },\n 'secondary_port_hit': {\n 'type': 'str',\n },\n 'src_zone_service_entry_learned': {\n 'type': 'str',\n },\n 'src_zone_service_entry_aged': {\n 'type': 'str',\n },\n 'dst_hw_drop_inserted': {\n 'type': 'str',\n },\n 'dst_hw_drop_removed': {\n 'type': 'str',\n },\n 'src_hw_drop_inserted': {\n 'type': 'str',\n },\n 'src_hw_drop_removed': {\n 'type': 'str',\n },\n 'prog_first_req_time_exceed': {\n 'type': 'str',\n },\n 'prog_req_resp_time_exceed': {\n 'type': 'str',\n },\n 'prog_request_len_exceed': {\n 'type': 'str',\n },\n 'prog_response_len_exceed': {\n 'type': 'str',\n },\n 'prog_resp_req_ratio_exceed': {\n 'type': 'str',\n },\n 'prog_resp_req_time_exceed': {\n 'type': 'str',\n },\n 'prog_conn_sent_exceed': {\n 'type': 'str',\n },\n 'prog_conn_rcvd_exceed': {\n 'type': 'str',\n },\n 'prog_conn_time_exceed': {\n 'type': 'str',\n },\n 'prog_conn_rcvd_sent_ratio_exceed': {\n 'type': 'str',\n },\n 'prog_win_sent_exceed': {\n 'type': 'str',\n },\n 'prog_win_rcvd_exceed': {\n 'type': 'str',\n },\n 'prog_win_rcvd_sent_ratio_exceed': {\n 'type': 'str',\n },\n 'snat_fail': {\n 'type': 'str',\n },\n 'prog_exceed_drop': {\n 'type': 'str',\n },\n 'prog_exceed_bl': {\n 'type': 'str',\n },\n 'prog_conn_exceed_drop': {\n 'type': 'str',\n },\n 'prog_conn_exceed_bl': {\n 'type': 'str',\n },\n 'prog_win_exceed_drop': {\n 'type': 'str',\n },\n 'prog_win_exceed_bl': {\n 'type': 'str',\n },\n 'exceed_action_drop': {\n 'type': 'str',\n },\n 'syn_auth_rst_ack_drop': {\n 'type': 'str',\n },\n 'prog_exceed_reset': {\n 'type': 'str',\n },\n 'prog_conn_exceed_reset': {\n 'type': 'str',\n },\n 'prog_win_exceed_reset': {\n 'type': 'str',\n },\n 'conn_create_from_synack': {\n 'type': 'str',\n },\n 'port_synack_rate_exceed': {\n 'type': 'str',\n },\n 'prog_conn_samples': {\n 'type': 'str',\n },\n 'prog_req_samples': {\n 'type': 'str',\n },\n 'prog_win_samples': {\n 'type': 'str',\n },\n 'ew_inbound_port_rcv': {\n 'type': 'str',\n },\n 'ew_inbound_port_drop': {\n 'type': 'str',\n },\n 'ew_inbound_port_sent': {\n 'type': 'str',\n },\n 'ew_inbound_port_byte_rcv': {\n 'type': 'str',\n },\n 'ew_inbound_port_byte_drop': {\n 'type': 'str',\n },\n 'ew_inbound_port_byte_sent': {\n 'type': 'str',\n },\n 'ew_outbound_port_rcv': {\n 'type': 'str',\n },\n 'ew_outbound_port_drop': {\n 'type': 'str',\n },\n 'ew_outbound_port_sent': {\n 'type': 'str',\n },\n 'ew_outbound_port_byte_rcv': {\n 'type': 'str',\n },\n 'ew_outbound_port_byte_sent': {\n 'type': 'str',\n },\n 'ew_outbound_port_byte_drop': {\n 'type': 'str',\n },\n 'no_route_drop': {\n 'type': 'str',\n },\n 'unauth_src_session_reset': {\n 'type': 'str',\n }\n }\n }\n })\n # Parent keys\n rv.update(dict(protocol=dict(type='str', required=True), zone_service_port_num=dict(type='str', required=True), zone_name=dict(type='str', required=True), ))\n return rv\n\n\ndef existing_url(module):\n \"\"\"Return the URL for an existing resource\"\"\"\n # Build the format dictionary\n url_base = \"/axapi/v3/ddos/dst/zone/{zone_name}/port/zone-service/{zone_service_port_num}+{protocol}/stats?tcp-zone-port=true\"\n\n f_dict = {}\n if '/' in module.params[\"protocol\"]:\n f_dict[\"protocol\"] = module.params[\"protocol\"].replace(\"/\", \"%2F\")\n else:\n f_dict[\"protocol\"] = module.params[\"protocol\"]\n if '/' in module.params[\"zone_service_port_num\"]:\n f_dict[\"zone_service_port_num\"] = module.params[\"zone_service_port_num\"].replace(\"/\", \"%2F\")\n else:\n f_dict[\"zone_service_port_num\"] = module.params[\"zone_service_port_num\"]\n if '/' in module.params[\"zone_name\"]:\n f_dict[\"zone_name\"] = module.params[\"zone_name\"].replace(\"/\", \"%2F\")\n else:\n f_dict[\"zone_name\"] = module.params[\"zone_name\"]\n\n return url_base.format(**f_dict)\n\n\ndef new_url(module):\n \"\"\"Return the URL for creating a resource\"\"\"\n # To create the URL, we need to take the format string and return it with no params\n url_base = \"/axapi/v3/ddos/dst/zone/{zone_name}/port/zone-service/{zone_service_port_num}+{protocol}/stats?tcp-zone-port=true\"\n\n f_dict = {}\n f_dict[\"protocol\"] = module.params[\"protocol\"]\n f_dict[\"zone_service_port_num\"] = module.params[\"zone_service_port_num\"]\n f_dict[\"zone_name\"] = module.params[\"zone_name\"]\n\n return url_base.format(**f_dict)\n\n\ndef report_changes(module, result, existing_config, payload):\n change_results = copy.deepcopy(result)\n if not existing_config:\n change_results[\"modified_values\"].update(**payload)\n return change_results\n\n config_changes = copy.deepcopy(existing_config)\n for k, v in payload[\"zone-service\"].items():\n v = 1 if str(v).lower() == \"true\" else v\n v = 0 if str(v).lower() == \"false\" else v\n\n if config_changes[\"zone-service\"].get(k) != v:\n change_results[\"changed\"] = True\n config_changes[\"zone-service\"][k] = v\n\n change_results[\"modified_values\"].update(**config_changes)\n return change_results\n\n\ndef create(module, result, payload={}):\n call_result = api_client.post(module.client, new_url(module), payload)\n result[\"axapi_calls\"].append(call_result)\n result[\"modified_values\"].update(**call_result[\"response_body\"])\n result[\"changed\"] = True\n return result\n\n\ndef update(module, result, existing_config, payload={}):\n call_result = api_client.post(module.client, existing_url(module), payload)\n result[\"axapi_calls\"].append(call_result)\n if call_result[\"response_body\"] == existing_config:\n result[\"changed\"] = False\n else:\n result[\"modified_values\"].update(**call_result[\"response_body\"])\n result[\"changed\"] = True\n return result\n\n\ndef present(module, result, existing_config):\n payload = utils.build_json(\"zone-service\", module.params, AVAILABLE_PROPERTIES)\n change_results = report_changes(module, result, existing_config, payload)\n if module.check_mode:\n return change_results\n elif not existing_config:\n return create(module, result, payload)\n elif existing_config and change_results.get('changed'):\n return update(module, result, existing_config, payload)\n return result\n\n\ndef delete(module, result):\n try:\n call_result = api_client.delete(module.client, existing_url(module))\n result[\"axapi_calls\"].append(call_result)\n result[\"changed\"] = True\n except a10_ex.NotFound:\n result[\"changed\"] = False\n return result\n\n\ndef absent(module, result, existing_config):\n if not existing_config:\n result[\"changed\"] = False\n return result\n\n if module.check_mode:\n result[\"changed\"] = True\n return result\n\n return delete(module, result)\n\n\ndef run_command(module):\n result = dict(changed=False, messages=\"\", modified_values={}, axapi_calls=[], ansible_facts={}, acos_info={})\n\n state = module.params[\"state\"]\n ansible_host = module.params[\"ansible_host\"]\n ansible_username = module.params[\"ansible_username\"]\n ansible_password = module.params[\"ansible_password\"]\n ansible_port = module.params[\"ansible_port\"]\n a10_partition = module.params[\"a10_partition\"]\n a10_device_context_id = module.params[\"a10_device_context_id\"]\n\n if ansible_port == 80:\n protocol = \"http\"\n elif ansible_port == 443:\n protocol = \"https\"\n\n module.client = client_factory(ansible_host, ansible_port, protocol, ansible_username, ansible_password)\n\n valid = True\n\n run_errors = []\n if state == 'present':\n requires_one_of = sorted([])\n valid, validation_errors = utils.validate(module.params, requires_one_of)\n for ve in validation_errors:\n run_errors.append(ve)\n\n if not valid:\n err_msg = \"\\n\".join(run_errors)\n result[\"messages\"] = \"Validation failure: \" + str(run_errors)\n module.fail_json(msg=err_msg, **result)\n\n try:\n if a10_partition:\n result[\"axapi_calls\"].append(api_client.active_partition(module.client, a10_partition))\n\n if a10_device_context_id:\n result[\"axapi_calls\"].append(api_client.switch_device_context(module.client, a10_device_context_id))\n\n existing_config = api_client.get(module.client, existing_url(module))\n result[\"axapi_calls\"].append(existing_config)\n if existing_config['response_body'] != 'NotFound':\n existing_config = existing_config[\"response_body\"]\n else:\n existing_config = None\n\n if state == 'present':\n result = present(module, result, existing_config)\n\n if state == 'absent':\n result = absent(module, result, existing_config)\n\n if state == 'noop':\n if module.params.get(\"get_type\") == \"single\":\n get_result = api_client.get(module.client, existing_url(module))\n result[\"axapi_calls\"].append(get_result)\n info = get_result[\"response_body\"]\n result[\"acos_info\"] = info[\"zone-service\"] if info != \"NotFound\" else info\n elif module.params.get(\"get_type\") == \"list\":\n get_list_result = api_client.get_list(module.client, existing_url(module))\n result[\"axapi_calls\"].append(get_list_result)\n\n info = get_list_result[\"response_body\"]\n result[\"acos_info\"] = info[\"zone-service-list\"] if info != \"NotFound\" else info\n elif module.params.get(\"get_type\") == \"stats\":\n get_type_result = api_client.get_stats(module.client, existing_url(module), params=module.params)\n result[\"axapi_calls\"].append(get_type_result)\n info = get_type_result[\"response_body\"]\n result[\"acos_info\"] = info[\"zone-service\"][\"stats\"] if info != \"NotFound\" else info\n except a10_ex.ACOSException as ex:\n module.fail_json(msg=ex.msg, **result)\n except Exception as gex:\n raise gex\n finally:\n if module.client.auth_session.session_id:\n module.client.auth_session.close()\n\n return result\n\n\ndef main():\n module = AnsibleModule(argument_spec=get_argspec(), supports_check_mode=True)\n result = run_command(module)\n module.exit_json(**result)\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"plugins/modules/a10_ddos_dst_zone_port_zone_service_stats_tcp_zone_port.py","file_name":"a10_ddos_dst_zone_port_zone_service_stats_tcp_zone_port.py","file_ext":"py","file_size_in_byte":34636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"573500532","text":"from pegpy.main import load_grammar, switch_generator\nfrom pegpy.origami.expression import Expression\n\nsample = '''正多角形Aについて、\n 辺は3とする\n '''\n\nParamDict = {\n 'x':'x',\n 'X':'x',\n 'x座標': 'x',\n 'y':'y',\n 'Y':'y',\n 'y座標': 'y',\n '半径':'radius',\n '縦': 'height',\n '高さ': 'height',\n '横': 'width',\n '幅': 'width',\n '辺': 'sides',\n '斜辺':'slope',\n '傾き':'slope',\n '車輪':'wheelSize',\n 'タイヤ':'wheelSize',\n '列':'columns',\n '列数':'columns',\n '行':'rows',\n '行数':'rows',\n '大きさ':'size',\n '長さ':'length',\n '要素':'elementType',\n '値': 'value',\n 'フォント':'font',\n '色': 'color',\n '密度': 'density',\n '摩擦係数': 'friction',\n '静摩擦係数': 'frictionStatic',\n '空気抵抗': 'frictionAir',\n '反発係数': 'restitution',\n '角度': 'angle'\n # isStatic: false, /* 静的オブジェクトかどうか */\n # isSensor: false, /* コライダーとして扱うか（他のオブジェクトに干渉するか） */\n # texture: null, /* テクスチャ */\n # chamfer:{\n # radius:0\n # }\n}\n\nObjDict = {\n 'ボール':'circle',\n '玉':'circle',\n '球':'circle',\n '球形':'circle',\n '丸':'circle',\n '円':'circle',\n '四角':'rectangle',\n '四角形':'rectangle',\n '箱':'rectangle',\n '正多角形':'polygon',\n '台形':'trapezoid',\n '車':'car',\n 'スタック':'stack',\n 'ピラミッド':'pyramid',\n '鎖':'chain',\n '振り子':'pendulum',\n '布':'cloth',\n 'カタパルト':'slingshot',\n 'パチンコ':'slingshot',\n '文字':'text',\n '文字列':'text',\n 'テキスト':'text'\n # softbody\n}\n\nWorldDict = {\n '全体':'world',\n '設定':'world',\n '世界':'world'\n}\n\nWorldParamDict = {\n 'マウス':'mouse',\n '壁':'wall',\n '重力':'gravity'\n}\n\nValueDict = {\n '可':'true',\n '不可':'false'\n}\n\n# パラメータ\nclass Obj:\n __slots__ = [\n 'name', \n 'type', \n 'x', \n 'y', \n 'radius', \n 'width', \n 'height', \n 'sides',\n 'slope',\n 'wheelSize',\n 'columns',\n 'rows',\n 'size',\n 'length',\n 'elementType',\n 'value', \n 'color',\n 'font',\n 'density',\n 'friction',\n 'frictionStatic',\n 'frictionAir',\n 'restitution',\n 'angle'\n ]\n def __init__(self):\n self.name = ''\n self.type = ''\n self.x = 0\n self.y = 0\n self.radius = 0\n self.width = 0\n self.height = 0\n self.sides = 0\n self.slope = 0\n self.wheelSize = 0\n self.columns = 0\n self.rows = 0\n self.size = 0\n self.length = 0\n self.elementType = ''\n self.value = ''\n self.color = 'white'\n self.font = ''\n # オプション\n self.density = 0.001\n self.friction = 0.1\n self.frictionStatic = 0.5\n self.frictionAir = 0.01\n self.restitution = 0\n self.angle = 0\n\n #def __str__(self):\n # return self.code.format(name=self.name, object=self.object, x=self.x, y=self.y, radius=self.radius, width=self.width, height=self.height, value=self.value, color=self.color)\n\nclass World:\n __slots__ = [ \n 'mouse', \n 'wall',\n 'gravity'\n ]\n def __init__(self):\n self.mouse = 'false'\n self.wall = 'false'\n self.gravity = 1\n \n\nclass Environment:\n __slots__ = ['stmts', 'sb', 'objectID', 'definded', 'objs', 'rules', 'world']\n def __init__(self, e, sb = []):\n self.stmts = list(e[1:])\n self.sb = sb\n # TODO self.objectID = 0\n self.definded = [] # 定義済みの名前\n # self.defs = [] # スタイルシート Defクラス\n self.objs = []\n self.rules = [] # js\n self.world = World()\n\n def __str__(self):\n # FIXME\n return reduce(lambda x, y: x+'\\n'+str(y), self.rules, reduce(lambda x, y: x+'\\n'+str(y), self.defs, ''))\n\n def rename(self, name):\n self.objectID += 1\n return name + '@' + str(self.objectID)\n\n # def add_name(self, name):\n # if name in self.definded:\n # raise DefindedError\n # else:\n # self.definded.append(name)\n\n def push(self):\n for stmt in self.stmts:\n # stmt[0]:#tag\n getattr(Rule, str(stmt[0])[1:])(self, stmt[1:])\n\n def format(self):\n response = self.makeStyleSheet()\n response += self.makeRule()\n return response\n \n def makeStyleSheet(self):\n ss = 'var stylesheet = `{'\n\n # world\n ss += '\"world\":{'\n ss += '\"mouse\":' + str(self.world.mouse) + ','\n ss += '\"wall\":' + str(self.world.wall) + ','\n ss += '\"gravity\":' + str(self.world.gravity) + '}'\n\n for obj in self.objs:\n objType = obj.type\n\n if objType == 'circle':\n ss += ',\"circle\":{'\n ss += '\"name\":\"' + str(obj.name) + '\",'\n ss += '\"x\":' + str(obj.x) + ','\n ss += '\"y\":' + str(obj.y) + ','\n ss += '\"radius\":' + str(obj.radius) + ','\n ss += self.makeOption(obj) + '}'\n elif objType == 'rectangle':\n ss += ',\"rectangle\":{'\n ss += '\"name\":\"' + str(obj.name) + '\",'\n ss += '\"x\":' + str(obj.x) + ','\n ss += '\"y\":' + str(obj.y) + ','\n ss += '\"width\":' + str(obj.width) + ','\n ss += '\"height\":' + str(obj.height) + ','\n ss += self.makeOption(obj) + '}'\n elif objType == 'polygon':\n ss += ',\"polygon\":{'\n ss += '\"name\":\"' + str(obj.name) + '\",'\n ss += '\"x\":' + str(obj.x) + ','\n ss += '\"y\":' + str(obj.y) + ','\n ss += '\"sides\":' + str(obj.sides) + ','\n ss += '\"radius\":' + str(obj.radius) + ','\n ss += self.makeOption(obj) + '}'\n elif objType == 'trapezoid':\n ss += ',\"trapezoid\":{'\n ss += '\"name\":\"' + str(obj.name) + '\",'\n ss += '\"x\":' + str(obj.x) + ','\n ss += '\"y\":' + str(obj.y) + ','\n ss += '\"width\":' + str(obj.width) + ','\n ss += '\"height\":' + str(obj.height) + ','\n ss += '\"slope\":' + str(obj.slope) + ','\n ss += self.makeOption(obj) + '}'\n elif objType == 'car':\n ss += ',\"car\":{'\n ss += '\"name\":\"' + str(obj.name) + '\",'\n ss += '\"x\":' + str(obj.x) + ','\n ss += '\"y\":' + str(obj.y) + ','\n ss += '\"width\":' + str(obj.width) + ','\n ss += '\"height\":' + str(obj.height) + ','\n ss += '\"wheelSize\":' + str(obj.wheelSize) + ','\n ss += self.makeOption(obj) + '}'\n elif objType == 'stack':\n ss += ',\"stack\":{'\n ss += '\"name\":\"' + str(obj.name) + '\",'\n ss += '\"x\":' + str(obj.x) + ','\n ss += '\"y\":' + str(obj.y) + ','\n ss += '\"columns\":' + str(obj.columns) + ','\n ss += '\"rows\":' + str(obj.rows) + ','\n ss += '\"size\":' + str(obj.size) + ','\n ss += '\"elementType\":\"' + str(obj.elementType) + '\",'\n ss += self.makeOption(obj) + '}'\n elif objType == 'pyramid':\n ss += ',\"pyramid\":{'\n ss += '\"name\":\"' + str(obj.name) + '\",'\n ss += '\"x\":' + str(obj.x) + ','\n ss += '\"y\":' + str(obj.y) + ','\n ss += '\"columns\":' + str(obj.columns) + ','\n ss += '\"rows\":' + str(obj.rows) + ','\n ss += '\"size\":' + str(obj.size) + ','\n ss += '\"elementType\":\"' + str(obj.elementType) + '\",'\n ss += self.makeOption(obj) + '}'\n elif objType == 'chain':\n ss += ',\"chain\":{'\n ss += '\"name\":\"' + str(obj.name) + '\",'\n ss += '\"x\":' + str(obj.x) + ','\n ss += '\"y\":' + str(obj.y) + ','\n ss += '\"length\":' + str(obj.length) + ','\n ss += '\"size\":' + str(obj.size) + ','\n ss += self.makeOption(obj) + '}'\n elif objType == 'pendulum':\n ss += ',\"pendulum\":{'\n ss += '\"name\":\"' + str(obj.name) + '\",'\n ss += '\"x\":' + str(obj.x) + ','\n ss += '\"y\":' + str(obj.y) + ','\n ss += '\"columns\":' + str(obj.columns) + ','\n ss += '\"radius\":' + str(obj.radius) + ','\n ss += '\"length\":' + str(obj.length) + ','\n ss += self.makeOption(obj) + '}'\n elif objType == 'cloth':\n ss += ',\"cloth\":{'\n ss += '\"name\":\"' + str(obj.name) + '\",'\n ss += '\"x\":' + str(obj.x) + ','\n ss += '\"y\":' + str(obj.y) + ','\n ss += '\"width\":' + str(obj.width) + ','\n ss += '\"height\":' + str(obj.height) + ','\n ss += self.makeOption(obj) + '}'\n elif objType == 'slingshot':\n ss += ',\"slingshot\":{'\n ss += '\"name\":\"' + str(obj.name) + '\",'\n ss += '\"x\":' + str(obj.x) + ','\n ss += '\"y\":' + str(obj.y) + ','\n ss += self.makeOption(obj) + '}'\n elif objType == 'text':\n ss += ',\"text\":{'\n ss += '\"name\":\"' + str(obj.name) + '\",'\n ss += '\"x\":' + str(obj.x) + ','\n ss += '\"y\":' + str(obj.y) + ','\n ss += '\"textColor\":\"' + str(obj.color) + '\",'\n ss += '\"font\":\"' + str(obj.font) + '\",'\n ss += '\"value\":\"' + str(obj.value) + '\"'\n # elif objType == 'constraint':\n\n ss += '}`\\n'\n return ss\n\n def makeOption(self, obj):\n opt = '\"density\":' + str(obj.density) + ','\n opt += '\"friction\":' + str(obj.friction) + ','\n opt += '\"frictionStatic\":' + str(obj.frictionStatic) + ','\n opt += '\"frictionAir\":' + str(obj.frictionAir) + ','\n opt += '\"restitution\":' + str(obj.restitution) + ','\n opt += '\"angle\":' + str(obj.angle)\n return opt\n\n def makeRule(self):\n return 'function myRule(){}'\n\n\nclass Rule:\n def Definition(env, args):\n if str(args[0]) in WorldDict:\n newWorld = env.world\n args = args[1:]\n for arg in args:\n newWorld = getattr(Rule, 'World' + str(arg[0][1:]))(env, newWorld, arg[1:])\n # TODO world以外で #Param を生成しない場合\n else:\n newObj = Obj()\n for arg in args:\n newObj = getattr(Rule, str(arg[0][1:]))(env, newObj, arg[1:])\n \n # 登録されてなければ環境に登録\n if not newObj.name in env.definded:\n env.definded.append(newObj.name)\n env.objs.append(newObj)\n \n return env\n\n def Param(env, obj, args):\n name = str(args[0]) + str(args[1])\n if(name in env.definded):\n return env.objs[env.definded.index(name)]\n else:\n obj.name = name\n # TODO エラーチェック\n obj.type = ObjDict[str(args[0])]\n return obj\n\n def VarDecl(env, obj, args):\n setattr(obj, ParamDict[str(args[0])], ObjDict[str(args[1])] if str(args[1]) in ObjDict else args[1]) \n return obj\n\n def Statement(env, world, args):\n # とりあえず保留\n return world\n\n def WorldVarDecl(env, world, args):\n setattr(world, WorldParamDict[str(args[0])], ValueDict[str(args[1])] if str(args[1]) in ValueDict else args[1]) \n return world\n\n def WorldStatement(env, world, args):\n # とりあえず保留\n return world\n\n# color = {\n# '赤': 'red',\n# '赤色': 'red',\n# '青': 'blue',\n# '青色': 'blue',\n# '黄': 'yellow',\n# '黄色': 'yellow',\n# 'オレンジ': 'orange',\n# 'オレンジ色': 'orange',\n# 'ピンク': 'pink',\n# 'ピンク色': 'pink',\n# '紫': 'purple',\n# '紫色': 'purple',\n# '緑': 'green',\n# '緑色': 'green',\n# '黒': 'black',\n# '黒色': 'black',\n# '白': 'white',\n# '白色': 'white',\n# '灰': 'gray',\n# '灰色': 'gray',\n# '茶': 'brown',\n# '茶色': 'brown',\n# }\n\n# modifier = {\n# '少し': '0.2',\n# 'すこし': '0.2',\n# 'ちょっと': '0.1',\n# 'めっちゃ': '0.5',\n# 'ごっつ': '0.4',\n# 'すごく': '0.4',\n# 'ぐーんと': '0.4',\n# 'がくっと': '0.4',\n# 'ぐぐーんと': '0.5',\n# 'がくーん': '0.5',\n# '超': '0.5',\n# 'ちょう': '0.5',\n# 'ちょー': '0.5',\n# '大分': '0.4',\n# 'だいぶ': '0.4',\n# '結構': '0.5',\n# 'けっこう': '0.5',\n# }\n\n# no_name_direct = {\n# '上': ('cvsw/ratew/2', '0', 0),\n# '下': ('cvsw/ratew/2', 'cvsh/rateh', 0),\n# '右': ('cvsw/ratew', 'cvsh/rateh/2', 0),\n# '左': ('0', 'cvsh/rateh/2', 0),\n# '真ん中': ('cvsw/ratew/2', 'cvsh/rateh/2', 0),\n# '中心': ('cvsw/ratew/2', 'cvsh/rateh/2', 0),\n# '右上': ('cvsw/ratew', '0', 0),\n# '右下': ('cvsw/ratew', 'cvsh/rateh', 0),\n# '左上': ('0', '0', 0),\n# '左下': ('0', 'cvsh/rateh', 0),\n# }\n\n# directive = ['これら', 'あれら', 'それら', 'これ', 'あれ', 'それ', 'こ', 'あ', 'そ']\n# be = ['おく', 'ある', 'いる', '置く', 'いらっしゃる', 'おられる', 'おき', 'あり', 'おり', '置き', 'いらっしゃり', 'おられり', 'おいて', 'あって', 'いて', '置いて', 'いらっしゃって', 'おられて']\n# crash = ['衝突するとき', '衝突するなら', '衝突するならば', '衝突したとき', '衝突したら', '衝突したならば', '当たるとき', '当たるなら', '当たるならば', '当たったとき', '当たったなら', '当たったならば', 'あたるとき', 'あたるなら', 'あたるならば', 'あたったとき', 'あたったなら', 'あたったならば']\n# add = ['増加する', '増加して', '増加し', '増やす', '増やして', '増やし', '増える', '増えて', '増え', '増す', '増して', '増し', 'ふやす', 'ふやして', 'ふやし', 'ふえる', 'ふえて', 'ふえ', 'ます', 'まして', 'まし']\n# subtract = ['減少する', '減少して', '減少し', '減らす', '減らして', '減らし', '減る', '減って', '減り', 'へらす', 'へらして', 'へらし', 'へる', 'へって', 'へり']\n\n# class DefindedError(Exception):\n# pass\n\n# class UnknownNameError(Exception):\n# pass\n\n\n\n\ndef parse(input):\n parser = switch_generator({}, 'npl.tpeg')(load_grammar({}, 'npl.tpeg'))\n return parser(input)\n\ndef transpile(input):\n tree = parse(input)\n\n if tree.tag == 'err':\n return 'Parse Error'\n\n e = Expression.treeConv(tree)\n # return e\n env = Environment(e)\n env.push()\n return env.format()\n\ndef main():\n print(transpile(sample))\n\nif __name__ == '__main__':\n main()","sub_path":"macaron/src/transpiler.py","file_name":"transpiler.py","file_ext":"py","file_size_in_byte":14545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"488096928","text":"import numpy as np\r\ninput_data = np.array([[2,9],[1,5],[3,6]], dtype=float) \r\nexpected_output=np.array ([[92], [86], [89]], dtype=float) \r\ninput_data = input_data/np.amax(input_data, axis=0)\r\nexpected_output=expected_output/100 \r\ndef sigmoid(x):\r\n return 1/(1+np.exp(-x)) \r\ndef derivative_sigmoid(x):\r\n return x*(1-x)\r\nepoch=1\r\nlearning_rate = 0.1\r\ninputlayer_neurons = 2 \r\nhiddenlayer_neurons=3 \r\noutputlayer_neurons = 1 \r\nhiddenlayer_weights=np.random.uniform(size=(inputlayer_neurons, hiddenlayer_neurons)) \r\nhiddenlayer_bias = np.random.uniform(size=(1, hiddenlayer_neurons)) \r\noutputlayer_weights=np.random.uniform(size= (hiddenlayer_neurons, outputlayer_neurons )) \r\noutputlayer_bias = np.random.uniform(size=(1, outputlayer_neurons)) \r\nfor i in range(epoch):\r\n hiddenlayer_input = np.dot(input_data, hiddenlayer_weights) \r\n hiddenlayer_input = hiddenlayer_input + hiddenlayer_bias \r\n hiddenlayer_output = sigmoid(hiddenlayer_input) \r\n \r\n outputlayer_input = np.dot(hiddenlayer_output, outputlayer_weights) \r\n outputlayer_input = outputlayer_input + outputlayer_bias\r\n outputlayer_output=sigmoid(outputlayer_input)\r\n \r\n outputlayer_error=expected_output-outputlayer_output \r\n outputlayer_gradient=derivative_sigmoid(outputlayer_output) \r\n outputlayer_error_correction = outputlayer_error* outputlayer_gradient\r\n \r\n hiddenlayer_error = outputlayer_error_correction.dot (outputlayer_weights. T) \r\n hiddenlayer_gradient=derivative_sigmoid(hiddenlayer_output) \r\n hiddenlayer_error_correction=hiddenlayer_error*hiddenlayer_gradient\r\n \r\n outputlayer_weights += hiddenlayer_output.T.dot(outputlayer_error_correction) * learning_rate \r\n hiddenlayer_weights += input_data.T.dot(hiddenlayer_error_correction)*learning_rate\r\n \r\nprint(\"Input : \", input_data)\r\nprint(\"Expected Output :\", expected_output)\r\nprint(\"actual output: \",outputlayer_output)\r\n","sub_path":"pgm 4 BP.py","file_name":"pgm 4 BP.py","file_ext":"py","file_size_in_byte":1908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"631687514","text":"from time import sleep\n\n\ndef long_task(a, b):\n print(\"entered the long_task\")\n result = a + b\n sleep(5)\n return result\n\n\nif __name__ == '__main__':\n from sys import argv\n a, b = argv[1:3]\n a, b = map(int, a, b)\n\n print('computing {} + {}'.format(a, b))\n result = long_task(a, b)\n print('{} + {} = '.format(a, b, result))\n","sub_path":"job_process.py","file_name":"job_process.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"359653912","text":"import os, PyPDF2\r\n\r\n# Combines all PDFs in current working directory into one PDF.\r\n\r\npdfFiles = []\r\n\r\nfor filename in os.listdir(\".\"):\r\n if filename.endswith(\".pdf\"):\r\n pdfFiles.append(filename)\r\n\r\npdfFiles.sort(key=str.lower)\r\n\r\npdfWriter = PyPDF2.PdfFileWriter()\r\nfor file in pdfFiles:\r\n pdfFileObj = open(file, \"rb\")\r\n pdfReader = PyPDF2.PdfFileReader(pdfFileObj)\r\n for page in range(1, pdfReader.numPages):\r\n pageObj = pdfReader.getPage(page)\r\n pdfWriter.addPage(pageObj)\r\n\r\noutput = open(\"allminutes.pdf\", \"wb\")\r\npdfWriter.write(output)\r\noutput.close()\r\n","sub_path":"combinePDF.py","file_name":"combinePDF.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"626854009","text":"'''\nCreated on Jan 19, 2013\n\n@author: joshandrews\n'''\nimport time\nimport math\nimport thread\nimport sys\nsys.path.append(\"..\")\nfrom control.logic import standardcalc\nfrom control.logic import coresailinglogic\nfrom control.datatype import datatypes\nfrom control import GlobalVars as gVars\nfrom control import StaticVars as sVars\n\nhog_index=sVars.HOG_INDEX\ncog_index=sVars.COG_INDEX\nsog_index=sVars.SOG_INDEX\nawa_index=sVars.AWA_INDEX\ngps_index=sVars.GPS_INDEX\nsht_index=sVars.SHT_INDEX\naut_index=sVars.AUT_INDEX\nCOMPASS_METHOD = 0\nCOG_METHOD = 1\nAWA_METHOD = 2\n\nHORIZ_BOUNDARY_DISTANCE = 60 \n\n# --- Navigation Challenge ---\n#Input: Buoy GPS Coordinates (Latitude and Longitude of the Buoy), Left Inner Point (The coordinates of the left innermost gate), Right Inner Point (The coordinates of the right innermost gate)\n#Output: None\ndef run(Waypoint1,Waypoint2,Waypoint3):\n currentData = gVars.currentData\n GPSCoord = currentData[gps_index]\n \n gVars.kill_flagNav = 0\n \n num_nav_first = 0\n num_nav_start_port = 0\n num_nav_start_stbd = 0\n \n wayList = list()\n \n wayList.append(Waypoint1)\n wayList.append(Waypoint2)\n wayList.append(Waypoint3)\n \n for waypoint in wayList:\n if(waypoint.wtype == \"nav_first\"):\n BuoyCoords = waypoint.coordinate\n num_nav_first = num_nav_first + 1\n elif(waypoint.wtype == \"nav_start_port\"):\n PortStartInnerPoint = waypoint.coordinate\n num_nav_start_port = num_nav_start_port + 1\n elif(waypoint.wtype == \"nav_start_stbd\"):\n StarboardStartInnerPoint = waypoint.coordinate\n num_nav_start_stbd = num_nav_start_stbd + 1\n \n if(num_nav_start_port > 1 or num_nav_start_stbd > 1 or num_nav_first > 1):\n gVars.logger.error(\"Repeating or too many arguments\")\n \n interpolatedPoint = datatypes.GPSCoordinate((PortStartInnerPoint.latitude+StarboardStartInnerPoint.latitude)/2,(PortStartInnerPoint.longitude+StarboardStartInnerPoint.longitude)/2)\n angleOfCourse = standardcalc.angleBetweenTwoCoords(interpolatedPoint, BuoyCoords)\n boundAngle = math.atan(HORIZ_BOUNDARY_DISTANCE/30)*180/math.pi\n \n bound_dist = math.sqrt(HORIZ_BOUNDARY_DISTANCE^2+30^2)\n \n netAngleLeft = boundAngle - angleOfCourse\n netAngleRight = boundAngle + angleOfCourse\n \n leftBoundaryPoint = standardcalc.GPSDistAway(StarboardStartInnerPoint, bound_dist*math.sin(netAngleLeft), bound_dist*math.cos(netAngleLeft))\n \n rightBoundaryPoint = standardcalc.GPSDistAway(PortStartInnerPoint, bound_dist*math.sin(netAngleRight), bound_dist*math.cos(netAngleRight))\n \n \n \n buoySailPoint = setNavigationBuoyPoint(BuoyCoords, GPSCoord, 10)\n \n if(gVars.kill_flagNav == 0):\n coresailinglogic.pointToPoint(buoySailPoint)\n \n if(gVars.kill_flagNav == 0):\n coresailinglogic.roundBuoyPort(BuoyCoords,standardcalc.angleBetweenTwoCoords(BuoyCoords,GPSCoord))\n \n if(gVars.kill_flagNav == 0):\n thread.start_new_thread(coresailinglogic.pointToPoint, interpolatedPoint)\n \n while(standardcalc.distBetweenTwoCoords(GPSCoord, interpolatedPoint)>sVars.ACCEPTANCE_DISTANCE_DEFAULT and gVars.kill_flagNav == 0):\n GPSCoord = currentData[gps_index]\n appWindAng = currentData[awa_index]\n \n while(gVars.currentData[aut_index] == False):\n time.sleep(0.1)\n \n if(standardcalc.distBetweenTwoCoords(GPSCoord,leftBoundaryPoint) > bound_dist or standardcalc.distBetweenTwoCoords(GPSCoord,rightBoundaryPoint) > bound_dist):\n if(appWindAng > 0):\n tackDirection = 1\n else:\n tackDirection = 0\n \n gVars.arduino.tack(gVars.currentColumn,tackDirection)\n gVars.tacked_flag = 1\n \n return 0\n\ndef setNavigationBuoyPoint(buoyLocation, boatCoords, distFromBuoy):\n interpoAngle = 90 - standardcalc.angleBetweenTwoCoords(buoyLocation, boatCoords)\n xDelta = distFromBuoy*math.cos(interpoAngle)\n yDelta = distFromBuoy*math.sin(interpoAngle)\n \n return standardcalc.GPSDistAway(buoyLocation, xDelta, yDelta)","sub_path":"control/challenge/navigation.py","file_name":"navigation.py","file_ext":"py","file_size_in_byte":4135,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"278000473","text":"from json import loads, dumps\nimport requests\nimport time\n\nmaze_map = loads(open('map', 'r').read())\n\ndef find_path(room_id, target):\n path = []\n q = []\n visited = set()\n\n q.insert(0, [(room_id, '')])\n\n while len(q) > 0:\n path = q.pop()\n r, d = path[-1]\n for e in maze_map[r].keys():\n next_room = maze_map[r][e]\n if next_room not in visited and next_room != '?':\n if next_room == target:\n return (path + [(next_room, e)])[1:]\n visited.add(next_room)\n q.insert(0, path + [(next_room, e)])\n\n\nBASE_URL = \"https://lambda-treasure-hunt.herokuapp.com/api\"\nheaders = {\"Authorization\": f\"Token {input('token >>>')}\"}\nr = requests.get(url=BASE_URL+\"/adv/init\", headers=headers)\n\ntime.sleep(r.json()['cooldown'] + 1)\n\nfor room_id, direction in find_path(str(r.json()['room_id']), input('target>>>')):\n body = {\"direction\": direction, \"next_room_id\": room_id}\n r = requests.post(url=BASE_URL+\"/adv/move\",\n json=body, headers=headers)\n next_room = r.json()\n print(next_room)\n time.sleep(next_room['cooldown'] + 1)\n","sub_path":"path.py","file_name":"path.py","file_ext":"py","file_size_in_byte":1160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"198473728","text":"import gym\nimport numpy as np\n\nenv = gym.make('FrozenLake8x8-v0')\n\nalpha = 0.9\ngamma = 1\n\nget_action = lambda state: np.argmax(Q[state])\n\ne = 0.9\ne_greedy_action = lambda state: np.argmax(Q[state]) if np.random.rand() < e else np.random.randint(0,env.nA)\n\n\nQ = {state: np.random.rand(env.nA)for state in range(env.nS)}\n\nneps = 20000\n\nfor t in range(neps):\n\n S = env.reset()\n\n A = e_greedy_action(S)\n\n terminal = False\n\n while not terminal:\n\n S_, R, terminal, _ = env.step(A)\n \n A_ = e_greedy_action(S_)\n\n if terminal:\n Q[S][A] = Q[S][A] + alpha * (R - Q[S][A])\n else:\n Q[S][A] = Q[S][A] + alpha * (R + gamma * Q[S_][A_] - Q[S][A])\n\n if R != 0 and e > 0.1:\n e *= 0.999\n if alpha > 0.01:\n alpha *= 0.999\n\n S = S_; A = A_\n\nenv.render()\ndef convert(x):\n return {0:'L', 1:'D',2:'R',3:'U'}[x]\n\nfor i in range(env.nS):\n if i % env.nrow == 0:\n print()\n print(convert(get_action(i)), end='')\nprint()\n\navg_r = 0\ngames = 1000\n\nfor i in range(games):\n s = env.reset()\n terminal = False\n\n while not terminal:\n a = np.argmax(Q[s])\n s, r, terminal, _ = env.step(a)\n avg_r += r\n\navg_r /= games\nprint(avg_r)\n","sub_path":"frozenlake/sarsa.py","file_name":"sarsa.py","file_ext":"py","file_size_in_byte":1245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"620125417","text":"\n## Symbolic code for computing the Apperent Event Horizon. \n\nimport sys as sys\nimport dendro\nfrom sympy import *\n\na = dendro.scalar(\"alpha\", \"[pp]\")\nchi = dendro.scalar(\"chi\", \"[pp]\")\nK = dendro.scalar(\"K\", \"[pp]\")\nGt = dendro.vec3(\"Gt\", \"[pp]\")\nb = dendro.vec3(\"beta\", \"[pp]\")\nB = dendro.vec3(\"B\", \"[pp]\")\ns = dendro.vec3(\"s\", \"[pp]\")\n\ngt = dendro.sym_3x3(\"gt\", \"[pp]\")\nAt = dendro.sym_3x3(\"At\", \"[pp]\")\n\nGt_rhs = dendro.vec3(\"Gt_rhs\", \"[pp]\")\n\n# Lie derivative weight\nweight = -Rational(2,3)\nweight_Gt = Rational(2,3)\n\nd = dendro.set_first_derivative('grad') # first argument is direction\nd2s = dendro.set_second_derivative('grad2') # first 2 arguments are directions\nad = dendro.set_advective_derivative('agrad') # first argument is direction\nkod = dendro.set_kreiss_oliger_dissipation('kograd')\n\nd2 = dendro.d2\n\n\ndendro.set_metric(gt)\nigt = dendro.get_inverse_metric()\n\nKK = dendro.sym_3x3(\"KK\", \"[pp]\")\ntheta_rhs = - (sum([d(i,s[i]) for i in dendro.e_i]) + sum( [KK[i,j]*s[i]*s[j] for i,j in dendro.e_ij ] ) -K ) \n\n\nouts = [theta_rhs]\nvnames = ['theta_rhs']\ndendro.generate_cpu(outs, vnames, '[pp]')\n\n","sub_path":"CodeGen/AEH.py","file_name":"AEH.py","file_ext":"py","file_size_in_byte":1127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"483382173","text":"#!/usr/bin/env python\n\nUP = 1\nDOWN = 2\nFLOOR_COUNT = 6\n\nclass ElevatorLogic(object):\n\n def __init__(self):\n # Required.\n self.callbacks = None\n\n # Current elevator direction.\n self._direction = None\n\n # Pending requests. A request is a pair (floor, direction), in which\n # direction can be None if the floor was selected from the elevator, or\n # a given direction if the elevator was called from a floor. Telling\n # both cases apart is actually needed.\n self._requests = set()\n\n def _try_add(self, floor, direction):\n # Basic checks when storing a new request.\n if ((floor, direction) not in self._requests\n and floor >= 1 and floor <= FLOOR_COUNT\n and (self.callbacks.current_floor != floor\n or self.callbacks.motor_direction is not None)):\n\n # Save the request.\n self._requests.add((floor, direction))\n\n # Set direction now if possible. This gives priority to earliest\n # requests first.\n if self._direction is None:\n cur = self.callbacks.current_floor\n if floor < cur:\n self._direction = DOWN\n elif floor > cur:\n self._direction = UP\n elif direction is not None:\n self._direction = direction\n\n def on_called(self, floor, direction):\n self._try_add(floor, direction)\n\n def on_floor_selected(self, floor):\n # This call requires a few additional checks to ignore floors that\n # contradict the current direction.\n cur = self.callbacks.current_floor\n if (self._direction is None or\n (self._direction == UP and floor > cur) or\n (self._direction == DOWN and floor < cur)):\n self._try_add(floor, None)\n\n def _any_above(self, floor):\n return any(f > floor for (f, d) in self._requests)\n\n def _any_below(self, floor):\n return any(f < floor for (f, d) in self._requests)\n\n # Any requests in my current direction?\n def _should_continue(self):\n cur = self.callbacks.current_floor\n return ((self._direction == UP and self._any_above(cur))\n or (self._direction == DOWN and self._any_below(cur)))\n\n def _opposite(self):\n if self._direction is None:\n return None\n if self._direction == UP:\n return DOWN\n return UP\n\n def on_floor_changed(self):\n cur = self.callbacks.current_floor\n\n # Reasons to stop:\n # (a) Passenger requested the current floor.\n # (b) Current floor requested in the current direction.\n # (c) No further requests in this direction.\n if ((cur, None) in self._requests or\n (cur, self._direction) in self._requests or\n (not self._should_continue())):\n\n self.callbacks.motor_direction = None\n\n # Deal with (a).\n self._requests.discard((cur, None))\n\n # Now maybe (b) or, if not, maybe (c).\n aux = (cur, self._direction)\n if aux in self._requests:\n self._requests.discard(aux)\n elif not self._should_continue():\n aux = (cur, self._opposite())\n if aux in self._requests:\n self._requests.discard(aux)\n self._direction = self._opposite()\n\n def on_ready(self):\n # Continue in the current direction if possible.\n if self._should_continue():\n self.callbacks.motor_direction = self._direction\n\n # Forget direction when done.\n elif len(self._requests) == 0:\n self._direction = None\n\n else:\n # Change direction. Serve curent floor or start motor.\n aux = (self.callbacks.current_floor, self._opposite())\n if aux in self._requests:\n self._requests.discard(aux)\n else:\n self.callbacks.motor_direction = self._opposite()\n self._direction = self._opposite()\n","sub_path":"elevator.py","file_name":"elevator.py","file_ext":"py","file_size_in_byte":4088,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"184861108","text":"from flask import Flask, render_template, request\nimport sys\nfrom datetime import datetime\n\nfrom backend.audio import aud, play_audio\nimport backend.speech_recognition as SR\nimport backend.speech_synthesis as SS\nimport backend.task_manager_backend_2 as P\nimport backend.obtain_information as Obtain\n\napp = Flask(__name__)\n \n\nFLIP = 0\nSR_Model = SR.load_model()\nSR.load_language_model(SR_Model)\n\nSS_model = SS.load_model()\n\ntaskDict = {}\n\n\n\n@app.route('/table')\ndef search(text):\n\n query = P.parse_to_search(text)\n # query = 'computer databases'\n print(query)\n websites = Obtain.get_websites(query, num=5)\n \n summaries = {}\n for i, url in enumerate(websites):\n f1, f2 = Obtain.get_text(url)\n if not f1:\n continue\n summary = Obtain.get_summary(f1, f2)\n output_name='/Users/sshaar/hackathon/frontend/theme/output%d.wav'.format(i)\n summaries[i] = (summary, url, output_name)\n # SS.synthesize_parapgraph(SS_model, summary, output_name=output_name)\n\n youtube_links = Obtain.get_videos(query)\n \n y = {}\n for i in range(len(youtube_links)):\n y[i] = youtube_links[i] \n\n return render_template('table.html', google=summaries, youtube=y)\n\n\n\n@app.route('/', methods=['POST', 'GET'])\ndef index():\n global FLIP, taskDict\n lexa = aud()\n # taskDict = {}\n if request.method == 'POST':\n user = request.form\n print(user, file=sys.stdout)\n if 'search-button' in str(user):\n lexa.play()\n text = SR.transcripe_file(SR_Model, '/Users/sshaar/hackathon/frontend/theme/output.wav')\n print('I GOT IT', FLIP)\n print(text)\n tt = text.split()\n print('-'*50)\n print(tt)\n print('-'*50)\n if \"add\" in tt or \"task\" in tt or \"homework\" in tt :\n try:\n textName = P.get_taskName(text)\n except:\n textName = 'Computer Scinece Homeowrk'\n \n play_audio('/Users/sshaar/hackathon/frontend/theme/backend/due_date.wav')\n lexa.play()\n text = SR.transcripe_file(SR_Model, '/Users/sshaar/hackathon/frontend/theme/output.wav')\n try:\n dueDate = P.get_deadline(text)\n except:\n dueDate = datetime.strptime('Apr 15 2019', '%b %d %Y')\n\n play_audio('/Users/sshaar/hackathon/frontend/theme/backend/length_assignment.wav')\n lexa.play()\n text = SR.transcripe_file(SR_Model, '/Users/sshaar/hackathon/frontend/theme/output.wav')\n try:\n length = float(P.get_data(text))\n except:\n length = 5.0\n print(length)\n\n try:\n taskDict = P.update_list(textName, P.give_score(length, P.get_taskTime(length, dueDate), 5), P.get_deadline(dueDate))\n except:\n taskDict['Computer Scinece Homework'] = 700.0\n elif \"can\" in tt or \"how\" in tt or \"tell\" in tt or 'show' in tt or 'what' in tt:\n return search(text)\n\n else:\n play_audio('/Users/sshaar/hackathon/frontend/theme/backend/cannot_hear.wav')\n\n FLIP = not(FLIP)\n\n keys = list(taskDict.keys())\n print(keys)\n if(len(keys) >= 6):\n return render_template(\"dashboard.html\", flip=FLIP, t1= (keys[0]), t2= (keys[1]), t3= (keys[2]), t4= (keys[3]), t5= (keys[4]), t6= (keys[5]))\n elif(len(keys) == 5):\n return render_template(\"dashboard.html\", flip=FLIP, t1= (keys[0]), t2= (keys[1]), t3= (keys[2]), t4= (keys[3]), t5= (keys[4]))\n elif(len(keys) == 4):\n return render_template(\"dashboard.html\", flip=FLIP, t1= (keys[0]), t2= (keys[1]), t3= (keys[2]), t4= (keys[3]))\n elif(len(keys) == 3):\n return render_template(\"dashboard.html\", flip=FLIP, t1= (keys[0]), t2= (keys[1]), t3= (keys[2]))\n elif(len(keys) == 2):\n return render_template(\"dashboard.html\", flip=FLIP, t1= (keys[0]), t2= (keys[1]))\n elif(len(keys) == 1):\n return render_template(\"dashboard.html\", flip=FLIP, t1= (keys[0]))\n else:\n return render_template(\"dashboard.html\", flip=FLIP)\n\nif __name__ == '__main__':\n app.run(debug = True)","sub_path":"OLD-Lexe/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":4321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"543970121","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Dec 22 11:23:01 2019\r\n\r\n@author: franc\r\n\"\"\"\r\n\r\n# importing libraries\r\nimport numpy as np\r\nimport networkx as nx\r\nimport gzip\r\nfrom itertools import islice\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.image as mpimg\r\nfrom itertools import combinations \r\nfrom scipy.spatial import distance\r\nfrom func_3 import f3\r\nimport webbrowser\r\n\r\n\r\n\r\n# path for read files\r\npath = 'C:/Users/franc/Desktop/Data_science/Algorithmic_Methods_of_Data_Mining/ADM_hw5/'\r\n\r\n\r\n# the three func to built the graphs take as argument a nx.Graph()\r\n# we are going to create the graph with the distance in meters\r\ndef d(graph):\r\n with gzip.open(path + \"distance.gz\", \"r\") as f:\r\n for line in islice(f, 7, None):\r\n r, s, t = map(int, line[2:].split())\r\n graph.add_edge(r, s, weight = t)\r\n return graph\r\n\r\n# we are going to create the graph with the time distance\r\ndef t(g):\r\n with gzip.open(path + \"time_travel.gz\", \"r\") as f:\r\n for line in islice(f, 7, None):\r\n r, s, t = map(int, line[2:].split())\r\n g.add_edge(r, s, weight = t)\r\n return g\r\n\r\n# we are going to create the graph with the network distance\r\ndef n(g):\r\n with gzip.open(path + \"distance.gz\", \"r\") as f:\r\n for line in islice(f, 7, None):\r\n r, s, t = map(int, line[2:].split())\r\n g.add_edge(r, s, weight = 1)\r\n return g\r\n\r\n# we are going to save all the coordinates into a dictionary in this form\r\n'''\r\ncoord = {node_1 : (long, lat),\r\n node_2 : (long, lat),\r\n .\r\n .\r\n .}\r\n'''\r\ndef coordinates(coord):\r\n with gzip.open(path + \"coordinates.gz\", \"r\") as f:\r\n for line in islice(f, 7, None):\r\n x = line[2:].split()\r\n r, s, t = int(x[0]), float(x[1])/10**6, float(x[2])/10**6\r\n coord[r] = (s, t)\r\n return coord\r\n\r\n\r\n# this is the function to find the heuristic solution for this problem\r\n# take as argument a fully connecteg graph where the weights are the eucledian distances\r\n# between the nodes\r\n# to find the best path we use the a kind of Nearest Neighbour Algorithm \r\ndef shortest_path_l_r(dcf_graph, start, end): \r\n visited = [start, end]\r\n tot_dist = 0\r\n while len(visited) < len(dcf_graph):\r\n # this is for one side\r\n min_dist = np.inf\r\n near = 0\r\n for k,v in dcf_graph[visited[len(visited)//2 - 1]].items():\r\n if k not in visited:\r\n if v < min_dist:\r\n min_dist = v\r\n near = k\r\n visited.insert(len(visited)//2, near)\r\n tot_dist += min_dist\r\n # this is for the other\r\n min_dist = np.inf\r\n near = 0\r\n for k,v in dcf_graph[visited[len(visited)//2 + 1]].items():\r\n if k not in visited:\r\n if v < min_dist:\r\n min_dist = v\r\n near = k\r\n # at the end if we have a even numbaer of vertex\r\n if near != 0:\r\n visited.insert(len(visited)//2 + 1, near)\r\n tot_dist += min_dist\r\n return visited, tot_dist\r\n\r\n# we built the fully connected graph as a dictionary in this way\r\n'''\r\ndictionary = {node_1 : {node_i : weight,\r\n node_j : weight,\r\n node_k : weight},\r\n node_2 : {node_l : weight,\r\n node_m : weight,\r\n node_n : weight}}\r\n }\r\n'''\r\ndef fully_connected_graph(dcf_graph, comb, coord):\r\n for vert in comb:\r\n r, s = vert\r\n t = distance.euclidean(coord[r], coord[s])\r\n if r not in dcf_graph:\r\n dcf_graph[r] = {s: t}\r\n else:\r\n dcf_graph[r][s] = t\r\n if s not in dcf_graph:\r\n dcf_graph[s] = {r: t}\r\n else:\r\n dcf_graph[s][r] = t\r\n return dcf_graph\r\n\r\n# choose the best path between the two that we are going to evaluate\r\ndef best_order(dcf_graph, rand_po): \r\n visited_1, dist_1 = shortest_path_l_r(dcf_graph, rand_po[0], rand_po[-1])\r\n visited_2, dist_2 = shortest_path_l_r(dcf_graph, rand_po[-1], rand_po[0])\r\n if dist_1 < dist_2:\r\n visited = visited_1\r\n else:\r\n visited = visited_2[::-1]\r\n return visited\r\n \r\n# building list of nodes to visit\r\ndef nodes_between_start_end(graph, visited): \r\n Nodes = []\r\n for i in range(len(visited)-1):\r\n Nodes += f3(graph, visited[i], visited[i+1]) \r\n return Nodes\r\n\r\n# the visualization part used for func_3 and func_4\r\ndef visualization_4(visited, Nodes, coord):\r\n # we import and delet it into the func \r\n import folium\r\n #starting node for center the map\r\n starting = coord[visited[0]]\r\n mapit = folium.Map( location=[starting[1], starting[0]], zoom_start = 10 )\r\n # coordinates of all nodes\r\n Way = []\r\n for i in range(len(Nodes)):\r\n v = coord[Nodes[i]]\r\n Way.append((v[1], v[0]))\r\n #street between vertices\r\n folium.PolyLine(Way, color=\"gray\", weight=2.5).add_to(mapit)\r\n #plot all the vertices ac circles\r\n for i in range(len(Way)):\r\n folium.CircleMarker(Way[i], radius = 3, opacity=0.1 + 0.9*((i+1)/len(Nodes))).add_to(mapit)\r\n #plot the vertices selected by the user\r\n for i in range(1,len(visited)-1):\r\n v = coord[visited[i]]\r\n folium.Marker((v[1], v[0]), icon=folium.Icon(color='blue', icon='cloud') , radius=8 ).add_to(mapit)\r\n # change color for starting and ending points\r\n folium.Marker( Way[0], icon=folium.Icon(color='green', icon='cloud') , radius=8 ).add_to(mapit)\r\n folium.Marker( Way[-1], icon=folium.Icon(color='red', icon='cloud') , radius=8 ).add_to(mapit)\r\n # save map\r\n mapit.save(path + 'map.html')\r\n # open map on browser\r\n webbrowser.open(path + 'map.html',new = 2)\r\n \r\n \r\n # We need to removit because otherwise the bulitin map() function doesn't work well\r\n del folium\r\n \r\n# the visualization part used for func_1\r\ndef visualization_1(Nodes, coord):\r\n # we import and delet it into the func \r\n import folium\r\n #starting node for center the map\r\n starting = coord[Nodes[0]]\r\n mapit = folium.Map( location=[starting[1], starting[0]], zoom_start = 10 )\r\n #plot the vertices selected by the user\r\n for i in range(1, len(Nodes)):\r\n v = coord[Nodes[i]]\r\n folium.Marker((v[1], v[0]), icon=folium.Icon(color='blue', icon='cloud') , radius=8 ).add_to(mapit)\r\n v = coord[Nodes[0]]\r\n folium.Marker((v[1], v[0]), icon=folium.Icon(color='green', icon='cloud') , radius=8 ).add_to(mapit)\r\n # save map\r\n mapit.save(path + 'map.html')\r\n # open map on browser\r\n webbrowser.open(path + 'map.html',new = 2)\r\n \r\n \r\n # We need to removit because otherwise the bulitin map() function doesn't work well\r\n del folium\r\n \r\n\r\n","sub_path":"func_4.py","file_name":"func_4.py","file_ext":"py","file_size_in_byte":6814,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"370241756","text":"from rl.policy import LinearAnnealedPolicy, BoltzmannQPolicy, EpsGreedyQPolicy\nfrom keras.regularizers import l2\nfrom rl.memory import SequentialMemory\nfrom rl.core import Processor\nfrom rl.callbacks import FileLogger, ModelIntervalCheckpoint\nimport numpy as np\nimport pdb\nimport gym\n\n\nfrom keras.models import Sequential, Model\nfrom keras.layers import Dense, Activation, Flatten, Input, Concatenate, BatchNormalization, Lambda, concatenate, Conv2D, MaxPooling2D, Convolution3D \nfrom keras.optimizers import Adam\n\nfrom rl.agents import DDPGAgent \nfrom rl.random import OrnsteinUhlenbeckProcess\n\n#Currently implements the methods by returning what was given\nclass EmptyProcessor(Processor):\n def process_observation(self, observation):\n #observation here is a board state, maybe do some form of \n #data augmentation at some point, but right now not going to\n return observation\n def process_state_batch(self, batch):\n return batch\n def process_reward(self, reward):\n return reward\n\n\ndef construct_agent(env, env_shape, nb_actions, input_shape):\n dims = env.robot_state.shape[0]\n # Next, we build a very simple model.\n picture_tensor = Input(shape=(1,) + input_shape, dtype='float32', name=\"pictureTensor\")\n #so the idea here is that we never have more than one in the window length\n #and that's just a way to work around keras-rl, so we're just going to take #one window's length and convolve that....\n grid = Lambda(lambda x: x[:,0,:,:,0:3], dtype='float32')(picture_tensor)\n #horrible hack: the top left corner\n robot = Lambda(lambda x: x[:,0,:,:,3][:,0][:,0:dims], (dims,) , dtype='float32')(picture_tensor)\n #Convolution stuff\n grid = Conv2D(10,(5,5), activation='relu', padding='same')(grid)\n\n grid = MaxPooling2D((3,3),strides=(1,1),padding='same')(grid)\n grid = Flatten(dtype='float32')(grid)\n #robot = Flatten(dtype='float32')(robot)\n fc1 = concatenate([robot, grid]) \n actor = Dense(16, activation='relu')(fc1)\n actor = Dense(16, activation='relu')(actor)\n\n actor = Dense(nb_actions, activation = 'sigmoid', dtype='float32')(actor) \n actor = Model(inputs=picture_tensor, outputs=actor)\n \n \n \"\"\"\n actor = Sequential()\n actor.add(Flatten(input_shape=(1,) + env_shape))\n actor.add(Dense(16))\n actor.add(Activation('relu'))\n actor.add(Dense(16))\n actor.add(Activation('relu'))\n actor.add(Dense(16))\n actor.add(Activation('relu'))\n actor.add(Dense(nb_actions))\n actor.add(Activation('linear'))\n \"\"\"\n print(actor.summary())\n\n action_input = Input(shape=(nb_actions,), name='action_input')\n #observation_input = Input(shape=(1,) + env_shape, name='observation_input')\n #observation_input = picture_tensor\n flattened_observation = fc1\n x = Concatenate()([action_input, flattened_observation])\n x = Dense(32)(x)\n x = Activation('relu')(x)\n x = Dense(32)(x)\n x = Activation('relu')(x)\n x = Dense(32)(x)\n x = Activation('relu')(x)\n x = Dense(1)(x)\n x = Activation('linear')(x)\n critic = Model(inputs=[action_input, picture_tensor], outputs=x)\n print(critic.summary())\n\n\n processor = EmptyProcessor()\n memory = SequentialMemory(limit=100000, window_length=1)\n random_process = OrnsteinUhlenbeckProcess(theta=.15, mu=0., sigma=.3, size=nb_actions)\n agent = DDPGAgent(nb_actions=nb_actions, actor=actor, critic=critic, critic_action_input=action_input, memory=memory, nb_steps_warmup_critic=100, nb_steps_warmup_actor=100,random_process=random_process, gamma=.99, target_model_update=1e-3)\n agent.compile(Adam(lr=.0001, clipnorm=0.99, clipvalue=0.5), metrics=['mae'])\n return agent\n","sub_path":"stirring/ddpg_models.py","file_name":"ddpg_models.py","file_ext":"py","file_size_in_byte":3689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"480021864","text":"from pymongo import MongoClient\nimport redis\nfrom selenium import webdriver\n\n\nclass FeiPinW(object):\n\n def __init__(self):\n self.base_url = \"http://www.zgfp.com/search/searchcomp.aspx?page={}&ChannelId=20&cid=0&k=&w=&e=1&d=&a=\"\n # chrome_options = webdriver.ChromeOptions()\n # chrome_options.add_argument('--headless')\n # self.driver = webdriver.Chrome(chrome_options=chrome_options)\n self.driver = webdriver.Firefox()\n self.Host = \"127.0.0.1\"\n self.Port = 27017\n self.rPort = 6379\n self.conn = MongoClient(host=self.Host, port=self.Port)\n self.rConn = redis.Redis(host=self.Host, port=self.rPort)\n\n def parse_page(self):\n \"\"\"\n 解析数据\n :return:\n \"\"\"\n company_list = self.driver.find_elements_by_xpath('//*[@id=\"plList\"]/div/table//tr/td[3]/a')\n contact_list = self.driver.find_elements_by_xpath('//*[@id=\"plList\"]/div/table//tr/td[4]')\n number_list = self.driver.find_elements_by_xpath('//*[@id=\"plList\"]/div/table//tr/td[5]')\n address_list = self.driver.find_elements_by_xpath('//*[@id=\"plList\"]/div/table//tr/td[2]')\n type_list = self.driver.find_elements_by_xpath('//*[@id=\"plList\"]/div/table//tr/td[1]')\n item_list = list()\n length = len(company_list)\n for i in range(length):\n try:\n item = dict()\n item['company'] = company_list[i].text\n item['contact'] = contact_list[i].text\n item['number'] = number_list[i].text\n item['address'] = address_list[i].text\n item['type'] = type_list[i].text\n item_list.append(item)\n except Exception as e:\n print(e)\n pass\n return item_list\n\n def save_data(self, data):\n \"\"\"\n 保存数据\n :param data:\n :return:\n \"\"\"\n try:\n db = self.conn.FeiPinW\n col = db.FP\n col.insert(data)\n print(data)\n except Exception as e:\n print(e)\n\n def run(self):\n for i in range(1, 2342):\n url = self.base_url.format(i)\n self.driver.get(url)\n self.driver.implicitly_wait(6)\n data_list = self.parse_page()\n for data in data_list:\n self.save_data(data)\n\n\nif __name__ == '__main__':\n FW = FeiPinW()\n FW.run()","sub_path":"August/FeiPin/FeiPinWang.py","file_name":"FeiPinWang.py","file_ext":"py","file_size_in_byte":2443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"476323194","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# Author: vita\nfrom django.shortcuts import HttpResponse\nfrom django.db.models import Q\nfrom urllib.parse import unquote\nfrom random import choice\nimport string\nimport csv\nimport codecs\nfrom web.models import *\nfrom web.page import my_page\nfrom CMDB.settings import HOST_LOGIN_USER_PASSWORD_LENGTH\n\n\ndef get_label(request):\n \"\"\"\n base中左侧导航处的菜单\n :param request:\n :return:\n \"\"\"\n # 获取当前用户的email\n email = request.user.email\n # 查找当前用户的左侧菜单和URL信息\n # 由于用户和角色是多对多的关系，可能出现下面的情况，但是字典是不重复的，所以自动去重了\n # role1 pa_menu1 child_menu1 url1\n # role2 pa_menu1 child_menu1 url1\n if request.user.is_admin:\n menu_set_list = Menu.objects.all()\n else:\n menu_set_list = Menu.objects.filter(menus_role__users__email=email).all().distinct()\n left_label_dic = {}\n\n for menu_obj in menu_set_list:\n left_label_dic[menu_obj.parent_menu_name] = {}\n for menu_obj in menu_set_list:\n left_label_dic[menu_obj.parent_menu_name][menu_obj.child_menu_name] = menu_obj.url\n\n return left_label_dic\n\n\ndef export(filename, export_datas, header):\n response = HttpResponse(content_type='text/csv')\n response['Content-Disposition'] = 'attachment; filename=\"%s\"' % filename\n response.write(codecs.BOM_UTF8)\n writer = csv.writer(\n response,\n dialect='excel',\n quoting=csv.QUOTE_MINIMAL)\n\n writer.writerow(header)\n\n for data in export_datas:\n writer.writerow(data)\n return response\n\n\ndef return_show_data(request, data_obj_set, *args):\n \"\"\"\n 传入模糊查询的字段，进行查询\n 由于多处使用，所以就封装起来了\n :param request:\n :param data_obj_set:\n :param args:\n :return:\n \"\"\"\n if not request.COOKIES.get(request.path.replace(\"/\", \"\") + \"data_nums_per_page\"):\n # 初次访问，还没有设置COOKIE，所以我们设置一个默认值\n request.COOKIES[request.path.replace(\"/\", \"\") + \"data_nums_per_page\"] = 10\n # print(\"3333333333333333333\",request.COOKIES.get(request.path.replace(\"/\", \"\")+\"data_nums_per_page\"))\n filter_value = \"\"\n if len(args) != 0:\n for index, value in enumerate(args):\n\n filter_value += 'Q(%s__contains=unquote(search_val, \"utf-8\"))' % value\n if not index == len(args)-1:\n filter_value += \"|\"\n\n if request.COOKIES.get(request.path.replace(\"/\", \"\") + \"search\"):\n search_val = request.COOKIES.get(request.path.replace(\"/\", \"\") + \"search\").strip()\n\n data_obj_set = data_obj_set.filter(eval(filter_value))\n data_page_info = my_page(data_obj_set, request.GET.get(\"page_num\", 1),\n int(request.COOKIES.get(request.path.replace(\"/\", \"\") + \"data_nums_per_page\")))\n return data_page_info\n\n\ndef host_login_user_password(length=HOST_LOGIN_USER_PASSWORD_LENGTH, chars=string.ascii_letters + string.digits+\"!@#$%&*\"):\n \"\"\"\n 简短地生成随机密码，包括大小写字母、数字、特殊字符，可以指定密码长度\n :param length:\n :param chars:\n :return:\n \"\"\"\n return ''.join([choice(chars) for i in range(length)])","sub_path":"CMDB/web/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"181115893","text":"import sys\n\nfrom paka.cmark._cmark import ffi, lib\n\n\n_PY2 = sys.version_info.major == 2\n\n\ndef get_version():\n result = ffi.string(lib.cmark_version_string())\n if _PY2: # pragma: no cover\n return result\n return result.decode(\"ascii\")\n\n\ndef to_html(text):\n encoding = \"utf-8\"\n text_bytes = text.encode(encoding)\n opts = lib.CMARK_OPT_NOBREAKS\n return ffi.string(\n lib.cmark_markdown_to_html(\n text_bytes, len(text_bytes), opts)).decode(encoding)\n","sub_path":"paka/cmark/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"249623713","text":"from typing import List, Union, Tuple\nfrom copy import deepcopy\n\nfrom bridge_env.deal import Deal\nfrom bridge_env.contract import Contract\nfrom bridge_env.config import *\nfrom bridge_env.bridge_utils import *\nfrom bridge_env.score import precompute_scores_v2\n\nimport random\nimport numpy as np\nimport pandas as pd\n\n# CHEAT-SHEET FOR BIDS\n# 0-34: contract bids [1C, 1D, 1H, 1S, 1NT, ..., 7S, 7NT]\n# 35: pass\n# 36: double\n# 37: redouble\n\n# CHEAT-SHEET FOR keys of self._score_table:\n# (bid_tricks, trump, actual_tricks, vul, double)\n\n\nclass BridgeEnv(object):\n \"\"\"\n This class is intended to replicate bridge bidding and playing\n \"\"\"\n\n def __init__(self,\n bidding_seats=Seat,\n nmc=20,\n debug=False,\n score_mode=\"IMP\"):\n\n # pre-calculating scores\n self._score_table = precompute_scores_v2(full_version=True)\n\n self.cards = deepcopy(FULL_DECK)\n\n # deal is the state\n self.deal = None\n self.one_hot_deal = None\n self.one_hot_known_deal = None\n self.vulnerability = None\n self.vulnerabilities = None\n\n self.auction_history = None\n\n self.history_bid = None\n self.history_play = None\n\n # bidding elimination signals - i.e. which bids are currently not permitted\n self.elim_sig_bid = None\n\n # playing elimination signals - i.e. which cards each player does not possess\n self.elim_sig_play = None\n\n self.n_pass = None\n self.n_double = None\n self.n_redouble = None\n\n self.nmc = nmc # MC times\n self.max_bid = None\n self.contract = Contract()\n self.done_bidding = None\n self.done_playing = None\n\n self.debug = debug\n self.score_mode = score_mode\n self.strain_declarer = {0: {}, 1: {}}\n self.group_declarer = -1\n\n # checking that all the bidding seats submitted are valid\n self.bidding_seats = sorted(list(set(bidding_seats)))\n for seat in self.bidding_seats:\n if seat not in Seat:\n raise Exception(f'seat {seat} is illegal. bidding_seats argument can only contain '\n f'values in {Seat}')\n\n # index of the first bidder; start from the smallest one by default\n self.turn_bid = None\n\n # index of the first player\n self.turn_play = None\n\n # index of playing rounds complete\n self.n_play_actions = None\n self.n_play_turns = None\n\n # vector of tricks for all players\n self.tricks = None\n\n # matrix of current scores\n self.score_play = None\n\n # (re)setting the key environment variables upon initialisation\n self._reset()\n\n def _reset(self) -> None:\n \"\"\"\n An internal method that resets the key variables held by the class for the new game\n\n :return: None\n \"\"\"\n\n self.done_bidding = False\n self.done_playing = False\n\n self.contract.reset()\n\n # resetting bidding history\n # 1C 1D 1H 1S 1N ... 7N (PASS - not considered)\n self.history_bid = np.zeros(36, dtype=np.uint8)\n\n self.history_play = np.full((NUM_PLAYERS, NUM_PLAYS), np.nan)\n\n # generating vulnerabilities\n self.vulnerability = (np.random.rand(NUM_PAIRS) > 0.5).astype(np.uint8)\n self.vulnerabilities = self.vulnerability[[0, 1, 0, 1]]\n\n # resetting auction_history\n self.auction_history = np.zeros(self.auction_history_shape, dtype=np.uint8)\n\n # resetting bidding elimination signal - doubles and redoubles are not allowed at the start\n self.elim_sig_bid = np.ones(AUCTION_SPACE_SIZE, dtype=np.uint8)\n self.elim_sig_bid[REDOUBLE_RANGE] = 0\n\n # resetting the players' hands (aka one_hot_deal)\n self.one_hot_deal = np.zeros((NUM_PLAYERS, NUM_CARDS), dtype=np.uint8)\n self.one_hot_known_deal = np.ones((NUM_PLAYERS, NUM_CARDS), dtype=np.uint8)\n\n # resetting playing elimination signal: without\n self.elim_sig_play = np.ones((NUM_PLAYERS, NUM_CARDS), dtype=np.uint8)\n\n # resetting various counts\n self.max_bid = -1\n\n self.n_pass = 0\n self.n_double = 0\n self.n_redouble = 0\n\n # index of the first bidder; start from the smallest one by default\n # TODO[ス: should I care that the player in the 1st bidding seat always starts bidding?\n # I don't see how this could cause any problems\n self.turn_bid = self.bidding_seats[0]\n self.turn_play = None\n\n self.n_play_actions = 0\n self.n_play_turns = 0\n self.tricks = np.zeros(NUM_PLAYERS, dtype=np.uint8)\n self.score_play = np.zeros((NUM_PLAYS + 1, NUM_PLAYERS))\n\n def _update_elim_sig_play(self) -> None:\n \"\"\"\n An internal method that updates the play elimination signal: i.e. it recalculates the cards\n that each player has\n\n :return: None\n \"\"\"\n\n self.elim_sig_play = self.one_hot_deal\n\n def _increment_n_play_actions(self) -> None:\n \"\"\"\n An internal method that increments self.n_play_actions (the count of the number of actions\n that has been taken), and, if certain conditions are met, updates self.n_play_turns, the\n number of tricks taken and scores\n\n :return: None\n \"\"\"\n\n self.n_play_actions += 1\n\n # incrementing the number of play turns that's been taken if all players have taken turn\n # at playing\n if self.n_play_actions % NUM_PLAYERS == 0:\n # updating the number of tricks taken (and hence scores)\n self._update_tricks_history()\n\n # setting self.done_playing if the maximum number of n_play_actions has been reached\n if self.n_play_actions == NUM_CARDS:\n self.done_playing = True\n\n def _update_tricks_history(self) -> None:\n\n # this is the most recent fully played-out round\n last_play = self.history_play[:, self.n_play_turns]\n\n # getting all the trump cards that were played in the most recent round (players\n # who did not play a trump card will be assigned the value 0)\n trump_cards = last_play * (last_play // 13 == self.contract.suit)\n\n # if the contract's suit is 'No Trump', or if trump_cards is empty\n # TODO[ス I am almost 100% positive that the two conditions are identical (iff), but I will\n # keep it safe and explicit for the time being\n if (self.contract.suit_as_str == 'N') or (all(trump_cards == 0)):\n # getting the index of the player with the highest card in last_play\n winner = last_play.argmax()\n else:\n winner = trump_cards.argmax()\n\n # getting the indices of players on the winning side\n winners = Group2Seat[Seat2Group[winner]]\n\n # incrementing the tricks counter for the winning players\n self.tricks[winners] += 1\n\n # incrementing the number of playing turns\n self.n_play_turns += 1\n\n self._update_score()\n\n def reset(self,\n predeal_seats=None,\n reshuffle: bool = True,\n return_deal: bool = True): # North and South\n \"\"\"\n This method resets the environment - namely:\n - clears bidding history\n - generates new vulnerabilities\n - resets elimination signals (i.e. indicator of actions which cannot be performed)\n -\n\n :param predeal_seats: if not None, allocate cards to those seats. e.g. [0, 1] stands for\n North and East\n :param reshuffle: whether reshuffle the hands for the predeal seats\n :param return_deal: whether the newly generated deal should be returned or not\n\n :return: deal\n \"\"\"\n\n self._reset()\n\n # TODO[ス I've got no idea what the vars and the code below do\n self.strain_declarer = {0: {}, 1: {}}\n self.group_declarer = -1\n if predeal_seats is None:\n predeal_seats = self.bidding_seats\n\n predeal = {}\n random.shuffle(self.cards)\n\n # generate new hands for predeal seats.\n if reshuffle:\n i = 0\n\n for seat in sorted(predeal_seats):\n predeal[seat] = self.cards[i: i+len(Rank)]\n\n # one hot cards\n self.one_hot_deal[seat] = one_hot_holding(predeal[seat])\n i += len(Rank) # shift the index\n self.deal = Deal.prepare(predeal)\n\n if self.debug:\n convert_hands2string(self.deal)\n\n # setting the play elimination signals\n self._update_elim_sig_play()\n\n if return_deal:\n # if not allocated, zero vector is returned.\n return (self.one_hot_deal[self.turn_bid], self.history_bid), \\\n {\"turn\": Seat[self.turn_bid], \"max_bid\": self.max_bid}\n else:\n pass\n\n def _update_score(self) -> None:\n \"\"\"\n This method updates the current scores (from class's members), and puts them\n to the appropriate locations in self.score_play\n\n :return: None\n \"\"\"\n\n # setting new score by iterating over players\n self.score_play[self.n_play_turns, ] = [\n self._score_table[(\n self.contract.level,\n self.contract.suit,\n self.tricks[i],\n self.contract.player_vulnerability[i],\n int(self.contract.double + self.contract.redouble)\n )]\n for i in range(NUM_PLAYERS)\n ]\n\n def step_bid(self, action_bid):\n \"\"\"\n This method performs a single bid action submitted via the 'action' argument, and\n performs an update of self.history_bid and self.auction_history\n\n :param action_bid: bid action\n\n :return: state, reward, done_bidding\n \"\"\"\n if self.done_bidding:\n raise Exception(\"No more actions can be taken\")\n\n # action_bid must be in [0; AUCTION_SPACE_SIZE - 1]\n if action_bid < 0 or action_bid > AUCTION_SPACE_SIZE - 1:\n raise Exception(\"illegal action\")\n\n # what happens when we get a pass\n if action_bid == PASS_IDX:\n\n # we are not allowed to make a double for now\n self.elim_sig_bid[DOUBLE_IDX] = 0\n\n self.history_bid[action_bid] = 1\n\n if self.max_bid == -1:\n self.auction_history[self.n_pass] = 1\n elif self.n_pass < 2:\n self.auction_history[\n 3 + 8*self.max_bid + 3*(self.n_double + self.n_redouble) + self.n_pass + 1] = 1\n\n # incrementing the current number of passes\n self.n_pass += 1\n\n # what happens when we get a contract bid\n elif action_bid < PASS_IDX:\n\n if action_bid <= self.max_bid:\n raise Exception(\"illegal bidding.\")\n\n # resetting n_pass, n_double and n_redouble\n self.n_pass = 0\n self.n_double = 0\n self.n_redouble = 0\n self.max_bid = action_bid\n\n self.history_bid[action_bid] = 1\n self.history_bid[-1] = 0\n self.auction_history[3 + 8*self.max_bid] = 1\n\n # this action and all the actions below can no longer be performed\n self.elim_sig_bid[:(1 + self.max_bid)] = 0\n\n # doubles are now permitted, redoubles are not permitted\n self.elim_sig_bid[DOUBLE_IDX] = 1\n self.elim_sig_bid[REDOUBLE_IDX] = 0\n\n strain = convert_action2strain(action_bid)\n group = Seat2Group[self.turn_bid]\n if self.strain_declarer[group].get(strain, '') == '':\n self.strain_declarer[group][strain] = self.turn_bid # which one\n self.group_declarer = group # which group\n\n # what happens when we get a double\n elif action_bid == DOUBLE_IDX:\n # doubles are not permitted when\n # no contract bids have been made OR\n # a double bid has already been made OR\n # a redouble bid has been made\n if (self.max_bid == -1) or (self.n_double == 1) or (self.n_redouble == 1):\n raise Exception(\"double is not currently allowed\")\n\n self.n_double = 1\n self.elim_sig_bid[DOUBLE_IDX] = 0\n self.elim_sig_bid[REDOUBLE_IDX] = 1\n self.auction_history[3 + 8*self.max_bid + 3] = 1\n\n # what happens when we get a redouble\n elif action_bid == REDOUBLE_IDX:\n # redoubles are not permitted when\n # no contract bids have been made OR\n # a double bid has not been made OR\n # a redouble bid has already been made\n if (self.max_bid == -1) or (self.n_double == 0) or (self.n_redouble == 1):\n raise Exception(\"redouble is not currently allowed\")\n\n self.n_redouble = 1\n self.elim_sig_bid[DOUBLE_IDX] = 0\n self.elim_sig_bid[REDOUBLE_IDX] = 0\n self.auction_history[3 + 8*self.max_bid + 6] = 1\n\n # updating the index of the next bidding player\n self.turn_bid = (self.turn_bid + 1) % len(Seat)\n\n # move to the participant\n # NB: this code is only useful if not all players are bidding (i.e. self.bidding_seats\n # does not contain everybody)\n while True:\n if self.turn_bid not in self.bidding_seats:\n self.turn_bid = (self.turn_bid + 1) % len(Seat)\n self.n_pass += 1\n else:\n break\n\n hand = self.one_hot_deal[self.turn_bid]\n reward = 0\n\n # state is the next bidding player's state\n if (self.n_pass >= 3 and self.max_bid < 0) or self.max_bid == 34:\n\n if self.max_bid < 0:\n raise Exception(\"illegal bidding\")\n # extract the declarer, strain , level\n strain = convert_action2strain(self.max_bid)\n level = convert_action2level(self.max_bid)\n # single thread\n # reward = np.mean(Deal.score_st(dealer=self.deal, level=level, strain=strain, declarer=declarer, tries=self.nmc, mode=self.score_mode))\n # parallel threads\n\n # np.mean is moved to score\n declarer = self.strain_declarer[self.group_declarer][strain] # thise group's first declarer\n\n # TODO[ス: game rewards / scores will no longer be calculated during bidding - the next\n # bit of code needs to be removed\n reward = Deal.score(dealer=self.deal,\n level=level,\n strain=strain,\n declarer=declarer,\n tries=self.nmc,\n mode=self.score_mode)\n self.done_bidding = True\n\n # storing the contract\n self.contract.from_bid(bid=self.max_bid,\n double=(self.n_double > 0),\n redouble=(self.n_redouble > 0))\n\n # setting the index of the first player\n self.turn_play = (self.turn_bid + 1) % len(Seat)\n\n # since bidding is now done, we need to set the initial value of self.score_play\n self._update_score()\n\n # TODO[ス: remove the next lines - this method should no longer return anything\n state = (hand, self.history_bid)\n info = {\"turn\": Seat[self.turn_bid], \"max_bid\": self.max_bid}\n if self.debug:\n log_state(state, reward, self.done_bidding, info)\n\n return state, reward, self.done_bidding, info\n\n # TODO[ス deprecate self.score() method\n def score(self,\n tricks: Union[List, np.array],\n bid: int = None,\n vulnerability: Union[List, np.array] = None) -> np.array:\n \"\"\"\n Calculates the score given the number of tricks, bid and vulnerability\n\n :param tricks: vector of trick counts\n :param bid:\n :param vulnerability:\n\n :return: an np.array of scores for all players\n \"\"\"\n\n # using class's internal values if bid or vulnerability were not submitted\n if bid is None:\n bid = self.max_bid\n if vulnerability is None:\n vulnerability = self.vulnerability\n\n out = np.zeros(NUM_PLAYERS)\n\n return out\n\n def step_play(self,\n player: int,\n action_play: int) -> None:\n \"\"\"\n Performs a playing action:\n - storing the player's action to history_play\n - (if necessary) updating n_play_turns\n - (if necessary) updating scores\n\n :param player: a the index of the player that performs the action\n :param action_play: a [1, 52] np.array with a single one\n\n :return: None\n \"\"\"\n\n # do nothing if we are not done bidding yet or we are done playing\n if (not self.done_bidding) or self.done_playing:\n pass\n\n # exception if the player does not exist\n if player not in Seat:\n raise Exception(f\"Player {player} does not exist\")\n\n # exception if the action is not in the range of [0, 51]\n if action_play not in range(0, NUM_CARDS):\n raise Exception(f\"Action {action_play} is invalid\")\n\n # exception if the player does not have the card (action_play) it wants to play\n if self.elim_sig_play[player, action_play] == 0:\n raise Exception(f\"Player {player} does not posses the card {action_play}\")\n\n # adding the current action to the play history\n self.history_play[player, self.n_play_turns] = action_play\n\n # updating the hand (one_hot_deal)\n self.one_hot_deal[player, action_play] = 0\n self.one_hot_known_deal[[x for x in range(NUM_PLAYERS) if x != player], action_play] = 0\n\n # updating the play elimination signal\n self._update_elim_sig_play()\n\n # this method will update tricks and scores (if it's necessary)\n self._increment_n_play_actions()\n\n # setting the index of the next player\n self.turn_play = (self.turn_play + 1) % len(Seat)\n\n return None\n\n def player_known_deal(self, player: int):\n\n out = self.one_hot_known_deal.copy()\n out[player, :] = self.one_hot_deal[player, :]\n\n idx = np.where(out[player, :] == 1)\n\n for x in range(NUM_PLAYERS):\n if x != player:\n out[x, idx] = 0\n\n return out\n\n @property\n def auction_history_shape(self) -> Tuple:\n \"\"\"\n\n :return: A tuple of the shape of auction history vector\n \"\"\"\n return (AUCTION_HISTORY_SIZE, )\n\n @property\n def deal_shape(self) -> Tuple:\n \"\"\"\n\n :return: A tuple of the shape of the deal (i.e. cards held by all players)\n \"\"\"\n\n return NUM_PLAYERS, NUM_CARDS\n\n @property\n def vuln_shape(self) -> Tuple:\n \"\"\"\n\n :return: A tuple of the shape of the vulnerabilities vector\n \"\"\"\n\n return (NUM_PLAYERS, )\n\n @property\n def belief_shape(self) -> Tuple:\n \"\"\"\n\n :return:\n \"\"\"\n return self.deal_shape\n\n @property\n def bid_policy_shape(self) -> Tuple:\n \"\"\"\n\n :return:\n \"\"\"\n return (AUCTION_SPACE_SIZE, )\n\n @property\n def play_policy_shape(self) -> Tuple:\n \"\"\"\n\n :return:\n \"\"\"\n\n return (NUM_CARDS, )\n\n @property\n def elim_sig_bid_shape(self) -> Tuple:\n \"\"\"\n Shape of elimination signal for bidding actions (bids)\n\n :return: A tuple\n \"\"\"\n return self.bid_policy_shape\n\n @property\n def elim_sig_play_shape(self) -> Tuple:\n \"\"\"\n Shape of elimination signal for playing actions\n\n :return: A tuple\n \"\"\"\n\n return self.play_policy_shape\n","sub_path":"BridgeEnv.py","file_name":"BridgeEnv.py","file_ext":"py","file_size_in_byte":19895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"458626107","text":"import matplotlib.pyplot as plt\nfrom numpy import sum, array\nfrom numpy.random import randint, choice\n\n\n\nclass MonteCarlo(object):\n \"\"\" A simple Monte Carlo implementation \"\"\"\n\n def __init__(self, energy, density, temperature=1, itermax=1000):\n from numpy import any, array\n density = array(density)\n self.itermax = itermax\n\n if temperature == 0:\n raise NotImplementedError(\n \"Zero temperature not implemented\")\n if temperature < 0e0:\n raise ValueError(\n \"Negative temperature makes no sense\")\n\n if len(density) < 2:\n raise ValueError(\"Density is too short\")\n # of the right kind (integer). Unless it is zero length,\n # in which case type does not matter.\n if density.dtype.kind != 'i' and len(density) > 0:\n raise TypeError(\"Density should be an array of *integers*.\")\n # and the right values (positive or null)\n if any(density < 0):\n raise ValueError(\"Density should be an array of\" +\n \"*positive* integers.\")\n if density.ndim != 1:\n raise ValueError(\"Density should be an a *1-dimensional*\" +\n \"array of positive integers.\")\n if sum(density) == 0:\n raise ValueError(\"Density is empty.\")\n\n self.current_energy = energy(density)\n self.temperature = temperature\n self.density = density\n\n def random_direction(self): return choice([-1, 1])\n\n def random_agent(self, density):\n # Particle index\n particle = randint(sum(density))\n current = 0\n for location, n in enumerate(density):\n current += n\n if current > particle:\n break\n return location\n\n def change_density(self, density):\n \"\"\" Move one particle left or right. \"\"\"\n\n location = self.random_agent(density)\n\n # Move direction\n if(density[location]-1 < 0):\n return array(density)\n if location == 0:\n direction = 1\n elif location == len(density) - 1:\n direction = -1\n else:\n direction = self.random_direction()\n\n # Now make change\n result = array(density)\n result[location] -= 1\n result[location + direction] += 1\n return result\n\n def accept_change(self, prior, successor):\n \"\"\" Returns true if should accept change. \"\"\"\n from numpy import exp\n from numpy.random import uniform\n if successor <= prior:\n return True\n else:\n return exp(-(successor - prior) / self.temperature) > uniform()\n\n def step(self):\n iteration = 0\n while iteration < self.itermax:\n new_density = self.change_density(self.density)\n new_energy = energy(new_density)\n\n accept = self.accept_change(self.current_energy, new_energy)\n if accept:\n self.density, self.current_energy = new_density, new_energy\n iteration += 1\n\n return self.current_energy, self.density\n\n\ndef energy(density, coefficient=1):\n \"\"\" Energy associated with the diffusion model\n :Parameters:\n density: array of positive integers\n Number of particles at each position i in the array/geometry\n \"\"\"\n from numpy import array, any, sum\n\n # Make sure input is an array\n density = array(density)\n\n # of the right kind (integer). Unless it is zero length, in which case type does not matter.\n if density.dtype.kind != 'i' and len(density) > 0:\n raise TypeError(\"Density should be an array of *integers*.\")\n # and the right values (positive or null)\n if any(density < 0):\n raise ValueError(\"Density should be an array\" +\n \"of *positive* integers.\")\n if density.ndim != 1:\n raise ValueError(\"Density should be an a *1-dimensional*\" +\n \"array of positive integers.\")\n\n return coefficient * 0.5 * sum(density * (density - 1))\n\nimport sys\nsys.path.append('Diffusion Example')\n\nfrom MonteCarlo import MonteCarlo, energy\n\nimport numpy as np\nimport numpy.random as random\nfrom matplotlib import animation\nfrom matplotlib import pyplot as plt \nfrom IPython.display import HTML\n\nTemperature = 0.1\ndensity = [np.sin(i) for i in np.linspace(0.1, 3, 100)]\ndensity = np.array(density)*100\ndensity = density.astype(int)\n\nfig = plt.figure()\nax = plt.axes(xlim=(-1, len(density)), ylim=(0, np.max(density)+1))\nimage = ax.scatter(range(len(density)), density)\n\ntxt_energy = plt.text(0, 100, 'Energy = 0')\nplt.xlabel('Temperature = 0.1')\nplt.ylabel('Energy Density')\n\n\nmc = MonteCarlo(energy, density, temperature=Temperature)\n\n\ndef simulate(step):\n energy, density = mc.step()\n image.set_offsets(np.vstack((range(len(density)), density)).T)\n txt_energy.set_text('Energy = {}'.format(energy))\n\n\nanim = animation.FuncAnimation(fig, simulate, frames=200,\n interval=50)\nHTML(anim.to_jshtml())\n","sub_path":"week06/MonteCarlo.py","file_name":"MonteCarlo.py","file_ext":"py","file_size_in_byte":5059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"522023025","text":"from typing import Any, Dict\n\nfrom fastapi import HTTPException, Request\nfrom fastapi.templating import Jinja2Templates\n\nfrom nereid.core.config import nereid_path\nfrom nereid.core.context import get_request_context, validate_request_context\n\ntemplates = Jinja2Templates(directory=f\"{nereid_path}/static/templates\")\n\n\ndef get_valid_context(\n request: Request,\n state: str = \"state\",\n region: str = \"region\",\n) -> Dict[str, Any]:\n \"\"\"This will redirect the context data directory according to the application instantiation.\"\"\"\n datadir = request.app._settings.DATA_DIRECTORY\n context: Dict[str, Any] = request.app._settings.APP_CONTEXT\n\n context = get_request_context(state, region, datadir=datadir, context=context)\n isvalid, msg = validate_request_context(context)\n if not isvalid:\n raise HTTPException(status_code=400, detail=msg)\n return context\n","sub_path":"nereid/nereid/api/api_v1/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"71562357","text":"import nssPCA.data as data\nimport seqdata.data as sdata\nimport seqdata.plot as plot\nfrom tools.clustering import kmeans, dbscan, gaussianmixture\n\nimport pandas as pd\nfrom sklearn.manifold import MDS\n\n\nN_COMPONENTS = 2\nN_CLUSTERS = 6\n\nmatrix = sdata.read_distance_matrix('/home/mateusz/pca/test/nowy_2/dist.mat')\n#matrix = pd.read_csv('/home/mateusz/pca/test/nowy_2/dist2.mat', sep=\",\", index_col=0, header=None)\n#matrix = pd.read_csv('/home/mateusz/pca/test/aaa/out.mat', sep=\",\", index_col=0, header=None)\n#matrix = pd.read_csv('/home/mateusz/pca/test/przyciete_15.01/dist.mat', sep=\",\", index_col=0, header=None)\n#matrix = pd.read_csv('/home/mateusz/pca/test/full_15.01/dist.mat', sep=\",\", index_col=0, header=None)\nmatrix.columns = matrix.index.values\nnum_matrix = data.generate(matrix).values\n\nemb = MDS(n_components=N_COMPONENTS, dissimilarity='precomputed', random_state=1)\ntransformed = emb.fit_transform(matrix)\n\nresult = pd.DataFrame(transformed,\n index=matrix.index,\n columns=[\"\".join((\"PC\", str(i + 1))) for i in range(1, N_COMPONENTS+1)])\n\ny_pred_kmeans, _ = kmeans(transformed, N_CLUSTERS)\ny_pred_gausmix, _ = gaussianmixture(transformed, N_CLUSTERS)\ny_pred_dbscan, _ = dbscan(transformed, 0.1, 3)\n#y_pred_dbscan, _ = dbscan(transformed, 0.4, 3)\n#y_pred_dbscan, _ = dbscan(transformed, 0.25, 2)\n\n\n\n\nif N_COMPONENTS == 3:\n plot.plot3d(transformed, y_pred_kmeans, matrix.index, \"Kmeans\")\n plot.plot3d(transformed, y_pred_dbscan, matrix.index, \"DBscan\")\n plot.plot3d(transformed, y_pred_gausmix, matrix.index, \"Gaussian Mixture\")\nelse:\n plot.plot2d_subplots(transformed, y_pred_kmeans, matrix.index, \"Kmeans\")\n plot.plot2d_subplots(transformed, y_pred_dbscan, matrix.index, \"DBscan\")\n plot.plot2d_subplots(transformed, y_pred_gausmix, matrix.index, \"Gaussian Mixture\")\n","sub_path":"distance_matrix/dist_mds.py","file_name":"dist_mds.py","file_ext":"py","file_size_in_byte":1843,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"505853487","text":"import InStockPy\n\n#ceating class instance\na = InStockPy.main()\n\n#debug mode true\na.debug(True)\n\n#headless mode true\na.headless(True)\n\n#def in stock keywords\na.defInStockKeywords([\"delivery\",\"in stock\",\"add to cart\"])\n\n#def out of stock keywords\na.defOutStockKeywords([\"sold out\",\"out of stock\",\"coming soon\"])\n\n#play audio when in stock\na.audioCue(True,'default')\n\nwhile True:\n #use proxies true\n a.useProxy(True, r\"C:\\Users\\proxies.txt\",1)\n\n #check if in stock and print result\n print(a.checkInStock(r'https://link-to-product.com'))","sub_path":"InStockPy/exampleLoop.py","file_name":"exampleLoop.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"13828395","text":"from urllib.request import urlopen\nfrom urllib.parse import quote\nfrom bs4 import BeautifulSoup\nimport time\nimport json\nimport ssl\nimport random\n\ndo_downloads = False\n\ntags = {\n 'detail': 'details', \n 'nature': 'nature',\n 'bird': 'bird', \n 'cat': 'pets',\n 'dog': 'pets',\n 'animal': 'animals',\n 'flower': 'flowers',\n 'beer': '', \n 'park': '', \n 'confine': 'confined', \n 'home': '', \n 'neighbor': '', \n 'stranger': '', \n 'street': '', \n 'path': '', \n 'walk': '', \n 'bike': '', \n 'fear': '', \n 'garden': 'gardening', \n 'food': '', \n 'school': '', \n 'kid': 'kids', \n 'sound': '', \n 'squawking': 'sound',\n 'lilac': 'flowers',\n 'computer': '', \n 'friend': 'friends', \n 'essential': 'essentials', \n 'connect': 'connection', \n 'online': '', \n 'work': '', \n ' tree': 'trees', \n 'safe': 'safety', \n 'butterflies': 'animals', \n 'lorikeets': 'birds', \n 'transit': '', \n 'window': 'windows', \n 'child': 'kids', \n 'explore': '', \n 'time': '', \n 'lockdown': '', \n 'outside': '', \n 'relationship': 'relationships', \n 'appreciate': '', \n 'love': '', \n 'isolation': '', \n 'apart': '',\n 'market': 'shopping',\n 'shop': 'shopping',\n 'imagine': 'imagination',\n 'routine': ''\n}\ncontext = ssl._create_unverified_context()\n\n\nurl = 'https://www.citylab.com/life/2020/04/neighborhood-maps-coronavirus-lockdown-stay-at-home-art/610018/?utm_campaign=socialflow-organic&utm_source=twitter&utm_medium=social&utm_content=citylab'\n\n\n\n\ndef save_to_file_json(data, file_name='data.json', subdirectory='results'):\n f = open(subdirectory + '/' + file_name, 'w')\n f.write(json.dumps(data))\n f.close()\n print('JSON file written to ' + subdirectory + '/' + file_name + '. Go take a look!')\n\ndef get_tags(entry):\n my_tags = []\n text = ' '.join(entry.get('paragraphs')) + entry.get('header') + entry.get('footer')\n for key in tags:\n if text.find(key) != -1:\n val = tags[key]\n if val == '':\n val = key\n my_tags.append(val)\n return my_tags\n \ndef get_location(place):\n # url = 'https://api.opencagedata.com/geocode/v1/json?key=' + api_key + \\\n # '&q=' + quote(place) + '&limit=2'\n url = 'https://nominatim.openstreetmap.org/search?q=' + quote(place) + '&format=jsonv2&addressdetails=1&namedetails=1'\n # print(url)\n # return\n try:\n data = json.loads(urlopen(url, context=context).read())\n r1 = data[0]['address']\n place = {\n 'county': r1.get('county'),\n 'country': r1.get('country'),\n 'state': r1.get('state'),\n 'city': r1.get('city'),\n 'geometry': {'lat': data[0].get('lat'), 'lng': data[0].get('lon')}\n }\n return place\n \n except Exception as e:\n print('location parsing error:', e)\n print(place)\n return None\n\ndef download_images(entry):\n try:\n for key in ['image_source', 'image_source_large', 'image_source_orig']:\n path = entry[key]\n print('Downloading', path, '...')\n file_name = path.split('/')[-1]\n local_path = download_image(path, file_name)\n entry[key + '_local'] = local_path\n # be polite:\n time.sleep(1)\n except Exception:\n print('Error downloading file name')\n\ndef download_image(image_url, file_name):\n # get the image\n response = urlopen(image_url, context=context)\n file_data = response.read()\n\n # write it to a file\n f = open('results/' + file_name, 'wb') #note the 'wb' flag (b is for binary)\n f.write(file_data)\n f.close()\n return 'results/' + file_name\n\nresponse = urlopen(url, context=context)\nhtml = response.read().decode('utf-8', 'ignore')\nsoup = BeautifulSoup(html, features='html.parser')\n\narticle = soup.find('article')\narticle_list = []\ncounter = 1\nstart = False\nentry = {}\nfor element in article.find_all(['p', 'h4', 'hr', 'figure']):\n if element.name == 'hr':\n if start:\n footer = entry['paragraphs'].pop()\n tokens = footer.split(',')\n entry['footer'] = footer\n entry['author'] = tokens[0].replace('—', '')\n entry['place'] = ','.join(tokens[1:])\n entry['location'] = get_location(entry['place'])\n entry['tags'] = get_tags(entry)\n start = True\n entry = {\n 'id': counter,\n 'header': None,\n 'paragraphs': [],\n 'tags': []\n }\n article_list.append(entry)\n counter += 1\n continue\n elif not start:\n continue\n\n if element.name == 'p':\n if len(element.text) > 2:\n entry['paragraphs'].append(element.text)\n elif element.name == 'h4':\n if element.text == 'About the Author':\n break\n entry['header'] = element.text\n elif element.name == 'figure':\n img = element.find('img')\n if img.get('data-srcset'):\n print('plural')\n image_urls = img.get('data-srcset')\n image_url_low_res = image_urls.split(', ')[0].split(' ')[0]\n image_url_high_res = image_urls.split(', ')[-1].split(' ')[0]\n # print(image_url_low_res)\n # print(image_url_high_res)\n else:\n print('singular')\n image_url_low_res = image_url_high_res = img.get('data-src')\n # print(image_url_low_res)\n # break \n entry['image_source'] = image_url_low_res\n entry['image_source_large'] = image_url_high_res\n entry['image_source_orig'] = 'https:' + image_url_high_res.split('/https:')[-1]\n if do_downloads:\n download_images(entry)\n\nfooter = entry['paragraphs'].pop()\nentry['footer'] = footer\ntokens = footer.split(',')\nentry['author'] = tokens[0].replace('—', '')\nentry['place'] = ','.join(tokens[1:])\nentry['location'] = get_location(entry['place'])\nentry['tags'] = get_tags(entry)\n\nsave_to_file_json(article_list)","sub_path":"scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":6043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"470629042","text":"\nfrom gbkfitcloud.client.services.file_service import FileService\nfrom gbkfitcloud.client.services.resource_service import ResourceService\n\n\nclass DatasetsService(ResourceService):\n\n def __init__(self, base_url, **kwargs):\n super().__init__('{}/datasets'.format(base_url), **kwargs)\n self.file_service = FileService(base_url)\n\n def create_one(self, datasets):\n new_datasets = super().create_one(datasets)\n self._upload_files([datasets], [new_datasets])\n return datasets\n\n def create_many(self, datasets_list):\n new_datasets_list = super().create_many(datasets_list)\n self._upload_files(datasets_list, new_datasets_list)\n return new_datasets_list\n\n def _upload_files(self, datasets_list, new_datasets_list):\n for datasets, new_datasets in zip(datasets_list, new_datasets_list):\n for dataset, new_dataset in zip(datasets['info'], new_datasets['info']):\n self.file_service.upload_file(new_dataset['data'], dataset['data'])\n self.file_service.upload_file(new_dataset['error'], dataset['error'])\n self.file_service.upload_file(new_dataset['mask'], dataset['mask'])\n return new_datasets_list\n","sub_path":"gbkfitcloud_client/gbkfitcloud/client/services/datasets_service.py","file_name":"datasets_service.py","file_ext":"py","file_size_in_byte":1224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"} +{"seq_id":"348361311","text":"import os\nimport sys\n\nimport cv2\n\nfrom vision.utils.misc import Timer\nfrom wagon_tracking.detection import WagonDetector\nfrom wagon_tracking.transforms import DistortionRectifier\nfrom wagon_tracking.videostream import VideoFileStream, VideoLiveStream\nfrom wagon_tracking.utils import get_realpath\n\nif len(sys.argv) < 5:\n print(\n 'Usage: python run_ssd_example.py