diff --git "a/3773.jsonl" "b/3773.jsonl" new file mode 100644--- /dev/null +++ "b/3773.jsonl" @@ -0,0 +1,698 @@ +{"seq_id":"536957562","text":"import numpy as np\nimport math\nimport pylab\n'''\nGoal: For each session:\n\t1. plot avg. time2target for All, Sc. Share in pre-blocks (bar +/- s.e.m)\n\t2. plot rew per min for All, Sc. Share\n\t3. plot perc. free choices to each (bar +/- s.e.m)\n\n\tSame axis: \n\t4. plot time course of choices (incl. instructed)\n\t5. plot time course of choices to ang. \n'''\n\ndef main():\n\tfa_dict = []\n\t#key: [[bmi_resetting], [fa_bmi_w_scaled_shared], [free_choice_30_perc.]]\n\tfa_dict.append([[5013], [5014], [5016, 5018, 5019], ['4_24_16']]) #4/24\n\tfa_dict.append([[5025], [5026], [5032, 5033], ['4_25_16']]) #4/25\n\tfa_dict.append([[5081], [5082], [5083, 5084, 5085, 5086, 5087, 5088], ['4_28_16']]) #4/28\n\tfa_dict.append([[5105, 5111], [5106, 5112], [5108, 5109, 5113, 5114], ['4_29_16']]) #4/29\n\tfa_dict.append([[5117], [5118], [5119, 5120, 5121, 5122, 5123], ['5_1_16']]) #5/1\n\tfa_dict.append([[5185], [5186], [5191, 5192, 5193, 5194, 5195, 5196], ['5_6_16']]) #5/6\n\tfa_dict.append([[5199], [5200], [5201, 5202, 5203, 5204], ['5_7_16']]) #5/7\n\tfa_dict.append([[5209], [5210], [5212, 5213], ['5_8_16']]) #5/8\n\n\tfor fff in fa_dict:\n\t\trpm, t2t, spd, ch_tc = plot_single_session_metrics(fff[0], fff[1], fff[2])\n\t\tax1 = plt.subplot2grid((5, 2), (0, 0))\n\n\t\t#RPM\n\t\tax1.bar(0, np.mean(rpm['all']), color='k')\n\t\tax1.bar(1, np.mean(rpm['shar']), color='b')\n\t\tax1.set_title('R. P. M')\n\n\t\t#T2t\n\t\tax2 = plt.subplot2grid((5, 2), (0, 1))\n\t\tax2.bar(0, np.mean(t2t['all'], axis=0)[0], yerr=np.mean(t2t['all'], axis=0)[1], color='k')\n\t\tax2.bar(1, np.mean(t2t['shar'], axis=0)[0], yerr=np.mean(t2t['shar'], axis=0)[1], color='b')\n\t\tax2.set_title('T2T')\n\n\t\tax0 = plt.subplot2grid((5, 2), (1, 1))\n\t\tax0.bar(0, np.mean(spd['all'], axis=0)[0], yerr=np.mean(spd['all'], axis=0)[1], color='k')\n\t\tax0.bar(1, np.mean(spd['shar'], axis=0)[0], yerr=np.mean(spd['shar'], axis=0)[1], color='b')\n\t\tax0.set_title('Speed')\n\n\t\tax3 = plt.subplot2grid((5, 2), (1,0))\n\t\tix_all = np.nonzero(ch_tc['choices']=='free_all')[0]\n\t\tix_shar = np.nonzero(ch_tc['choices']=='free_shar')[0]\n\t\tax3.bar(0, len(ix_all)/float(len(ix_all)+len(ix_shar)), color='k')\n\t\tax3.bar(1, len(ix_shar)/float(len(ix_all)+len(ix_shar)), color='b')\n\t\tax3.set_title('Percent of Free Choices')\t\t\n\n\t\tax4 = plt.subplot2grid((5, 2), (2,0), colspan=2)\n\t\ttry:\n\t\t\tix_i_all = np.nonzero(ch_tc['choices']=='inst_all')[0]\n\t\t\tix_i_shar = np.nonzero(ch_tc['choices']=='inst_shar')[0]\n\t\texcept:\n\t\t\tfff[3][0] = fff[3][0] + '_no_instruc'\n\t\t\tix_i_all = np.array([])\n\t\t\tix_i_shar = np.array([])\n\n\t\tfor i, (ix, y, col) in enumerate(zip((ix_i_all, ix_i_shar, ix_all, ix_shar), [0, 0, 3, 3], ['k', 'b', 'k', 'b'])):\n\t\t\tax4.plot(ch_tc['t'][ix], [y]*len(ix), '.', color=col, markersize=15)\n\t\tax4.plot(ch_tc['t'], np.array([1.5]*len(ch_tc['t'])), 'r--')\n\t\tax4.set_title('Timecourse of Instructed (low) vs. Free (high)')\n\t\tax4.set_ylim([-1, 5])\n\n\t\tax5 = plt.subplot2grid((5, 2), (3,0), colspan=2)\n\n\t\tfor i, (ix, y, col) in enumerate(zip((ix_i_all, ix_i_shar, ix_all, ix_shar), [0, 1, 3, 4], ['k', 'b', 'k', 'b'])):\n\t\t\tax5.plot(ch_tc['t'][ix], ch_tc['choice_loc'][ix], '.', color=col, markersize=15)\n\n\t\tax5.set_title('Chosen target location')\n\n\t\tax6 = plt.subplot2grid((5, 2), (4, 0), colspan=2)\n\t\tfor i, (ix, y, col) in enumerate(zip((ix_i_all, ix_i_shar, ix_all, ix_shar), [0, 1, 3, 4], ['k', 'b', 'k', 'b'])):\n\t\t\tax6.plot(ch_tc['t'][ix], ch_tc['dot_prod'][ix], '.', color=col, markersize=15)\t\n\t\tax6.set_title('Norm Dot Prod b/w Prev. Vel. and Chosen Targ')\t\n\t\tplt.tight_layout()\n\n\t\tplt.savefig('test_'+fff[3][0]+'.png')\n\n\n\ndef plot_single_session_metrics(all_te, fa_te, choice_te):\n\t'''\n\tall inputs are lists of task entry numbers\n\t'''\n\trpm = dict(all=[], shar=[])\n\tt2t = dict(all=[], shar=[])\n\tspd = dict(all=[], shar=[])\n\tch_tc = dict(t=[], choices=[], choice_loc=[], task_entry=[], dot_prod=[])\n\n\t#Get rew_per_min and t2t for bmi\n\tfor a in all_te:\n\t\tte = dbfn.TaskEntry(a)\n\t\trec = te.record.offline_report()\n\t\trpm['all'].append(rec['Rewards/min'])\n\t\tt2t_ = t2tcalc(te.hdf)\n\t\tt2t['all'].append([np.mean(t2t_), np.std(t2t_)/float(np.sqrt(len(t2t_)))])\n\t\t\n\t\tspd_ = spdcalc(te.hdf)\n\t\tspd['all'].append([np.mean(spd_), np.std(spd_)/float(np.sqrt(len(spd_)))])\n\tfor f in fa_te:\n\t\tte = dbfn.TaskEntry(f)\n\t\trec = te.record.offline_report()\n\t\trpm['shar'].append(rec['Rewards/min'])\n\t\tt2t_ = t2tcalc(te.hdf)\n\t\tt2t['shar'].append([np.mean(t2t_), np.std(t2t_)/float(np.sqrt(len(t2t_)))])\n\t\tspd_ = spdcalc(te.hdf)\n\t\tspd['shar'].append([np.mean(spd_), np.std(spd_)/float(np.sqrt(len(spd_)))])\n\n\tn_all = 0\n\tn_shar = 0\n\tt0 = 0\n\n\tfor c in choice_te:\n\t\tte = dbfn.TaskEntry(c)\n\t\thdf = te.hdf\n\n\t\t#Get choice made:\n\t\tchoice_ix = np.array([hdf.root.task_msgs[i+1]['time'] for i, j in enumerate(hdf.root.task_msgs[:])\n\t\t\tif j['msg'] == 'choice_target'])\n\n\t\tfree_ix = np.array([(i, j) for j, i in enumerate(choice_ix) if hdf.root.task[i]['trial_type'] == 'Free'])\n\t\tinst_ix = np.array([(i, j) for j, i in enumerate(choice_ix) if hdf.root.task[i]['trial_type'] == 'Instructed'])\n\n\t\tif len(free_ix) > 0:\n\t\t\tfree_all_ix = np.nonzero(hdf.root.task[free_ix[:,0].astype(int)]['choice_ix'] == 0)[0]\n\t\t\tfree_shar_ix = np.nonzero(hdf.root.task[free_ix[:,0].astype(int)]['choice_ix'] == 1)[0]\n\t\telse:\n\t\t\tfree_all_ix = np.array([])\n\t\t\tfree_shar_ix = np.array([])\n\n\t\tif len(inst_ix) > 0:\n\t\t\tinst_all_ix = np.nonzero(hdf.root.task[inst_ix[:,0].astype(int)]['choice_ix'] == 0)[0]\n\t\t\tinst_shar_ix = np.nonzero(hdf.root.task[inst_ix[:,0].astype(int)]['choice_ix'] == 1)[0]\n\t\telse:\n\t\t\tinst_all_ix = np.array([])\n\t\t\tinst_shar_ix = np.array([])\n\t\t\n\t\tt = (choice_ix/60.) + t0\n\t\tt0 = t[-1]\n\n\t\tchoices = np.chararray((len(choice_ix),), itemsize=10)\n\t\tif len(free_all_ix) > 0:\n\t\t\tchoices[free_ix[free_all_ix, 1]] = 'free_all'\n\t\tif len(free_shar_ix) > 0:\n\t\t\tchoices[free_ix[free_shar_ix, 1]] = 'free_shar'\n\t\tif len(inst_all_ix) > 0:\n\t\t\tchoices[inst_ix[inst_all_ix, 1]] = 'inst_all'\n\t\tif len(inst_shar_ix) > 0:\n\t\t\tchoices[inst_ix[inst_shar_ix, 1]] = 'inst_shar'\n\n\t\tchoice_made = hdf.root.task[choice_ix.astype(int)]['choice_ix'][:,0]\n\t\tloc = hdf.root.task[choice_ix.astype(int)]['choice_targ_loc']\n\t\tang = []\n\t\tdp = []\n\t\tfor i, l in enumerate(loc):\n\t\t\tl_chosen = l[choice_made[i], :]\n\t\t\tang.append(math.atan2(l_chosen[2], l_chosen[0]))\n\n\t\t\t#Prev velocity: \n\t\t\tprev_vel = hdf.root.task[int(choice_ix[i])-60:int(choice_ix[i])]['internal_decoder_state'][:, [3, 5], 0]\n\t\t\tavg_vel = np.mean(prev_vel, axis=0)\n\n\t\t\tdp.append(np.dot(avg_vel, l_chosen[[0, 2]])/(np.linalg.norm(avg_vel)*np.linalg.norm(l_chosen[[0, 2]])))\n\n\t\tch_tc['t'] = np.hstack((ch_tc['t'], t))\n\t\tch_tc['choices'] = np.hstack((ch_tc['choices'], choices))\n\t\tch_tc['choice_loc'] = np.hstack((ch_tc['choice_loc'], np.array(ang))) \n\t\tch_tc['dot_prod'] = np.hstack((ch_tc['dot_prod'], np.array(dp)))\n\t\tch_tc['task_entry'] = np.hstack((ch_tc['task_entry'], np.array([c]*len(choice_ix)))) \n\treturn rpm, t2t, spd, ch_tc\n\ndef t2tcalc(hdf):\n\n\t#Get time to target: \n\t#Get rew_indices:\n\trew_ix = np.nonzero(hdf.root.task_msgs[:]['msg'] == 'reward')[0]\n\tend_trl = rew_ix - 2\n\ttarg_on = rew_ix - 3 \n\n\tt2t = hdf.root.task_msgs[end_trl]['time'] - hdf.root.task_msgs[targ_on]['time']\n\treturn t2t/60. #in seconds\n\ndef spdcalc(hdf):\n\trew_ix = np.nonzero(hdf.root.task_msgs[:]['msg'] == 'reward')[0]\n\tend_trl = hdf.root.task_msgs[rew_ix - 2]['time']\n\ttarg_on = hdf.root.task_msgs[rew_ix - 3]['time']\n\n\tspd = []\n\tfor e, end in enumerate(end_trl):\n\t\tv = hdf.root.task[targ_on[e]:end]['internal_decoder_state'][:, [3, 5], 0]\n\t\tfor iv in v:\n\t\t\tspd.extend([np.linalg.norm(iv)])\n\treturn spd\n\n","sub_path":"online_analysis/free_choice/free_choice_metrics.py","file_name":"free_choice_metrics.py","file_ext":"py","file_size_in_byte":7468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"542111314","text":"from urllib import request\nimport threading\nimport time\n\n\nclass ThreadUsable(threading.Thread):\n def __init__(self, in_queue, out_queue):\n threading.Thread.__init__(self)\n self.in_queue = in_queue\n self.out_queue = out_queue\n\n def run(self):\n while True:\n each = self.in_queue.get()\n print(self.in_queue.qsize())\n if each[2].lower() != 'http' and each[2].lower() != 'https':\n self.in_queue.task_done()\n continue\n proxy = '{}://{}:{}'.format(each[2].lower(), each[0], each[1])\n proxy_support = request.ProxyHandler({each[2].lower(): proxy})\n opener = request.build_opener(proxy_support)\n request.install_opener(opener)\n req = request.Request('https://www.baidu.com')\n req.add_header(\"Accept-Language\", \"zh-cn\")\n req.add_header(\"Content-Type\", \"text/html; charset=gb2312\")\n req.add_header(\"User-Agent\", \"Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.2; .NET CLR 1.1.4322)\")\n try:\n with request.urlopen(req) as f:\n if f.status == 200:\n print(\"{}\\tyes\".format(proxy))\n self.out_queue.put((time.time(), each[0], each[1], each[2]))\n else:\n print(\"{}\\tno\".format(proxy))\n except Exception:\n print(\"{}\\tno\".format(proxy))\n finally:\n self.in_queue.task_done()\n","sub_path":"proxy/threads.py","file_name":"threads.py","file_ext":"py","file_size_in_byte":1523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"639589664","text":"import unittest\nfrom urllib.parse import parse_qs\n\nimport advertools as adv\n\n\nclass AdTestCase(unittest.TestCase):\n \n def test_correct_split(self):\n s = 'this is a short ad'\n s2 = 'string-separated-by-dashes'\n self.assertEqual(adv.ad_from_string(s), ['this is a short ad','', '', '', '', ''])\n self.assertEqual(adv.ad_from_string(s, slots=[5, 5]), ['this', 'is a', 'short ad'])\n self.assertEqual(adv.ad_from_string(s2, sep='-'), ['string separated by dashes', '', '', '', '', ''])\n self.assertEqual(adv.ad_from_string(s2, sep='-', slots=(8, 10, 3, 7)), ['string', 'separated', 'by', 'dashes', ''])\n with self.assertRaises(AttributeError):\n adv.ad_from_string(123)\n\n \n def test_correct_url(self):\n base_url = 'http://www.site.com'\n source = 'the_source'\n medium = 'The medium'\n campaign = 'campaign%%'\n term = 'keyword1 keyword 2'\n content = 'banner_728x90'\n self.assertEqual(parse_qs(adv.url_utm_ga(url=base_url,\n utm_source=source, \n utm_medium=medium,\n utm_campaign=campaign,\n utm_term=term,\n utm_content=content)),\n parse_qs('http://www.site.com?utm_term=keyword1 keyword 2&utm_content=banner_728x90&utm_campaign=campaign%25%25&utm_medium=The++medium&utm_source=the_source'))\n\n def test_ad_create(self):\n template = 'my car is a {}'\n replacements = ['bmw', 'honda', 'lamborghini']\n self.assertEqual(adv.ad_create(template, replacements, 'car', 30),\n ['my car is a bmw', 'my car is a honda', 'my car is a lamborghini'])\n\n self.assertEqual(adv.ad_create(template, replacements, 'car', 20),\n ['my car is a bmw', 'my car is a honda', 'my car is a car'])\n\n with self.assertRaises(ValueError):\n adv.ad_create(template, replacements, 'this is long and should raise an exception', 20)\n \n\n\nif __name__ == '__main__':\n unittest.main()","sub_path":"tests/test_cases.py","file_name":"test_cases.py","file_ext":"py","file_size_in_byte":2181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"240208579","text":"# CCC '14 S1 - Party Invitation\n\nfriends = [int(i) for i in range(1, int(input()) + 1)]\n\nround_actions = [int(input()) for i in range(int(input()))]\n\nfor action in round_actions:\n friends = [element for index, element in enumerate(friends) if (index+1) % action]\n\n[print(friend) for friend in friends]","sub_path":"CCC/2014/2014s1.py","file_name":"2014s1.py","file_ext":"py","file_size_in_byte":304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"168480250","text":"from sys import path\nimport time\npath.append('..\\\\')\nimport pandas\nimport wikiAPI\nfrom change_path import *\n\n\n\"\"\"\nThis code can be used to get all pages for a specific language\n\"\"\"\n\nFILE_FROM = '%s_all_pages.csv'\nFILE_TO = '%s_coordinates.csv'\nLANG = 'uk'\n\n\ndef get_page_coordinates(file_from=None, file_to=None,\n language=LANG, state_file=None):\n\n if not (isinstance(language, str) and len(language) == 2):\n raise ValueError('Language should be a 2 char code: en, uk, etc.')\n if not (file_from is None or isinstance(file_from, str)):\n raise ValueError('File should be file path string.')\n if not (file_to is None or isinstance(file_to, str)):\n raise ValueError('File should be file path string')\n if not (isinstance(state_file, str) or state_file is None):\n raise ValueError('State File should be file path string.')\n file_from = file_from if file_from else FILE_FROM % language\n file_to = file_to if file_to else FILE_TO % language\n\n to_files()\n\n dataframe = pandas.read_csv(file_from, sep='\\t')\n dataframe = dataframe.astype({'pageid': 'int64'})\n pageid = dataframe['pageid']\n\n params = {\n 'prop': 'revisions',\n 'rvprop': 'content',\n 'pageids': pageid\n }\n\n response_table = {\n 'revisions': {\n 'pageid': 'int64',\n 'ns': 'int64',\n 'title': str,\n 'coordinates': str,\n 'lat': 'float64',\n 'long': 'float64'\n }\n }\n\n custom = {\n 'revisions': {\n ('coordinates', 'lat', 'long'): wikiAPI.get_coordinates_data\n }\n }\n\n response = wikiAPI.WikiResponse(response_table,\n custom=custom, file=file_to)\n print(response)\n\n request = wikiAPI.WikiSafeRequestMultiplePage(\n params,\n language,\n on_response=response,\n state_file=state_file\n )\n request.language = language\n request.send_all()\n\n response.save()\n print(response.show())\n\n to_functions()\n\n\ndef timeit(func, *args, **kwargs):\n start = time.time()\n\n func(*args, **kwargs)\n\n print('Time elapsed in this session: ')\n print(time.strftime('%H:%M:%S', time.gmtime(time.time() - start)))\n\n\nif __name__ == '__main__':\n timeit(get_page_coordinates, language='en')\n","sub_path":"modules/wikiAPIFunctions/get_page_coordinates_safe.py","file_name":"get_page_coordinates_safe.py","file_ext":"py","file_size_in_byte":2321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"561713000","text":"'''\nCreated on 10.04.2015\n\n@author: Andreas\n'''\n\nimport codecs\nimport logging\nfrom lexicons.Lexicon import Lexicon\n\nlogger = logging.getLogger(__name__)\n\nclass BingLiuLexicon(Lexicon):\n '''\n classdocs\n '''\n\n def __init__(self, file_path_positive,file_path_negative):\n '''\n Constructor\n '''\n Lexicon.__init__(self,file_path_positive,file_path_negative)\n self.fv = []\n self.no_of_pol_tokens = 0\n self.tweet_score = 0\n self.score_of_last_token_in_tweet = 0\n self.polarity_words = {}\n self.__process_lexicon()\n \n def __process_lexicon(self):\n \"\"\" Positive file \"\"\"\n f = codecs.open(self.file_path, 'r', \"utf-8\")\n for line in f:\n self.polarity_words[line.rstrip()] = 1\n f.close()\n \"\"\" Negative file \"\"\"\n f = codecs.open(self.file_path_2, 'r', \"utf-8\")\n for line in f:\n self.polarity_words[line.rstrip()] = -1\n f.close()\n \n for key in self.polarity_words:\n logger.debug(key+\": \"+str(self.polarity_words[key]))\n \n def get_feature_vector(self,tweet):\n self.fv = []\n self.no_of_pol_tokens = 0\n self.tweet_score = 0\n self.score_of_last_token_in_tweet = 0\n\n tokens = tweet.plain_text().split()\n for token in tokens:\n if token in self.polarity_words:\n self.no_of_pol_tokens += 1\n self.tweet_score += self.polarity_words[token]\n self.score_of_last_token_in_tweet = self.polarity_words[token]\n self.fv.append(self.no_of_pol_tokens)\n self.fv.append(self.tweet_score)\n self.fv.append(self.score_of_last_token_in_tweet)\n# print self.fv\n return self.fv\n \n def get_polarity_of(self,word):\n if word in self.polarity_words:\n return self.polarity_words[word]\n else:\n return 0","sub_path":"src/lexicons/BingLiuLexicon.py","file_name":"BingLiuLexicon.py","file_ext":"py","file_size_in_byte":1728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"525890434","text":"import paho.mqtt.client as mqtt\nfrom encoder import Encoder\nfrom gpiozero import Motor\nfrom simple_pid import PID\nimport threading\nimport time\n\n\nbroker='192.168.0.102'\nclient=mqtt.Client('dataPubr')\n\ndef on_log(client,userdata,level,buf):\n print('log: '+buf)\n\ndef on_connect(client,userdata,flags,rc):\n if rc==0:\n print('Connected OK')\n else:\n print('Not connected : ',rc)\n\n\nclient.on_connect=on_connect\n# client.on_log=on_log\n\nprint('Connecting to broker : ',broker)\nclient.connect(broker)\n\nclient.loop_start()\n\npid_sample_time = 0.1\nencoder_sample_time=60\n\nKP = 0.5\nKI =0\nKD = 0\n\nsp=8\n\nlpid=PID(Kp=KP,Ki=KI,Kd=KD,setpoint=sp,sample_time=pid_sample_time,output_limits=(0,1),auto_mode=True)\nrpid=PID(Kp=KP,Ki=KI,Kd=KD,setpoint=sp,sample_time=pid_sample_time,output_limits=(0,1),auto_mode=True)\n\nmotl_a_pin=4\nmotl_b_pin=12\nmotr_a_pin=1\nmotr_b_pin=15\nservo_pin=14\n\nenc_l_pin=18\nenc_r_pin=0\nenc_l_rate=0\nenc_r_rate=0\n\n\nmotl= Motor(motl_a_pin,motl_b_pin)\nmotr = Motor(motr_a_pin, motr_b_pin)\n\nel=Encoder(enc_l_pin)\ner=Encoder(enc_r_pin)\n\nmotl.forward(1)\n# motr.forward(0.2)\nl_pwr=0\ndef get_encoder_reading():\n global enc_l_rate,enc_r_rate,l_pwr\n while True:\n enc_l_rate=el.value\n enc_r_rate=er.value\n print('L : ',enc_l_rate)\n el.reset()\n er.reset()\n time.sleep(encoder_sample_time)\n \nthreading.Thread(target=get_encoder_reading,daemon=True).start()\n\nwhile True:\n l_pwr=lpid(enc_l_rate)\n # motl.forward(l_pwr)\n client.publish('y',str(enc_l_rate))\n time.sleep(0.1)","sub_path":"misc/zmisc/pid_t/simplepid.py","file_name":"simplepid.py","file_ext":"py","file_size_in_byte":1549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"234049903","text":"#!/usr/bin/env python3\n# -*- coding = utf-8 -*-\n'''\n @Author : RuiJia Li \n @Time : 2022/4/7 23:34\n @File : 换水瓶盖游戏\n @Desc :\n'''\n\n\"\"\"\n 给一个初始饮料瓶数（空的），三个空瓶换一瓶，可以赊一瓶，求最后能喝几瓶\n \n 没有换之前能喝到的水瓶数：nums \n 能喝到的水的瓶数：nums/3 -> 总共能换的次数 -- 本轮可以喝的水\n 当前有的空瓶为：喝了水的水瓶 + 剩余没换的瓶数\n 不断循环迭代，直到 当前剩余空瓶数 <= m-1 循环终止\n 迭代 递归\n\"\"\"\n\ndef solve(nums,m=3):\n \"\"\"\n :param nums: 空瓶个数\n :return: 可以喝的瓶数\n \"\"\"\n cout_1=0\n if numsm-1:\n cout_1+=nums//m\n nums=nums//m+nums%m\n if nums==m-1:\n cout_1+=1\n return cout_1\n\nif __name__ == '__main__':\n ss = solve(15)\n print(ss)\n\n # 10个空瓶 3瓶一换， 能喝的瓶数 10/(3-1)\n # 15个空瓶 3瓶一换， 能喝的瓶数为 15/(3-1)\n # n个空瓶，m瓶一换，能喝的瓶数为 n/(m-1)\n # 主要是 m个空瓶换一个水，也即m-1瓶喝到水 递归 f(n-(m-1)) + 1","sub_path":"SZ_CODE/hot/换水瓶盖游戏.py","file_name":"换水瓶盖游戏.py","file_ext":"py","file_size_in_byte":1187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"433169456","text":"import asyncio\nimport random\n\nimport discord\n\nfrom queuebot.cog import Cog\n\nSTATUSES = [\n (discord.ActivityType.watching, '{user.name}'),\n (discord.ActivityType.watching, '#submissions'),\n (discord.ActivityType.watching, 'blobs as they come in'),\n (discord.ActivityType.playing, 'with blobs'),\n (discord.ActivityType.listening, 'blob radio')\n]\n\n\nclass PlayingStatus(Cog):\n def __init__(self, bot):\n super().__init__(bot)\n\n self.task = bot.loop.create_task(self.rotate_forever())\n\n def __unload(self):\n self.task.cancel()\n\n def generate_activity(self):\n \"\"\"Generate a random :class:`discord.Activity`.\"\"\"\n random_council_member = self.get_random_council()\n activity_type, format_string = random.choice(STATUSES)\n\n return discord.Activity(\n type=activity_type,\n name=format_string.format(user=random_council_member)\n )\n\n def get_random_council(self) -> discord.Member:\n \"\"\"Return a random council member.\"\"\"\n council_role_id = list(self.bot.council_roles)[0]\n council_role = discord.utils.get(self.bot.blob_emoji.roles, id=council_role_id)\n online_council_members = [\n member\n for member in council_role.members\n if member.status is not discord.Status.offline\n ]\n return random.choice(online_council_members)\n\n async def rotate_forever(self):\n await self.bot.wait_until_ready()\n\n while True:\n await self.rotate()\n await asyncio.sleep(60 * 60)\n\n async def rotate(self):\n \"\"\"Change the bot's presence to a random activity.\"\"\"\n activity = self.generate_activity()\n await self.bot.change_presence(activity=activity)\n\n\ndef setup(bot):\n bot.add_cog(PlayingStatus(bot))\n","sub_path":"queuebot/cogs/playing_status.py","file_name":"playing_status.py","file_ext":"py","file_size_in_byte":1807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"370006191","text":"__author__ = 'Elisabetta Ronchieri'\n\nimport commands\nimport os\nfrom tstorm.utils import utils\n\nclass Ldd:\n def __init__(self, library):\n self.library = library\n self.cmd = {\n 'name':'ldd'\n }\n self.otpt = {\n 'status':'',\n 'otpt':''}\n\n def get_command(self):\n a = self.cmd['name']\n a += ' ' + self.library\n return a\n\n def run_command(self):\n a=()\n if utils.cmd_exist(self.cmd['name']):\n a=commands.getstatusoutput(self.get_command())\n return a\n\n def get_output(self):\n a=self.run_command()\n if a[0] == 0:\n self.otpt['status'] = 'PASS'\n self.otpt['otpt'] = a[1]\n else:\n self.otpt['status'] = 'FAILURE'\n\n return self.otpt\n","sub_path":"packages/tstorm/sources/tstorm/utils/library_dependencies.py","file_name":"library_dependencies.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"551357620","text":"#!/usr/bin/python\n\nfor val in range(1,42):\n print(\"Je dois refaire les exercices\")\n\nn=10\nwhile not(n == 0):\n if n == 1:\n print(\"C'est dans % dan je m'en irai j'entends le loup le renard chanter\" %n)\n else:\n print(\"C'est dans % dans je m'en irai j'entends le loup le renard chanter\" %n)\n n = n - 1\n","sub_path":"exercice5_loopdiff_data.py","file_name":"exercice5_loopdiff_data.py","file_ext":"py","file_size_in_byte":323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"500173865","text":"l= int(input(\"Length of rectangle : \"))\r\nw= int(input(\"Width of rectangle : \"))\r\n\r\nclass Rectangle():\r\n def __init__(self, l, w):\r\n self.length = l\r\n self.width = w\r\n\r\n def rectangle_area(self):\r\n return self.length*self.width\r\n\r\nnewRectangle = Rectangle(l, w)\r\nprint(newRectangle.rectangle_area())","sub_path":"Task10.py","file_name":"Task10.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"555823954","text":"from django.test import TestCase\n\nfrom .models import Starship, Crewman, Species, Planet\n\n# Create your tests here.\nclass StarshipModelTests(TestCase):\n\n def test_count_number_of_crew(self):\n # creating planets instances\n vulcan_planet = Planet.objects.create(planet_name='Vulcan')\n earth_planet = Planet.objects.create(planet_name='Earth')\n vulcan_planet.save()\n earth_planet.save()\n \n # creating species instances\n human = Species.objects.create(description='Human', planet_of_origin=earth_planet)\n vulcan = Species.objects.create(description='Vulcan', planet_of_origin=vulcan_planet)\n human.save()\n vulcan.save()\n \n # creating a startship instance\n starship = Starship.objects.create(name='USS Enterprise', record='NCC-1701')\n starship.save()\n\n # creating crewmen instances\n kirk = Crewman.objects.create(name='James Tiberius Kirk', species=human, starship=starship)\n spock = Crewman.objects.create(name='Spock', species=vulcan, starship=starship)\n scotty = Crewman.objects.create(name='Montgomery Scott', species=human, starship=starship)\n kirk.save()\n spock.save()\n scotty.save()\n\n self.assertIs(starship.count_number_of_crew(), 3)\n\n","sub_path":"starfleet/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"271432471","text":"#!/usr/bin/python\r\n# -*- coding: utf-8 -*-\r\n\r\n\"\"\"\r\nAnsible module to manage A10 Networks objects\r\n(c) 2016, Fadi Hafez \r\n\r\nThis file is part of Ansible\r\n\r\nAnsible is free software: you can redistribute it and/or modify\r\nit under the terms of the GNU General Public License as published by\r\nthe Free Software Foundation, either version 3 of the License, or\r\n(at your option) any later version.\r\n\r\nAnsible is distributed in the hope that it will be useful,\r\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\r\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\r\nGNU General Public License for more details.\r\n\r\nYou should have received a copy of the GNU General Public License\r\nalong with Ansible. If not, see .\r\n\"\"\"\r\n\r\nDOCUMENTATION = '''\r\n---\r\nmodule: a10_acl\r\nversion_added: 1.8\r\nshort_description: Manage A10 Networks AX/SoftAX/Thunder/vThunder devices\r\ndescription:\r\n - Manage extended acl objects on A10 Networks devices via aXAPI\r\nauthor: Fadi Hafez using works of Mischa Peters\r\nnotes:\r\n - Requires A10 Networks aXAPI 2.1\r\noptions:\r\n host:\r\n description:\r\n - hostname or ip of your A10 Networks device\r\n required: true\r\n username:\r\n description:\r\n - admin account of your A10 Networks device\r\n required: true\r\n aliases: ['user', 'admin']\r\n password:\r\n description:\r\n - admin password of your A10 Networks device\r\n required: true\r\n aliases: ['pass', 'pwd']\r\n partition:\r\n description:\r\n - L3V partition to add the ACL to. Will be added to 'shared' if not specified\r\n required: false\r\n default: null\r\n choices: []\r\n state:\r\n description:\r\n - create, update or remove acl\r\n required: false\r\n default: present\r\n choices: ['present', 'absent']\r\n acl_id:\r\n description:\r\n - acl ID (100 - 199)\r\n required: true\r\n aliases: ['acl']\r\n remark_list:\r\n description:\r\n - List of remarks\r\n Must contain a list of seq_num and remark_string pairs\r\n required: false\r\n default: null\r\n aliases: ['rem']\r\n item_list:\r\n description:\r\n - List of extended ACL items\r\n Must contain seq_num (range 1 - 8192)\r\n Can contain \r\n - action (0=deny,1=permit,2=l3-vlan-fwd-disable, default:0), \r\n log (0=deny,1=permit,2=only log transparent sessions, default:0),\r\n protocol (0=icmp,1=ip,2=tcp,3=udp, default:0)\r\n src_ip, \r\n src_mask,\r\n dst_ip,\r\n dst_mask,\r\n vlan_id,\r\n src_port_start,\r\n src_port_end,\r\n dst_port_start,\r\n dst_port_end\r\n required: true\r\n aliases: ['items']\r\n write_config:\r\n description:\r\n - If C(yes), any changes will cause a write of the running configuration\r\n to non-volatile memory. This will save I(all) configuration changes,\r\n including those that may have been made manually or through other modules,\r\n so care should be taken when specifying C(yes).\r\n required: false\r\n version_added: 2.2\r\n default: \"no\"\r\n choices: [\"yes\", \"no\"]\r\n validate_certs:\r\n description:\r\n - If C(no), SSL certificates will not be validated. This should only be used\r\n on personally controlled devices using self-signed certificates.\r\n required: false\r\n version_added: 2.2\r\n default: 'yes'\r\n choices: ['yes', 'no']\r\n\r\n'''\r\n\r\nEXAMPLES = '''\r\n# Create a new acl\r\n- a10_acl: \r\n host: a10.mydomain.com\r\n username: myadmin\r\n password: mypassword\r\n acl_id: 100\r\n remark_list:\r\n - seq_num: 12\r\n remark_string: \"something to allow\"\r\n - seq_num: 16\r\n remark_string: \"something to block\"\r\n acl_item_list:\r\n - seq_num: 1\r\n action: 1\r\n log: 1\r\n protocol: 2\r\n src_ip: \"0.0.0.0\"\r\n src_mask: \"255.255.255.255\"\r\n dst_ip: \"0.0.0.0\"\r\n dst_mask: \"255.255.255.255\"\r\n vlan_id: 0\r\n\r\n'''\r\n\r\nVALID_ACL_REM_FIELDS = ['seq_num', 'remark_string']\r\nVALID_ACL_LIST_FIELDS = ['seq_num','action','log','protocol','src_ip','src_mask','dst_ip','dst_mask','vlan_id','name','dst_port_start','dst_port_end','src_port_start','src_port_end']\r\n\r\ndef validate_keys(module, rem_or_list, keys):\r\n if (rem_or_list == 'rem'):\r\n VALID_FIELDS = VALID_ACL_REM_FIELDS\r\n else:\r\n VALID_FIELDS = VALID_ACL_LIST_FIELDS\r\n\r\n for item in keys:\r\n for key in item:\r\n if key not in VALID_FIELDS:\r\n module.fail_json(msg=\"invalid field (%s), must be one of: %s\" % (key, ','.join(VALID_FIELDS)))\r\n\r\n\r\ndef main():\r\n argument_spec = a10_argument_spec()\r\n argument_spec.update(url_argument_spec())\r\n argument_spec.update(\r\n dict(\r\n state=dict(type='str', default='present', choices=['present', 'absent']),\r\n partition=dict(type='str', aliases=['partition','part'], required=False),\r\n acl_id=dict(type='int', aliases=['id'], required=True),\r\n remark_list=dict(type='list', aliases=['rem'], default=[]),\r\n acl_item_list=dict(type='list', aliases=['acl'], default=[]),\r\n )\r\n )\r\n\r\n module = AnsibleModule(\r\n argument_spec=argument_spec,\r\n supports_check_mode=False\r\n )\r\n\r\n host = module.params['host']\r\n username = module.params['username']\r\n password = module.params['password']\r\n part = module.params['partition']\r\n state = module.params['state']\r\n write_config = module.params['write_config']\r\n acl_id = module.params['acl_id']\r\n acl_remarks = module.params['remark_list']\r\n acl_items = module.params['acl_item_list']\r\n\r\n if acl_id is None:\r\n module.fail_json(msg='acl id is required')\r\n\r\n axapi_base_url = 'https://%s/services/rest/V2.1/?format=json' % host\r\n session_url = axapi_authenticate(module, axapi_base_url, username, password)\r\n\r\n # change partitions if we need to\r\n if part:\r\n result = axapi_call(module, session_url + '&method=system.partition.active', json.dumps({'name': part}))\r\n if (result['response']['status'] == 'fail'):\r\n module.fail_json(msg=result['response']['err']['msg'])\r\n\r\n # validate the ports data structure\r\n validate_keys(module, 'rem', acl_remarks)\r\n validate_keys(module, 'items', acl_items)\r\n\r\n json_post = {\r\n 'ext_acl': {\r\n 'id': acl_id,\r\n 'acl_item_list': acl_items,\r\n }\r\n }\r\n\r\n if acl_remarks and len(acl_remarks) > 0:\r\n json_post['ext_acl']['remark_list'] = acl_remarks\r\n\r\n acl_data = axapi_call(module, session_url + '&method=network.acl.ext.search', json.dumps({'id': acl_id}))\r\n acl_exists = not axapi_failure(acl_data)\r\n\r\n changed = False\r\n if state == 'present':\r\n\r\n if not acl_exists:\r\n result = axapi_call(module, session_url + '&method=network.acl.ext.create', json.dumps(json_post))\r\n if axapi_failure(result):\r\n module.fail_json(msg=\"failed to create the acl: %s\" % result['response']['err']['msg'])\r\n else:\r\n result = axapi_call(module, session_url + '&method=network.acl.ext.update', json.dumps(json_post))\r\n if axapi_failure(result):\r\n module.fail_json(msg=\"failed to create the acl: %s\" % result['response']['err']['msg'])\r\n\r\n changed = True\r\n\r\n elif state == 'absent':\r\n if acl_exists:\r\n result = axapi_call(module, session_url + '&method=network.acl.ext.delete', json.dumps({'id': acl_id}))\r\n changed = True\r\n else:\r\n result = dict(msg=\"the acl was not present\")\r\n\r\n # if the config has changed, save the config unless otherwise requested\r\n if changed and write_config:\r\n write_result = axapi_call(module, session_url + '&method=system.action.write_memory')\r\n if axapi_failure(write_result):\r\n module.fail_json(msg=\"failed to save the configuration: %s\" % write_result['response']['err']['msg'])\r\n\r\n # log out of the session nicely and exit\r\n axapi_call(module, session_url + '&method=session.close')\r\n module.exit_json(changed=changed, content=result)\r\n\r\n# standard ansible module imports\r\nfrom ansible.module_utils.basic import *\r\nfrom ansible.module_utils.urls import *\r\nfrom ansible.module_utils.a10 import *\r\n\r\nif __name__ == '__main__':\r\n main()\r\n","sub_path":"library/a10_acl_v2.py","file_name":"a10_acl_v2.py","file_ext":"py","file_size_in_byte":8343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"195320860","text":"\"\"\"\nhttps://leetcode.com/submissions/detail/20786527/\n\n\r\nGiven an input string, reverse the string word by word.\r\n\r\n\r\n\r\nFor example,\r\nGiven s = \"the sky is blue\",\r\nreturn \"blue is sky the\".\r\n\r\n\r\n\r\nUpdate (2015-02-12):\r\nFor C programmers: Try to solve it in-place in O(1) space.\r\n\r\n\r\nclick to show clarification.\r\n\r\nClarification:\r\n\r\n\r\n\r\nWhat constitutes a word?\r\nA sequence of non-space characters constitutes a word.\r\nCould the input string contain leading or trailing spaces?\r\nYes. However, your reversed string should not contain leading or trailing spaces.\r\nHow about multiple spaces between two words?\r\nReduce them to a single space in the reversed string.\r\n\r\n\r\n\"\"\"\n\n\nclass Solution:\r\n # @param s, a string\r\n # @return a string\r\n def reverseWords(self, s):\r\n s = (' ').join(x for x in s.split())\r\n a = s.split(' ')\r\n thelen = len(a)\r\n b = ''\r\n for i in range(thelen):\r\n b +=a[-1*(i+1)]\r\n if i != thelen -1:\r\n b += ' ' \r\n return b\r\n \r\n ","sub_path":"reverse-words-in-a-string.py","file_name":"reverse-words-in-a-string.py","file_ext":"py","file_size_in_byte":1042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"85978262","text":"# test_misc.py\n# Copyright (c) 2013-2016 Pablo Acosta-Serafini\n# See LICENSE for details\n# pylint: disable=C0103,C0111,C0302,E0611,E1101,E1129,F0401,R0915,W0621\n\n# Standard library imports\nfrom __future__ import print_function\nfrom datetime import datetime\nimport inspect\nimport os\nimport platform\nimport re\nimport struct\nimport sys\nimport time\nfrom fractions import Fraction\nif sys.hexversion >= 0x03000000:\n import unittest.mock as mock\n# PyPI imports\nfrom numpy import array\nimport pytest\nif sys.hexversion < 0x03000000:\n import mock\n# Putil imports\nimport putil.misc\nfrom putil.test import AE, AI, GET_EXMSG\nif sys.hexversion < 0x03000000:\n from putil.compat2 import _unicode_to_ascii, _write\nelse:\n from putil.compat3 import _unicode_to_ascii, _write\n\n\n###\n# Test functions\n###\ndef test_ignored():\n \"\"\" Test ignored context manager behavior \"\"\"\n with putil.misc.TmpFile() as fname:\n with open(fname, 'w') as output_obj:\n output_obj.write('This is a test file')\n assert os.path.exists(fname)\n with putil.misc.ignored(OSError):\n os.remove(fname)\n assert not os.path.exists(fname)\n with putil.misc.ignored(OSError):\n os.remove('_some_file_')\n with pytest.raises(OSError) as excinfo:\n with putil.misc.ignored(RuntimeError):\n os.remove('_some_file_')\n\n assert excinfo.value.strerror == (\n 'The system cannot find the file specified'\n if platform.system().lower() == 'windows' else\n 'No such file or directory'\n )\n assert excinfo.value.filename == '_some_file_'\n assert excinfo.value.errno == 2\n\n\ndef test_timer(capsys):\n \"\"\" Test Timer context manager behavior \"\"\"\n # Test argument validation\n with pytest.raises(RuntimeError) as excinfo:\n with putil.misc.Timer(5):\n pass\n assert GET_EXMSG(excinfo) == 'Argument `verbose` is not valid'\n # Test that exceptions within the with statement are re-raised\n with pytest.raises(RuntimeError) as excinfo:\n with putil.misc.Timer():\n raise RuntimeError('Error in code')\n assert GET_EXMSG(excinfo) == 'Error in code'\n # Test normal operation\n with putil.misc.Timer() as tobj:\n time.sleep(0.5)\n assert isinstance(tobj.elapsed_time, float) and (tobj.elapsed_time > 0)\n tregexp = re.compile(r'Elapsed time: [\\d|\\.]+\\[msec\\]')\n with putil.misc.Timer(verbose=True) as tobj:\n time.sleep(0.5)\n out, _ = capsys.readouterr()\n assert tregexp.match(out.rstrip())\n\n\ndef test_tmp_file():\n \"\"\" Test TmpFile context manager behavior \"\"\"\n def write_data(file_handle):\n _write(file_handle, 'Hello world!')\n # Test argument validation\n with pytest.raises(RuntimeError) as excinfo:\n with putil.misc.TmpFile(5) as fname:\n pass\n assert GET_EXMSG(excinfo) == 'Argument `fpointer` is not valid'\n # Test behavior when no function pointer is given\n with putil.misc.TmpFile() as fname:\n assert isinstance(fname, str) and (len(fname) > 0)\n assert os.path.exists(fname)\n assert not os.path.exists(fname)\n # Test that exceptions within the with statement are re-raised\n with pytest.raises(OSError) as excinfo:\n with putil.misc.TmpFile(write_data) as fname:\n raise OSError('No data')\n assert GET_EXMSG(excinfo) == 'No data'\n assert not os.path.exists(fname)\n # Test behaviour under \"normal\" circumstances\n with putil.misc.TmpFile(write_data) as fname:\n with open(fname, 'r') as fobj:\n line = fobj.readlines()\n assert line == ['Hello world!']\n assert os.path.exists(fname)\n assert not os.path.exists(fname)\n\n\ndef test_binary_string_to_octal_string():\n \"\"\" Test binary_string_to_octal_string function behavior \"\"\"\n obj = putil.misc.binary_string_to_octal_string\n if sys.hexversion < 0x03000000:\n ref = (\n '\\\\1\\\\0\\\\2\\\\0\\\\3\\\\0\\\\4\\\\0\\\\5\\\\0\\\\6\\\\0\\\\a\\\\0'\n '\\\\b\\\\0\\\\t\\\\0\\\\n\\\\0\\\\v\\\\0\\\\f\\\\0\\\\r\\\\0\\\\16\\\\0'\n )\n actual = obj(''.join([struct.pack('h', num) for num in range(1, 15)]))\n assert ref == actual\n else:\n ref = (\n r'\\o1\\0\\o2\\0\\o3\\0\\o4\\0\\o5\\0\\o6\\0\\a\\0'\n r'\\b\\0\\t\\0\\n\\0\\v\\0\\f\\0\\r\\0\\o16\\0'\n )\n code = lambda x: struct.pack('h', x).decode('ascii')\n actual = obj(''.join([code(num) for num in range(1, 15)]))\n assert ref == actual\n\n\ndef test_char_string_to_decimal():\n \"\"\" Test char_string_to_decimal_string function \"\"\"\n ref = '72 101 108 108 111 32 119 111 114 108 100 33'\n assert putil.misc.char_to_decimal('Hello world!') == ref\n\n\ndef test_elapsed_time_string():\n \"\"\" Test elapsed_time_string function behavior \"\"\"\n obj = putil.misc.elapsed_time_string\n assert obj(datetime(2015, 1, 1), datetime(2015, 1, 1)) == 'None'\n AE(\n obj, RuntimeError, 'Invalid time delta specification',\n start_time=datetime(2015, 2, 1), stop_time=datetime(2015, 1, 1)\n )\n items = [\n ((2014, 1, 1), (2015, 1, 1), '1 year'),\n ((2014, 1, 1), (2016, 1, 1), '2 years'),\n ((2014, 1, 1), (2014, 1, 31), '1 month'),\n ((2014, 1, 1), (2014, 3, 2), '2 months'),\n ((2014, 1, 1, 10), (2014, 1, 1, 11), '1 hour'),\n ((2014, 1, 1, 10), (2014, 1, 1, 12), '2 hours'),\n ((2014, 1, 1, 1, 10), (2014, 1, 1, 1, 11), '1 minute'),\n ((2014, 1, 1, 1, 10), (2014, 1, 1, 1, 12), '2 minutes'),\n ((2014, 1, 1, 1, 10, 1), (2014, 1, 1, 1, 10, 2), '1 second'),\n ((2014, 1, 1, 1, 10, 1), (2014, 1, 1, 1, 10, 3), '2 seconds'),\n (\n (2014, 1, 1, 1, 10, 1),\n (2015, 1, 1, 1, 10, 2),\n '1 year and 1 second'\n ),\n (\n (2014, 1, 1, 1, 10, 1),\n (2015, 1, 1, 1, 10, 3),\n '1 year and 2 seconds'),\n (\n (2014, 1, 1, 1, 10, 1),\n (2015, 1, 2, 1, 10, 3),\n '1 year, 1 day and 2 seconds'),\n (\n (2014, 1, 1, 1, 10, 1),\n (2015, 1, 3, 1, 10, 3),\n '1 year, 2 days and 2 seconds'\n ),\n ]\n for date1, date2, ref in items:\n assert obj(datetime(*date1), datetime(*date2)) == ref\n\n\ndef test_flatten_list():\n \"\"\" Test flatten_list function behavior \"\"\"\n obj = putil.misc.flatten_list\n assert obj([1, 2, 3]) == [1, 2, 3]\n assert obj([1, [2, 3, 4], 5]) == [1, 2, 3, 4, 5]\n assert obj([1, [2, 3, [4, 5, 6]], 7]) == [1, 2, 3, 4, 5, 6, 7]\n ref = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n assert obj([1, [2, 3, [4, [5, 6, 7], 8, 9]], [10, 11], 12]) == ref\n\n\ndef test_gcd():\n \"\"\" Test gcd function behavior \"\"\"\n assert putil.misc.gcd([]) is None\n assert putil.misc.gcd([7]) == 7\n assert putil.misc.gcd([48, 18]) == 6\n assert putil.misc.gcd([20, 12, 16]) == 4\n ref = [Fraction(5, 3), Fraction(2, 3), Fraction(10, 3)]\n assert putil.misc.gcd(ref) == Fraction(1, 3)\n\n\ndef test_isalpha():\n \"\"\" Test isalpha function behavior \"\"\"\n assert putil.misc.isalpha('1.5')\n assert putil.misc.isalpha('1E-20')\n assert not putil.misc.isalpha('1EA-20')\n\n\ndef test_ishex():\n \"\"\" Test ishex function behavior \"\"\"\n assert not putil.misc.ishex(5)\n assert not putil.misc.ishex('45')\n assert putil.misc.ishex('F')\n\n\ndef test_isiterable():\n \"\"\" Test isiterable function behavior \"\"\"\n assert putil.misc.isiterable([1, 2, 3])\n assert putil.misc.isiterable({'a':5})\n assert putil.misc.isiterable(set([1, 2, 3]))\n assert not putil.misc.isiterable(3)\n\n\ndef test_isnumber():\n \"\"\" Test isnumber function behavior \"\"\"\n assert putil.misc.isnumber(5)\n assert putil.misc.isnumber(1.5)\n assert putil.misc.isnumber(complex(3.2, 9.5))\n assert not putil.misc.isnumber(True)\n\n\ndef test_isreal():\n \"\"\" Test isreal function behavior \"\"\"\n assert putil.misc.isreal(5)\n assert putil.misc.isreal(1.5)\n assert not putil.misc.isreal(complex(3.2, 9.5))\n assert not putil.misc.isreal(True)\n\n\ndef test_make_dir(capsys):\n \"\"\" Test make_dir function behavior \"\"\"\n def mock_os_makedir(file_path):\n print(file_path)\n home_dir = os.path.expanduser('~')\n with mock.patch('os.makedirs', side_effect=mock_os_makedir):\n fname = os.path.join(home_dir, 'some_dir', 'some_file.ext')\n putil.misc.make_dir(fname)\n stdout, _ = capsys.readouterr()\n actual = repr(os.path.dirname(fname).rstrip())[1:-1]\n ref = repr(_unicode_to_ascii(stdout.rstrip()))[1:-1]\n assert actual == ref\n putil.misc.make_dir(\n os.path.join(os.path.abspath(os.sep), 'some_file.ext')\n )\n stdout, _ = capsys.readouterr()\n assert stdout == ''\n\n\ndef test_normalize():\n \"\"\" Test normalize function behavior \"\"\"\n obj = putil.misc.normalize\n AI(obj, 'value', value='a', series=[2, 5], offset=10)\n AI(obj, 'offset', value=5, series=[2, 5], offset='a')\n AI(obj, 'series', value=5, series=['a', 'b'])\n exmsg = 'Argument `offset` has to be in the [0.0, 1.0] range'\n AE(obj, ValueError, exmsg, value=5, series=[2, 5], offset=10)\n exmsg = 'Argument `value` has to be within the bounds of argument `series`'\n AE(obj, ValueError, exmsg, value=0, series=[2, 5], offset=0)\n assert putil.misc.normalize(15, [10, 20]) == 0.5\n assert putil.misc.normalize(15, [10, 20], 0.5) == 0.75\n\n\ndef test_normalize_windows_fname():\n \"\"\" Test normalize_windows_fname behavior \"\"\"\n obj = putil.misc.normalize_windows_fname\n in_windows = platform.system().lower() == 'windows'\n ref = r'a\\b\\c' if in_windows else 'a/b/c//'\n assert obj('a/b/c//') == ref\n ref = r'a\\b\\c' if in_windows else 'a/b/c'\n assert obj('a/b/c//', True) == ref\n ref = r'\\\\a\\b\\c' if in_windows else r'\\\\a\\\\b\\\\c'\n assert obj(r'\\\\\\\\\\\\\\\\a\\\\\\\\b\\\\c', True) == ref\n ref = r'C:\\a\\b\\c' if in_windows else r'C:\\\\a\\\\b\\\\c'\n assert obj(r'C:\\\\\\\\\\\\\\\\a\\\\\\\\b\\\\c', True) == ref\n ref = (\n '\\\\apps\\\\temp\\\\new\\\\file\\\\wire'\n if in_windows else\n r'\\apps\\temp\\new\\\\file\\\\wire'\n )\n assert obj(r'\\apps\\temp\\new\\\\\\\\file\\\\\\\\\\\\\\\\\\\\wire', True) == ref\n\n\ndef test_per():\n \"\"\" Test per function behavior \"\"\"\n obj = putil.misc.per\n AI(obj, 'prec', arga=5, argb=7, prec='Hello')\n AI(obj, 'arga', arga='Hello', argb=7, prec=1)\n AI(obj, 'argb', arga=5, argb='Hello', prec=1)\n exmsg = 'Arguments are not of the same type'\n AE(obj, TypeError, exmsg, arga=5, argb=[5, 7], prec=1)\n assert obj(3, 2, 1) == 0.5\n assert obj(3.1, 3.1, 1) == 0\n ttuple = zip(obj([3, 1.1, 5], [2, 1.1, 2], 1), [0.5, 0, 1.5])\n assert all([test == ref for test, ref in ttuple])\n ttuple = zip(obj(array([3, 1.1, 5]), array([2, 1.1, 2]), 1), [0.5, 0, 1.5])\n assert all([test == ref for test, ref in ttuple])\n assert obj(4, 3, 3) == 0.333\n assert obj(4, 0, 3) == 1e20\n ttuple = zip(\n obj(array([3, 1.1, 5]), array([2, 0, 2]), 1), [0.5, 1e20, 1.5]\n )\n assert all([test == ref for test, ref in ttuple])\n\n\ndef test_pcolor():\n \"\"\" Test pcolor function behavior \"\"\"\n obj = putil.misc.pcolor\n AI(obj, 'text', text=5, color='red', indent=0)\n AI(obj, 'color', text='hello', color=5, indent=0)\n AI(obj, 'indent', text='hello', color='red', indent=5.1)\n exmsg = 'Unknown color hello'\n AE(obj, ValueError, exmsg, text='hello', color='hello', indent=5)\n assert putil.misc.pcolor('Text', 'none', 5) == ' Text'\n assert putil.misc.pcolor('Text', 'blue', 2) == '\\033[34m Text\\033[0m'\n # These statements should not raise any exception\n putil.misc.pcolor('Text', 'RED')\n putil.misc.pcolor('Text', 'NoNe')\n\n\ndef test_pgcd():\n \"\"\" Test pgcd function behavior \"\"\"\n assert putil.misc.pgcd(48, 18) == 6\n assert putil.misc.pgcd(3, 4) == 1\n assert putil.misc.pgcd(0.05, 0.02) == 0.01\n assert putil.misc.pgcd(5, 2) == 1\n assert putil.misc.pgcd(Fraction(5, 3), Fraction(2, 3)) == Fraction(1, 3)\n\n\ndef test_quote_str():\n \"\"\" Test quote_str function behavior \"\"\"\n assert putil.misc.quote_str(5) == 5\n assert putil.misc.quote_str('Hello!') == '\"Hello!\"'\n assert putil.misc.quote_str('He said \"hello!\"') == \"'He said \\\"hello!\\\"'\"\n\n\ndef test_strframe():\n \"\"\" Test strframe function behavior \"\"\"\n obj = putil.misc.strframe\n def check_basic_frame(lines):\n assert lines[0].startswith('\\x1b[33mFrame object ID: 0x')\n assert lines[1] == 'File name......: {0}'.format(\n os.path.realpath(__file__)\n )\n assert lines[2].startswith('Line number....: ')\n assert lines[3] == 'Function name..: test_strframe'\n assert (\n lines[4] ==\n r\"Context........: [' fobj = inspect.stack()[0]\\n']\"\n )\n assert lines[5] == 'Index..........: 0'\n fobj = inspect.stack()[0]\n lines = obj(fobj).split('\\n')\n check_basic_frame(lines)\n assert len(lines) == 6\n lines = [\n line\n for num, line in enumerate(obj(fobj, extended=True).split('\\n'))\n if (num < 6) or line.startswith('f_')\n ]\n check_basic_frame(lines)\n assert lines[6].startswith('f_back ID......: 0x')\n assert lines[7].startswith('f_builtins.....: {')\n assert lines[8].startswith(\n 'f_code.........: '\n ' 0:\n self.direction = self.direction.normalize()\n angle = self.direction.angle_to((0, -1))\n self.image = pygame.transform.rotate(self.original_image, angle)\n self.rect = self.image.get_rect(center=self.rect.center)\n\n def move(self, x, y):\n self.position -= self.direction * y * self.velocity\n self.position += pygame.math.Vector2(-self.direction.y, self.direction.x) * x * self.velocity\n self.rect.center = round(self.position.x), round(self.position.y)\n\n\n def reflect(self, NV):\n self.direction = self.direction.reflect(pygame.math.Vector2(NV))\n\n def update(self):\n self.position += self.direction * self.velocity\n self.rect.center = round(self.position.x), round(self.position.y)\n\n def hit(self, player):\n if self.health > 0:\n self.health -= 1\n else:\n self.visible = False\n distance = math.sqrt(math.pow(player.x - player.x(), 2) + math.pow(player.y - player.y(), 2))\n if distance < 20:\n return True\n\n else:\n return False\n\n\n\n def move(self, x, y, clamp_rect):\n self.position -= self.direction * y * self.velocity\n self.position += pygame.math.Vector2(-self.direction.y, self.direction.x) * x * self.velocity\n self.rect.center = round(self.position.x), round(self.position.y)\n\n if self.rect.left < clamp_rect.left:\n self.rect.left = clamp_rect.left\n self.position.x = self.rect.centerx\n if self.rect.right > clamp_rect.right:\n self.rect.right = clamp_rect.right\n self.position.x = self.rect.centerx\n if self.rect.top < clamp_rect.top:\n self.rect.top = clamp_rect.top\n self.position.y = self.rect.centery\n if self.rect.bottom > clamp_rect.bottom:\n self.rect.bottom = clamp_rect.bottom\n self.position.y = self.rect.centery\n\n class Projectile(object):\n def __init__(self, x, y, radius, color, facing):\n self.x = x\n self.y = y\n self.radius = radius\n self.color = color\n self.facing = facing\n self.vel = 8 * facing\n\n def draw(self, win):\n pygame.draw.circle(win, self.color, (self.x, self.y), self.radius)\n\n\n\n\n\n\nplayer = Player(200, 200)\nall_sprites = pygame.sprite.Group(player)\n\n\nrun = True\nwhile run:\n clock.tick(60)\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n run = False\n elif event.type == pygame.MOUSEMOTION:\n player.point_at(*event.pos)\n\n pygame.draw.rect(wn, (0, 0, 0), (50, 50, 10000, 100000))\n\n\n\n keys = pygame.key.get_pressed()\n if keys[pygame.K_w] or keys[pygame.K_UP]:\n player.move(0, -1, wn.get_rect())\n\n\n wn.fill((255, 255, 255))\n all_sprites.draw(wn)\n pygame.display.update()","sub_path":"Other/d.py","file_name":"d.py","file_ext":"py","file_size_in_byte":4008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"363372654","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nimport calendar\nimport datetime\n\n\ndef journal(request):\n c = calendar.Calendar(0)\n current = datetime.datetime.today()\n current_month = c.itermonthdays2(current.year, current.month)\n students = (\n {'id': 1,\n 'firstname': 'Виталий',\n 'lastname': 'Ермак'},\n {'id': 2,\n 'firstname': 'Александр',\n 'lastname': 'Коломоец'},\n {'id': 3,\n 'firstname': 'Дмитрий',\n 'lastname': 'Мельник'},\n )\n\n context = {\"students\": students, \"month\": current_month}\n return render(request, \"students/journal.html\", context)\n\n\ndef journal_list(request, gid):\n return HttpResponse('

Group %s Journal

', gid)\n\n\ndef journal_edit(request, gid):\n return HttpResponse(\"

Edit Group journal %s

\", gid)\n","sub_path":"students/views/journal.py","file_name":"journal.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"159132557","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\ndf = pd.read_csv('../Dataset/datalog.csv')\n\ndef correlation(data):\n plt.figure(figsize=(16,16))\n\n colormap = plt.cm.gist_heat\n sns.heatmap(df.corr(), linewidths = 0.1, vmax = 0.5, cmap = plt.cm.gist_heat, linecolor='white', annot = True)\n\n plt.show()\n\ndef graph(data):\n grid = sns.FacetGrid(df, col = 'label')\n # 가운데 컬럼을 바꿔서 라벨값과 관계 비교\n grid.map(plt.hist, 'meta_data', bins = 10)\n\n plt.show()\n\ncorrelation(df)\n#graph(df)","sub_path":"Dataset_map/datagraph.py","file_name":"datagraph.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"221481","text":"#!/usr/bin/python\n# Copyright (C) 2010 McAfee, Inc. All rights reserved.\n# TestcaseID: 16397\n# TestcaseDescription: Testcase to verify the multi-path support for applications\n\nimport sys\nimport logging\nimport os\nimport shutil\nimport subprocess\n\n# Import CommonTest module into current namespace\ncommon_path=os.path.dirname(os.path.abspath(sys.argv[0])) + \"/../\"\nsys.path.append(common_path + \"/Common\")\n\n\nimport commonFns\nimport CommonAppProFns\n# Import CommonTest module into current namespace\nfrom CommonTest import *\n\n# Get testcase name\ntestcaseName = sys.argv[0][:-3]\n\nclass TestCase(BaseTest):\n def __init__(self):\n logging.info(\"TestcaseID : 16397\")\n logging.info(\"Description : Testcase to verify the multi-path support for applications\")\n\n def init(self):\n logging.info(\"Initializing testcase %s\" % testcaseName)\n # Call the common initialization check\n _retval = BaseTest.init(self)\n if _retval != 0 :\n return _retval\n # Install the test tool\n logging.debug(\"Installing appProtTestTool\")\n if CommonAppProFns.installAppProtTestTool() != True :\n logging.error(\"Failed to install appProtTestTool\")\n return 1\n\n\n logging.debug(\"Resetting the application protection to defaults\")\n CommonAppProFns.resetAppProtToDefaults()\n\n # Set exclusion for appProtTestTool\n logging.debug(\"Exclude APTT \")\n CommonAppProFns.setAppProExclusions([os.path.dirname(\n os.path.abspath(sys.argv[0])) + \"/data/aptt/appProtTestTool\"])\n\n\n # Additional init code will go here !\n self.app_actual = os.path.dirname(os.path.abspath(sys.argv[0]))\\\n + \"/data/SampleApplications/UDPClient\"\n self.app_new = self.app_actual + \"_new\"\n self.rule1 = {\"AppPath\" : self.app_actual, \"Enabled\" : \"1\",\n \"ExecAllowed\":\"0\",\n \"NwAction\":\"1\"}\n if os.path.exists(self.app_new) :\n try :\n logging.debug(\"Already app exist removing it.\")\n os.remove(self.app_new)\n except :\n logging.error(\"Failed to remove pre-existing app \")\n return 1\n return 0\n\n def execute(self):\n logging.info(\"Executing testcase %s\" % testcaseName)\n # Add rule to deny application execution\n logging.debug(\"Add rule to deny application execution\")\n if CommonAppProFns.addAppProtRule(self.rule1) != CommonAppProFns.SUCCESS :\n logging.error(\"Failed to set deny rule for %s\" % self.rule1[\"AppPath\"])\n return 1\n\n # Copy the application to another location.\n try :\n logging.debug(\"Copying %s to %s\" % (self.app_actual, self.app_new))\n shutil.copy(self.app_actual, self.app_new)\n except :\n logging.error(\"Failed to copy!\")\n return 1\n\n # Now launch app_new\n try :\n logging.debug(\"Launching %s\" % self.app_new)\n subprocess.Popen([self.app_new, \"127.0.0.1\", \"1000\", \"Test\"],\\\n stdout=subprocess.PIPE)\n # We expect an exception, thus coming here fails the testcase\n logging.error(\"Application got launced.\")\n return 1\n except:\n logging.debug(\"Failed to launch application as expected\")\n return 0\n\n def verify(self):\n logging.info(\"Verifying testcase %s\" % testcaseName)\n # Check the McAfeeSecurity Log for Deny Message.\n if commonFns.searchProductLog(CommonAppProFns.REGEX_DENIED_RULE) != True :\n logging.error(\"Application is not blocked by AppProt\")\n return 1\n\n\n # Additional verify code will go here !\n\n return 0\n\n def cleanup(self):\n logging.info(\"Performing cleanup for testcase %s\" % testcaseName)\n # Copy logs and clean them.\n foundCrash = 0\n foundCrash = commonFns.copyLogs()\n commonFns.cleanLogs()\n logging.debug(\"Resetting the application protection to defaults\")\n CommonAppProFns.resetAppProtToDefaults()\n\n\n # Additional clean code will go here !\n # Remove the copied application\n try :\n os.remove(self.app_new)\n except :\n logging.error(\"Failed to remove %s\" % self.app_new)\n\n if foundCrash != 0:\n logging.error(\"copylogs returned failure status. Maybe a product crash\")\n\n return foundCrash\n\n def __del__(self):\n pass\n\nif __name__ == \"__main__\":\n # Setup testcase\n setupTestcase(sys.argv)\n\n testObj = TestCase()\n\n # Perform testcase operations\n retVal = testObj.init()\n\n # Perform execute once initialization succeeds...\n if(retVal == 0):\n retVal = testObj.execute()\n\n # Once execution succeeds, perform verification...\n if(retVal == 0):\n retVal = testObj.verify()\n\n # Perform testcase cleanup\n retVal += testObj.cleanup()\n\n if(retVal == 0):\n resultString = \"PASS\"\n else:\n resultString = \"FAIL\"\n\n logging.info(\"Result of testcase %s: %s\" % (testcaseName, resultString) )\n sys.exit(retVal)\n","sub_path":"McAfee/src/TestAutomation/Testcases/FVT/AppProtection/Appprot_ApplicationLaunch_1.py","file_name":"Appprot_ApplicationLaunch_1.py","file_ext":"py","file_size_in_byte":5150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"12456676","text":"n = int(input())\nl = list(input().split(' '))\nl = list(map(int, l))\n\n\ndef max_n(l):\n m = l[0]\n pos = 0\n\n for i in range(len(l)):\n if l[i] > m:\n m = l[i]\n pos = i\n\n return pos\n\nscore = 0\nstring = ''\n\nwhile len(l) > 0:\n pos = max_n(l)\n if pos == 0:\n l = l[1:]\n l.reverse()\n score += 1\n string += str(len(l)+1) + ' '\n elif pos == len(l)-1:\n l = l[:-1]\n else:\n l = l[pos+1::-1] + l[pos+1:]\n l.reverse()\n l = l[:-1]\n score += 2\n string += str(pos+1) + ' ' + str(len(l)) + ' '\n\nprint(score)\nprint(string[:-1])\n","sub_path":"5.py","file_name":"5.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"858788","text":"### 根据合并表的数据，制作新结构的数据表 ###\r\nfrom db.database import singleton_Scrub_DB\r\nfrom common import common\r\nfrom dragon import source_datas\r\nfrom dragon import export\r\nimport datetime\r\nfrom config.myconfig import singleton_cfg\r\n\r\n\r\ndef __searchAllRaceCardData(today_date):\r\n all = []\r\n # history\r\n now = datetime.datetime.now()\r\n for year in range(2014, now.year + 1):\r\n tableName = common.RECE_CARD_TABLE.replace('{0}', str(year))\r\n if singleton_Scrub_DB.table_exists(tableName):\r\n singleton_Scrub_DB.cursor.execute('select * from {}'.format(tableName))\r\n rows = singleton_Scrub_DB.cursor.fetchall()\r\n singleton_Scrub_DB.connect.commit()\r\n for row in rows:\r\n if int(row['race_date']) < int(today_date):\r\n draw = row['draw'].replace('\\xa0', '')\r\n if '' != draw:\r\n all.append(row)\r\n else:\r\n common.log('dragon: Table[' + tableName + '] not exist.')\r\n count = len(all)\r\n print('without future raceCard=', count)\r\n\r\n # today\r\n future_table = common.FUTURE_RECE_CARD_TABLE.replace('{0}', today_date[: len(today_date) - 4])\r\n if singleton_Scrub_DB.table_exists(future_table):\r\n singleton_Scrub_DB.cursor.execute('select * from {} where race_date=%s'.format(future_table), today_date)\r\n rows = singleton_Scrub_DB.cursor.fetchall()\r\n singleton_Scrub_DB.connect.commit()\r\n for row in rows:\r\n draw = row['draw'].replace('\\xa0', '')\r\n if '' != draw:\r\n all.append(row)\r\n else:\r\n print('future draw error:', row)\r\n else:\r\n common.log('dragon: Table[' + future_table + '] not exist.')\r\n print('all raceCard=', len(all), ' future=', len(all) - count)\r\n return all\r\n\r\n\r\ndef __getAllResultsRows(today_date):\r\n all = {} # race_date_No & {horse_code & row}\r\n now = datetime.datetime.now()\r\n for year in range(2014, now.year + 1):\r\n tableName = common.RESULTS_TABLE.replace('{0}', str(year))\r\n if singleton_Scrub_DB.table_exists(tableName):\r\n singleton_Scrub_DB.cursor.execute('select * from {}'.format(tableName))\r\n rows = singleton_Scrub_DB.cursor.fetchall()\r\n singleton_Scrub_DB.connect.commit()\r\n for row in rows:\r\n array_date = row['race_date'].split('/')\r\n race_date = array_date[2] + array_date[1] + array_date[0]\r\n if int(race_date) < int(today_date):\r\n race_date_No = race_date + common.toDoubleDigitStr(row['race_No'])\r\n if race_date_No not in all.keys():\r\n all[race_date_No] = {}\r\n horse_code = row['horse_code'].strip()\r\n if horse_code not in all[race_date_No].keys():\r\n all[race_date_No][horse_code] = row\r\n else:\r\n common.log('results: Table[' + tableName + '] not exist.')\r\n count = 0\r\n for race_date_No, dict in all.items():\r\n count += len(dict)\r\n print('all results=', count)\r\n return all\r\n\r\n\r\n# 将原数据转成目标数据结构\r\ndef __toTargetStruct(row, last_race_id, pedigree_dst_dict,\r\n horse_record_dict, horse_recent_record_dict, horse_site_record_dict, horse_go_record_dict,\r\n horse_course_record_dict, horse_dst_record_dict, horse_cls_record_dict, horse_draw_record_dict,\r\n horse_jockey_record_dict, horse_speed_dict, horse_recent_speed_dict, horse_site_speed_dict,\r\n horse_go_speed_dict, horse_course_speed_dict, horse_dst_speed_dict, horse_cls_speed_dict,\r\n horse_draw_speed_dict, horse_gear_speed_dict, horse_jockey_speed_dict,\r\n horse_last_dst_time_prev_dict, horse_last_dst_time_ave_dict, horse_last_dst_time_min_dict,\r\n rtg_dict, horse_dct_trend_dict, horse_act_trend_dict,\r\n horse_rest_dict, horse_new_dis_dict, draw_record_dict, trainer_record_dict,\r\n jockey_record_dict, jockey_recent_dict, jockey_hot_dict, jockey_hot_before4_dict):\r\n race_date = row['race_date'] # 20190202 str\r\n race_no = row['race_No']\r\n __race_date_No = race_date + common.toDoubleDigitStr(race_no)\r\n horse_no = row['horse_No']\r\n horse_code = row['horse_code'].strip()\r\n race_id = race_no\r\n pla_odds = 1\r\n win_odds = 1\r\n plc = 999\r\n\r\n array_trainer = row['trainer'].split('(')\r\n trainer_code = array_trainer[0].strip()\r\n\r\n array_jockey = row['jockey'].split('(')\r\n jockey_code = array_jockey[0].strip()\r\n\r\n site_label = row['site'].replace(' ', '')\r\n\r\n go_label = row['going'].replace(' ', '').upper()\r\n if go_label == '':\r\n go_label = 'GOOD'\r\n\r\n course_label = row['course'].strip()\r\n dst_label = int(row['distance'])\r\n cls_label = row['cls'].strip()\r\n draw_label = int(row['draw'])\r\n gear_label = row['gear']\r\n count_bucket = ''\r\n odd_bucket = ''\r\n pedigree_growth = False\r\n pedigree_dst = source_datas.getPedigreeDst(__race_date_No, horse_code, pedigree_dst_dict)\r\n pedigree_track = False\r\n horse_age = int(row['age'])\r\n\r\n horse_record = source_datas.getHorseRecord(__race_date_No, horse_code, horse_record_dict)\r\n horse_recent_record = source_datas.getHorseRecentRecord(__race_date_No, horse_code, horse_recent_record_dict)\r\n horse_site_record = source_datas.getHorseSiteRecord(__race_date_No, horse_code, horse_site_record_dict)\r\n horse_go_record = source_datas.getHorseGoingRecord(__race_date_No, horse_code, horse_go_record_dict)\r\n horse_course_record = source_datas.getHorseCourseRecord(__race_date_No, horse_code, horse_course_record_dict)\r\n horse_dst_record = source_datas.getHorseDstRecord(__race_date_No, horse_code, horse_dst_record_dict)\r\n horse_cls_record = source_datas.getHorseClsRecord(__race_date_No, horse_code, horse_cls_record_dict)\r\n horse_draw_record = source_datas.getHorseDrawRecord(__race_date_No, horse_code, horse_draw_record_dict)\r\n # horse_count_record\r\n horse_jockey_record = source_datas.getHorseJockeyRecord(__race_date_No, horse_code, horse_jockey_record_dict)\r\n # horse_odd_record\r\n\r\n horse_speed = source_datas.getHorseSpeed(__race_date_No, horse_code, horse_speed_dict)\r\n horse_recent_speed = source_datas.getHorseRecentSpeed(__race_date_No, horse_code, horse_recent_speed_dict)\r\n horse_site_speed = source_datas.getHorseSiteSpeed(__race_date_No, horse_code, horse_site_speed_dict)\r\n horse_go_speed = source_datas.getHorseGoSpeed(__race_date_No, horse_code, horse_go_speed_dict)\r\n horse_course_speed = source_datas.getHorseCourseSpeed(__race_date_No, horse_code, horse_course_speed_dict)\r\n horse_dst_speed = source_datas.getHorseDstSpeed(__race_date_No, horse_code, horse_dst_speed_dict)\r\n horse_cls_speed = source_datas.getHorseClsSpeed(__race_date_No, horse_code, horse_cls_speed_dict)\r\n horse_draw_speed = source_datas.getHorseClsSpeed(__race_date_No, horse_code, horse_draw_speed_dict)\r\n horse_gear_speed = source_datas.getHorseGearSpeed(__race_date_No, horse_code, horse_gear_speed_dict)\r\n # horse_count_speed\r\n horse_jockey_speed = source_datas.getHorseJockeySpeed(__race_date_No, horse_code, horse_jockey_speed_dict)\r\n # horse_odd_speed\r\n horse_last_dst_time_prev = source_datas.getHorseLastDstTimePrev(__race_date_No, horse_code, horse_last_dst_time_prev_dict)\r\n horse_last_dst_time_ave = source_datas.getHorseLastDstTimeAve(__race_date_No, horse_code, horse_last_dst_time_ave_dict)\r\n horse_last_dst_time_min = source_datas.getHorseLastDstTimeMin(__race_date_No, horse_code, horse_last_dst_time_min_dict)\r\n\r\n current_rating = source_datas.getRtg(__race_date_No, horse_code, rtg_dict)\r\n\r\n declar_horse_wt = row['horse_wt_dec']\r\n if ('\\xa0' in declar_horse_wt) and (plc == None):\r\n declar_horse_wt = 0\r\n else:\r\n declar_horse_wt = int(declar_horse_wt)\r\n\r\n actual_wt = int(row['wt'])\r\n\r\n rating_trend = row['rtg_as']\r\n if '-' == rating_trend:\r\n rating_trend = 0\r\n else:\r\n rating_trend = int(rating_trend)\r\n\r\n dct_trend = source_datas.getHorseDctTrend(__race_date_No, horse_code, horse_dct_trend_dict)\r\n act_trend = source_datas.getHorseActTrend(__race_date_No, horse_code, horse_act_trend_dict)\r\n odd_trend = 0\r\n odd_sectional_trend = 1\r\n\r\n rest = source_datas.getHorseRest(__race_date_No, horse_code, horse_rest_dict)\r\n # age_match\r\n # dst_match\r\n # track_match\r\n new_dis = source_datas.getHorseNewDis(__race_date_No, horse_code, horse_new_dis_dict)\r\n\r\n draw_record = source_datas.getDrawRecord(race_date, draw_label, draw_record_dict)\r\n trainer_record = source_datas.getTrainerRecord(race_date, trainer_code, trainer_record_dict)\r\n jockey_record = source_datas.getJockeyRecord(race_date, jockey_code, jockey_record_dict)\r\n jockey_recent = source_datas.getJockeyRecent(race_date, jockey_code, jockey_recent_dict)\r\n\r\n jockey_hot = source_datas.getJockeyHot(__race_date_No, horse_code, jockey_hot_dict)\r\n jockey_hot_before4 = source_datas.getJockeyHotBefore4(__race_date_No, horse_code, jockey_hot_before4_dict)\r\n\r\n item = (race_date, race_id, race_no, horse_no, pla_odds, win_odds, plc, horse_code, trainer_code, jockey_code,\r\n site_label, go_label, course_label, dst_label, cls_label, draw_label, gear_label, count_bucket, odd_bucket, pedigree_growth,\r\n pedigree_dst, pedigree_track, horse_age,\r\n horse_record[0] + horse_record[1] + horse_record[2] + horse_record[3], horse_record[4],\r\n horse_recent_record[0] + horse_recent_record[1] + horse_recent_record[2] + horse_recent_record[3], horse_recent_record[4],\r\n horse_site_record[0] + horse_site_record[1] + horse_site_record[2] + horse_site_record[3], horse_site_record[4],\r\n horse_go_record[0] + horse_go_record[1] + horse_go_record[2] + horse_go_record[3], horse_go_record[4],\r\n horse_course_record[0] + horse_course_record[1] + horse_course_record[2] + horse_course_record[3], horse_course_record[4],\r\n horse_dst_record[0] + horse_dst_record[1] + horse_dst_record[2] + horse_dst_record[3], horse_dst_record[4],\r\n horse_cls_record[0] + horse_cls_record[1] + horse_cls_record[2] + horse_cls_record[3], horse_cls_record[4],\r\n horse_draw_record[0] + horse_draw_record[1] + horse_draw_record[2] + horse_draw_record[3], horse_draw_record[4],\r\n horse_jockey_record[0] + horse_jockey_record[1] + horse_jockey_record[2] + horse_jockey_record[3], horse_jockey_record[4],\r\n horse_speed[0], horse_speed[1], horse_speed[2],\r\n horse_recent_speed[0], horse_recent_speed[1], horse_recent_speed[2],\r\n horse_site_speed[0], horse_site_speed[1], horse_site_speed[2],\r\n horse_go_speed[0], horse_go_speed[1], horse_go_speed[2],\r\n horse_course_speed[0], horse_course_speed[1], horse_course_speed[2],\r\n horse_dst_speed[0], horse_dst_speed[1], horse_dst_speed[2],\r\n horse_cls_speed[0], horse_cls_speed[1], horse_cls_speed[2],\r\n horse_draw_speed[0], horse_draw_speed[1], horse_draw_speed[2],\r\n horse_gear_speed[0], horse_gear_speed[1], horse_gear_speed[2],\r\n horse_jockey_speed[0], horse_jockey_speed[1], horse_jockey_speed[2],\r\n current_rating, declar_horse_wt, actual_wt, rating_trend, dct_trend, act_trend, odd_trend, rest, new_dis,\r\n draw_record[0] + draw_record[1] + draw_record[2] + draw_record[3], draw_record[4],\r\n trainer_record[0] + trainer_record[1] + trainer_record[2] + trainer_record[3], trainer_record[4],\r\n jockey_record[0] + jockey_record[1] + jockey_record[2] + jockey_record[3], jockey_record[4],\r\n jockey_recent[0] + jockey_recent[1] + jockey_recent[2] + jockey_recent[3], jockey_recent[4],\r\n horse_last_dst_time_prev, horse_last_dst_time_ave, horse_last_dst_time_min,\r\n jockey_hot, jockey_hot_before4, odd_sectional_trend)\r\n return item\r\n\r\n\r\ndef main():\r\n today_date = singleton_cfg.getRaceDate()\r\n raceCard_rows = __searchAllRaceCardData(today_date)\r\n results_rows = __getAllResultsRows(today_date) # race_date_No & {horse_code & row}\r\n if len(raceCard_rows) > 0:\r\n data_dict = source_datas.prepareDatas(raceCard_rows, results_rows)\r\n last_race_id = source_datas.getLastRaceId(today_date, results_rows) # race_date: '20190101'\r\n all_list = []\r\n print('start data=>')\r\n for row in raceCard_rows:\r\n race_date = row['race_date']\r\n if today_date == race_date:\r\n # pedigree_dst\r\n pedigree_dst_dict = data_dict['pedigree_dst']\r\n # horse_record\r\n horse_record_dict = data_dict['horse_record']\r\n # horse_recent_record\r\n horse_recent_record_dict = data_dict['horse_recent_record']\r\n # horse_site_record\r\n horse_site_record_dict = data_dict['horse_site_record']\r\n # horse_go_record\r\n horse_go_record_dict = data_dict['horse_go_record']\r\n # horse_course_record\r\n horse_course_record_dict = data_dict['horse_course_record']\r\n # horse_dst_record\r\n horse_dst_record_dict = data_dict['horse_dst_record']\r\n # horse_cls_record\r\n horse_cls_record_dict = data_dict['horse_cls_record']\r\n # horse_draw_record\r\n horse_draw_record_dict = data_dict['horse_draw_record']\r\n # horse_jockey_record\r\n horse_jockey_record_dict = data_dict['horse_jockey_record']\r\n # horse_speed\r\n horse_speed_dict = data_dict['horse_speed']\r\n # horse_recent_speed\r\n horse_recent_speed_dict = data_dict['horse_recent_speed']\r\n # horse_site_speed\r\n horse_site_speed_dict = data_dict['horse_site_speed']\r\n # horse_go_speed\r\n horse_go_speed_dict = data_dict['horse_go_speed']\r\n # horse_course_speed\r\n horse_course_speed_dict = data_dict['horse_course_speed']\r\n # horse_dst_speed\r\n horse_dst_speed_dict = data_dict['horse_dst_speed']\r\n # horse_cls_speed\r\n horse_cls_speed_dict = data_dict['horse_cls_speed']\r\n # horse_draw_speed\r\n horse_draw_speed_dict = data_dict['horse_draw_speed']\r\n # horse_gear_speed\r\n horse_gear_speed_dict = data_dict['horse_gear_speed']\r\n # horse_jockey_speed\r\n horse_jockey_speed_dict = data_dict['horse_jockey_speed']\r\n # horse_last_dst_time_prev\r\n horse_last_dst_time_prev_dict = data_dict['horse_last_dst_time_prev']\r\n # horse_last_dst_time_ave\r\n horse_last_dst_time_ave_dict = data_dict['horse_last_dst_time_ave']\r\n # horse_last_dst_time_min\r\n horse_last_dst_time_min_dict = data_dict['horse_last_dst_time_min']\r\n # rtg\r\n rtg_dict = data_dict['rtg']\r\n # horse_dct_trend\r\n horse_dct_trend_dict = data_dict['horse_dct_trend']\r\n # horse_act_trend\r\n horse_act_trend_dict = data_dict['horse_act_trend']\r\n # horse_rest\r\n horse_rest_dict = data_dict['horse_rest']\r\n # horse_new_dis\r\n horse_new_dis_dict = data_dict['horse_new_dis']\r\n # draw_record\r\n draw_record_dict = data_dict['draw_record']\r\n # trainer_record\r\n trainer_record_dict = data_dict['trainer_record']\r\n # jockey_record\r\n jockey_record_dict = data_dict['jockey_record']\r\n # jockey_recent\r\n jockey_recent_dict = data_dict['jockey_recent']\r\n # jockey_hot\r\n jockey_hot_dict = data_dict['jockey_hot']\r\n # jockey_hot_before4\r\n jockey_hot_before4_dict = data_dict['jockey_hot_before4']\r\n\r\n # 将数据组装成目标数据结构\r\n cur_row = __toTargetStruct(row, last_race_id, pedigree_dst_dict,\r\n horse_record_dict, horse_recent_record_dict, horse_site_record_dict, horse_go_record_dict,\r\n horse_course_record_dict, horse_dst_record_dict, horse_cls_record_dict, horse_draw_record_dict,\r\n horse_jockey_record_dict, horse_speed_dict, horse_recent_speed_dict, horse_site_speed_dict,\r\n horse_go_speed_dict, horse_course_speed_dict, horse_dst_speed_dict, horse_cls_speed_dict,\r\n horse_draw_speed_dict, horse_gear_speed_dict, horse_jockey_speed_dict,\r\n horse_last_dst_time_prev_dict, horse_last_dst_time_ave_dict, horse_last_dst_time_min_dict,\r\n rtg_dict, horse_dct_trend_dict, horse_act_trend_dict,\r\n horse_rest_dict, horse_new_dis_dict, draw_record_dict, trainer_record_dict,\r\n jockey_record_dict, jockey_recent_dict, jockey_hot_dict, jockey_hot_before4_dict)\r\n item = (cur_row)\r\n all_list.append(item)\r\n\r\n export.export(all_list)\r\n\r\n source_datas.showLostPedigreeDst()\r\n else:\r\n pass\r\n\r\n\r\n\r\n","sub_path":"20190413/futureData_model3/dragon/dragon.py","file_name":"dragon.py","file_ext":"py","file_size_in_byte":17803,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"340913468","text":"# Copyright 2019 Streamlit Inc. All rights reserved.\n\nimport sys\nimport traceback\n\nfrom streamlit.logger import get_logger\nLOGGER = get_logger(__name__)\n\n\ndef marshall(exception_proto, exception, exception_traceback=None):\n \"\"\"Marshalls an Exception.proto message.\n\n Parameters\n ----------\n exception_proto : Exception.proto\n The Exception protobuf to fill out\n\n exception : Exception\n The exception whose data we're extracting\n\n exception_traceback : Exception Traceback or None\n If None or False, does not show display the trace. If True,\n tries to capture a trace automatically. If a Traceback object,\n displays the given traceback.\n \"\"\"\n exception_proto.type = type(exception).__name__\n\n stack_trace = get_stack_trace(exception, exception_traceback)\n exception_proto.stack_trace.extend(stack_trace)\n\n try:\n exception_proto.message = str(exception)\n except Exception as str_exception:\n # Sometimes the exception's __str__/__unicode__ method itself\n # raises an error.\n exception_proto.message = ''\n LOGGER.warning('''\n\nStreamlit was unable to parse the data from an exception in the user's script.\nThis is usually due to a bug in the Exception object itself. Here is some info\nabout that Exception object, so you can report a bug to the original author:\n\nException type:\n %(etype)s\n\nProblem:\n %(str_exception)s\n\nTraceback:\n%(str_exception_tb)s\n\n ''' % {\n 'etype': type(exception).__name__,\n 'str_exception': str_exception,\n 'str_exception_tb': '\\n'.join(get_stack_trace(str_exception)),\n })\n\n\ndef get_stack_trace(exception, exception_traceback=None):\n # Get and extract the traceback for the exception.\n if exception_traceback is not None:\n extracted_traceback = traceback.extract_tb(exception_traceback)\n elif hasattr(exception, '__traceback__'):\n # This is the Python 3 way to get the traceback.\n extracted_traceback = traceback.extract_tb(exception.__traceback__)\n else:\n # Hack for Python 2 which will extract the traceback as long as this\n # method was called on the exception as it was caught, which is\n # likely what the user would do.\n _, live_exception, live_traceback = sys.exc_info()\n if exception == live_exception:\n extracted_traceback = traceback.extract_tb(live_traceback)\n else:\n extracted_traceback = None\n\n # Format the extracted traceback and add it to the protobuf element.\n if extracted_traceback is None:\n stack_trace = [\n 'Cannot extract the stack trace for this exception. '\n 'Try calling exception() within the `catch` block.']\n else:\n stack_trace = traceback.format_list(extracted_traceback)\n\n return stack_trace\n","sub_path":"lib/streamlit/elements/exception_proto.py","file_name":"exception_proto.py","file_ext":"py","file_size_in_byte":2842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"640299867","text":"# project/server/admin/racer/views.py\n\nimport sys, datetime\nfrom flask import render_template, Blueprint, url_for, \\\n redirect, flash, request\nfrom flask_login import login_required, current_user\n\nfrom project.server import bcrypt, db\nfrom project.server.models import Racer, Car, CarRacer, Sponsor, RacerSponsor\nfrom project.server.admin.racer.forms import RacerForm\n\n# Blueprints\nadmin_racer_blueprint = Blueprint('admin_racer', __name__,)\n\n# Helper Functions\n\n\ndef get_racers():\n return db.session.query(Racer)\n\ndef get_cars():\n return db.session.query(Car)\n\ndef get_sponsors():\n return db.session.query(Sponsor)\n\ndef get_carChoices():\n cars = get_cars()\n car_list = [(0, \"---\")]\n [car_list.append((c.id, c.number + ' ' + c.make + ' ' + c.model)) for c in cars.order_by(Car.number).all()]\n print(\"car_list\")\n print(car_list)\n return car_list\n\ndef get_sponsorChoices():\n sponsors = get_sponsors()\n sponsor_list = [(0, \"---\")]\n [sponsor_list.append((s.id, s.name)) for s in sponsors.order_by(Sponsor.name).all()]\n print(\"sponsor_list\")\n print(sponsor_list)\n return sponsor_list\n\ndef remove_car_association(racer_id):\n racer = db.session.query(Racer).filter_by(id=racer_id)\n r = racer.first()\n carRacers = db.session.query(CarRacer).filter_by(racerId=racer_id).all()\n for carRacer in carRacers:\n car = db.session.query(Car).filter_by(id=carRacer.carId).first()\n r.cars.remove(car)\n\ndef remove_sponsor_association(racer_id):\n racer = db.session.query(Racer).filter_by(id=racer_id)\n r = racer.first()\n racerSponsors = db.session.query(RacerSponsor).filter_by(racerId=racer_id).all()\n for racerSponsor in racerSponsors:\n sponsor = db.session.query(Sponsor).filter_by(id=racerSponsor.sponsorId).first()\n r.sponsors.remove(sponsor)\n\ndef get_pghead():\n return 'Racers'\n\n# Route Handlers\n\n# Racer\n@admin_racer_blueprint.route('/racer/main')\n@login_required\ndef main():\n if current_user.is_admin():\n return render_template('admin/racer/main.html', racers=get_racers(), pghead=get_pghead())\n else:\n flash('You are not an admin!', 'danger')\n return redirect(url_for(\"user.members\"))\n\n@admin_racer_blueprint.route('/racer/create', methods=['GET', 'POST'])\n@login_required\ndef create():\n if current_user.is_admin():\n form = RacerForm(request.form)\n form.cars.choices = get_carChoices()\n form.sponsors.choices = get_sponsorChoices()\n\n if form.validate_on_submit():\n racer = Racer(\n email=form.email.data,\n name=form.name.data,\n city=form.city.data,\n state=form.state.data,\n points=form.points.data,\n )\n for c in form.cars.data:\n if c != 0:\n car = db.session.query(Car).filter_by(id=c).first()\n racer.cars.append(car)\n \n for s in form.sponsors.data:\n if s != 0:\n sponsor = db.session.query(Sponsor).filter_by(id=s).first()\n racer.sponsors.append(sponsor)\n\n db.session.add(racer)\n db.session.commit()\n\n flash('New racer created.', 'success')\n return redirect(url_for(\"admin_racer.main\", pghead=get_pghead()))\n return render_template('admin/racer/create.html', form=form, pghead=get_pghead())\n else:\n flash('You are not an admin!', 'danger') \n return redirect(url_for(\"user.members\"))\n\n@admin_racer_blueprint.route('/racer/update//', methods=['GET', 'POST'])\n@login_required\ndef update(racer_id):\n if current_user.is_admin():\n racer = Racer.query.filter_by(id=racer_id).first()\n form = RacerForm(request.form)\n form.cars.choices = get_carChoices()\n form.sponsors.choices = get_sponsorChoices()\n \n if form.validate_on_submit():\n racer.email = form.email.data\n racer.name = form.name.data\n racer.city = form.city.data\n racer.state = form.state.data\n racer.points = form.points.data\n\n for c in form.cars.data:\n if c != 0:\n car = db.session.query(Car).filter_by(id=c).first()\n racer.cars.append(car)\n else:\n remove_car_association(racer_id)\n cars=[]\n \n for s in form.sponsors.data:\n if s != 0:\n sponsor = db.session.query(Sponsor).filter_by(id=s).first()\n racer.sponsors.append(sponsor)\n else:\n remove_sponsor_association(racer_id)\n sponsors=[]\n racer.updated_date = datetime.datetime.now()\n db.session.commit()\n\n flash('Racer Updated.', 'success')\n return redirect(url_for(\"admin_racer.main\", pghead=get_pghead()))\n \n if racer:\n form.email.data = racer.email\n form.name.data = racer.name\n form.city.data = racer.city\n form.state.data = racer.state\n form.points.data = racer.points\n\n return render_template('admin/racer/update.html', racer=racer, form=form, pghead=get_pghead())\n else:\n flash('You are not an admin!', 'danger')\n return redirect(url_for(\"user.members\"))\n\n@admin_racer_blueprint.route('/racer/delete//')\n@login_required\ndef delete(racer_id):\n if current_user.is_admin():\n racer = db.session.query(Racer).filter_by(id=racer_id)\n remove_car_association(racer_id)\n remove_sponsor_association(racer_id)\n racer.delete()\n db.session.commit()\n flash('The racer was deleted.', 'success')\n return redirect(url_for('admin_racer.main', pghead=get_pghead()))\n else:\n flash('You are not an admin!', 'danger')\n return redirect(url_for(\"user.members\"))","sub_path":"project/server/admin/racer/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"424592093","text":"import gurobipy as grb\nimport time\nimport pandas as pd\nimport multiprocessing as mp \nimport traceback\nimport random\nimport time\nimport colorama\nfrom colorama import Fore, Back, Style\nfrom termcolor import colored\ncolorama.init(autoreset=True)\n\ndef set_constrained_variables():\n '''\n This function sets the games which must be zero based on the constraints to zero (i.e. zero upper and lower bound)\n '''\n\n myConstrs = NFLmodel.getConstrs()\n for c in myConstrs:\n SoftlinkingConstr = False\n if c.sense =='<'and c.RHS == 0:\n row = NFLmodel.getRow(c)\n #check if the row contains a linking term or penalty term\n for r in range(row.size()):\n if 'GO_' not in row.getVar(r).varName:\n SoftlinkingConstr = True\n break\n # if it doesnt contain one of those terms then set all the variables in that constraint to zero\n if not SoftlinkingConstr:\n for r in range(row.size()):\n row.getVar(r).lb = 0\n row.getVar(r).ub = 0\n NFLmodel.update()\n return\n \n\ndef get_variables():\n '''\n makes two dictionarys keyed over cleaned up tuples of the game variables:\n (1) freevars - are the gurobi objects of the variables which are not yet assigned\n (2) var_status contains a tuple indicating the bounds of the variables\n '''\n free_vars = {}\n var_status = {}\n temp=[]\n myVars = NFLmodel.getVars()\n for v in myVars:\n if 'GO' == v.varName[:2]:\n temp = v.varName.split('_')\n if 'PRIME' in temp:\n free_vars[tuple(temp[1:])] = v\n var_status[tuple(temp[1:])]=(v.lb,v.ub)\n free_vars = cleanfreevars(free_vars,var_status)\n print(len(var_status))\n print(len(free_vars) )\n return free_vars, var_status\n\ndef cleanfreevars(free_vars,var_status):\n '''\n this kills any variables in freevars which now have fixed bounds as recorded in var_status\n '''\n for v in var_status:\n if var_status[v][0]==var_status[v][1]:\n if v in free_vars:\n free_vars.pop(v,None)\n return free_vars\n\ndef varProb(iq,oq):\n NFLR = grb.read('updated.lp')\n NFLR.setParam('OutputFlag', False )\n NFLR.setParam('TimeLimit',10)\n NFLR.setParam('Threads',1)\n \n while True:\n try:\n task = iq.get()\n try:\n if task[0] == None:\n break\n start_time = time.localtime()\n ##operate on the variable\n myVar = NFLR.getVarByName(task)\n myVar.lb = 1\n NFLR.update()\n NFLR.optimize()\n if NFLR.Status == grb.GRB.INFEASIBLE:\n myVar.lb = 0\n myVar.ub = 0\n NFLR.update()\n mymessage = task + ' -- is infeasible Time: ' + str(time.mktime(time.localtime())-time.mktime(start_time))\n else:\n myVar.lb = 0\n NFLR.update()\n mymessage = Fore.BLACK + Back.GREEN +task + ' -- is good. Time: ' + str(time.mktime(time.localtime())-time.mktime(start_time))\n NFLR.update()\n oq.put((1,mymessage))\n except:\n oq.put((2,traceback.format_exc()))\n except:\n time.sleep(4)\n return\n\ndef MyHandler(free_vars,pool_size, var_status, start_time, NFLmodel):\n\n def populate_queue(freevars,inputqueue, counter):\n for v in freevars:\n if freevars[v].lb != freevars[v].ub:\n varname = 'GO_' + '_'.join(list(v))\n inputqueue.put(varname) # dont have to pickle just needs to be pickeable\n counter+=1\n print('(MASTER): COMPLETED LOADING QUEUE WITH TASKS WITH A TOTAL RUN TIME OF %s' % str(time.mktime(time.localtime())-time.mktime(start_time)))\n return inputqueue, counter\n \n def killswitch(pool_size, iq):\n for i in range(pool_size*10):\n iq.put((None,None))\n print('(MASTER): COMPLETED LOADING QUEUE WITH NONES WITH A TOTAL RUN TIME OF %s' % str(time.mktime(time.localtime())-time.mktime(start_time)))\n\n NFLmodel.write('updated.lp')\n iq = mp.Queue()\n oq = mp.Queue()\n Stop = False\n while not Stop:\n iq = mp.Queue()\n oq = mp.Queue()\n Stop = True\n counter=0\n iq, counter = populate_queue(free_vars,iq,counter) #only free variables are populated\n print(counter)\n myprocesses = [mp.Process(target=varProb,args=(iq,oq)) for _ in range(pool_size)]\n for p in myprocesses:\n p.start()\n\n #manage output queue\n count = 0\n while count < counter: \n try: \n result = oq.get()\n if result[0]==1:\n count+=1\n my_message = result[1]\n if 'infeasible' in my_message:\n m=tuple(my_message.split()[0][3:].split('_')) #format string back to freevar format\n var_status[m] = (0,0)\n free_vars[m].lb=0\n free_vars[m].ub=0\n Stop=False\n NFLmodel.update()\n print( my_message + ' Queue: ' + str(count) + '/' + str(counter) )\n elif result[0] == 0:\n my_message = result[1]\n print( my_message + ' Queue: ' + str(count) + '/' + str(counter))\n else:\n print(result)\n except:\n time.sleep(.5)\n NFLmodel.write('updated.lp')\n \n killswitch(pool_size,iq)\n# stop the routine from moving forward until all processes have completed\n # their assigned task. Without this, you will get an error\n for p in myprocesses:\n p.join()\n\n # now that all processes are completed, terminate them all - you don't want\n # to tie up the CPU with zombie processes\n for p in myprocesses:\n p.terminate()\n\n number_tasks = oq.qsize()\n for i in range(number_tasks):\n print(oq.get_nowait()[1])\n\n # There may be some left over \"Nones\" in the input queue. Let's clear \n # them out since we want to account for all tasks (good housekeeping)\n number_tasks = iq.qsize()\n for i in range(number_tasks):\n try:\n iq.get_nowait()\n except:\n pass\n\n print('(MASTER): COMPLETED FLUSHING QUEUE WITH A TOTAL RUN TIME OF %s' % str(time.mktime(time.localtime())-time.mktime(start_time)))\n return free_vars, var_status\ndef main():\n\n #load constraints\n set_constrained_variables()\n free_vars, var_status = get_variables()\n free_vars, var_status = MyHandler(free_vars,pool_size, var_status, start_time, NFLmodel) #do probing and all that parallel\n write = pd.DataFrame.from_dict(var_status,orient=\"index\") #write solution\n write.to_csv(\"GameBounds.csv\")\n print('(MASTER): ALL PROCESSES HAVE COMPLETED WITH A TOTAL RUN TIME OF %s' % str(time.mktime(time.localtime())-time.mktime(start_time)))\n\nif __name__ == \"__main__\":\n start_time = time.localtime()\n #set worker information\n pool_size = 4\n my_seed = 11111124\n random.seed(my_seed)\n #load model\n NFLmodel=grb.read('OR604 Model File v2.lp')\n main()","sub_path":"HW10/NFLparallel.py","file_name":"NFLparallel.py","file_ext":"py","file_size_in_byte":7419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"617508083","text":"import json\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport dash_bootstrap_components as dbc\nfrom dash.exceptions import PreventUpdate\nfrom dash.dependencies import Input, Output, State\nfrom ... import app\nfrom ...utils import stl_toast\n\n\ndef load_macros_json(key, add_blank=False, add_null=False, filename='./macros.json'):\n '''Read and load macros configuration from JSON file.'''\n with open(filename, 'r') as f:\n text = json.load(f)\n\n dic = text[key]\n \n if add_blank:\n dic.insert(0, dict(label=None, value=None))\n\n if add_null:\n dic.extend([dict(label='(Vazio)', value='NULL')])\n\n return dic\n\n\ndef update_macros_json(key, dic, filename='./macros.json'):\n '''Update macros.json file.'''\n\n try: \n with open(filename, 'r') as f:\n text = json.load(f)\n\n # Update dic\n text[key] = dic\n\n # Save file\n with open(filename, 'w') as f:\n json.dump(text, f, indent=4)\n \n return True\n\n except: return False\n\n\ndef read_macros_content_keys(ch):\n '''Read labels and values in config-macros-content` children.'''\n out = []\n\n for i in range(len(ch)):\n label = ch[i]['props']['children'][0]['props']['children']['props']['children'][1]['props']['value']\n value = ch[i]['props']['children'][1]['props']['children']['props']['children'][1]['props']['value']\n out.append(dict(label=label, value=value))\n\n return out\n\n\n_empty_macro = dict(label='', value='')\n\n\n_macros_options = dbc.InputGroup([\n dbc.InputGroupAddon(\n \"Macro\", addon_type=\"prepend\", \n className='input-group-prepend-110'\n ),\n \n dbc.Select(id='config-macros-drop',\n options=[\n dict(label='Equipamento > Controles', value='eqp_controle'),\n dict(label='Equipamento > Tipos', value='eqp_tipos'),\n dict(label='Equipamento > Varreduras', value='scan'),\n dict(label='Fibra > Meios de comunição', value='fibra_comunicacao'),\n dict(label='Rádio > Larguras de banda', value='radio_bw'),\n dict(label='Rádio > Modelos', value='radio_modelo'),\n dict(label='Rádio > Modelos (equip.)', value='radio_modelo_eqp'),\n dict(label='Rádio > Modos de compatibilidade', value='radio_compat'),\n dict(label='Rádio > Modos de operação', value='radio_mode'), \n dict(label='Regiões', value='regiao'),\n dict(label='SNMP > Comunidades', value='snmp_c'),\n ]\n ),\n])\n\n\nlayout = html.Div([\n # Header\n html.H4('Configurações > Macros'),\n html.Hr(),\n \n # Select options\n _macros_options,\n html.Hr(),\n \n # Content\n html.Div(id='config-macros-content'),\n\n # Buttons & Storage\n dcc.Store(\n id='config-macros-store', \n data=dict(macro=None, n_add=0, n_rem=0)\n ),\n dbc.Button(\n id='config-macros-bt-sav', n_clicks=0, \n className='fas fa-save'\n ),\n dbc.Button(\n id='config-macros-bt-rem', n_clicks=0, \n className='fas fa-minus-square'\n ),\n \n dbc.Button(\n id='config-macros-bt-add', n_clicks=0, \n className='fas fa-plus-square'\n ),\n \n\n # Buttons tooltips\n dbc.Tooltip(\n 'Adicionar linha.', \n target='config-macros-bt-add'\n ),\n dbc.Tooltip(\n 'Remover linha.', \n target='config-macros-bt-rem'\n ),\n dbc.Tooltip(\n 'Salvar alterações.', \n target='config-macros-bt-sav'\n ),\n\n # Toast\n dbc.Toast(\n id=\"config-macros-toast\", header=\"Macros\",\n is_open=False, dismissable=True,\n duration=3000, style=stl_toast,\n ),\n])\n\n\ndef macros_options(i, dic):\n return dbc.Row([\n\n dbc.Col(dbc.InputGroup([\n dbc.InputGroupAddon(\n 'Label', addon_type='prepend',\n className='input-group-prepend-110'\n ),\n\n dbc.Input(\n value=dic['label'], \n id=f'macro-item-label-{i}'\n )\n ]), xs=12, sm=12, md=6, lg=6, xl=6),\n\n dbc.Col(dbc.InputGroup([\n dbc.InputGroupAddon(\n 'Valor', addon_type='prepend',\n className='input-group-prepend-110'\n ),\n\n dbc.Input(\n value=dic['value'], \n id=f'macro-item-value-{i}'\n )\n ]), className='breakColLine', xs=12, sm=12, md=6, lg=6, xl=6),\n\n ], className='breakRowLine')\n\n\n@app.callback([\n Output('config-macros-content', 'children'),\n Output('config-macros-store', 'data')], [\n Input('config-macros-drop', 'value'),\n Input('config-macros-bt-add', 'n_clicks'),\n Input('config-macros-bt-rem', 'n_clicks')], [\n State('config-macros-store', 'data'),\n State('config-macros-content', 'children')])\ndef show_macros_children(m_id, n_add, n_rem, store, ch):\n \n # Default children\n if m_id == None:\n raise PreventUpdate\n\n # Id has not change\n if m_id == store['macro']:\n if ch is not None:\n n_ch = len(ch)\n\n if (n_add > 0) and (n_add > store['n_add']):\n store['n_add'] = n_add\n ch.extend([macros_options(n_ch, _empty_macro)])\n\n elif (n_rem > 0) and (n_rem > store['n_rem']) and (n_ch > 0):\n store['n_rem'] = n_rem\n ch.pop()\n\n return ch, store\n\n # Id has change\n else:\n store = dict(macro=m_id, n_add=0, n_rem=0)\n macro_list = load_macros_json(m_id)\n return [macros_options(i, dic) for i, dic in enumerate(macro_list)], store\n\n\n@app.callback([\n Output('config-macros-toast', 'icon'),\n Output('config-macros-toast', 'is_open'),\n Output('config-macros-toast', 'children')], [\n Input('config-macros-bt-sav', 'n_clicks')], [\n State('config-macros-store', 'data'),\n State('config-macros-content', 'children')])\ndef update_macros(n_clicks, store, ch):\n '''Update macros.json file.'''\n\n if n_clicks == 0 or ch is None:\n raise PreventUpdate\n \n key = store['macro']\n dic = read_macros_content_keys(ch)\n status = update_macros_json(key, dic)\n \n if status:\n return 'success', True, 'Dados atualizados!'\n else:\n return 'danger', True, 'Falha na atualização!'\n","sub_path":"app/layout/config/macros.py","file_name":"macros.py","file_ext":"py","file_size_in_byte":6302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"383641463","text":"from django.shortcuts import render\nfrom events.models import Event\nfrom jobs.models import Job\nfrom communities.models import Community\nfrom meetups.models import Meetup\n\ndef home_view(request):\n events = Event.objects.all()\n jobs = Job.objects.all()\n communities = Community.objects.all()\n meetups = Meetup.objects.all()\n context = {\n 'events': events,\n 'jobs': jobs,\n 'communities': communities,\n 'meetups': meetups,\n 'title': 'Home',\n }\n return render(request, 'home/home.html', context)\n\n\ndef about_view(request):\n context = {\n 'title': 'About Us',\n }\n return render(request, 'home/about.html', context)\n\n\ndef contact_view(request):\n context = {\n 'title': 'Contact',\n }\n return render(request, 'home/contact.html', context)\n","sub_path":"home/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"554279765","text":"from pynamodb.models import Model\nfrom pynamodb.attributes import UnicodeAttribute\nfrom pynamodb.attributes import NumberAttribute\nfrom pynamodb.attributes import BooleanAttribute\nfrom pynamodb.attributes import UTCDateTimeAttribute\nfrom pynamodb.connection import Connection\nimport re\n\n\nclass ServiceModel(Model):\n \"\"\"\n Service Model\n \"\"\"\n class Meta:\n table_name = \"service\"\n name = UnicodeAttribute(hash_key=True)\n fqdn = UnicodeAttribute()\n healthcheck_path = UnicodeAttribute(default='/')\n healthcheck_interval = NumberAttribute(default=5000)\n healthcheck_rise = NumberAttribute(default=10)\n healthcheck_fall = NumberAttribute(default=3)\n connection_draining = NumberAttribute(default=20)\n failover_pool_fqdn = UnicodeAttribute(default=\"\")\n failover_pool_use_https = BooleanAttribute(default=0)\n failover_pool_ssl_allow_self_signed_certs = BooleanAttribute(default=0)\n dns_resolver = UnicodeAttribute(default=\"\")\n\n def as_dict(self):\n return {\n 'name': self.name,\n 'fqdn': self.fqdn,\n 'healthcheck_interval': self.healthcheck_interval,\n 'healthcheck_path': self.healthcheck_path,\n 'healthcheck_rise': self.healthcheck_rise,\n 'healthcheck_fall': self.healthcheck_fall,\n 'connection_draining': self.connection_draining,\n 'failover_pool_fqdn': self.failover_pool_fqdn,\n 'failover_pool_use_https': self.failover_pool_use_https,\n 'failover_pool_ssl_allow_self_signed_certs': self.failover_pool_ssl_allow_self_signed_certs,\n 'dns_resolver': self.dns_resolver,\n }\n\n def __eq__(self, other):\n return self.__dict__ == other.__dict__\n\n\nclass TargetGroupModel(Model):\n \"\"\"\n Service Target Group\n \"\"\"\n class Meta:\n table_name = \"target-group\"\n service_name = UnicodeAttribute(hash_key=True)\n target_group_name = UnicodeAttribute(range_key=True)\n weight = NumberAttribute()\n\n def as_dict(self):\n return {\n 'weight': self.weight,\n 'target_group_name': self.target_group_name,\n }\n\n def __eq__(self, other):\n return self.__dict__ == other.__dict__\n\n\nclass BackendModel(Model):\n \"\"\"\n Service Backend Model\n \"\"\"\n class Meta:\n table_name = \"backend\"\n service_name = UnicodeAttribute(hash_key=True)\n target_group_name = UnicodeAttribute()\n host = UnicodeAttribute(range_key=True)\n dns_resolver = UnicodeAttribute(default=\"\")\n\n updated_at = UTCDateTimeAttribute()\n status = UnicodeAttribute(default=\"HEALTHY\")\n\n def as_dict(self):\n return {\n 'host': self.host,\n 'status': self.status\n }\n\n def __eq__(self, other):\n return self.__dict__ == other.__dict__\n\n\nclass ResolverModel(Model):\n \"\"\"\n DNS Resolver Model\n \"\"\"\n\n class Meta:\n table_name = \"resolver\"\n\n resolver_name = UnicodeAttribute(hash_key=True)\n nameserver_address = UnicodeAttribute()\n nameserver_port = NumberAttribute(default=53)\n resolve_retries = NumberAttribute(default=10)\n timeout_retry = UnicodeAttribute(default=\"5s\")\n hold_valid = UnicodeAttribute(default=\"30s\")\n\n def as_dict(self):\n return {\n 'name': self.resolver_name,\n 'nameserver_address': self.nameserver_address,\n 'nameserver_port': self.nameserver_port,\n 'resolve_retries': self.resolve_retries,\n 'timeout_retry': self.timeout_retry,\n 'hold_valid': self.hold_valid\n }\n\n def __eq__(self, other):\n return self.__dict__ == other.__dict__\n\n\nclass DynamoTableManagement:\n\n def update_capacity(self, dynamo_host, dynamo_region, table_root, logger, config):\n \"\"\"\n Manage the DynamoDB tables:\n - Create if the tables don't exist\n - Update the read/write capacity if they do exist and there is a conflict between required and current\n :param dynamo_host: optional host, for local testing\n :param dynamo_region: aws region for the table\n :param table_root: prefix for table_name (e.g. flyby)\n :param logger:\n :return:\n \"\"\"\n models = [BackendModel, ServiceModel, TargetGroupModel, ResolverModel]\n for model in models:\n if dynamo_host:\n model.Meta.host = dynamo_host\n conn = Connection(host=dynamo_host)\n else:\n conn = Connection(region=dynamo_region)\n default_table_name = model.Meta.table_name\n if not model.Meta.table_name.startswith(table_root):\n model.Meta.table_name = \"{0}-{1}\".format(table_root, model.Meta.table_name)\n model.Meta.region = dynamo_region\n\n read_capacity_units = config[default_table_name]['read_capacity_units']\n write_capacity_units = config[default_table_name]['write_capacity_units']\n\n if not model.exists():\n logger.info(\"Creating {} table\".format(model.Meta.table_name))\n model.create_table(read_capacity_units=read_capacity_units,\n write_capacity_units=write_capacity_units,\n wait=True\n )\n else:\n dynamo_table = conn.describe_table(table_name=model.Meta.table_name)\n actual_read_capacity = dynamo_table['ProvisionedThroughput']['ReadCapacityUnits']\n actual_write_capacity = dynamo_table['ProvisionedThroughput']['WriteCapacityUnits']\n if actual_read_capacity == read_capacity_units and actual_write_capacity == write_capacity_units:\n continue\n\n if dynamo_table['ProvisionedThroughput']['NumberOfDecreasesToday'] >= 4:\n logger.error(\"Unable to decrease capacity on {} table\".format(model.Meta.table_name))\n else:\n conn.update_table(\n table_name=model.Meta.table_name,\n read_capacity_units=read_capacity_units,\n write_capacity_units=write_capacity_units\n )\n logger.info(\"Updating {} table read/write capacity\".format(model.Meta.table_name))\n","sub_path":"flyby/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":6300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"72964662","text":"from bs4 import BeautifulSoup\nimport requests\n\nheaders = {'User-Agent': \n 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.106 Safari/537.36'}\n\npage = \"https://www.transfermarkt.de/holstein-kiel/startseite/verein/269\"\npageTree = requests.get(page, headers=headers)\npageSoup = BeautifulSoup(pageTree.content, 'html.parser')\n\nplayer_names = pageSoup.find_all(\"span\", class_=\"hide-for-small\")\nglobal player_list\nplayer_list = []\n\n\nfor _ in player_names:\n player_list.append(_.a.get_text(separator=\"\\n\"))\n\nprint(', '.join(player_list))","sub_path":"transfermarkt.py","file_name":"transfermarkt.py","file_ext":"py","file_size_in_byte":584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"355192155","text":"#!/usr/bin/env python3\n##########################################################################\n# Copyright(c) 2016, 2019, Oracle and/or its affiliates. All rights reserved.\n# showoci.py\n#\n# @author: Adi Zohar\n#\n# Supports Python 3 and above\n#\n# coding: utf-8\n##########################################################################\n# OCI Report Tool SHOWOCI:\n#\n# require OCI read only user with OCI authentication:\n# ALLOW GROUP ReadOnlyUsers to read all-resources IN TENANCY\n#\n# config file should contain:\n# [TENANT_NAME]\n# user = user_ocid\n# fingerprint = fingerprint of the api ssh key\n# key_file = the path to the private key\n# tenancy = tenancy ocid\n# region = region\n#\n# Recommend to set below for display interactive\n# export PYTHONUNBUFFERED=TRUE\n##########################################################################\n#\n# Modules Included:\n# - oci.core.VirtualNetworkClient\n# - oci.core.ComputeClient\n# - oci.core.ComputeManagementClient\n# - oci.core.BlockstorageClient\n# - oci.file_storage.FileStorageClient\n# - oci.object_storage.ObjectStorageClient\n# - oci.database.DatabaseClient\n# - oci.identity.IdentityClient\n# - oci.load_balancer.LoadBalancerClient\n# - oci.email.EmailClient\n# - oci.container_engine.ContainerEngineClient\n# - oci.streaming.StreamAdminClient\n# - oci.budget.BudgetClient\n# - oci.autoscaling.AutoScalingClient\n# - oci.monitoring.MonitoringClient\n# - oci.ons.NotificationControlPlaneClient\n# - oci.ons.NotificationDataPlaneClient\n# - oci.healthchecks.HealthChecksClient\n# - oci.announcements_service.AnnouncementClient\n# - oci.limits.LimitsClient\n#\n# Modules Not Yet Covered:\n# - oci.waas.WaasClient\n# - oci.dns.DnsClient\n#\n##########################################################################\nfrom __future__ import print_function\nfrom showoci_data import ShowOCIData\nfrom showoci_output import ShowOCIOutput, ShowOCISummary, ShowOCICSV\nfrom showoci_service import ShowOCIFlags\nimport json\nimport sys\nimport argparse\nimport datetime\n\nversion = \"19.9.11\"\n\n##########################################################################\n# execute_extract\n##########################################################################\n\n\ndef execute_extract():\n\n # get parset cmd\n cmd = set_parser_arguments()\n if cmd is None:\n return\n\n # Start time\n start_time = str(datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n\n # get flags object for calling cache\n flags = set_service_extract_flags(cmd)\n\n ############################################\n # create data instance\n ############################################\n data = ShowOCIData(flags)\n\n ############################################\n # output and summary instances\n ############################################\n output = ShowOCIOutput()\n summary = ShowOCISummary()\n csv = ShowOCICSV(start_time)\n\n ############################################\n # print showoci config\n ############################################\n cmdline = ' '.join(x for x in sys.argv[1:])\n showoci_config = data.get_showoci_config(cmdline, start_time)\n output.print_showoci_config(showoci_config['data'])\n\n ############################################\n # load oci data to cache\n ############################################\n output.print_header('Load OCI data to Memory', 1)\n\n if not data.load_service_data():\n return\n\n ############################################\n # if print service data to file or screen\n ############################################\n if cmd.servicefile or cmd.servicescr:\n if cmd.servicefile:\n if cmd.servicefile.name:\n print_to_json_file(cmd.servicefile.name, data.get_service_data(), \"Service Data\")\n\n elif cmd.servicescr:\n print(json.dumps(data.get_service_data(), indent=4, sort_keys=False))\n\n else:\n ############################################\n # process the data into data json\n ############################################\n output.print_header(\"Start Processing Data\", 1)\n extracted_data = data.process_oci_data()\n\n ############################################\n # if JSON and screen\n ############################################\n if cmd.sjoutfile:\n # print nice\n output.print_data(extracted_data)\n summary.print_summary(extracted_data)\n\n # Add summary to JSON and print to JSON file\n extracted_data.append({'summary': summary.get_summary_json()})\n if cmd.sjoutfile.name:\n print_to_json_file(output, cmd.sjoutfile.name, extracted_data, \"JSON Data\")\n\n ############################################\n # JSON File only\n ############################################\n elif cmd.joutfile:\n if cmd.joutfile.name:\n summary.print_summary(extracted_data)\n extracted_data.append({'summary': summary.get_summary_json()})\n print_to_json_file(output, cmd.joutfile.name, extracted_data, \"JSON Data\")\n\n ############################################\n # JSON to screen only\n ############################################\n elif cmd.joutscr:\n summary.print_summary(extracted_data)\n extracted_data.append({'summary': summary.get_summary_json()})\n print(json.dumps(extracted_data, indent=4, sort_keys=False))\n\n ############################################\n # print summary only\n ############################################\n elif cmd.sumonly:\n summary.print_summary(extracted_data)\n\n ############################################\n # print nice output as default to screen\n # and summary\n ############################################\n else:\n output.print_data(extracted_data)\n summary.print_summary(extracted_data)\n\n ############################################\n # if print to CSV\n ############################################\n if cmd.csv:\n csv.generate_csv(extracted_data, cmd.csv)\n\n ############################################\n # print completion\n ############################################\n complete_message = return_error_message(data.get_service_errors(), data.get_service_warnings(), data.error)\n\n # if reboot migration\n if data.get_service_reboot_migration() > 0:\n output.print_header(str(data.get_service_reboot_migration()) + \" Reboot Migration Alert for Compute\", 0)\n\n # print completion\n output.print_header(\"Completed \" + complete_message + \" at \" + str(datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")), 0)\n\n\n##########################################################################\n# compile the error message\n##########################################################################\ndef return_error_message(service_error, service_warning, data_error):\n\n complete_message = \"Successfully\"\n\n if service_error > 0 or service_warning > 0 or data_error > 0:\n complete_message = \"With \"\n\n if service_error > 0:\n complete_message += str(service_error) + \"x(Service Errors) \"\n\n if service_warning > 0:\n complete_message += str(service_warning) + \"x(Service Warnings) \"\n\n if data_error > 0:\n complete_message += str(data_error) + \" (Processing Errors) \"\n return complete_message\n\n\n##########################################################################\n# set parser\n##########################################################################\ndef set_parser_arguments():\n parser = argparse.ArgumentParser()\n parser.add_argument('-a', action='store_true', default=False, dest='all', help='Print All Resources')\n parser.add_argument('-ani', action='store_true', default=False, dest='allnoiam', help='Print All Resources but identity')\n parser.add_argument('-an', action='store_true', default=False, dest='announcement', help='Print Announcements')\n parser.add_argument('-b', action='store_true', default=False, dest='budgets', help='Print Budgets')\n parser.add_argument('-n', action='store_true', default=False, dest='network', help='Print Network')\n parser.add_argument('-i', action='store_true', default=False, dest='identity', help='Print Identity')\n parser.add_argument('-c', action='store_true', default=False, dest='compute', help='Print Compute')\n parser.add_argument('-cn', action='store_true', default=False, dest='container', help='Print Containers')\n parser.add_argument('-o', action='store_true', default=False, dest='object', help='Print Object Storage')\n parser.add_argument('-l', action='store_true', default=False, dest='load', help='Print Load Balancer')\n parser.add_argument('-d', action='store_true', default=False, dest='database', help='Print Database')\n parser.add_argument('-f', action='store_true', default=False, dest='file', help='Print File Storage')\n parser.add_argument('-e', action='store_true', default=False, dest='email', help='Print EMail')\n parser.add_argument('-m', action='store_true', default=False, dest='monitoring', help='Print Monitoring and Notifications')\n parser.add_argument('-s', action='store_true', default=False, dest='streams', help='Print Streams')\n parser.add_argument('-rm', action='store_true', default=False, dest='orm', help='Print Resource management')\n parser.add_argument('-so', action='store_true', default=False, dest='sumonly', help='Print Summary Only')\n parser.add_argument('-edge', action='store_true', default=False, dest='edge', help='Print Edge Services (Healthcheck)')\n parser.add_argument('-lq', action='store_true', default=False, dest='limits', help='Print Limits and Quotas')\n parser.add_argument('-mc', action='store_true', default=False, dest='mgdcompart', help='exclude ManagedCompartmentForPaaS')\n parser.add_argument('-nr', action='store_true', default=False, dest='noroot', help='Not include root compartment')\n parser.add_argument('-ip', action='store_true', default=False, dest='instance_principals', help='Use Instance Principals for Authentication')\n parser.add_argument('-t', default=\"\", dest='profile', help='Config file section to use (tenancy profile)')\n parser.add_argument('-p', default=\"\", dest='proxy', help='Set Proxy (i.e. www-proxy-server.com:80) ')\n parser.add_argument('-rg', default=\"\", dest='region', help='Filter by Region')\n parser.add_argument('-cp', default=\"\", dest='compart', help='Filter by Compartment')\n parser.add_argument('-cpath', default=\"\", dest='compartpath', help='Filter by Compartment path ,(i.e. -cpath \"Adi / Sub\"')\n parser.add_argument('-cf', type=argparse.FileType('r'), dest='config', help=\"Config File\")\n parser.add_argument('-csv', default=\"\", dest='csv', help=\"Output to CSV files, Input as file header\")\n parser.add_argument('-jf', type=argparse.FileType('w'), dest='joutfile', help=\"Output to file (JSON format)\")\n parser.add_argument('-js', action='store_true', default=False, dest='joutscr', help=\"Output to screen (JSON format)\")\n parser.add_argument('-sjf', type=argparse.FileType('w'), dest='sjoutfile', help=\"Output to screen (nice format) and JSON File\")\n parser.add_argument('-cachef', type=argparse.FileType('w'), dest='servicefile', help=\"Output Cache to file (JSON format)\")\n parser.add_argument('-caches', action='store_true', default=False, dest='servicescr', help=\"Output Cache to screen (JSON format)\")\n parser.add_argument('--version', action='version', version='%(prog)s ' + version)\n\n result = parser.parse_args()\n\n if len(sys.argv) < 2:\n parser.print_help()\n return None\n\n if not (result.all or result.allnoiam or result.network or result.identity or\n result.compute or result.object or\n result.load or result.database or result.file or result.email or result.orm or result.container or\n result.streams or result.budgets or result.monitoring or result.edge or result.announcement or result.limits):\n\n parser.print_help()\n\n print(\"******************************************************\")\n print(\"*** You must choose at least one parameter!! ***\")\n print(\"******************************************************\")\n return None\n\n return result\n\n\n##########################################################################\n# set cache flags for extract\n##########################################################################\ndef set_service_extract_flags(cmd):\n\n prm = ShowOCIFlags()\n\n prm.showoci_version = version\n\n if cmd.proxy:\n prm.proxy = cmd.proxy\n\n if cmd.mgdcompart:\n prm.read_ManagedCompartmentForPaaS = False\n\n if cmd.all or cmd.identity:\n prm.read_identity = True\n\n if cmd.all or cmd.allnoiam or cmd.network:\n prm.read_network = True\n\n if cmd.all or cmd.allnoiam or cmd.compute:\n prm.read_compute = True\n\n if cmd.all or cmd.allnoiam or cmd.database:\n prm.read_database = True\n\n if cmd.all or cmd.allnoiam or cmd.file:\n prm.read_file_storage = True\n\n if cmd.all or cmd.allnoiam or cmd.object:\n prm.read_object_storage = True\n\n if cmd.all or cmd.allnoiam or cmd.orm:\n prm.read_resource_management = True\n\n if cmd.all or cmd.allnoiam or cmd.load:\n prm.read_load_balancer = True\n\n if cmd.all or cmd.allnoiam or cmd.email:\n prm.read_email_distribution = True\n\n if cmd.all or cmd.allnoiam or cmd.container:\n prm.read_containers = True\n\n if cmd.all or cmd.allnoiam or cmd.streams:\n prm.read_streams = True\n\n if cmd.all or cmd.allnoiam or cmd.budgets:\n prm.read_budgets = True\n\n if cmd.all or cmd.allnoiam or cmd.limits:\n prm.read_limits = True\n\n if cmd.all or cmd.allnoiam or cmd.monitoring:\n prm.read_monitoring_notifications = True\n\n if cmd.all or cmd.allnoiam or cmd.announcement:\n prm.read_announcement = True\n\n if cmd.all or cmd.allnoiam or cmd.edge:\n prm.read_edge = True\n\n if cmd.noroot:\n prm.read_root_compartment = False\n\n if cmd.config:\n if cmd.config.name:\n prm.config_file = cmd.config.name\n\n if cmd.profile:\n prm.config_section = cmd.profile\n\n if cmd.region:\n prm.filter_by_region = str(cmd.region)\n\n if cmd.compart:\n prm.filter_by_compartment = str(cmd.compart)\n\n if cmd.compartpath:\n prm.filter_by_compartment_path = str(cmd.compartpath)\n\n if cmd.instance_principals:\n prm.use_instance_principals = True\n\n return prm\n\n\n############################################\n# print data to json file\n############################################\ndef print_to_json_file(output, file_name, data, header):\n\n with open(file_name, 'w') as outfile:\n json.dump(data, outfile, indent=4, sort_keys=False)\n\n output.print_header(header + \" exported to \" + file_name, 0)\n\n\n##########################################################################\n# Main\n##########################################################################\nexecute_extract()\n","sub_path":"OCI_Python_Scripts/showoci/showoci.py","file_name":"showoci.py","file_ext":"py","file_size_in_byte":15198,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"605543238","text":"# Given Variables\nbalance = 320000\nannualInterestRate = 0.2\n\n# My Variables\nmonthly_interest = annualInterestRate / 12\nlower = balance / 12\nupper = (balance * (1 + monthly_interest) ** 12) / 12\npayment = (upper + lower) / 2\nepsilon = .01\n\n\n# Calculate Here While Loop is Broken, need to include interest\nwhile True:\n nb = balance\n\n for i in range(12):\n nb = (nb - payment) * (1 + monthly_interest)\n\n if nb < 0 and abs(nb) > epsilon:\n upper = payment\n elif nb > 0 and abs(nb) > epsilon:\n lower = payment\n else:\n break\n\n payment = (upper + lower) / 2\n\nprint(round(payment, 2))\n","sub_path":"6.00.1x/creditcard3.py","file_name":"creditcard3.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"25839211","text":"MEDIA_APP = 1\nMEDIA_MACAPP = 2\nMEDIA_IBOOK = 3\nMEDIA_TV = 4\nMEDIA_MOVIE = 5\nMEDIA_MUSIC = 6\nMEDIA_IPAD_APP = 7\n\nMEDIA_TYPE_CHOICES = (\n (MEDIA_APP, 'iOS App'),\n (MEDIA_MACAPP, 'Mac App'),\n (MEDIA_IBOOK, 'iBook'),\n (MEDIA_TV, 'TV'),\n (MEDIA_MOVIE, 'Movie'),\n (MEDIA_MUSIC, 'Music'),\n (MEDIA_IPAD_APP, 'iPad App'),\n)\n","sub_path":"bbase/bbase/main/globals.py","file_name":"globals.py","file_ext":"py","file_size_in_byte":336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"609021979","text":"import backtrader as bt\nimport pandas as pd\nfrom strategies.MacdLong import *\nfrom strategies.MacdShort import *\nfrom strategies.Momentum import *\nimport itertools\n\n\nclass Cerebro_GA(bt.Cerebro):\n def optstrategy_GA(self, strategy, paras, *args):\n self._dooptimize = True\n\n args = self.iterize(args)\n optargs = itertools.product(*args)\n\n paraskwargs = paras.to_dict('index')\n paraskwargsmap = map(dict, paraskwargs.values())\n # print(paraskwargsmap)\n it = itertools.product([strategy], optargs, paraskwargsmap)\n\n self.strats.append(it)\n # return it\n\n\ndef evaluate(candidates, data, no_paras,\n strat, stock, capital, testpara):\n\n cerebro = Cerebro_GA(stdstats=False)\n cerebro.broker.setcash(capital)\n if stock == 'HSI' or stock == 'hsi' or stock == 'future':\n cerebro.resampledata(data,\n timeframe=bt.TimeFrame.Minutes,\n compression=60)\n else:\n cerebro.adddata(data)\n cerebro.addanalyzer(bt.analyzers.Returns, _name='RT')\n if testpara['useMargin']:\n cerebro.broker.setcommission(margin=testpara['stop_loss'])\n # cerebro.addanalyzer(bt.analyzers.CalmarAlt, _name='CMA')\n # cerebro.addobserver(bt.observers.Value)\n # cerebro.addobserver(bt.observers.Trades)\n # cerebro.addobserver(bt.observers.BuySell)\n # cerebro.addobserver(bt.observers.DrawDown)\n\n paras = candidates.iloc[:, 0:no_paras].copy()\n paras.loc[:, 'stop_loss'] = testpara['stop_loss']\n paras.loc[:, 'usePercent'] = testpara['usePercent']\n paras.loc[:, 'useMargin'] = testpara['useMargin']\n if testpara['usePercent']:\n paras.loc[:, 'percent'] = testpara['percent']\n\n cerebro.optstrategy_GA(strat, paras)\n # print(it)\n # print(type(it))\n # for i in it:\n # print(i)\n\n result = cerebro.run()\n profit_list = []\n for run in result:\n for strategy in run:\n # profit = strategy.analyzers.getbyname('CMA').calmaralt\n # profit_list.append(profit)\n profit = strategy.analyzers.RT.get_analysis()\n profit_list.append(profit['rtot'])\n # print(type(strategy))\n # profit = (strategy.broker.get_value() - capital) / capital\n candidates['profit'] = pd.Series(profit_list)\n # print(profit_list)\n return candidates\n\n # print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())\n #\n # print('Final Portfolio: %.2f%%' %\n # (cerebro.broker.getvalue() * 100 / capital))\n","sub_path":"GA_fitness.py","file_name":"GA_fitness.py","file_ext":"py","file_size_in_byte":2548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"201777013","text":"import argparse\r\nimport pickle\r\nfrom predictor_2 import PitchDiffPredictor\r\nimport torch\r\nfrom dataset_former_note import PitchDiffDataset\r\n\r\nif __name__ == '__main__':\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument('train_dataset')\r\n parser.add_argument('val_dataset')\r\n\r\n args = parser.parse_args()\r\n\r\n with open(args.train_dataset, 'rb') as f:\r\n train_pkl = pickle.load(f)\r\n\r\n with open(args.val_dataset, 'rb') as f:\r\n val_pkl = pickle.load(f)\r\n\r\n device = 'cpu'\r\n if torch.cuda.is_available():\r\n device = 'cuda:0'\r\n\r\n predictor = PitchDiffPredictor(train_pkl, val_pkl, device=device, pretrained_path=None)\r\n pitch_curve_pred, pitch_curve_gt, segments = predictor.fit(model_dir=\"model\", save_prefix=\"pitch\", plot_path=\"loss.png\")\r\n predictor.plot_pitch_line(pitch_curve_gt, pitch_curve_pred, segments, plot_path=\"pitch.png\")\r\n # predictor.plot_pitch_line(test_dataset=val_pkl, plot_path=\"pitch.png\")","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"397948778","text":"\"\"\"\nraven.transport.http\n~~~~~~~~~~~~~~~~~~~~\n\n:copyright: (c) 2010-2012 by the Sentry Team, see AUTHORS for more details.\n:license: BSD, see LICENSE for more details.\n\"\"\"\nfrom __future__ import absolute_import\n\nimport sys\n\nfrom raven.conf import defaults\nfrom raven.transport.base import Transport\nfrom raven.utils import six\nfrom raven.utils.compat import urlopen, Request\n\n\nclass HTTPTransport(Transport):\n\n scheme = ['sync+http', 'sync+https']\n\n def __init__(self, parsed_url, timeout=defaults.TIMEOUT):\n self.check_scheme(parsed_url)\n\n self._parsed_url = parsed_url\n self._url = parsed_url.geturl().split('+', 1)[-1]\n\n if isinstance(timeout, six.string_types):\n timeout = int(timeout)\n self.timeout = timeout\n\n def send(self, data, headers):\n \"\"\"\n Sends a request to a remote webserver using HTTP POST.\n \"\"\"\n req = Request(self._url, headers=headers)\n\n if sys.version_info < (2, 6):\n response = urlopen(req, data).read()\n else:\n response = urlopen(req, data, self.timeout).read()\n return response\n","sub_path":"raven/transport/http.py","file_name":"http.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"324815656","text":"#!/usr/bin/env python\n\nfrom __future__ import print_function\n\nimport argparse\nimport base64\nimport json\nimport os\nimport os.path as osp\nimport shutil\n\nfrom labelme import utils\nimport PIL.Image\n\n\ndef save_image_from_json(json_file, out_dir):\n data = json.load(open(json_file))\n\n if data['imageData']:\n imageData = data['imageData']\n else:\n imagePath = osp.join(osp.dirname(json_file), data['imagePath'])\n with open(imagePath, 'rb') as f:\n imageData = f.read()\n imageData = base64.b64encode(imageData).decode('utf-8')\n img = utils.img_b64_to_arr(imageData)\n PIL.Image.fromarray(img).save(osp.join(out_dir, 'image.png'))\n\n\ndef save_label_from_json(json_file, out_dir):\n data = json.load(open(json_file))\n\n if data['imageData']:\n imageData = data['imageData']\n else:\n imagePath = osp.join(osp.dirname(json_file), data['imagePath'])\n with open(imagePath, 'rb') as f:\n imageData = f.read()\n imageData = base64.b64encode(imageData).decode('utf-8')\n img = utils.img_b64_to_arr(imageData)\n\n label_name_to_value = {'_background_': 0}\n for shape in sorted(data['shapes'], key=lambda x: x['label']):\n label_name = shape['label']\n if label_name in label_name_to_value:\n label_value = label_name_to_value[label_name]\n else:\n label_value = len(label_name_to_value)\n label_name_to_value[label_name] = label_value\n lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)\n\n label_names = [None] * (max(label_name_to_value.values()) + 1)\n for name, value in label_name_to_value.items():\n label_names[value] = name\n lbl_viz = utils.draw_label(lbl, img, label_names)\n\n utils.lblsave(osp.join(out_dir, 'label.png'), lbl)\n PIL.Image.fromarray(lbl_viz).save(\n osp.join(out_dir, 'label_viz.png'))\n\n with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:\n for lbl_name in label_names:\n f.write(lbl_name + '\\n')\n\n\ndef copy_depth(depth_file, out_dir):\n shutil.copy(depth_file, osp.join(out_dir, 'depth.npz'))\n\n\ndef main(src_dir, dst_dir, split):\n raw_split_dir = osp.join(src_dir, split)\n out_split_dir = osp.join(dst_dir, split)\n for stamp_dir in sorted(os.listdir(raw_split_dir)):\n json_file = osp.join(raw_split_dir, stamp_dir, 'image.json')\n if not osp.exists(json_file):\n print('{} does not exist.'.format(json_file))\n exit(1)\n\n depth_file = osp.join(raw_split_dir, stamp_dir, 'depth.npz')\n if not osp.exists(depth_file):\n print('{} does not exist.'.format(depth_file))\n exit(1)\n\n out_dir = osp.join(out_split_dir, stamp_dir)\n if not osp.exists(out_dir):\n os.makedirs(out_dir)\n\n save_image_from_json(json_file, out_dir)\n save_label_from_json(json_file, out_dir)\n copy_depth(depth_file, out_dir)\n print('Saved to: %s' % out_dir)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(\n '-s, ''--src_dir', type=str, required=True,\n help='Input data directory. It must have [split] dirs.')\n parser.add_argument(\n '-d', '--dst_dir', type=str, required=True,\n help='Output dataset directory.')\n\n args = parser.parse_args()\n src_dir = args.src_dir\n dst_dir = args.dst_dir\n\n for split in ['train', 'test']:\n main(src_dir, dst_dir, split)\n","sub_path":"learning_scripts/02_make_labeled_mirror_with_depth_dataset_from_json.py","file_name":"02_make_labeled_mirror_with_depth_dataset_from_json.py","file_ext":"py","file_size_in_byte":3483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"531674127","text":"import urllib2\r\nimport hashlib\r\nimport MySQLdb\r\nimport datetime\r\nimport time\r\n\r\nurl='https://www.python.org/'\r\n\r\nresponse = urllib2.urlopen(url)\r\nhtml = response.read()\r\nnow=datetime.datetime.now()\r\n\r\n#Hash md5\r\nhash=hashlib.md5(html).hexdigest()\r\n\r\n#Crear fichero html\r\nfrom datetime import datetime\r\ndatestring = datetime.strftime(datetime.now(), '%Y-%m-%d-%H-%M-%S')\r\n#Ejemplo de path c:\\XXXX\r\nfilename = open('c:\\\\XXXX\\\\html_' + datestring + '_' + hash + '.html', 'w')\r\nfilename.write(html)\r\nfilename.close()\r\n\r\n#Conexion Base de datos \r\nconn = MySQLdb.connect(host= \"XXXXXX\",\r\n user=\"XXXXXX\",\r\n passwd=\"XXXXX\",\r\n db=\"XXXXXX\")\r\ncursor = conn.cursor()\r\n \r\ncursor.execute (\"INSERT INTO XXXXX.watcher(URL,LastHash,DateLast) VALUES (%s,%s,%s)\",(url,hash,now))\r\nconn.commit()\r\n \r\n","sub_path":"WebHash.py","file_name":"WebHash.py","file_ext":"py","file_size_in_byte":830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"235508769","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Bookmark',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('title', models.CharField(max_length=200)),\n ],\n ),\n migrations.CreateModel(\n name='Friendship',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('from_friend', models.ForeignKey(related_name='friend_set', to=settings.AUTH_USER_MODEL)),\n ('to_friend', models.ForeignKey(related_name='to_friend_set', to=settings.AUTH_USER_MODEL)),\n ],\n ),\n migrations.CreateModel(\n name='Invitation',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=50)),\n ('email', models.EmailField(max_length=254)),\n ('code', models.CharField(max_length=20)),\n ('sender', models.ForeignKey(to=settings.AUTH_USER_MODEL)),\n ],\n ),\n migrations.CreateModel(\n name='Link',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('url', models.URLField(unique=True)),\n ],\n ),\n migrations.CreateModel(\n name='SharedBookmark',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('date', models.DateTimeField(auto_now_add=True)),\n ('votes', models.IntegerField(default=1)),\n ('bookmark', models.ForeignKey(to='bookmarks.Bookmark', unique=True)),\n ('users_voted', models.ManyToManyField(to=settings.AUTH_USER_MODEL)),\n ],\n ),\n migrations.CreateModel(\n name='Tag',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(unique=True, max_length=64)),\n ('bookmarks', models.ManyToManyField(to='bookmarks.Bookmark')),\n ],\n ),\n migrations.AddField(\n model_name='bookmark',\n name='link',\n field=models.ForeignKey(to='bookmarks.Link'),\n ),\n migrations.AddField(\n model_name='bookmark',\n name='user',\n field=models.ForeignKey(to=settings.AUTH_USER_MODEL),\n ),\n migrations.AlterUniqueTogether(\n name='friendship',\n unique_together=set([('to_friend', 'from_friend')]),\n ),\n ]\n","sub_path":"django_bookmarks/bookmarks/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":3151,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"333415618","text":"from collections import deque\nimport sys\ninput = sys.stdin.readline\n\nresult = []\n\nn, k = list(map(int, input().split()))\nq = deque()\n\nfor num in range(1, n+1):\n q.append(num)\n\nwhile(len(q) != 0):\n for _ in range(k-1):\n q.append(q.popleft())\n result.append(q.popleft())\n\nprint(\"<\", end =\"\")\nfor r in result[:-1]:\n print(r, end=\", \")\nprint(result[-1], end=\">\")\n","sub_path":"by date/2021.02.24/11866.py","file_name":"11866.py","file_ext":"py","file_size_in_byte":378,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"54601150","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[3]:\n\n\nimport pandas as pd\nimport numpy as np\n\nfrom keras import optimizers, losses, activations, models\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping, LearningRateScheduler, ReduceLROnPlateau\nfrom keras.layers import Dense, Input, Dropout, concatenate, LSTM, RNN, Embedding, Bidirectional\nfrom keras.models import Sequential\nfrom sklearn.metrics import f1_score, accuracy_score, roc_auc_score, average_precision_score\nfrom sklearn.model_selection import train_test_split\n\n\n# In[4]:\n\n\ndf_1 = pd.read_csv(\"../../input/ptbdb_normal.csv\", header=None)\ndf_2 = pd.read_csv(\"../../input/ptbdb_abnormal.csv\", header=None)\ndf = pd.concat([df_1, df_2])\n\ndf_train, df_test = train_test_split(df, test_size=0.2, random_state=1337, stratify=df[187])\n\n\nY = np.array(df_train[187].values).astype(np.int8)\nX = np.array(df_train[list(range(187))].values)[..., np.newaxis]\n\nY_test = np.array(df_test[187].values).astype(np.int8)\nX_test = np.array(df_test[list(range(187))].values)[..., np.newaxis]\n\n\n# In[11]:\n\n\ndef get_model():\n nclass = 1\n model = Sequential()\n model.add(Bidirectional(LSTM(10, return_sequences=True), input_shape=(187,1)))\n model.add(Bidirectional(LSTM(10)))\n model.add(Dense(1, activation=\"sigmoid\"))\n opt = optimizers.Adam(0.01)\n\n model.compile(optimizer=opt, loss=losses.binary_crossentropy, metrics=['acc'])\n model.summary()\n return model\n\n\n# In[ ]:\n\n\nmodel = get_model()\nfile_path = \"rnn_bi_ptbdb.h5\"\ncheckpoint = ModelCheckpoint(file_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max')\nearly = EarlyStopping(monitor=\"val_acc\", mode=\"max\", patience=6, verbose=1)\nredonplat = ReduceLROnPlateau(monitor=\"val_acc\", mode=\"max\", patience=3, verbose=2)\ncallbacks_list = [checkpoint, early, redonplat] # early\n\nmodel.fit(X, Y, epochs=1000, verbose=2, callbacks=callbacks_list, validation_split=0.1)\nmodel.load_weights(file_path)\n\n\n# In[7]:\n\n\npred_test = model.predict(X_test)\npred_test = (pred_test>0.5).astype(np.int8)\n\nf1 = f1_score(Y_test, pred_test)\n\nprint(\"Test f1 score : %s \"% f1)\n\n\n# In[8]:\n\n\nacc = accuracy_score(Y_test, pred_test)\n\nprint(\"Test accuracy score : %s \"% acc)\n\n\n# In[9]:\n\n\nroc = roc_auc_score(Y_test, pred_test)\n\nprint(\"Test auroc score : %s \"% roc)\n\n\n# In[10]:\n\n\nauprc = average_precision_score(Y_test, pred_test)\n\nprint(\"Test auprc score : %s \"% auprc)\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"ml_project_1_final/rnn_bi/RNNbi_ptbdb.py","file_name":"RNNbi_ptbdb.py","file_ext":"py","file_size_in_byte":2394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"529939915","text":"\"\"\"\r\nAuthor: John Schulz\r\nDate: 11/17/2020\r\nProject: Logic Gate Delayed Neural Network (LGDNN)\r\nDescription: Utilize Logic-Gates to create a network to solve different logic gates.\r\n\r\nNeural Architecture: 2 input nodes, 2 hidden layers, 1 output node.\r\nOutput Type: Boolean Output\r\nInput Type: Boolean Input\r\n\r\nResults:\r\n* Successfully forwardly computes: AND, OR, NOR, XNOR, NAND, XOR\r\n\"\"\"\r\n\r\n\r\nimport numpy as np\r\nimport logic\r\nbool = logic.bool()\r\n\r\n# --------------------------- User Vars ------------------------------\r\nmax_delay = 1 # Max Delay\r\n\r\n# --------------------------- Inputs ---------------------------------\r\ninput = np.array([[False, False], [False, True], [True, False], [True, True]])\r\n\r\n# ---------------------- Output Solutions ---------------------------\r\nand_table = np.array([False, False, False, True])\r\nnand_table = np.array([True, True, True, False])\r\nor_table = np.array([False, True, True, True])\r\nxor_table = np.array([False, True, True, False])\r\nxnor_table = np.array([True, False, False, True])\r\nnor_table = np.array([True, False, False, False])\r\n\r\n\r\n# Generate Random Delays\r\nd1 = np.random.randint(0, max_delay+1, size=6)\r\nd2 = np.random.randint(0, max_delay+1, size=9)\r\nd3 = np.random.randint(0, max_delay+1, size=3)\r\nd4 = np.random.randint(0, max_delay+1, size=1)\r\n\r\n\r\n\r\n\r\ndef forward_prop(input, d1, d2, d3, d4):\r\n\r\n s0 = input\r\n s1 = np.array([False, False, False])\r\n s2 = np.array([False, False, False])\r\n s3 = np.array([False])\r\n\r\n for tick in range(0, d4[0]+1):\r\n\r\n # Input --> Hidden Layer 1\r\n s1[0] = bool.OR(np.logical_and(d1[0:2] <= tick, s0))\r\n s1[1] = bool.NOR(np.logical_and(d1[2:4] <= tick, s0))\r\n s1[2] = bool.OR(np.logical_and(d1[4:6] <= tick, s0))\r\n\r\n # Hidden Layer 1 --> Hidden Layer 2\r\n s2[0] = bool.NOR(np.logical_and(d2[0:3] <= tick, s1))\r\n s2[1] = bool.OR(np.logical_and(d2[3:6] <= tick, s1))\r\n s2[2] = bool.NOR(np.logical_and(d2[6:9] <= tick, s1))\r\n\r\n # Hidden Layer 2 --> Output\r\n s3[0] = bool.XNOR(np.logical_and(d3 <= tick, s2))\r\n\r\n return s3[0]\r\n\r\n# Guess for a solution\r\ndef rand_find_solution(input_table, max_delay):\r\n\r\n # Init Var\r\n output = np.array([False, False, False, False])\r\n\r\n # Keep searching until solution found\r\n while not(np.array_equal(input_table, output)):\r\n\r\n # Generate Random Delays\r\n d1 = np.random.randint(0, max_delay+1, size=6)\r\n d2 = np.random.randint(0, max_delay+1, size=9)\r\n d3 = np.random.randint(0, max_delay+1, size=3)\r\n d4 = np.random.randint(0, max_delay+1, size=1)\r\n\r\n for index in range(0, 4):\r\n output[index] = forward_prop(input[index, :], d1, d2, d3, d4)\r\n\r\n # Print solution\r\n if np.array_equal(input_table, output):\r\n print(d1, d2, d3, d4)\r\n\r\n for index in range(0, 4):\r\n print(forward_prop(input[index, :], d1, d2, d3, d4))\r\n\r\n\r\n# -------------------------- Print Results ----------------------------\r\nprint('XNOR:')\r\nrand_find_solution(xnor_table, max_delay)\r\nprint('\\nNOR')\r\nrand_find_solution(nor_table, max_delay)\r\nprint('\\nOR')\r\nrand_find_solution(or_table, max_delay)\r\nprint('\\nNAND')\r\nrand_find_solution(nand_table, max_delay)\r\nprint('\\nXOR')\r\nrand_find_solution(xor_table, max_delay)\r\nprint('\\nAND')\r\nrand_find_solution(and_table, max_delay)\r\n\r\n\"\"\"\r\nprint(d1, d2, d3, d4)\r\n\r\nfor index in range(0, 4):\r\n print(forward_prop(input[index, :], d1, d2, d3, d4))\r\n\"\"\"\r\n","sub_path":"LGDNNv2/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"235813825","text":"from bootstrap3_datetime.widgets import DateTimePicker\nfrom crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import Submit, Layout\nfrom django import forms\nfrom django.conf import settings\nfrom phonenumber_field.widgets import PhoneNumberInternationalFallbackWidget\n\nfrom .models import Order\n\n\nclass OrderForm(forms.ModelForm):\n\n class Meta:\n model = Order\n fields = (\n 'quantity',\n 'requester_name',\n 'requester_email',\n 'requester_phone_number',\n 'pickup_date',\n 'comments',)\n\n def __init__(self, *args, submit_button_name=\"Submit\", **kwargs):\n super(OrderForm, self).__init__(*args, **kwargs)\n self.fields['requester_name'].label = \"Your full name\"\n self.fields['requester_email'].label = \"Your email address\"\n self.fields['requester_phone_number'].label = \"Your phone number\"\n self.fields['quantity'].label = \"Number of pounds of blueberries\"\n self.fields['requester_phone_number'].widget = PhoneNumberInternationalFallbackWidget(\n region='us')\n self.fields['pickup_date'].widget = \\\n DateTimePicker(options={\"format\": \"MM/DD/YYYY\"})\n\n if settings.DEBUG:\n self.fields['quantity'].initial = 1000\n self.fields['requester_name'].initial = \"Charles Reid\"\n self.fields['requester_email'].initial = \"creid@example.com\"\n self.fields['requester_phone_number'].initial = \"5035555678\"\n self.fields['pickup_date'].initial = \"06/20/2017\"\n\n self.helper = FormHelper()\n self.helper.form_method = 'POST'\n self.helper.form_action = 'submit_survey'\n self.helper.form_class = 'form-inline'\n self.helper.field_template = 'bootstrap3/layout/inline_field.html'\n self.helper.layout = Layout(*self.fields.keys())\n self.helper.add_input(Submit('submit', submit_button_name))\n","sub_path":"orders/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"404874672","text":"from gtnlplib.preproc import conll_seq_generator\nfrom gtnlplib.constants import START_TAG, TRANS, END_TAG, EMIT, OFFSET\nfrom gtnlplib import naive_bayes, most_common\nimport numpy as np\nfrom collections import defaultdict\n\ndef hmm_features(tokens,curr_tag,prev_tag,m):\n \"\"\"Feature function for HMM that returns emit and transition features\n\n :param tokens: list of tokens \n :param curr_tag: current tag\n :param prev_tag: previous tag\n :param i: index of token to be tagged\n :returns: dict of features and counts\n :rtype: dict\n\n \"\"\"\n returnDict = {}\n if(curr_tag!=END_TAG):\n returnDict[(curr_tag,tokens[m],EMIT)]=1\n returnDict[(curr_tag,prev_tag,TRANS)]=1\n else:\n returnDict[(curr_tag,prev_tag,TRANS)]=1\n return returnDict;\n \n\ndef compute_HMM_weights(trainfile,smoothing):\n \"\"\"Compute all weights for the HMM\n\n :param trainfile: training file\n :param smoothing: float for smoothing of both probability distributions\n :returns: defaultdict of weights, list of all possible tags (types)\n :rtype: defaultdict, list\n\n \"\"\"\n # hint: these are your first two lines\n tag_trans_counts = most_common.get_tag_trans_counts(trainfile)\n all_tags = tag_trans_counts.keys()\n\n # hint: call compute_transition_weights\n weights = compute_transition_weights(tag_trans_counts,smoothing)\n\n # hint: set weights for illegal transitions to -np.inf\n for prev_tag in all_tags:\n weights[(prev_tag,END_TAG,TRANS)]=-np.inf\n weights[(END_TAG,END_TAG,TRANS)]=-np.inf\n weights[(START_TAG,prev_tag,TRANS)]=-np.inf\n weights[(START_TAG,START_TAG,TRANS)]=-np.inf\n weights[(START_TAG,END_TAG,TRANS)]=-np.inf\n weights[(END_TAG,START_TAG,TRANS)]=-np.inf\n\n # hint: call get_tag_word_counts and estimate_nb_tagger\n tag_word_counts = most_common.get_tag_word_counts(trainfile);\n # print tag_word_counts\n counter_items = np.array(tag_word_counts.items())\n counters = counter_items[:,1] \n update_nb_tagger = defaultdict(float)\n nb_tagger = naive_bayes.estimate_nb_tagger(tag_word_counts,smoothing)\n for key in nb_tagger:\n value = nb_tagger[key];\n # print \"key: \", key\n # print type(key)\n # print \"value: \", value\n if(key[0]!=OFFSET and key[1]!=OFFSET):\n new_key = (key[0],key[1],EMIT)\n update_nb_tagger[new_key]=value\n # print nb_tagger\n # print counters\n # print \"counters: \", counters\n # for count in counters:\n # nb_tagger = naive_bayes.estimate_nb_tagger(counters,smoothing)\n # print \"nb_tagger: \", nb_tagger\n # hint: Counter.update() combines two Counters\n newDict = defaultdict(float)\n newDict.update(update_nb_tagger)\n newDict.update(weights)\n\n # hint: return weights, all_tags\n return newDict, all_tags\n # raise NotImplementedError\n\n\ndef compute_transition_weights(trans_counts, smoothing):\n \"\"\"Compute the HMM transition weights, given the counts.\n Don't forget to assign smoothed probabilities to transitions which\n do not appear in the counts.\n \n This will also affect your computation of the denominator.\n\n :param trans_counts: counts, generated from most_common.get_tag_trans_counts\n :param smoothing: additive smoothing\n :returns: dict of features [(curr_tag,prev_tag,TRANS)] and weights\n\n \"\"\"\n weights = defaultdict(float)\n counter_items = np.array(trans_counts.items())\n y = counter_items[:,0] ### List of labels\n x = counter_items[:,1] ### List of Counters\n for prev_tag in y:\n denominator = trans_counts[prev_tag]\n denominator_value = np.sum(denominator.values())+(len(y)*smoothing)\n for current_tag in y:\n nominator = trans_counts[prev_tag][current_tag]\n nominator+=smoothing\n denominator = trans_counts[prev_tag]\n weights[(current_tag,prev_tag,TRANS)] = np.log(nominator/denominator_value)\n # weights[(prev_tag,END_TAG,TRANS)]=0\n weights[(START_TAG,prev_tag,TRANS)]=-np.inf\n for prev_tag in y:\n denominator = trans_counts[prev_tag]\n denominator_value = np.sum(denominator.values())+(len(y)*smoothing)\n nominator = trans_counts[prev_tag][END_TAG] + smoothing\n weights[(END_TAG,prev_tag,TRANS)] = np.log(nominator/denominator_value)\n return weights\n \n\n","sub_path":"2017-materials/psets/ps2/gtnlplib/hmm.py","file_name":"hmm.py","file_ext":"py","file_size_in_byte":4346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"561422195","text":"import os\n\nimport requests\nfrom parsel import Selector\n\nfrom libs.polish import polish_content, polish_title\n\n\ndef parse_content(url):\n page = requests.get(url)\n html = page.text\n sel = Selector(text=html)\n title = sel.xpath('//h1/text()').extract()[0]\n title = polish_title(title, 'yushuwu')\n\n file_path = os.path.join(os.getcwd(), '..')\n file_path = os.path.join(file_path, 'userData')\n file_path = os.path.join(file_path, 'downloads')\n file_path = os.path.join(file_path, title + '.txt')\n print(file_path)\n if os.path.isfile(file_path):\n return 0\n\n article = ''\n idx = 1\n while True:\n html = page.text\n sel = Selector(text=html)\n contents = sel.xpath('//div[@id=\"nr1\"]/p/text()').extract()\n cc = polish_content(contents)\n article += cc\n\n tmp = sel.xpath('//div[@class=\"nr_page\"]/table/tr')\n next_page = tmp.xpath('td[@class=\"next\"]/a/@href').extract()[0]\n mulu = tmp.xpath('td[@class=\"mulu\"]/a/@href').extract()[0]\n if next_page == mulu:\n break\n page = requests.get(next_page)\n idx += 1\n print(idx, next_page)\n\n save_to_file(file_path, article)\n\ndef save_to_file(file_path, article):\n print(article)\n with open(file_path, 'w', encoding='utf-8') as f:\n f.write(article)\n\n\nif __name__ == '__main__':\n url = 'https://m.yubook.net/novel/61472/7940223.html'\n parse_content(url)\n\n","sub_path":"simpleSpiders/m-yubook-net.py","file_name":"m-yubook-net.py","file_ext":"py","file_size_in_byte":1443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"218161347","text":"\"\"\"\n48. How to format all the values in a dataframe as percentages?\n\"\"\"\n\"\"\"\nDifficulty Level: L2\n\"\"\"\n\"\"\"\nFormat the values in column 'random' of df as percentages.\n\"\"\"\n\"\"\"\nInput\n\"\"\"\n\"\"\"\ndf = pd.DataFrame(np.random.random(4), columns=['random'])\ndf\n#> random\n#> 0 .689723\n#> 1 .957224\n#> 2 .159157\n#> 3 .21082\n\"\"\"\n\"\"\"\nDesired Output\n\"\"\"\n\"\"\"\n#> random\n#> 0 68.97%\n#> 1 95.72%\n#> 2 15.91%\n#> 3 2.10%\n\"\"\"\n\n# Input\ndf = pd.DataFrame(np.random.random(4), columns=['random'])\n\n# Solution\nout = df.style.format({\n 'random': '{0:.2%}'.format,\n})\n\nout","sub_path":"pset_pandas_ext/101problems/solutions/p48.py","file_name":"p48.py","file_ext":"py","file_size_in_byte":578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"609070812","text":"from django.shortcuts import render, redirect\nfrom .models import Dish, Reservation, Order, TodaysSpecials\nfrom django.urls import reverse\nfrom datetime import datetime\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom foodcart.views import cart_clear_silent\nfrom django.core.exceptions import ObjectDoesNotExist\n# Create your views here.\ndef home(request):\n menu=dict()\n menu['Starters']=Dish.objects.filter(dish_type='Starters')|Dish.objects.filter(dish_type='Momos')|Dish.objects.filter(dish_type='Soups')\n menu['Main_Dishes']=Dish.objects.filter(dish_type='Main Course')|Dish.objects.filter(dish_type='Bread')|Dish.objects.filter(dish_type='Others')\n menu['Noodles_and_Rice']=Dish.objects.filter(dish_type='Fried Rice and Noodles')\n menu['Rolls']=Dish.objects.filter(dish_type='Rolls')\n todaysSpecials=TodaysSpecials.objects.last().dishes.all()\n return render(request,'index.html',{'menu':menu, 'todaysSpecials': todaysSpecials})\n\ndef reservation(request):\n if request.method=='POST':\n reservation=Reservation(\n name=request.POST.get('form_name'),\n email=request.POST.get('email'),\n phone_number=(request.POST.get('phone')),\n num_people=request.POST.get('no_of_persons'),\n date_and_time=datetime.strptime('{} {}'.format(\n request.POST.get('date-picker'),\n request.POST.get('time-picker')\n ), '%d.%m.%Y %H:%M %p'),\n )\n if request.POST.get('occasion'):\n reservation.occasion=request.POST.get('occasion')\n reservation.save()\n if request.user.is_authenticated:\n request.user.reservation_set.add(reservation)\n messages.add_message(request,messages.INFO, 'Your Reservation Has been Made Successfully')\n return redirect(reverse('home'))\n return render(request,'reservation.html')\n\n@login_required\ndef order(request):\n if 'cart' not in request.session:\n return redirect(reverse('home'))\n menu=dict()\n menu['Starters']=list(Dish.objects.filter(dish_type='Starters')|Dish.objects.filter(dish_type='Momos')|Dish.objects.filter(dish_type='Soups'))\n menu['Main_Dishes']=list(Dish.objects.filter(dish_type='Main Course')|Dish.objects.filter(dish_type='Bread')|Dish.objects.filter(dish_type='Others'))\n menu['Noodles_and_Rice']=list(Dish.objects.filter(dish_type='Fried Rice and Noodles'))\n menu['Rolls']=list(Dish.objects.filter(dish_type='Rolls'))\n subtotal=sum((int(request.session['cart'][i]['price'])*int(request.session['cart'][i]['quantity']) for i in request.session['cart']))\n if 'q' in request.GET:\n for dishType in menu:\n menu[dishType]=list(filter(lambda x: (request.GET['q'].lower() in x.name.lower()) or (request.GET['q'].lower() in x.description.lower()), menu[dishType]))\n return render(request, 'order.html', {'menu': menu, 'subtotal': subtotal, 'searched_for': request.GET['q']})\n else:\n return render(request, 'order.html', {'menu':menu, 'subtotal': subtotal})\n\n@login_required\ndef checkout(request):\n if 'cart' not in request.session:\n return redirect(reverse('home'))\n subtotal=sum((int(request.session['cart'][i]['price'])*int(request.session['cart'][i]['quantity']) for i in request.session['cart']))\n delivery_charge = 20 if subtotal<300 else 0\n to_pay = subtotal+delivery_charge\n try:\n currentCustomer=request.user.customer\n except ObjectDoesNotExist:\n messages.add_message(request,messages.INFO,\"Please complete your profile to place an order.\")\n return redirect(reverse('profile_create'))\n if request.method=='POST':\n try:\n dishesOrdered={ request.session['cart'][i]['product_id']:(request.session['cart'][i]['name'],request.session['cart'][i]['quantity']) for i in request.session['cart'] }\n order=Order(\n date_and_time=datetime.now(),\n customer=currentCustomer,\n dishes_ordered=repr(dishesOrdered),\n dishes_total=subtotal,\n delivery_charge=delivery_charge,\n delivery_address=request.POST.get('delivery_address'),\n )\n order.save()\n messages.add_message(request, messages.INFO, 'Order placed Successfully. Your Food is on it\\'s way!')\n cart_clear_silent(request)\n return redirect(reverse('home'))\n except:\n messages.add_message(request, messages.INFO, \"Something went wrong! Please order by call or try after sometime.\")\n return redirect(reverse('home'))\n else:\n return render(request,'checkout.html',{'subtotal': subtotal, 'delivery_charge': delivery_charge, 'to_pay': to_pay})\n","sub_path":"coreapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"76327901","text":"from itertools import count, cycle\n\nfor i in count(10):\n if i > 20:\n break\n else:\n print(i)\n\n\nresult = 0\nfor el in cycle('PYTHON'):\n if result > 20:\n break\n print(el)\n result += 1\n","sub_path":"lesson04/task_6.py","file_name":"task_6.py","file_ext":"py","file_size_in_byte":216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"22002313","text":"import sys\nimport requests\nimport json\nimport time\nimport random\n\ndef check(player, hero, mode):\n url = 'https://owapi.net/api/v3/u/'+player+'/blob'\n time.sleep(random.randint(1,7))\n\n #Prepare the connections for the HTTPS Request\n debug = {'verbose': sys.stderr}\n user_agent = {'User-agent': 'Mozilla/5.0'}\n imprime = requests.get(url, headers = user_agent)\n toParse = imprime.json()\n print(url)\n\n #Check if user exist\n try:\n print('Error: ',toParse['error'])\n\n if(toParse['error'] == 429):\n return check(player, hero, mode)\n else:\n return 'Usuario no existe'\n except:\n pass\n\n #Check if hero is played long enough to get data\n if toParse['eu']['heroes']['playtime'][mode][hero] < 1:\n return 'Tiempo de juego insuficiente'\n\n #If all requirements met, give permission to scrap data\n return [True, toParse]\n\n\n#return True if the message has already been processed or False in it hasn't\ndef processed_or_not(dm_id):\n\n id_list = []\n evaluar = str(dm_id)+'\\n'\n\n #opens the list of processed messages and converts it to a list\n rFich = open('processed.txt', 'r')\n for a in rFich:\n id_list.append(a)\n\n #check if the message is in the list aka was already processed\n if evaluar in id_list:\n print('Mensaje ya procesado')\n return False\n\n #writes the id of the message and gives permission to the main to continue\n else:\n wFich = open('processed.txt', 'a')\n wFich.write(evaluar)\n wFich.close()\n\n return True\n","sub_path":"check.py","file_name":"check.py","file_ext":"py","file_size_in_byte":1577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"40145141","text":"import os\nimport cv2\nimport numpy as np\n\n# return path of all image in folder\ndef get_filepaths(directory):\n file_paths = []\n\n for root, directories, files in os.walk(directory):\n for filename in files:\n filepath = os.path.join(root, filename)\n file_paths.append(filepath)\n\n return file_paths\n\n#convert image into binary matrix\ndef convert_image(path):\n img = cv2.imread(path,0)\n\n row, column = img.shape\n\n for x in range(row):\n for y in range(column):\n if img[x][y] == 255:\n img[x][y] = 1\n else:\n img[x][y] = 0\n return img\n\n#convert data to training set\ndef image_to_matrix(lis_dir):\n lis = np.array([])\n lis_test = np.array([])\n\n for i in range(7500):\n if i < 7500*3/4:\n matrix = convert_image(lis_dir[i])\n matrix = matrix.reshape((1,400))\n matrix = np.insert(matrix,0,[1])\n lis = np.append(lis, matrix)\n else:\n matrix = convert_image(lis_dir[i])\n matrix = matrix.reshape((1,400))\n matrix = np.insert(matrix,0,[1])\n lis_test = np.append(lis_test, matrix)\n x = 7500*3/4\n y = 7500 - 7500*3/4\n return lis, x, lis_test, y\n\n\n\ndef getdata():\n #lis = os.listdir()\n lis = get_filepaths('/home/thangnx/code/triangletest/triangle_competition/train/triangle')\n X1, len1, X1_test, len_test1 = image_to_matrix(lis)\n Y1 = np.ones((len1, 1))\n Y1_test = np.ones((len_test1, 1))\n\n lis = get_filepaths('/home/thangnx/code/triangletest/triangle_competition/train/non-triangle')\n X2, len2, X2_test, len_test2 = image_to_matrix(lis)\n Y2 = np.zeros((len2, 1))\n Y2_test = np.zeros((len_test2, 1))\n\n X = np.append(X1, X2)\n X = X.reshape((len1 + len2, 401)) \n X = np.matrix(X, dtype = float)\n\n X_test = np.append(X1_test, X2_test)\n X_test = X_test.reshape((len_test1 + len_test2, 401))\n X_test = np.matrix(X_test, dtype = float)\n\n Y = np.append(Y1, Y2)\n Y = Y.reshape((len1 + len2, 1))\n Y = np.matrix(Y.reshape((len1+len2,1)), dtype = float)\n\n Y_test = np.append(Y1_test, Y2_test)\n Y_test = np.matrix(Y_test.reshape((len_test1 + len_test2, 1)), dtype =float)\n\n return X, Y, X_test, Y_test\n\ndef get_final_test():\n lisdir = os.listdir('/home/thangnx/code/triangletest/triangle_competition/test')\n x = len(lisdir)\n lis = np.array([])\n\n for each in lisdir:\n matrix = convert_image('/home/thangnx/code/triangletest/triangle_competition/test/'+each)\n matrix = matrix.reshape((1,400))\n matrix = np.insert(matrix,0,[1])\n lis = np.append(lis, matrix)\n \n Xtest = np.matrix(lis.reshape((x, 401)))\n #Xtest = lis.reshape((x,400)) #knn\n \n return Xtest, lisdir\n\ndef result(h, lisdir):\n f = open('result_knn1.txt','w')\n\n for i in range(len(h)):\n f.write(str(h[i])+ str(' ') + str(lisdir[i]) +str('\\n') )\n\n return\n","sub_path":"readfile.py","file_name":"readfile.py","file_ext":"py","file_size_in_byte":2938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"489008364","text":"import math\n\n\n\n\nclass super_point():\n def __init__(self,x,y):\n self.x=x\n self.y=y\n print('父类初始化')\n\nclass point(super_point):\n def __init__(self,x,y):\n super().__init__(x,y)\n\n def distance(self,object):\n dis=math.sqrt((self.x-object.x)**2+(self.y-object.y)**2)\n return dis\n\n\n\nif __name__==\"__main__\":\n\n p1=point(3,4)\n p2=point(6,8)\n a=p1.distance(p2)\n print(a)\n\n","sub_path":"fit9133-m3-p2/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"174231067","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /home/pawel/Dropbox/NiftyPET/NIPET/niftypet/nipet/dinf/__init__.py\n# Compiled at: 2020-01-22 17:43:14\n# Size of source mod 2**32: 271 bytes\nfrom .dinf import dev_info\n\ndef gpuinfo(extended=False):\n \"\"\" Run the CUDA dev_info shared library to get info about the installed GPU devices. \n \"\"\"\n if extended:\n info = dev_info(1)\n print(info)\n else:\n info = dev_info(0)\n return info","sub_path":"pycfiles/nipet-1.1.21.tar/__init__.cpython-37.py","file_name":"__init__.cpython-37.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"570689029","text":"from basemodel import *\nfrom factory import *\n\ndef get_frames(videoname):\n frame = []\n video = Video.get(videoname)\n width = video.width\n height = video.height\n frame_count = video.frame_count\n step = video.step\n current = 0;\n while current < frame_count - 1:\n fname = '%s/%s.jpg' % (videoname, str(current))\n frame.push(path.join(FRAME_DIR,fname))\n current = current + step\n\n fname = '%s/%s.jpg' % (videoname, str(frame_count - 1))\n frame.push(path.join(FRAME_DIR,fname))\n\n return frame\n","sub_path":"libs/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"88920423","text":"from django.shortcuts import render, render_to_response\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.http import HttpResponse\nfrom django.template import RequestContext\nfrom app.models import Word\nfrom django.template import Context\nfrom django.template.loader import get_template\nimport predict\nimport os,time\nimport subprocess as sub\nimport json\n# Create your views here.\n\ndef home(request):\n\treturn render(request,'sign.html')\n\n@csrf_exempt\ndef keyword(request):\n\treturn render(request,'keyword.html',content_type=\"\")\n\n@csrf_exempt\ndef text(request):\n\treturn render(request,'textword.html',content_type=\"\")\n\n@csrf_exempt\ndef keyres(request):\n\tif request.method == \"POST\":\n\t\tm = request.POST.get('key')\n\ttemp = get_template('keyres.html')\n\n\ttweet=[]\n\tsentiment=[]\n\tcount1=0\n\tcount2=0\n\tvar=['java -classpath' , '\"lib/*:.\"' , 'SimpleStream2']\n\tvar.append(m)\n\tcommand = \" \".join(var)\n\tp = os.popen(command,\"r\")\n\tcount=0\n\twhile count<13:\n\t\tline = p.readline()\n\t\tif count>2:\n\t\t\tif count%2==0:\n\t\t\t\tif \"Positive\" in line:\n\t\t\t\t\tcount1+=1\n\t\t\t\t\tsentiment.append(line)\n\t\t\t\telif \"Negative\" in line:\n\t\t\t\t\tcount2+=1\n\t\t\t\t\tsentiment.append(line)\n\t\t\t\telse:\n\t\t\t\t\tsentiment.append(\"No keyword found\")\n\t\t\telif count%2==1:\n\t\t\t\ttweet.append(line)\n\t\tcount+=1\n\n\tjson_tweet = json.dumps(tweet)\n\tjson_sentiment = json.dumps(sentiment)\n\n\tcont = RequestContext(request,{'tweets_array':json_tweet,'sent_array':json_sentiment,'count1':count1,'count2':count2})\n\n\n\treturn HttpResponse(temp.render(cont))\n\n\n@csrf_exempt\ndef normalres(request):\n\ttemp = get_template('normalres.html')\n\ttweet=[]\n\tsentiment=[]\n\tcount1=0\n\tcount2=0\n\tvar=['java -classpath' , '\"lib/*:.\"' , 'SimpleStream']\n\tcommand = \" \".join(var)\n\tp = os.popen(command,\"r\")\n\tcount=0\n\twhile count<13:\n\t\tline = p.readline()\n\t\tif count>2:\n\t\t\tif count%2==0:\n\t\t\t\tif \"Positive\" in line:\n\t\t\t\t\tcount1+=1\n\t\t\t\t\tsentiment.append(line)\n\t\t\t\telif \"Negative\" in line:\n\t\t\t\t\tcount2+=1\n\t\t\t\t\tsentiment.append(line)\n\t\t\t\telse:\n\t\t\t\t\tsentiment.append(\"No keyword found\")\n\t\t\telif count%2==1:\n\t\t\t\ttweet.append(line)\n\t\t\n\n\tjson_tweet = json.dumps(tweet)\n\tjson_sentiment = json.dumps(sentiment)\n\n\tcont = RequestContext(request,{'tweets_array':json_tweet,'sent_array':json_sentiment,'count1':count1,'count2':count2})\n\n\n\treturn HttpResponse(temp.render(cont))\n\n\n\n@csrf_exempt\ndef textres(request):\n\tif request.method == \"POST\":\n\t\tx = request.POST.get('content')\n\ttemp = get_template('textres.html')\n\tnew_x = str(x)\n\n\tval = predict.main(new_x)\n\n\tcont = RequestContext(request,{'string':x,'sentiment':val})\n\treturn HttpResponse(temp.render(cont))","sub_path":"backend/app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"448793995","text":"# Copyright (C) 2017 Google Inc.\n# Licensed under http://www.apache.org/licenses/LICENSE-2.0 \n\"\"\"Utility classes and functions for page objects used in tests.\nDetails:\nMost of tests require sequence of primitive methods of page\nobject. If sequence repeats itself among tests, it should be shared in\nthis module.\n\"\"\"\n# pylint: disable=too-few-public-methods\n\nimport re\nimport uuid\n\nfrom lib import base\nfrom lib.constants.test import modal_create_new, modal_custom_attribute\n\n\ndef append_random_string(text):\n return text + str(uuid.uuid4())\n\n\ndef prepend_random_string(text):\n return str(uuid.uuid4()) + text\n\n\nclass HtmlParser(object):\n \"\"\"The HtmlParser class simulates what happens with (non-rich)text in HTML.\n \"\"\"\n @staticmethod\n def parse_text(text):\n \"\"\"Simulates text parsed by html.\n Args: text (basestring)\n \"\"\"\n return re.sub(r'\\s+', \" \", text)\n\n\nclass ModalInput(base.TestUtil):\n \"\"\"Base class for filling out modals.\"\"\"\n @staticmethod\n def enter_test_data(modal):\n modal.enter_title(append_random_string(modal_create_new.SHORT_TITLE))\n\n\nclass ModalNewControls(ModalInput):\n \"\"\"Methods for simulating common user actions.\"\"\"\n\n @staticmethod\n def enter_test_data(modal):\n \"\"\"Fills out all fields in lhn_modal.\n Args: modal (lib.page.lhn_modal.new_control.Controls)\n \"\"\"\n modal.enter_title(append_random_string(modal_create_new.SHORT_TITLE))\n modal.enter_description(append_random_string(\n modal_create_new.SHORT_TITLE))\n modal.enter_test_plan(append_random_string(\n modal_create_new.SHORT_TITLE))\n modal.enter_notes(append_random_string(modal_create_new.SHORT_TITLE))\n modal.enter_code(append_random_string(modal_create_new.SHORT_TITLE))\n\n\nclass ModalNewPrograms(ModalInput):\n \"\"\"Methods for simulating common user actions.\"\"\"\n\n @staticmethod\n def set_start_date(modal, day_start):\n \"\"\"Sets start date from datepicker in new program/edit modal.\n Args:\n modal (lib.page.modal.edit_object.Programs)\n day_start (int): for more info see\n base.DatePicker.select_day_in_current_month\n \"\"\"\n modal.enter_effective_date_start_month(day_start)\n\n @staticmethod\n def enter_test_data(modal):\n \"\"\"Fills out all fields in lhn_modal.\n Args: modal (lib.page.modal.edit_object.Programs)\n \"\"\"\n modal.enter_title(append_random_string(modal_create_new.SHORT_TITLE))\n modal.enter_description(append_random_string(\n modal_create_new.SHORT_TITLE))\n modal.enter_notes(append_random_string(modal_create_new.SHORT_TITLE))\n modal.enter_code(append_random_string(modal_create_new.SHORT_TITLE))\n modal.enter_program_ref_url(prepend_random_string(\n modal_create_new.Programs.PROGRAM_URL))\n ModalNewPrograms.set_start_date(modal, 0)\n\n\nclass ModalNewProgramCustomAttribute(ModalInput):\n @staticmethod\n def enter_test_data(modal):\n \"\"\"Fills out all fields in lhn_modal.\n Args: modal (lib.page.modal.custom_attribute.ModalCustomAttributes)\n \"\"\"\n modal.enter_title(append_random_string(\n modal_custom_attribute.Programs.TITLE))\n modal.enter_inline_help(append_random_string(\n modal_custom_attribute.Programs.INLINE_HELP))\n modal.enter_placeholder(append_random_string(\n modal_custom_attribute.Programs.PLACEHOLDER))\n\n\nclass ModalNewOrgGroups(ModalInput):\n \"\"\"Utils for data entry for Org Group modals.\"\"\"\n\n\nclass ModalNewRisks(ModalInput):\n \"\"\"Utils for data entry for Risk modals.\"\"\"\n @staticmethod\n def enter_test_data(modal):\n \"\"\"Fills out all fields in lhn_modal.\n Args: modal (lib.page.modal.edit_object.EditRisksModal)\n \"\"\"\n modal.enter_title(append_random_string(modal_create_new.SHORT_TITLE))\n modal.enter_description(append_random_string(\n modal_create_new.SHORT_TITLE))\n\n\nclass ModalNewIssues(ModalInput):\n \"\"\"Utils for data entry for Issues modals.\"\"\"\n\n\nclass ModalNewProcesses(ModalInput):\n \"\"\"Utils for data entry for Process modals.\"\"\"\n\n\nclass ModalNewDataAssets(ModalInput):\n \"\"\"Utils for data entry for Data asset modals.\"\"\"\n\n\nclass ModalNewSystems(ModalInput):\n \"\"\"Utils for data entry for System modals.\"\"\"\n\n\nclass ModalNewProducts(ModalInput):\n \"\"\"Utils for data entry for Product modals.\"\"\"\n\n\nclass ModalNewProjects(ModalInput):\n \"\"\"Utils for data entry for Project modals.\"\"\"\n","sub_path":"test/selenium/src/lib/utils/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":4294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"189887344","text":"import torch\nimport pytest\nfrom binding_prediction.layers import GraphAndConv\n\n\ndef _permute_tensors(features, adj_mats, p_indices):\n permuted_adj_mats = []\n permuted_feature_mats = []\n for i, pidx in enumerate(p_indices):\n permuted_adj_mats.append(adj_mats[i][pidx][:, pidx])\n permuted_feature_mats.append(features[i][pidx])\n permuted_adj_mats = torch.stack(permuted_adj_mats, dim=0)\n permuted_feature_mats = torch.stack(permuted_feature_mats, dim=0)\n return permuted_feature_mats, permuted_adj_mats\n\n\nclass TestGraphAndConv(object):\n @pytest.mark.parametrize('num_intermediate', [None, 4])\n def test_permutation_equivariance(self, sample_batch, num_intermediate):\n features, adj_mats = sample_batch\n B, N, __ = adj_mats.shape\n p_indices = [torch.randperm(N) for i in range(B)]\n\n feature_perm, adj_mats_perm = _permute_tensors(features, adj_mats, p_indices)\n\n gconv = GraphAndConv(3, 4, 1, intermediate_dim = num_intermediate)\n output = gconv(adj_mats, features)\n permed_output = _permute_tensors(output, adj_mats, p_indices)[0]\n # print(feature_perm.shape, adj_mats_perm.shape, 'blah!')\n output_from_perm = gconv(adj_mats_perm, feature_perm)\n # print(output_from_perm.shape, permed_output.shape, 'perm')\n assert(torch.norm(permed_output - output_from_perm) < 1e-4)\n\n def test_translational_equivariance(self, sample_batch):\n features, adj_mats = sample_batch\n features[:, :, -1] = 0.\n trans_features = torch.zeros(features.shape)\n trans_features[:, :, 1:] = features[:, :, :-1]\n\n gconv = GraphAndConv(3, 4, 1)\n output = gconv(adj_mats, features)\n output_from_translation = gconv(adj_mats, trans_features)\n assert(torch.norm(output[:, :, :-1] - output_from_translation[:, :, 1:]) < 1e-4)\n","sub_path":"tests/test_layers.py","file_name":"test_layers.py","file_ext":"py","file_size_in_byte":1863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"259572193","text":"#exercise1\nimport matplotlib.pyplot as plt \nfig, ax1 = plt.subplots()\ntime = range(7)\nco2 = [250, 265, 272, 260, 300, 320, 389]\nax1.plot(time, co2, \"b--\")\nax1.set_ylabel(\"CO2]\")\nax2 = ax1.twinx()\ntemp = [14.1, 15.5, 16.3, 18.1, 17.3, 19.1, 20.2]\nax2.plot(time, temp, \"r*-\")\nax2.set_ylabel(\"Temp (degc)\")\nplt.show()\n\n#exercise2\nplt.subplot(1, 2, 3)\nx = range(0, 10, 1)\nplt.plot (x)\nplt.subplot(1, 3, 2)\ny = range(10, 0, -1)\nplt.plot(y)\nplt.subplot(1, 3, 3)\nz = [4] * 10\nplt.plot(z)\nplt.show\n","sub_path":"python/plotting2.py","file_name":"plotting2.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"622123505","text":"import pandas as pd\nimport numpy as np\nimport pickle\n\ndf = pd.read_csv('processed_input.txt',delimiter=\"\\t\", encoding = \"utf-8\")\nindex = [i for i in range(0,len(df['Word']))]\ndf = df.fillna(method='ffill')\n\n\nclass SentenceGetter(object):\n \n def __init__(self, data):\n self.n_sent = 1\n self.data = data\n self.empty = False\n agg_func = lambda s: [(w, p) for w, p in zip(s['Word'].values.tolist(), \n s['POS'].values.tolist()) \n ]\n self.grouped = self.data.groupby('Sentence#').apply(agg_func)\n self.sentences = [s for s in self.grouped]\n \n def get_next(self):\n try: \n s = self.grouped['Sentence: {}'.format(self.n_sent)]\n self.n_sent += 1\n return s \n except:\n return None\ngetter = SentenceGetter(df)\n\nsentences = getter.sentences\n\ndef word2features(sent, i):\n word = sent[i][0]\n postag = sent[i][1]\n \n features = {\n 'bias': 1.0, \n 'word[-3:]': word[-3:],\n 'word[-2:]': word[-2:],\n 'word.isdigit()': word.isdigit(),\n 'postag': postag,\n 'postag[:2]': postag[:2],\n }\n if i > 0:\n word1 = sent[i-1][0]\n postag1 = sent[i-1][1]\n features.update({\n '-1:postag': postag1,\n '-1:postag[:2]': postag1[:2],\n })\n else:\n features['BOS'] = True\n if i < len(sent)-1:\n word1 = sent[i+1][0]\n postag1 = sent[i+1][1]\n features.update({\n\n '+1:postag': postag1,\n '+1:postag[:2]': postag1[:2],\n })\n else:\n features['EOS'] = True\n return features\ndef sent2features(sent):\n return [word2features(sent, i) for i in range(len(sent))]\ndef sent2labels(sent):\n return [label for token, postag, label in sent]\ndef sent2tokens(sent):\n return [token for token, postag, label in sent]\n\ny_pred=[]\nX = [sent2features(s) for s in sentences]\nprint(X)\n\n\nwith open(\"ner.pkl\",\"rb\") as f:\n crf2 = pickle.load(f)\n\n\n#y_pred = crf.predict(X)\ny_pred = crf2.predict(X)\n\nprint(y_pred)\ny=[]\n\nfor i in y_pred:\n for j in i:\n y.append(j)\n\ndf['Tag'] = y\n\n#f = open('output.txt' , mode= 'w', encoding='utf-8')\ndf.to_csv('output.txt', sep='\\t', encoding='utf-8')\n\n\n","sub_path":"ner.py","file_name":"ner.py","file_ext":"py","file_size_in_byte":2346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"406657226","text":"# Definition for singly-linked list.\n# class ListNode(object):\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution(object):\n def isPalindrome(self, head):\n \"\"\"\n :type head: ListNode\n :rtype: bool\n \"\"\"\n\n if head is None or head.next is None:\n return True\n\n if head.next.next is None:\n return head.val == head.next.val\n\n p,q =head,head\n while q.next is not None and q.next.next is not None:\n p=p.next\n q=q.next.next\n middle = p\n seconde= middle.next\n\n p=None\n # reverse second to the end\n while seconde is not None:\n q = seconde\n seconde = seconde.next\n q.next = p\n middle.next = q\n p = q\n\n p,q = head,middle.next\n while q is not None:\n if p.val!=q.val:\n return False\n p=p.next\n q=q.next\n\n return True","sub_path":"234. Palindrome Linked List.py","file_name":"234. Palindrome Linked List.py","file_ext":"py","file_size_in_byte":1004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"230102398","text":"from django.core.management.base import BaseCommand\nfrom django.db import models\n\n\nclass Command(BaseCommand):\n help = 'Prints all project models and the count of objects in every model'\n\n def handle(self, *args, **options):\n for model in models.get_models(include_auto_created=True):\n self.stdout.write(\"%s, %s\" % (model,\n model.objects.all().count()))\n self.stderr.write(\"error: %s, %s\" % (model,\n model.objects.all().count()))\n","sub_path":"apps/model_logger/management/commands/print_models.py","file_name":"print_models.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"473961962","text":"import os\nimport gensim\nimport datetime\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import Dropout\nfrom keras.models import load_model\nfrom Models.base import BaseModel\nfrom Models.dataPreparation import DataPreparation\nfrom Models.metrics import ModelMetrics\nfrom Utils.utils import get_abs_path, get_formatted_date, test_path\n\n\nclass SnnModel(BaseModel):\n def __init__(self, Config):\n super().__init__(Config)\n if self.Config[\"w2vmodel\"] == None:\n test_path(Config, \"model_path\", \"Wrong path to W2V model. Stop.\")\n try:\n self.validation_data_size = float(Config[\"validation_data_size\"])\n except ValueError:\n self.validation_data_size = 0\n if self.validation_data_size <= 0 or self.validation_data_size >= 1:\n raise ValueError(\"Wrong size of validation data set. Stop.\")\n try:\n self.ndim = int(self.Config[\"vectors_dimension\"])\n except ValueError:\n raise ValueError(\"Wrong size of vectors' dimentions. Stop.\")\n self.addValSet = True\n self.handleType = \"wordVectorsSum\"\n self.save_intermediate_results = Config[\"save_intermediate_results\"] == \"True\"\n self.useProbabilities = True\n self.w2vModel = None\n self.load_w2v_model()\n if Config[\"type_of_execution\"] != \"crossvalidation\":\n self.prepareData()\n self.launch_process()\n\n def prepareData(self):\n print (\"Start data preparation...\")\n dp = DataPreparation(self, self.addValSet)\n dp.getWordVectorsSum()\n\n def create_model(self):\n model = Sequential()\n model.add(Dense(256, activation='relu', input_dim=self.ndim))\n model.add(Dropout(0.2))\n model.add(Dense(256, activation='relu'))\n model.add(Dropout(0.2))\n model.add(Dense(256, activation='relu'))\n model.add(Dropout(0.2))\n model.add(Dense(len(self.Config[\"predefined_categories\"]), activation='sigmoid'))\n model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n return model\n\n def load_model(self):\n self.model = self.loadNNModel()\n\n def train_model(self):\n self.trainNNModel()\n\n def test_model(self):\n self.testNNModel()\n\n def save_additions(self):\n self.resources[\"w2v\"] = \"True\"\n self.resources[\"handleType\"] = \"wordVectorsSum\"\n","sub_path":"TrainAndTest/Models/snn.py","file_name":"snn.py","file_ext":"py","file_size_in_byte":2431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"463426593","text":"def modinv(a,m):\n b, u, v = m, 1, 0\n while b:\n t = a//b\n a -= t*b\n a,b = b,a\n u -= t * v\n u,v = v,u\n u %= m\n return u\n\nn=int(input())\nk=int(input())\n\nMOD=10**9+7\ntop=1\nbottom=1\nfor i in range(k):\n top*=(n+k-1-i)\n bottom*=(i+1)\n top%=MOD\n bottom%=MOD\nans = top*modinv(bottom,MOD)\nprint(ans%MOD)","sub_path":"2_kakomon/abc001-041/abc021_d.py","file_name":"abc021_d.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"547839037","text":"from geopy.geocoders import Nominatim\nfrom geopy.distance import great_circle\nfrom geopy.extra.rate_limiter import RateLimiter\nimport copy\nimport string\nimport folium\n\n\ndef get_location_of_user(latitude, longitude):\n '''\n (str, str) -> list\n Function returns the location of the user.\n\n >>> get_location_of_user(\"32.8246758\", \"-117.1559805\")[:2]\n ['4646,', 'Convoy']\n '''\n geolocator = Nominatim(user_agent=\"specify_your_app_name_here\")\n s = ''\n s += latitude + ', ' + longitude\n location = geolocator.reverse(s, language='en')\n loc = location.address.split()\n if loc[-1] == 'America':\n loc.append('USA')\n if loc[-1] == 'Kingdom':\n loc.append('UK')\n return loc\n\n\ndef locations(file, year, loc_lst):\n '''\n (NoneType, str, list) -> list\n\n Function that reads the file with film locations.\n '''\n locations = []\n with open(file, 'r') as f:\n for row in f:\n row = row.strip().split()\n j = 0\n new_lst = []\n for i in row:\n if '(' in i:\n row[:j] = [' '.join(new_lst[:j])]\n break\n else:\n new_lst.append(i)\n j += 1\n if len(row) > 1:\n row[1] = row[1].replace('(', '').replace(')', '')\n if row[1] == year and loc_lst[-1] in row:\n locations.append(row)\n if 'USA' or 'Canada' in loc_lst:\n if len(locations) == 240:\n break\n locations = locations[14:]\n return locations\n\n\ndef get_location(location_lst, loc_lst):\n '''\n (list, list) -> dict\n\n Function that returns the dictionary where\n keys are locations and values are films.\n\n >>> get_location([['Ty pomnish?', '2002', 'Kiev', 'Ukraine']], ['Ukraine'])\n {'Kiev Ukraine': ['Ty pomnish?']}\n '''\n new_dct = {}\n for sublist in location_lst:\n new_lst = []\n i = 1\n index = sublist.index(loc_lst[-1])\n if ')' in sublist[index - 1] or '(' in sublist[index - 1]:\n new_lst.append(loc_lst[-1])\n new2 = copy.copy(new_lst)\n new2 = ' '.join(new2)\n if new2 not in new_dct.keys():\n new_dct[new2] = [sublist[0]]\n else:\n new_dct[new2].append(sublist[0])\n else:\n while not sublist[index - i].isdigit():\n new_lst.append(sublist[index - i])\n i += 1\n new_lst.append(loc_lst[-1])\n new1 = copy.copy(new_lst)\n new1 = ' '.join(new1)\n if new1 not in new_dct.keys():\n new_dct[new1] = [sublist[0]]\n else:\n new_dct[new1].append(sublist[0])\n return new_dct\n\n\ndef location_identifier(locations_dct):\n '''\n (dict) -> dict\n\n Function to find the latitude and the\n longitude of the specific places and returns\n the dictionary where keys are coordinates and\n values films\n\n >>> location_identifier({'Kiev, Ukraine': ['Ty pomnish?']})\n {(50.4500336, 30.5241361): ['Ty pomnish?']}\n >>>\n '''\n geo_dct = {}\n geolocator = Nominatim(user_agent=\"specify_your_app_name_here\")\n geocode = RateLimiter(geolocator.geocode, min_delay_seconds=1)\n for key, value in locations_dct.items():\n try:\n location = geolocator.geocode(key)\n lst_loc = (location.latitude, location.longitude)\n geo_dct[lst_loc] = value\n except Exception:\n continue\n return geo_dct\n\n\ndef find_distance(geo_dct, latitude, longitude):\n '''\n (dict, str, str) -> dict\n\n Function that returns the distance between the inputted\n place and film locations\n\n >>> n = ['Ty pomnish?']\n >>> find_distance({(50.4500336, 30.5241361): n}, '49.841952', '24.0315921')\n {'467.401892504032': ['Ty pomnish?']}\n '''\n distance_dct = {}\n my_geo_lst = (latitude, longitude)\n for key, value in geo_dct.items():\n dist = great_circle(my_geo_lst, key)\n dist1 = str(dist).replace('Distance(', '').replace(')', '')\n dist1 = dist1.replace(' km', '')\n if dist1 == '0.0':\n distance_dct['0'] = value\n else:\n distance_dct[dist1] = value\n return distance_dct\n\n\ndef top_locations(distance_dct, geo_dct):\n '''\n (dict, dict) -> dict\n\n Function that returns dictionary with 10 or less locations.\n\n >>> n = ['Ty pomnish?']\n >>> top_locations({'467.401892504032': n}, {(50.4500336, 30.5241361): n})\n {(50.4500336, 30.5241361): ['Ty pomnish?']}\n '''\n total_dct = {}\n i = 1\n if len(distance_dct) <= 10:\n total_dct = copy.copy(distance_dct)\n else:\n while i != 10:\n key_min = min(distance_dct.keys())\n total_dct[key_min] = distance_dct[key_min]\n del distance_dct[key_min]\n i += 1\n new_dct = {}\n for key, value in geo_dct.items():\n if value in total_dct.values() and value not in new_dct.values():\n new_dct[key] = value\n return new_dct\n\n\ndef make_a_dict(totl):\n '''\n (dict) -> dict\n\n Function that сonverts the dictionary to another one.\n\n >>> make_a_dict({(50.4500336, 30.5241361): ['Ty pomnish?']})\n {'lat': [50.4500336], 'lon': [30.5241361], 'films': [['Ty pomnish?']]}\n '''\n new_dct = {'lat': [], 'lon': [], 'films': []}\n for key, value in totl.items():\n key1 = list(key)\n new_dct['lat'].append(key1[0])\n new_dct['lon'].append(key1[1])\n new_dct['films'].append(value)\n return new_dct\n\n\ndef popular_countries():\n '''\n (NoneType) -> dict\n\n Function that returns top 5 countries of\n cinematograph.\n '''\n con_dct = {'lat': [], 'lon': [], 'country': []}\n popular_countries = ['USA', 'UK', 'China', 'France', 'Japan']\n geolocator = Nominatim(user_agent=\"specify_your_app_name_here\")\n for i in popular_countries:\n location = geolocator.geocode(i)\n loc = [location.latitude, location.longitude]\n con_dct['lat'].append(loc[0])\n con_dct['lon'].append(loc[1])\n con_dct['country'].append(i)\n return con_dct\n\n\ndef create_map(total_dct, con_dct, latitude, longitude):\n '''\n Function that creates html file with map.\n '''\n lat = total_dct['lat']\n lon = total_dct['lon']\n films = total_dct['films']\n\n map = folium.Map(location=[latitude, longitude], zoom_start=8)\n\n fg_fl = folium.FeatureGroup(name=\"Films\")\n\n for lt, ln, fl in zip(lat, lon, films):\n fg_fl.add_child(folium.Marker(location=[lt, ln],\n radius=10,\n popup=', '.join(fl),))\n lat1 = con_dct['lat']\n lon1 = con_dct['lon']\n con = con_dct['country']\n\n def color(con):\n '''\n Function that returns the color depending\n on the country.\n '''\n\n if con == 'USA':\n return 'red'\n else:\n return 'orange'\n\n fg_fl_pop = folium.FeatureGroup(name=\"Top 5 countries of cinematograph\")\n\n for lt, ln, con in zip(lat1, lon1, con):\n fg_fl_pop.add_child(folium.CircleMarker(location=[lt, ln],\n radius=10,\n popup=con,\n fill_color=color(con),\n color='red',\n fill_opacity=1))\n map.add_child(fg_fl)\n map.add_child(fg_fl_pop)\n map.add_child(folium.LayerControl())\n map.save('Map_with_films.html')\n\n\nif __name__ == \"__main__\":\n year = input('Please enter a year you would like to have a map for: ')\n latitude = input('Please enter your latitude: ')\n longitude = input('Please enter your longitude: ')\n print('The map with markers is generating... ')\n location = get_location_of_user(latitude, longitude)\n films_lst = locations('locations.list', year, location)\n loc_film_dct = get_location(films_lst, location)\n address = location_identifier(loc_film_dct)\n print('Please wait a bit more...')\n distance = find_distance(address, latitude, longitude)\n top_loc = top_locations(distance, address)\n new_lst = make_a_dict(top_loc)\n pop_con = popular_countries()\n create_map(new_lst, pop_con, latitude, longitude)\n print('Finished.The map is saved in file Map_with_films.html.Have a look!')","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"462246340","text":"from django.conf.urls import url\n\nfrom . import views\n\nurlpatterns = [\n url(r'^$', views.index, name='index'),\n url(r'^(?P[0-9]+)/$', views.detail, name='detail'),\n url(r'^(?P[0-9]+)/submit/$', views.submit, name='submit'),\n url(r'^dearsenior/$', views.dear, name='dear'),\n url(r'^dearsenior/(?P[0-9]+)/$', views.viewmsg, name='viewmsg'),\n]\n","sub_path":"message/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"518681929","text":"import random\n\ndef throw_dices():\n first_dice = range(1, 7)\n second_dice = range(1, 7)\n counter = 0\n while True:\n counter = counter + 1\n elem , elem2 = random.choice(first_dice), random.choice(second_dice)\n summ_elem = elem + elem2\n if summ_elem == 8:\n print(elem, elem2, counter)\n break\n\nthrow_dices()","sub_path":"throw_dices.py","file_name":"throw_dices.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"226578690","text":"from django import forms\nfrom .models import Message\n\nclass MessageForm(forms.ModelForm):\n\n class Meta:\n model = Message\n fields= ('text',)\n labels = {'text': 'Send message'}\n widgets = {'text': forms.Textarea(attrs={'colls': 50,\n 'rows': 4, 'id': 'mess-text', 'required': True})}","sub_path":"chat_app/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"120996719","text":"import urllib\nimport requests\n\ni = 0\ndownload_url = input(\"What mirror do you wanna use? \")\nloc = input(\"Where is your osu! songs folder? \")\n\ndef report_hook(block_num, block_size, total_size):\n read_so_far = block_num * block_size\n percent = read_so_far * 1e2 / total_size\n print(\"[{}%]: {}\".format(str(int(percent)).rjust(3), download_url))\n\nwhile True:\n\n try:\n urllib.request.urlretrieve(download_url + \"/d/{}\".format(str(i)), loc + \"\\{}.osz\".format(str(i)), report_hook)\n print(\"done: {}\".format(download_url))\n\n except Exception as ex:\n print(\"failed: {})\".format(download_url))\n \n i = i + 1\n","sub_path":"meme.py","file_name":"meme.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"26116978","text":"total_number_of_simulation = 400\nBATCH_SIZE_PER_THREAD = 3\nUPDATE_SIZE = 6 # batch size per model update\nNUM_EPOCHS = int(total_number_of_simulation/(UPDATE_SIZE/BATCH_SIZE_PER_THREAD))\nHIDDEN_SIZE = 128 # number of hidden nodes we have in our dnn\nALPHA = 1e-3 # learning rate\nGAMMA = 0.99 # discount rate\nBETA = 0.1 # the entropy bonus multiplier\n\n\nuse_cuda = True\n\nmax_simulation_length = 200\n\nobservation_space_size = 4\naction_space_size = 2","sub_path":"working_algo_for_gym/neural_network_mini_batch_rpc_gpu/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"317129897","text":"from templates.text import TextTemplate\n\ndef process(input, entities=None):\n help = '''Hi there! I'm AskHilti, your personal assistant.\\nTell me things like the following:\\n\n - info MX1500\\n - price if MX1500\\n - suggest a drill\\n \nI'm always learning, so do come back and say hi from time to time!\\nHave a nice day.'''\n output = {\n 'input': input,\n 'output': TextTemplate(help).get_message(),\n 'success': True\n }\n return output\n","sub_path":"modules/src/help.py","file_name":"help.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"245121253","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# This file is part of the SCICO package. Details of the copyright\n# and user license can be found in the 'LICENSE.txt' file distributed\n# with the package.\n\nr\"\"\"\nComplex Total Variation Denoising with NLPADMM Solver\n=====================================================\n\nThis example demonstrates solution of a problem of the form\n\n$$\\argmin_{\\mb{x}} \\; f(\\mb{x}) + g(\\mb{z}) \\; \\text{such that}\\;\nH(\\mb{x}, \\mb{z}) = 0 \\;,$$\n\nwhere $H$ is a nonlinear function, via a variant of the proximal ADMM\nalgorithm for problems with a non-linear operator constraint\n:cite:`benning-2016-preconditioned`. The example problem represents\ntotal variation (TV) denoising applied to a complex image with\npiece-wise smooth magnitude and non-smooth phase. (This example is rather\ncontrived, and was not constructed to represent a specific real imaging\nproblem, but it does have some properties in common with synthetic\naperture radar single look complex data in which the magnitude has much\nmore discernible structure than the phase.) The appropriate TV denoising\nformulation for this problem is\n\n$$\\argmin_{\\mb{x}} \\; (1/2) \\| \\mb{y} - \\mb{x} \\|_2^2 + \\lambda\n\\| C(\\mb{x}) \\|_{2,1} \\;,$$\n\nwhere $\\mb{y}$ is the measurement, $\\|\\cdot\\|_{2,1}$ is the\n$\\ell_{2,1}$ mixed norm, and $C$ is a non-linear operator consisting of\na linear difference operator applied to the magnitude of a complex array.\nThis problem is represented in the form above by taking $H(\\mb{x},\n\\mb{z}) = C(\\mb{x}) - \\mb{z}$. The standard TV solution, which is\nalso computed for comparison purposes, gives very poor results since\nthe difference is applied independently to real and imaginary\ncomponents of the complex image.\n\"\"\"\n\n\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nfrom xdesign import SiemensStar, discrete_phantom\n\nimport scico.numpy as snp\nimport scico.random\nfrom scico import function, functional, linop, loss, metric, operator, plot\nfrom scico.examples import phase_diff\nfrom scico.optimize import NonLinearPADMM, ProximalADMM\nfrom scico.util import device_info\n\n\"\"\"\nCreate a ground truth image.\n\"\"\"\nN = 256 # image size\nphantom = SiemensStar(16)\nx_mag = snp.pad(discrete_phantom(phantom, N - 16), 8) + 1.0\nx_mag /= x_mag.max()\n# Create reference image with structured magnitude and random phase\nx_gt = x_mag * snp.exp(-1j * scico.random.randn(x_mag.shape, seed=0)[0])\n\n\n\"\"\"\nAdd noise to create a noisy test image.\n\"\"\"\nσ = 0.25 # noise standard deviation\nnoise, key = scico.random.randn(x_gt.shape, seed=1, dtype=snp.complex64)\ny = x_gt + σ * noise\n\n\n\"\"\"\nDenoise with standard total variation.\n\"\"\"\nλ_tv = 6e-2\nf = loss.SquaredL2Loss(y=y)\ng = λ_tv * functional.L21Norm()\n# The append=0 option makes the results of horizontal and vertical finite\n# differences the same shape, which is required for the L21Norm.\nC = linop.FiniteDifference(input_shape=y.shape, input_dtype=snp.complex64, append=0)\n\nsolver_tv = ProximalADMM(\n f=f,\n g=g,\n A=C,\n rho=1.0,\n mu=8.0,\n nu=1.0,\n maxiter=200,\n itstat_options={\"display\": True, \"period\": 20},\n)\nprint(f\"Solving on {device_info()}\\n\")\nx_tv = solver_tv.solve()\nprint()\nhist_tv = solver_tv.itstat_object.history(transpose=True)\n\n\n\"\"\"\nDenoise with total variation applied to the magnitude of a complex image.\n\"\"\"\nλ_nltv = 2e-1\ng = λ_nltv * functional.L21Norm()\n# Redefine C for real input (now applied to magnitude of a complex array)\nC = linop.FiniteDifference(input_shape=y.shape, input_dtype=snp.float32, append=0)\n# Operator computing differences of absolute values\nD = C @ operator.Abs(input_shape=x_gt.shape, input_dtype=snp.complex64)\n# Constraint function imposing z = D(x) constraint\nH = function.Function(\n (C.shape[1], C.shape[0]),\n output_shape=C.shape[0],\n eval_fn=lambda x, z: D(x) - z,\n input_dtypes=(snp.complex64, snp.float32),\n output_dtype=snp.float32,\n)\n\nsolver_nltv = NonLinearPADMM(\n f=f,\n g=g,\n H=H,\n rho=5.0,\n mu=6.0,\n nu=1.0,\n maxiter=200,\n itstat_options={\"display\": True, \"period\": 20},\n)\nx_nltv = solver_nltv.solve()\nhist_nltv = solver_nltv.itstat_object.history(transpose=True)\n\n\n\"\"\"\nPlot results.\n\"\"\"\nfig, ax = plot.subplots(nrows=1, ncols=3, sharex=True, sharey=False, figsize=(27, 6))\nplot.plot(\n snp.vstack((hist_tv.Objective, hist_nltv.Objective)).T,\n ptyp=\"semilogy\",\n title=\"Objective function\",\n xlbl=\"Iteration\",\n lgnd=(\"Standard TV\", \"Magnitude TV\"),\n fig=fig,\n ax=ax[0],\n)\nplot.plot(\n snp.vstack((hist_tv.Prml_Rsdl, hist_nltv.Prml_Rsdl)).T,\n ptyp=\"semilogy\",\n title=\"Primal residual\",\n xlbl=\"Iteration\",\n lgnd=(\"Standard TV\", \"Magnitude TV\"),\n fig=fig,\n ax=ax[1],\n)\nplot.plot(\n snp.vstack((hist_tv.Dual_Rsdl, hist_nltv.Dual_Rsdl)).T,\n ptyp=\"semilogy\",\n title=\"Dual residual\",\n xlbl=\"Iteration\",\n lgnd=(\"Standard TV\", \"Magnitude TV\"),\n fig=fig,\n ax=ax[2],\n)\nfig.show()\n\n\nfig, ax = plot.subplots(nrows=2, ncols=4, figsize=(20, 10))\nnorm = plot.matplotlib.colors.Normalize(\n vmin=min(snp.abs(x_gt).min(), snp.abs(y).min(), snp.abs(x_tv).min(), snp.abs(x_nltv).min()),\n vmax=max(snp.abs(x_gt).max(), snp.abs(y).max(), snp.abs(x_tv).max(), snp.abs(x_nltv).max()),\n)\nplot.imview(snp.abs(x_gt), title=\"Ground truth\", cbar=None, fig=fig, ax=ax[0, 0], norm=norm)\nplot.imview(\n snp.abs(y),\n title=\"Measured: PSNR %.2f (dB)\" % metric.psnr(snp.abs(x_gt), snp.abs(y)),\n cbar=None,\n fig=fig,\n ax=ax[0, 1],\n norm=norm,\n)\nplot.imview(\n snp.abs(x_tv),\n title=\"Standard TV: PSNR %.2f (dB)\" % metric.psnr(snp.abs(x_gt), snp.abs(x_tv)),\n cbar=None,\n fig=fig,\n ax=ax[0, 2],\n norm=norm,\n)\nplot.imview(\n snp.abs(x_nltv),\n title=\"Magnitude TV: PSNR %.2f (dB)\" % metric.psnr(snp.abs(x_gt), snp.abs(x_nltv)),\n cbar=None,\n fig=fig,\n ax=ax[0, 3],\n norm=norm,\n)\ndivider = make_axes_locatable(ax[0, 3])\ncax = divider.append_axes(\"right\", size=\"5%\", pad=0.2)\nfig.colorbar(ax[0, 3].get_images()[0], cax=cax)\nnorm = plot.matplotlib.colors.Normalize(\n vmin=min(snp.angle(x_gt).min(), snp.angle(x_tv).min(), snp.angle(x_nltv).min()),\n vmax=max(snp.angle(x_gt).max(), snp.angle(x_tv).max(), snp.angle(x_nltv).max()),\n)\nplot.imview(\n snp.angle(x_gt),\n title=\"Ground truth\",\n cbar=None,\n fig=fig,\n ax=ax[1, 0],\n norm=norm,\n)\nplot.imview(\n snp.angle(y),\n title=\"Measured: Mean phase diff. %.2f\" % phase_diff(snp.angle(x_gt), snp.angle(y)).mean(),\n cbar=None,\n fig=fig,\n ax=ax[1, 1],\n norm=norm,\n)\nplot.imview(\n snp.angle(x_tv),\n title=\"Standard TV: Mean phase diff. %.2f\"\n % phase_diff(snp.angle(x_gt), snp.angle(x_tv)).mean(),\n cbar=None,\n fig=fig,\n ax=ax[1, 2],\n norm=norm,\n)\nplot.imview(\n snp.angle(x_nltv),\n title=\"Magnitude TV: Mean phase diff. %.2f\"\n % phase_diff(snp.angle(x_gt), snp.angle(x_nltv)).mean(),\n cbar=None,\n fig=fig,\n ax=ax[1, 3],\n norm=norm,\n)\ndivider = make_axes_locatable(ax[1, 3])\ncax = divider.append_axes(\"right\", size=\"5%\", pad=0.2)\nfig.colorbar(ax[1, 3].get_images()[0], cax=cax)\nax[0, 0].set_ylabel(\"Magnitude\")\nax[1, 0].set_ylabel(\"Phase\")\nfig.tight_layout()\nfig.show()\n\n\ninput(\"\\nWaiting for input to close figures and exit\")\n","sub_path":"examples/scripts/denoise_cplx_tv_nlpadmm.py","file_name":"denoise_cplx_tv_nlpadmm.py","file_ext":"py","file_size_in_byte":7193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"652693429","text":"from random import randint\nfrom colorama import Fore, Back, Style\n\nprint('')\nmodo = int(input('Esse é um jogo de batalha naval, se quiser jogar um modo simples digite \"0\", se quiser jogar um modo customizavel digite \"1\"! '))\n\nif modo == 0:\n board = []\n for x in range(5):\n board.append([\"O\"] * 5)\n\n def print_board(board):\n for row in board:\n print (Fore.BLUE + ' '.join(row))\n print(Style.RESET_ALL)\n\n def random_row(board):\n return randint(0, len(board) - 1)\n \n def random_col(board):\n return randint(0, len(board[0]) - 1)\n\n def random_row2(board):\n return randint(0, len(board) - 1)\n if naviu2 == 0:\n naviu2 = (str(naviu2), str(randint(1, naviu2)))\n elif naviu2 == 4:\n naviu2 = (str(naviu2), str(randint(3, naviu2)))\n else:\n naviu2 = (str(naviu2), str(randint(naviu2 - 1, naviu2 + 1)))\n return naviu2\n\n def random_col2(board):\n navio2 = randint(0, len(board[0]) - 1)\n if navio2 == 0:\n navio2 = (str(navio2), str(randint(1, navio2)))\n elif navio2 == 4:\n navio2 = (str(navio2), str(randint(3, navio2)))\n else:\n navio2 = (str(navio2), str(randint(navio2 - 1, navio2 + 1)))\n return navio2\n\n print (' ')\n print ('Você tem 4 turnos para acertar o navio!')\n print ('Escolha dois numeros entre 1 e 5!')\n ship_row = random_row(board)\n ship_col = random_col(board)\n ship_col2 = random_col2(board)\n ship_row2 = random_row2(board)\n #print (ship_row)\n #print (ship_col)\n print (ship_row2)\n print (ship_col2)\n guess_row = ''\n guess_col = ''\n turn = 1\n guess = (guess_row, guess_col)\n right_guess = (ship_row, ship_col)\n print (int(right_guess[0]) + 1, int(right_guess[1]) + 1) \n while guess != right_guess and turn < 5:\n\n print (' ')\n print (' ')\n print (('Turn: ') + str(turn))\n print (' ')\n guess_row = int(input(\"Advinhe a fileira: \"))-1\n guess_col = int(input(\"Advinhe a coluna: \"))-1\n guess = (guess_row, guess_col)\n print (' ')\n\n if guess_row == ship_row and guess_col == ship_col:\n print (\"Parabéns! Você afundou o navio!\")\n print (' ')\n\n elif guess_row not in range(5) or guess_col not in range(5):\n if turn != 5:\n print (\"Isso não é nem no oceano.\")\n print (' ')\n print (print_board(board))\n\n elif board[guess_row][guess_col] == (Fore.RED + 'X' + Fore.BLUE):\n if turn != 5:\n print (\"Você já acertou esse lugar!.\")\n print (' ')\n print (print_board(board))\n\n else:\n if turn != 5:\n print (\"Você errou o navio!\")\n print (' ')\n board[guess_row][guess_col] = (Fore.RED + 'X' + Fore.BLUE)\n print (print_board(board))\n turn += 1\n\n if turn == 5:\n print (' ')\n print ('You lost!')\n print (' ')\n \nelif modo == 1:\n print(' ')\n board_size = str(input(\"Quanto por quanto você quer que seja o seu tabuleiro?(use 4X4 por exemplo, e menor que 10x10) \")) + 'l'\n while 'x' in board_size[2:].lower() or board_size[3] != 'l' or board_size[1].lower() != 'x':\n board_size = str(input(\"Menor que 10X10! \"))\n board_size += 'l'\n board = []\n turns = int(input('Quantos turnos você quer ter? '))\n\n for x in range(int(board_size[0])):\n board.append([\"O\"] * int(board_size[2]))\n\n def print_board(board):\n for row in board:\n print(Fore.BLUE + ' '.join(row))\n print(Style.RESET_ALL)\n def random_row(board):\n return randint(0, len(board) - 1)\n\n def random_col(board):\n return randint(0, len(board[0]) - 1)\n\n print(' ')\n print_board(board)\n print (' ')\n print ('Você tem ' + str(turns) + ' turnos para fundar um navio!')\n print ('Escolha as cordenadas onde você pensa que esta o navio!')\n ship_row = random_row(board)\n ship_col = random_col(board)\n #print (ship_row)\n #print (ship_col)\n guess_row = ''\n guess_col = ''\n turn = 0\n guess = (guess_row, guess_col)\n right_guess = (ship_row, ship_col)\n #print(' ')\n #print (int(right_guess[0]) + 1, int(right_guess[1]) + 1) \n\n while guess != right_guess and turn < turns:\n print (' ')\n print (' ')\n print (('Turno: ') + str(turn + 1))\n print (' ')\n guess_row = int(input(\"Advinhe a fileira: \"))-1\n guess_col = int(input(\"Advinhe a coluna: \"))-1\n guess = (guess_row, guess_col)\n print (' ')\n\n if guess == right_guess:\n print (\"Parabéns! Você afundou o navio!\")\n print (' ')\n\n elif guess_row not in range(int(board_size[0])) or guess_col not in range(int(board_size[2])):\n if turn != 5:\n print (\"Isso não é nem no oceano.\")\n print (' ')\n print_board(board)\n\n elif board[guess_row][guess_col] == (Fore.RED + 'X' + Fore.BLUE):\n if turn != turns:\n print (\"Você já acertou esse lugar!\")\n print (' ')\n print_board(board)\n\n else:\n if turn != turns:\n print (\"Você errou o navio!\")\n print (' ')\n board[guess_row][guess_col] = (Fore.RED + 'X' + Fore.BLUE)\n print_board(board)\n turn += 1\n\n if turn == turns:\n print (' ')\n print ('Você perdeu!')\n print (' ') \n\n","sub_path":"BattleShip.py","file_name":"BattleShip.py","file_ext":"py","file_size_in_byte":5644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"603006051","text":"import h5py\nimport numpy as np\n\nname_file1 = 'best_weights.h5'\nname_file2 = 'weights.h5'\n\nwith h5py.File(name_file1, 'r') as hdf1, h5py.File(name_file2, 'r') as hdf2:\n\tls1 = list(hdf1.keys())\n\tls2 = list(hdf2.keys())\n\tprint(f\"Keys_f1: {ls1}\")\n\tprint(\"**--**\")\n\tprint(f\"Keys_f2: {ls2}\")\n\t\n\tnp.array(hdf1.get(ls1[0]))\n\t\n\t\n\n\n","sub_path":"compare_h5.py","file_name":"compare_h5.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"477893691","text":"\"\"\"\n为了避免全局变量值，我们需要一个多线程锁。继承Thread类，写一个线程类。\n\"\"\"\n\nfrom threading import Thread, Lock\n\n\nclass MyThread(Thread):\n\n def __init__(self, name):\n super().__init__()\n self.name = name\n\n def run(self):\n global money\n n = 0\n global gLock\n # 获得锁\n gLock.acquire()\n while True:\n if n >= 100000:\n break\n money += 1\n n += 1\n gLock.release()\n\n def __str__(self):\n return \"{}赚到了{}元钱\".format(self.name, money)\n\n\nif __name__ == '__main__':\n money = 10000\n gLock = Lock()\n t1 = MyThread(\"Andy\")\n t2 = MyThread(\"Tom\")\n\n t1.start()\n t2.start()\n\n print(t1)\n print(t2)\n\n t2.join()\n t1.join()\n\n print(\"最终的钱为：{}\".format(money))\n\n","sub_path":"part07/threads_Lock.py","file_name":"threads_Lock.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"154584476","text":"import scipy.io.wavfile as wav\r\nimport numpy as np\r\nfrom matplotlib import pyplot as mp\r\nfs,data=wav.read('ishu1.wav')\r\nprint(fs,len(data),data)\r\nmp.subplot(211)\r\nmp.plot(data)\r\nt=np.arange(0,len(data)/fs,1.0/fs)\r\nmp.subplot(212)\r\nmp.plot(t,data)\r\nmp.show( )","sub_path":"ishu1.py","file_name":"ishu1.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"251896039","text":"from create_tables import *\nimport csv\n\nif __name__ == \"__main__\":\n connection.setup(IP_ADDRESS, KEY_SPACE[0])\n statistics = [0] * 15\n warehouses = Warehouse.all()\n districts = District.all()\n customers = Customer.all()\n orders = Order.all()\n order_lines = OrderLine.all()\n stocks = Stock.all()\n for w in warehouses:\n statistics[0] += w.W_YTD\n for d in districts:\n statistics[1] += d.D_YTD\n statistics[2] += d.D_NEXT_O_ID\n for c in customers:\n statistics[3] += c.C_BALANCE\n statistics[4] += c.C_YTD_PAYMENT\n statistics[5] += c.C_PAYMENT_CNT\n statistics[6] += c.C_DELIVERY_CNT\n for o in orders:\n statistics[7] = max(statistics[7], o.O_ID)\n statistics[8] += o.O_OL_CNT\n for ol in order_lines:\n statistics[9] += ol.OL_AMOUNT\n statistics[10] += ol.OL_QUANTITY\n for s in stocks:\n statistics[11] += s.S_QUANTITY\n statistics[12] += s.S_YTD\n statistics[13] += s.S_ORDER_CNT\n statistics[14] += s.S_REMOTE_CNT\n\n with open('output/db-state.csv', 'a') as f:\n writer = csv.writer(f)\n writer.writerow(statistics)\n","sub_path":"collect_database_state.py","file_name":"collect_database_state.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"3316819","text":"from tkinter import *\nimport db.db\n\n\nclass IncomeWindow:\n\n def __init__(self):\n self.win = Tk()\n canvas = Canvas(self.win, width=600, height=500, bg='white')\n canvas.pack(expand=YES, fill=BOTH)\n\n width = self.win.winfo_screenwidth()\n height = self.win.winfo_screenheight()\n\n x = int(width / 2 - 600 / 2)\n y = int(height / 2 - 400 / 2)\n\n str1 = \"600x400+\"+str(x)+\"+\"+ str(y)\n self.win.geometry(str1)\n self.win.resizable(width=False, height=False)\n self.win.title(\"Add Income | Administrator\")\n\n\n def add_frame(self):\n\n self.frame = Frame(self.win, height=350, width=450)\n self.frame.place(x=80, y=20)\n\n x, y = 70, 20\n\n self.label = Label(self.frame, text=\"Add Income Source\")\n self.label.config(font=('Courier', 20, 'bold'))\n self.label.place(x = x + 30, y = y + 50)\n\n self.income = Label(self.frame, text='Enter Source')\n self.income.config(font=('Courier', 12, 'bold'))\n self.income.place(x = 40, y = y + 100)\n\n self.inc = Entry(self.frame, font='Courier 12')\n self.inc.place(x=220, y = y + 100)\n\n self.income = Label(self.frame, text='Enter Description')\n self.income.config(font=('Courier', 12, 'bold'))\n self.income.place(x=40, y=y + 130)\n\n self.des = Entry(self.frame, font='Courier 12')\n self.des.place(x=220, y=y + 130)\n\n self.button = Button(self.frame, text='SUBMIT', font='Courier 12 bold',\n command=self.add_income)\n self.button.place(x=170, y = y + 180)\n\n self.labelmsg = Label(self.frame, text='')\n self.labelmsg.config(font=('Courier', 12, 'bold'))\n self.labelmsg.place(x = 120, y = y + 210)\n\n self.win.mainloop()\n\n def add_income(self):\n data = (\n self.inc.get(),\n self.des.get()\n )\n\n if self.inc.get() == '':\n self.labelmsg.config(fg='red')\n self.labelmsg.config(text='Please Enter Income')\n\n elif self.des.get() == '':\n self.labelmsg.config(fg='red')\n self.labelmsg.config(text='Please Enter Description')\n\n else:\n res = db.db.add_income(data)\n if res:\n self.labelmsg.config(fg='green')\n self.labelmsg.config(text='Data Added Successfully')\n # code to clear the data from input box after submission\n self.inc.delete(0, 'end')\n self.des.delete(0, 'end')\n else:\n self.labelmsg.config(fg='red')\n self.labelmsg.config(text='Alert! Please try again')\n\n","sub_path":"income/add_income.py","file_name":"add_income.py","file_ext":"py","file_size_in_byte":2671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"314328297","text":"from django.http import Http404\nfrom django.contrib import messages\nfrom django.views.generic import CreateView, DetailView, TemplateView\nfrom django.shortcuts import render, redirect, get_object_or_404\n\nfrom bag_transfer.models import (\n BagItProfile,\n BagItProfileBagInfo,\n BagItProfileBagInfoValues,\n Organization,\n)\nfrom bag_transfer.rights.models import RecordType, RightsStatement\nfrom bag_transfer.rights.forms import CopyrightFormSet, LicenseFormSet, StatuteFormSet, OtherFormSet, RightsGrantedFormSet, RightsForm\nfrom bag_transfer.mixins.authmixins import ManagingArchivistMixin, OrgReadViewMixin\nfrom bag_transfer.mixins.formatmixins import JSONResponseMixin\n\n\nclass RightsManageView(ManagingArchivistMixin, CreateView):\n template_name = \"rights/manage.html\"\n model = RightsStatement\n form_class = RightsForm\n\n def get_formset(self, rights_basis):\n if rights_basis == \"Copyright\":\n return {\"key\": \"copyright_form\", \"class\": CopyrightFormSet}\n elif rights_basis == \"License\":\n return {\"key\": \"license_form\", \"class\": LicenseFormSet}\n elif rights_basis == \"Statute\":\n return {\"key\": \"statute_form\", \"class\": StatuteFormSet}\n else:\n return {\"key\": \"other_form\", \"class\": OtherFormSet}\n\n def get_applies_to_type_choices(self, organization):\n values = BagItProfileBagInfoValues.objects.filter(\n bagit_profile_baginfo__in=BagItProfileBagInfo.objects.filter(\n bagit_profile__in=BagItProfile.objects.filter(\n applies_to_organization=organization\n ),\n field=\"record_type\",\n )\n )\n applies_to_type_choices = []\n for v in values:\n record_type = RecordType.objects.get_or_create(name=v.name)[0]\n applies_to_type_choices.append((record_type.pk, record_type.name))\n return sorted(applies_to_type_choices, key=lambda tup: tup[1])\n\n def get(self, request, *args, **kwargs):\n if self.kwargs.get(\"pk\"):\n rights_statement = RightsStatement.objects.get(pk=self.kwargs.get(\"pk\"))\n organization = rights_statement.organization\n applies_to_type_choices = self.get_applies_to_type_choices(organization)\n formset_data = self.get_formset(rights_statement.rights_basis)\n formset = formset_data[\"class\"](instance=rights_statement)\n basis_form = RightsForm(\n applies_to_type_choices=applies_to_type_choices,\n instance=rights_statement,\n organization=organization,\n )\n granted_formset = RightsGrantedFormSet(instance=rights_statement)\n return render(\n request,\n self.template_name,\n {\n \"organization\": organization,\n formset_data[\"key\"]: formset,\n \"basis_form\": basis_form,\n \"granted_formset\": granted_formset,\n },\n )\n else:\n organization = Organization.objects.get(pk=self.request.GET.get(\"org\"))\n applies_to_type_choices = self.get_applies_to_type_choices(organization)\n basis_form = RightsForm(\n applies_to_type_choices=applies_to_type_choices,\n organization=organization,\n )\n return render(\n request,\n self.template_name,\n {\n \"copyright_form\": CopyrightFormSet(),\n \"license_form\": LicenseFormSet(),\n \"statute_form\": StatuteFormSet(),\n \"other_form\": OtherFormSet(),\n \"organization\": organization,\n \"basis_form\": basis_form,\n \"granted_formset\": RightsGrantedFormSet(),\n },\n )\n\n def post(self, request, *args, **kwargs):\n applies_to_type = request.POST.getlist(\"applies_to_type\")\n\n if not self.kwargs.get(\"pk\"):\n organization = Organization.objects.get(pk=self.request.GET.get(\"org\"))\n applies_to_type_choices = self.get_applies_to_type_choices(organization)\n form = RightsForm(\n request.POST,\n applies_to_type_choices=applies_to_type_choices,\n organization=organization,\n )\n if not form.is_valid():\n messages.error(\n request,\n \"There was a problem with your submission. Please correct the error(s) below and try again.\",\n )\n return render(\n request,\n self.template_name,\n {\n \"copyright_form\": CopyrightFormSet(),\n \"license_form\": LicenseFormSet(),\n \"statute_form\": StatuteFormSet(),\n \"other_form\": OtherFormSet(),\n \"organization\": organization,\n \"basis_form\": form,\n },\n )\n rights_statement = form.save(commit=False)\n else:\n rights_statement = RightsStatement.objects.get(pk=self.kwargs.get(\"pk\"))\n organization = rights_statement.organization\n applies_to_type_choices = self.get_applies_to_type_choices(organization)\n\n formset_data = self.get_formset(rights_statement.rights_basis)\n basis_formset = formset_data[\"class\"](request.POST, instance=rights_statement)\n rights_granted_formset = RightsGrantedFormSet(\n request.POST, instance=rights_statement\n )\n\n for formset in [rights_granted_formset, basis_formset]:\n if not formset.is_valid():\n messages.error(\n request,\n \"There was a problem with your submission. Please correct the error(s) below and try again.\",\n )\n form = RightsForm(\n request.POST,\n applies_to_type_choices=applies_to_type_choices,\n organization=organization,\n )\n return render(\n request,\n self.template_name,\n {\n formset_data[\"key\"]: formset_data[\"class\"](request.POST),\n \"organization\": organization,\n \"basis_form\": form,\n \"granted_formset\": rights_granted_formset,\n },\n )\n\n rights_statement.save()\n rights_statement.applies_to_type.clear()\n for record_type in applies_to_type:\n rights_statement.applies_to_type.add(record_type)\n rights_statement.save()\n\n for formset in [rights_granted_formset, basis_formset]:\n formset.save()\n\n messages.success(request, \"Rights statement saved!\")\n return redirect(\"orgs:detail\", organization.pk)\n\n\nclass RightsAPIAdminView(ManagingArchivistMixin, JSONResponseMixin, TemplateView):\n def render_to_response(self, context, **kwargs):\n if not self.request.is_ajax():\n raise Http404\n resp = {\"success\": 0}\n\n if \"action\" in self.kwargs:\n obj = get_object_or_404(RightsStatement, pk=context[\"pk\"])\n if self.kwargs[\"action\"] == \"delete\":\n obj.delete()\n resp[\"success\"] = 1\n\n return self.render_to_json_response(resp, **kwargs)\n\n\nclass RightsDetailView(OrgReadViewMixin, DetailView):\n template_name = \"rights/detail.html\"\n model = RightsStatement\n\n def get_context_data(self, *args, **kwargs):\n context = super(RightsDetailView, self).get_context_data(**kwargs)\n context[\"meta_page_title\"] = \"{} PREMIS rights statement\".format(\n self.object.organization\n )\n context[\"rights_basis_info\"] = self.object.get_rights_info_object\n context[\"rights_granted_info\"] = self.object.get_rights_granted_objects\n return context\n","sub_path":"aurora/bag_transfer/rights/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8099,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"645791273","text":"from collections import OrderedDict\n\nfrom lxml import etree\n\nfrom zeep import xsd\nfrom zeep.xsd.valueobjects import CompoundValue\n\n\ndef serialize_object(obj):\n \"\"\"Serialize zeep objects to native python data structures\"\"\"\n if obj is None:\n return obj\n\n if isinstance(obj, etree._Element):\n return obj\n\n if isinstance(obj, list):\n return [serialize_object(sub) for sub in obj]\n\n result = OrderedDict()\n for key in obj:\n value = obj[key]\n if isinstance(value, (list, CompoundValue)):\n value = serialize_object(value)\n result[key] = value\n return result\n\n\ndef create_xml_soap_map(values):\n \"\"\"Create an http://xml.apache.org/xml-soap#Map value.\"\"\"\n Map = xsd.ComplexType(\n xsd.Sequence([\n xsd.Element(\n 'item',\n xsd.AnyType(),\n min_occurs=1,\n max_occurs=\"unbounded\"),\n ]),\n qname=etree.QName('{http://xml.apache.org/xml-soap}Map'))\n\n KeyValueData = xsd.Element(\n '{http://xml.apache.org/xml-soap}KeyValueData',\n xsd.ComplexType(\n xsd.Sequence([\n xsd.Element(\n 'key',\n xsd.AnyType(),\n ),\n xsd.Element(\n 'value',\n xsd.AnyType(),\n ),\n ]),\n ),\n )\n\n return Map(item=[\n KeyValueData(\n xsd.AnyObject(xsd.String(), key),\n xsd.AnyObject(guess_xsd_type(value), value)\n ) for key, value in values.items()\n ])\n\n\ndef guess_xsd_type(obj):\n if isinstance(obj, bool):\n return xsd.Boolean()\n if isinstance(obj, int):\n return xsd.Integer()\n return xsd.String()\n\n\ndef Nil():\n \"\"\"Return an xsi:nil element\"\"\"\n return xsd.AnyObject(None, None)\n","sub_path":"src/zeep/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":1852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"104909785","text":"import os, unittest\nimport lsst.daf.persistence as dafPersistence\nfrom lsst.ts.wep.Utility import getModulePath\n\nclass SliceFacade(object):\n\n def __init__(self, dataButlerObject):\n\n self.dataButlerObject = dataButlerObject\n\n def getDimensions(self, key=None):\n \"\"\"\n\n Get the dimension of exposure image.\n\n Arguments:\n key {[string]} -- Name of amplifier. (default: {None})\n\n Returns:\n [float] -- Dimension of exposure image.\n \"\"\"\n\n return self.__getItem(key).getDimensions()\n\n def getBoresightRotAngle(self, key=None):\n \"\"\"\n\n Get rotation angle at boresight at middle of exposure. The meaning of rotation\n angle depends on rotType. For example, if rotType is SKY the angle is the position\n angle of the focal plane +Y with respect to North.\n\n Arguments:\n key {[string]} -- Name of amplifier. (default: {None})\n\n Returns:\n [float] -- Rotation angle at boresight.\n \"\"\"\n\n return self.__getItem(key).getInfo().getVisitInfo().getBoresightRotAngle()\n\n def getRotType(self, key=None):\n \"\"\"\n\n Get rotation type of boresightRotAngle\n\n Arguments:\n key {[string]} -- Name of amplifier. (default: {None})\n\n Returns:\n [string] -- Get the rotation type.\n \"\"\"\n\n return self.__getItem(key).getInfo().getVisitInfo().getRotType()\n\n def getBoresightAzAlt(self, key=None):\n \"\"\"\n\n Get the boresight of telescope in Az and Alt.\n\n Arguments:\n key {[string]} -- Name of amplifier. (default: {None})\n\n Returns:\n [float] -- Boresight of telescope in Az and Alt.\n \"\"\"\n\n return self.__getItem(key).getInfo().getVisitInfo().getBoresightAzAlt()\n\n def getBoresightRaDec(self, key=None):\n \"\"\"\n\n Get the boresight of telescope in Ra and Dec.\n\n Arguments:\n key {[string]} -- Name of amplifier. (default: {None})\n\n Returns:\n [float] -- Boresight of telescope in Ra and Dec.\n \"\"\"\n\n return self.__getItem(key).getInfo().getVisitInfo().getBoresightRaDec()\n\n def getGain(self, key=None):\n \"\"\"\n\n Get the gain value of amplifier.\n\n Arguments:\n key {[string]} -- Name of amplifier. (default: {None})\n\n Returns:\n [float] -- Gain of amplifier.\n \"\"\"\n\n return self.__getItem(key).getMetadata().get(\"GAIN\")\n\n def __getItem(self, key=None):\n \"\"\"\n \n Get the specific exposure.\n \n Keyword Arguments:\n key {[string]} -- Name of amplifier. (default: {None})\n \n Returns:\n [butler] -- Exposure butler object.\n \"\"\"\n\n if key is not None:\n return self.dataButlerObject[key]\n else:\n return self.dataButlerObject\n\nclass SliceFacadeTest(unittest.TestCase):\n\n \"\"\"\n Test the function of SliceFacade.\n \"\"\"\n\n def setUp(self):\n\n # Get the path of module\n modulePath = getModulePath()\n\n # Path of data folder\n dataFolderPath = os.path.join(modulePath, \"test\")\n self.dataFolderPath = dataFolderPath\n\n def testFunction(self):\n\n # Constuct the butler\n butler = dafPersistence.Butler(inputs=self.dataFolderPath)\n\n # Get the amplifier slice data\n obsId = 99999999\n snap = 0\n raft = \"2,2\"\n sensor = \"1,1\"\n channel = \"1,4\"\n\n dataId = dict(visit=obsId, snap=snap, raft=raft, sensor=sensor, channel=channel)\n exposure = butler.get(\"raw\", dataId=dataId)\n ampSlice = SliceFacade(exposure)\n ampTwoSlice = SliceFacade(dict([(\"S1\", exposure), (\"S2\", exposure)]))\n\n # Test Slice functions\n self.assertEqual(ampSlice.getDimensions()[0], 513)\n self.assertEqual(ampSlice.getDimensions()[1], 2001)\n\n self.assertEqual(ampSlice.getGain(), 1.83546)\n\n self.assertEqual(ampSlice.getBoresightAzAlt()[0], 0)\n\n self.assertEqual(ampSlice.getBoresightRotAngle(), 0)\n\n self.assertEqual(ampSlice.getBoresightRaDec()[0], 0)\n self.assertEqual(ampSlice.getBoresightRaDec()[1], 0)\n\n # Test Slice functions for a dictionary\n self.assertEqual(ampTwoSlice.getDimensions(\"S1\")[0], 513)\n self.assertEqual(ampTwoSlice.getDimensions(\"S2\")[1], 2001)\n\nif __name__ == \"__main__\":\n\n # Do the unit test\n unittest.main()","sub_path":"python/lsst/ts/wep/isr/SliceFacade.py","file_name":"SliceFacade.py","file_ext":"py","file_size_in_byte":4466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"292526982","text":"import tweepy\nimport crawler.authenticator as auth\nimport json\nimport time\nfrom os.path import join\nimport os\n\n\napi = tweepy.API(auth.authenticate(), wait_on_rate_limit=True, wait_on_rate_limit_notify=True)\ncursor = tweepy.Cursor(api.search,\n q='ethereum OR #ethereum',\n rpp=50,\n lang='en',\n # result_typr='recent',\n include_entities=True).items()\njsonDir = 'resources/rawTweets/ethereumTweets'\n\n\ndef now():\n return int(round(time.time()))\n\n\ndef crawlTwitter(cursor, outputFolder):\n if not os.path.exists(outputFolder):\n os.makedirs(outputFolder)\n\n def newJsonFile():\n \"\"\"Creates a new Json File and inserts initial Json.\"\"\"\n print('creating new file')\n f = open(join(outputFolder, 'tweets-' + str(now()) + '.json'), 'a')\n f.write('[\\n')\n return f\n\n def closeJson(f):\n \"\"\"Closes file and ends Json.\"\"\"\n print('closing file')\n f.write(']')\n f.close()\n\n print('writing first tweet')\n # Writes first tweet to the file from the iterator, to prevent trailing Comma\n f = newJsonFile()\n f.write(json.dumps(cursor.next()._json) + '\\n')\n\n print('starting twitter crawl')\n for count, tweet in enumerate(cursor):\n print(count)\n f.write(',' + json.dumps(tweet._json) + '\\n')\n if(count % 2500 == 0):\n print('create new file')\n closeJson(f)\n f = newJsonFile()\n\n print('End of Cursor')\n closeJson(f)\n\n\ndef twitterToCsv(inputfolder, outputFolder):\n if not os.path.exists(outputFolder):\n os.makedirs(outputFolder)\n\n for file in os.listdir(inputfolder):\n print(file)\n # nltk.twitter.common.json2csv\n\n\ndef crawl():\n outputFolder = jsonDir + '_' + str(now())\n crawlTwitter(cursor, outputFolder)\n","sub_path":"crawler/twitterCrawler.py","file_name":"twitterCrawler.py","file_ext":"py","file_size_in_byte":1878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"632975400","text":"#Code adapted from http://charliemeyer.net/2012/12/email-to-sms-gateway-solution-for-google-voice-users/\n#Created on 29 August 2014 by David O Stewart at GeauxHack\n#Created an email-sms gateway using dummy gmail account and googlevoice forwarding\n#working in tandem with SendGrid api\n#this is a listener, opens connection to gmail account and listens for incoming mail. If it fits pattern, forwards it to\n#google voice number, which forwards to phone.\nfrom bs4 import BeautifulSoup\nfrom time import sleep\nfrom time import strftime\nimport imaplib\nimport email\nfrom googlevoice import Voice\nimport sendgrid\nimport re\n\nclass EmailReceiver:\n \n def __init__(self):\n self.username = \"gmailtiedtogvoicehere\" #edit this\n self.password = \"gmailpassword\" #edit this\n self.server = \"imap.gmail.com\"\n self.port = 993\n self.M = None\n self.response = None\n \n def login(self):\n self.M = imaplib.IMAP4_SSL(self.server, self.port)\n rc, self.response = self.M.login(self.username, self.password)\n return rc\n \n def logout(self):\n self.M.close()\n self.M.logout()\n # self.M == mailbox\n def get_messages(self):\n self.M.select(\"INBOX\")\n status, response = self.M.status('INBOX', \"(UNSEEN)\")\n status, email_ids = self.M.search(None, '(UNSEEN)')\n messages = []\n for e_id in email_ids[0].split():\n rc, data = self.M.FETCH(e_id, '(RFC822)')\n email_body = data[0][1] # getting the mail content\n if 'sendsms' in str(data[0][1]): # don't EVER look at data[0][1].\n junk, useful =str(data[0][1]).split(\"sendsms \", 1)\n useful, junk = re.split('\\\\\\\\|[<>]',useful, maxsplit=1) \n useful = \"sendsms \" + useful\n elif 'sendemail' in str(data[0][1]): \n junk, useful =str(data[0][1]).split(\"sendemail \", 1)\n useful, junk = re.split('\\\\\\\\|[<>]',useful, maxsplit=1)\n useful = \"sendemail \" + useful \n messages.append(useful)\n\n self.M.store(e_id, '+FLAGS', '\\\\Deleted')\n self.M.expunge()\n return messages\n\ng = EmailReceiver()\ng.login()\nprint(\"Logged in to email server. Retrieving messages\")\nsg = sendgrid.SendGridClient()\nprint(\"SendGrid functionality enabled\")\nvoice = Voice()\nprint(\"SMS functionality enabled\")\nvoice.login()\nvoice.sms()\n\ndef extractsms(htmlsms) : #same sort of functionality as above, but for GVoice. Not used yet, will be\n\t\t\t\t\t\t #in the future.\n \"\"\"\n extractsms -- extract SMS messages from BeautifulSoup tree of Google Voice SMS HTML.\n\n Output is a list of dictionaries, one per message.\n \"\"\"\n msgitems = []\t\t\t\t\t\t\t\t\t\t# accum message items here\n #\tExtract all conversations by searching for a DIV with an ID at top level.\n tree = BeautifulSoup(htmlsms)\t\t\t# parse HTML into tree\n conversations = tree.findAll(\"div\",attrs={\"id\" : True},recursive=False)\n for conversation in conversations :\n #\tFor each conversation, extract each row, which is one SMS message.\n rows = conversation.findAll(attrs={\"class\" : \"gc-message-sms-row\"})\n for row in rows :\t\t\t\t\t\t\t\t# for all rows\n #\tFor each row, which is one message, extract all the fields.\n msgitem = {\"id\" : conversation[\"id\"]}\t\t# tag this message with conversation ID\n spans = row.findAll(\"span\",attrs={\"class\" : True}, recursive=False)\n for span in spans : # for all spans in row\n for cl in span[\"class\"]:\n cl = cl.replace('gc-message-sms-','')\n msgitem[cl] = (\" \".join(span.findAll(text=True))).strip()\t# put text in dict\n msgitems.append(msgitem)\t\t\t\t\t# add msg dictionary to list\n return msgitems\n\n\ncounter = 0\nmessagenum = 0\nwhile True:\n counter+=1\n messages = g.get_messages()\n for message in messages:\n if 'sendsms' in message:\n print(message)\n messagenum+=1\n print(\"Time: \", strftime(\"%Y-%m-%d %H:%M:%S\"))\n print(\"Message \" + str(messagenum) + \" received: parsing...\")\n num,msg = message.replace('sendsms ','').split(' ', 1)\n msg = msg + \"\\n\\n If you would like to respond, reply to this message with format 'sendemail [response@email.here] subject [message]'\"\n voice.send_sms(num,msg)\n print(\"Text sent to \", num, \" at \", strftime(\"%Y-%m-%d %H:%M:%S\"))\n if 'sendemail' in message:\n messagenum+=1\n print(\"Time: \", strftime(\"%Y-%m-%d %H:%M:%S\"))\n print(\"Message \" + str(messagenum) + \" received: parsing...\")\n emailaddress, subject, content = message.replace('sendemail ','').split(' ', 2)\n content = content + \"\\n\\n\\n If you would like to respond, reply to this message with format 'sendsms [response-number-here] [message here]'\"\n email = sendgrid.Mail(to=emailaddress, subject=subject, html=content, text=content, from_email='dummygmailacc')\n status, msg = sg.send(email)\n print(\"Email sent to \", emailaddress, \" at \", strftime(\"%Y-%m-%d %H:%M:%S\"))\n sleep(1)\n print(\"Loop \", counter)\n sleep(15)\n \n","sub_path":"PyBroadcast.py","file_name":"PyBroadcast.py","file_ext":"py","file_size_in_byte":5206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"65640829","text":"import sys\nimport random\nimport time\ndef strepen():\n print(\"=\"* 40)\n\ndef welkom_keuze():\n strepen()\n print(\"Welkom bij mijn overhoor progamma!\")\n print(\"Tip: begin met een woordenlijst maken!\")\n strepen()\n print(\"nieuwe lijst: n\")\n print(\"bekijk lijst: b\")\n print(\"wijzig lijst: w\")\n print(\"overhoor lijst: o\")\n print(\"Stop mijn programma: q\")\n strepen()\n\ndef main():\n welkom_keuze()\n keuze = input(\"Welke letter geef je mij mee?:\")\n woorden = {}\n while keuze != 'q':\n if (keuze == 'b'):\n bekijk_lijst(woorden)\n if (keuze == 'n'):\n woorden = nieuwe_lijst(woorden)\n if (keuze == 'w'):\n wijzig_lijst(woorden)\n if (keuze == 'o'):\n overhoren_lijst(woorden)\n strepen()\n keuze = input(\"Welke letter geef je mij mee?: \")\n stoppen()\n\n\ndef bekijk_lijst(woorden):\n strepen()\n print(\"Nederlands : Engels\")\n for key in woorden:\n print(\"{key} : {value}\".format(key=key, value=woorden[key]))\n\ndef nieuwe_lijst(woorden):\n strepen()\n print(\"'q' om te stoppenn\")\n print(\"Laten we beginnen!\")\n woorden = {}\n key = input(\"Nederlands:\")\n while key != \"q\":\n value = input(\"Engels:\")\n woorden[key] = value\n key = input(\"Nederlands:\")\n f = open('lijstwoorden.txt', 'w')\n for key in woorden:\n f.write(\"{}:{} \".format(key, woorden[key]))\n f.close()\n print(\"Je bent klaar met je lijst!\")\n print(\"Als je nog meer woorden wilt wijzigen, kan dat in het menu\")\n return woorden\n\ndef wijzig_lijst(woorden):\n bekijk_lijst(woorden)\n strepen()\n key = input(\"Woord (NL) dat je wilt veranderen:\")\n newnl = input(\"Nieuw woord:\")\n newen = input(\"Nieuwe vertaling:\")\n woorden[newnl] = newen\n del woorden[key]\n print(\"De nieuwe wijziging is aangebracht in je woorden lijst!\")\n return woorden\n\ndef verwijder_woord(woorden):\n bekijk_lijst(woorden)\n strepen()\n key = input(\"Woord (NL) die je wilt verwijderen:\")\n vraag = input(\"Weet je zeker dat je dit woord wilt verwijderen?(ja/nee)\")\n if (vraag == 'ja'):\n del woorden[key]\n return woorden\n\ndef overhoren_lijst(woorden):\n punten = 0\n while punten < len(woorden.keys())*2:\n strepen()\n nlwoord = random.choice(list(woorden))\n print(\"Wat is de vertaling van dit woord?\")\n enwoord = input(nlwoord + \":\")\n if (enwoord == woorden[nlwoord]):\n print(\"Dat is juist!\")\n punten += 1\n print(\"Aantal punten: \",punten)\n elif (enwoord == 'q'):\n break\n else:\n print(\"Dat is helaas fout\")\n print(\"Het goede antwoord:\",woorden[nlwoord])\n punten -= 1\n print(\"Aantal punten: \", punten)\n\ndef stoppen():\n print(\"Dit programma sluit over 5 seconden,\")\n print(\"5\")\n time.sleep(0.5)\n print(\"4\")\n time.sleep(0.5)\n print(\"3\")\n time.sleep(0.5)\n print(\"2\")\n time.sleep(0.5)\n print(\"1\")\n time.sleep(0.5)\n print(\"0\")\n time.sleep(0.5)\n sys.exit()\n\nmain()","sub_path":"overhoren.py","file_name":"overhoren.py","file_ext":"py","file_size_in_byte":3081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"58454264","text":"\"\"\"\n##############################################################################\nПорядок использования: “python diffall.py dir1 dir2”.\nВыполняет рекурсивное сравнение каталогов: сообщает об уникальных файлах,\nсуществующих только в одном из двух каталогов, dir1 или dir2; сообщает о файлах\nс одинаковыми именами и с разным содержимым, присутствующих в каталогах dir1\nи dir2; сообщает об разнотипных элементах с одинаковыми именами, присутствующих\nв каталогах dir1 и dir2; то же самое выполняется для всех подкаталогов\nс одинаковыми именами, находящихся внутри деревьев каталогов dir1\nи dir2. Сводная информация об обнаруженных отличиях помещается в конец вывода,\nоднако в процессе поиска в вывод добавляется дополнительная информация об\nотличающихся и уникальных файлах с метками “DIFF” и “unique”. Новое: (в 3\nиздании) для больших файлов введено ограничение на размер читаемых блоков в 1\nМбайт, (3 издание) обнаруживаются одинаковые имена файлов/каталогов, (4 издание)\nисключены лишние вызовы os.listdir() в dirdiff.comparedirs() за счет передачи\nрезультатов.\n##############################################################################\n\"\"\"\nimport os, dirdiff\n\nblocksize = 1024 * 1024 # не более 1 Мб на одну операцию чтения\n\n\ndef intersect(seq1, seq2):\n \"\"\"\n Возвращает все элементы, присутствующие одновременно в seq1 и seq2;\n выражение set(seq1) & set(seq2) возвращает тот же результат, но множества\n являются неупорядоченными коллекциями, поэтому при их использовании может\n быть утерян порядок следования элементов, если он имеет значение для\n некоторых платформ\n \"\"\"\n return [item for item in seq1 if item in seq2]\n\n\ndef comparetrees(dir1, dir2, diffs, verbose=False):\n \"\"\"\n Сравнивает все подкаталоги и файлы в двух деревьях каталогов;\n для предотвращения кодирования/декодирования содержимого и преобразования\n символов конца строки использует двоичный режим доступа к файлам,\n так как деревья могут содержать произвольные двоичные и текстовые файлы;\n функции listdir может потребоваться передавать аргумент типа bytes, если\n могут встречаться имена файлов, недекодируемые на других платформах\n \"\"\"\n # сравнить списки с именами файлов\n print('-' * 20)\n names1 = os.listdir(dir1)\n names2 = os.listdir(dir2)\n if not dirdiff.comparedirs(dir1, dir2, names1, names2):\n diffs.append('unique files at %s - %s' % (dir1, dir2))\n\n print('Comparing contents')\n common = intersect(names1, names2)\n missed = common[:]\n\n # сравнить содержимое фалов с одинаковыми именами\n for name in common:\n path1 = os.path.join(dir1, name)\n path2 = os.path.join(dir2, name)\n if os.path.isfile(path1) and os.path.isfile(path2):\n missed.remove(name)\n file1 = open(path1, 'rb')\n file2 = open(path2, 'rb')\n while True:\n bytes1 = file1.read(blocksize)\n bytes2 = file2.read(blocksize)\n if (not bytes1) and (not bytes2):\n if verbose: print(name, 'matches')\n break\n if bytes1 != bytes2:\n diffs.append('files differ at %s - %s' % (path1, path2))\n print(name, 'DIFFERS')\n break\n # рекурсивно сравнить каталоги с одинаковыми именами\n for name in common:\n path1 = os.path.join(dir1, name)\n path2 = os.path.join(dir2, name)\n if os.path.isdir(path1) and os.path.isdir(path2):\n missed.remove(name)\n comparetrees(path1, path2, diffs, verbose)\n\n # одинаковые имена, но оба не являются одновременно файломи или каталогами?\n for name in missed:\n diffs.append('files missed at %s - %s: %s' % (dir1, dir2, name))\n print(name, 'DIFFERS')\n\n\nif __name__ == '__main__':\n dir1, dir2 = dirdiff.getargs()\n diffs = []\n comparetrees(dir1, dir2, diffs, True)\n print('=' * 40)\n if not diffs:\n print('No diffs found.')\n else:\n print('Diffs found:', len(diffs))\n for diff in diffs: print('-', diff)\n","sub_path":"System/Filetools/diffall.py","file_name":"diffall.py","file_ext":"py","file_size_in_byte":5574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"97783575","text":"\n\nfrom xai.brain.wordbase.nouns._lawbreaker import _LAWBREAKER\n\n#calss header\nclass _LAWBREAKERS(_LAWBREAKER, ):\n\tdef __init__(self,): \n\t\t_LAWBREAKER.__init__(self)\n\t\tself.name = \"LAWBREAKERS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"lawbreaker\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_lawbreakers.py","file_name":"_lawbreakers.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"506659844","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Apr 20 17:06:44 2021\r\n\r\n@author: ANIL\r\n\"\"\"\r\n\r\n'''\r\nBreak a list into chunks of size N in Python\r\n'''\r\n\r\nmy_list = ['geeks', 'for', 'geeks', 'like',\r\n 'geeky','nerdy', 'geek', 'love',\r\n 'questions','words', 'life']\r\n\r\nn = 4\r\n\r\nchunk_list = []\r\n\r\nfor i in range(0,(len(my_list) + n - 1)//n):\r\n list1 = my_list[i*n : (i+1) * n]\r\n chunk_list.append(list1)\r\n \r\nprint(chunk_list)\r\n\r\n\r\n\r\n\r\nchunk_list1 = [ my_list[i:i+n] for i in range(0,len(my_list),n)]\r\nprint(chunk_list1)\r\n\r\n\r\n\r\ndef divide_chunks(l, n):\r\n for i in range(0, len(l), n): \r\n yield l[i:i + n]\r\n \r\nx = list(divide_chunks(my_list, n))\r\nprint (x)","sub_path":"GeeksforGeeks/vAnil/03. List Programs/29.py","file_name":"29.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"250002479","text":"#!/usr/bin/env python\nimport rospy\nfrom std_msgs.msg import String\nfrom sensor_msgs.msg import Imu\n\ndef callback(data):\n AX = (int(data.data[data.data.index(\"A\") + 1:data.data.index(\"B\")]))\n AY = (int(data.data[data.data.index(\"B\") + 1:data.data.index(\"C\")]))\n AZ = (int(data.data[data.data.index(\"C\") + 1:data.data.index(\"D\")]))\n\n GX = (int(data.data[data.data.index(\"D\") + 1:data.data.index(\"E\")]))\n GY = (int(data.data[data.data.index(\"E\") + 1:data.data.index(\"F\")]))\n GZ = (int(data.data[data.data.index(\"F\") + 1:data.data.index(\"G\")]))\n print(AX)\n\n talker(AX, AY, AZ, GX, GY, GZ)\n\n\ndef talker(AX, AY, AZ, GX, GY, GZ):\n pub = rospy.Publisher('imu', Imu, queue_size=10)\n rate = rospy.Rate(10)\n\n\n imu = Imu()\n imu.header.stamp = rospy.Time.now()\n imu.header.frame_id = 'odom'\n imu.linear_acceleration.x = AX\n imu.linear_acceleration.y = AY\n imu.linear_acceleration.z = AZ\n imu.angular_velocity.x = GX\n imu.angular_velocity.y = GY\n imu.angular_velocity.z = GZ\n \n pub.publish(imu)\n rate.sleep()\n\ndef listener():\n\n print('What it is')\n # In ROS, nodes are uniquely named. If two nodes with the same\n # name are launched, the previous one is kicked off. The\n # anonymous=True flag means that rospy will choose a unique\n # name for our 'listener' node so that multiple listeners can\n # run simultaneously.\n rospy.init_node('listener', anonymous=True)\n\n print('Mario')\n\n rospy.Subscriber(\"imu_arduino\", String, callback)\n\n print('Luigi')\n\n # spin() simply keeps python from exiting until this node is stopped\n\n rospy.spin()\n\nif __name__ == '__main__':\n listener()\n","sub_path":"src/imu/imu_publisher.py","file_name":"imu_publisher.py","file_ext":"py","file_size_in_byte":1665,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"154401491","text":"print('Hello world')\n\n\nfrom Tkinter import *\n\n\n'''\nclass Application(tk.Frame): \n\tdef __init__(self, master=None):\n\t\ttk.Frame.__init__(self, master) \n\t\tself.grid() \n\t\tself.createWidgets()\n\tdef createWidgets(self):\n\t\tself.quitButton = tk.Button(self, text='Quit', command=self.quit) \n\t\tself.quitButton.grid() \n\n\napp = Application() \napp.master.title('Sample application') \napp.mainloop() \n'''\n'''\ndef create_layout(frame):\n\tframe = Frame(frame, )\n'''\n\nwin = Tk()\nfor i in range(0, 8):\n\tfor j in range(0, 8):\n\t\tb1 = Button(win)\n\t\tb1.grid(row = i, column = j)\n\t\tb1.pack()","sub_path":"Othello.py","file_name":"Othello.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"346883706","text":"# -*- coding:utf-8 -*-\n\"\"\"\ncreated by server on 14-6-27下午6:49.\n\"\"\"\nfrom shared.db_opear.configs_data import game_configs\nfrom app.game.component.Component import Component\nfrom app.game.redis_mode import tb_character_info\nfrom gfirefly.server.logobj import logger\nfrom shared.utils.const import const\nfrom gfirefly.server.globalobject import GlobalObject\nfrom shared.tlog import tlog_action\n\n\n# const.COIN = 1\n# const.GOLD = 2\n# const.HERO_SOUL = 3\n# const.JUNIOR_STONE = 4\n# const.MIDDLE_STONE = 5\n# const.HIGH_STONE = 6\n# const.STAMINA = 7\n# const.PVP = 8\n# const.CONSUME_GOLD = 9\n# const.GUILD2 = 11\n# const.TEAM_EXPERIENCE = 12\n# const.NECTAR = 13\n# const.STONE1 = 14\n# const.STONE2 = 15\n# const.EQUIPMENT_ELITE = 21\n# const.RESOURCE_MAX = 23\n\nclass CharacterFinanceComponent(Component):\n \"\"\"货币\"\"\"\n\n def __init__(self, owner, coin=0, gold=0, hero_soul=0):\n super(CharacterFinanceComponent, self).__init__(owner)\n self._finances = [0] * const.RESOURCE_MAX\n\n def init_data(self, data):\n print(\"init data ============finance\")\n self._finances = data['finances']\n while len(self._finances) < const.RESOURCE_MAX:\n self._finances.append(0)\n\n def save_data(self):\n character_obj = tb_character_info.getObj(self.owner.base_info.id)\n character_obj.hset('finances', self._finances)\n\n def new_data(self):\n self._finances = [0] * const.RESOURCE_MAX\n for t, v in game_configs.base_config.get('resource_for_InitUser').items():\n self._finances[t] = v\n return {'finances': self._finances}\n\n def __getitem__(self, res_type):\n if res_type >= len(self._finances):\n logger.error('get error resource type:%s', res_type)\n return None\n return self._finances[res_type]\n\n def __setitem__(self, res_type, value):\n if res_type >= len(self._finances):\n logger.error('set error resource type:%s', res_type)\n return\n self._finances[res_type] = value\n\n @property\n def coin(self):\n return self._finances[const.COIN]\n\n @coin.setter\n def coin(self, coin):\n self._finances[const.COIN] = coin\n\n @property\n def hero_soul(self):\n return self._finances[const.HERO_SOUL]\n\n @hero_soul.setter\n def hero_soul(self, value):\n self._finances[const.HERO_SOUL] = value\n\n @property\n def gold(self):\n \"\"\"\n get gold num.\n \"\"\"\n return self._finances[const.GOLD]\n\n @gold.setter\n def gold(self, gold):\n self._finances[const.GOLD] = gold\n\n @property\n def pvp_score(self):\n return self._finances[const.PVP]\n\n @pvp_score.setter\n def pvp_score(self, value):\n self._finances[const.PVP] = value\n\n def is_afford(self, fType, value):\n if fType >= len(self._finances):\n logger.error('afford error finance type:%s', fType)\n return False\n if self._finances[fType] < value:\n return False\n return True\n\n def consume(self, fType, num, reason):\n logger.debug(\"consume============= %s\" % num)\n if fType >= len(self._finances):\n logger.error('consume error finance type:%s', fType)\n return False\n if num > self._finances[fType]:\n logger.error('not enough finance:%s:%s:%s',\n fType, self._finances[fType], num)\n return False\n if fType != const.GOLD:\n self._finances[fType] -= int(num)\n self._owner.add_activity.add_currency(fType, num)\n self._owner.act.add_currency(fType, num)\n if reason:\n tlog_action.log('ItemFlow', self.owner, const.REDUCE, const.RESOURCE, num,\n fType, 0, reason, self._finances[fType], '')\n return True\n\n def add(self, fType, num, reason=0):\n if fType >= len(self._finances):\n logger.error('consume error finance type:%s', fType)\n return False\n logger.debug(\"fType %s num %s\" % (fType, num))\n if fType == const.GOLD:\n self.add_gold(num, reason)\n else:\n self._finances[fType] += int(num)\n if reason:\n tlog_action.log('ItemFlow', self.owner, const.ADD, const.RESOURCE, num,\n fType, 0, reason, self._finances[fType], '')\n return True\n\n def add_coin(self, num, reason):\n self._finances[const.COIN] += num\n tlog_action.log('ItemFlow', self.owner, const.ADD, const.RESOURCE, num,\n 1, 0, reason, self._finances[const.COIN], '')\n tlog_action.log('MoneyFlow', self.owner, self._finances[const.COIN],\n num, reason, const.ADD, 1)\n\n def consume_coin(self, num, reason):\n if num < self._finances[const.COIN]:\n return False\n self._finances[const.COIN] -= num\n tlog_action.log('ItemFlow', self.owner, const.REDUCE, const.RESOURCE, num,\n 1, 0, reason, self._finances[const.COIN], '')\n tlog_action.log('MoneyFlow', self.owner, self._finances[const.COIN],\n num, reason, const.REDUCE, 1)\n return True\n\n def add_gold(self, num, reason):\n if self._owner.pay.REMOTE_DEPLOYED:\n self._owner.pay.present(num)\n else:\n self._finances[const.GOLD] += num\n if reason:\n tlog_action.log('ItemFlow', self.owner, const.ADD, const.RESOURCE, num,\n 2, 0, reason, self._finances[const.GOLD], '')\n tlog_action.log('MoneyFlow', self.owner, self._finances[const.GOLD],\n num, reason, const.ADD, 2)\n\n def consume_gold(self, num, reason):\n \"\"\"\n 消耗元宝\n \"\"\"\n logger.debug(\"consume_gold============= %s\" % num)\n if num > self._finances[const.GOLD]:\n return False\n self._finances[const.CONSUME_GOLD] += num\n self._finances[const.GOLD] -= num\n self._owner.add_activity.add_currency(const.GOLD, num)\n self._owner.act.add_currency(const.GOLD, num)\n if reason:\n tlog_action.log('ItemFlow', self.owner, const.REDUCE, const.RESOURCE, num,\n 2, 0, reason, self._finances[const.GOLD], '')\n tlog_action.log('MoneyFlow', self.owner, self._finances[const.GOLD],\n num, reason, const.REDUCE, 2)\n return True\n\n def add_hero_soul(self, num):\n self._finances[const.HERO_SOUL] += num\n\n def consume_hero_soul(self, num):\n self._finances[const.HERO_SOUL] -= num\n","sub_path":"app/game/component/finance/finance.py","file_name":"finance.py","file_ext":"py","file_size_in_byte":6639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"435990502","text":"import requests\n\ntry:\n host = 'https://api-cn.faceplusplus.com/cardpp/v1/ocridcard'\n url = host\n files = {'image_file': open('/sun/tmp/test-idcard.jpg', 'rb')}\n data = {'api_key': '','api_secret': ''}\n\n response = requests.post(url, files = files, data = data)\n result = response.json()\n print(result)\nexcept Exception as e:\n print(\"type error: \" + str(e))","sub_path":"py/req.py","file_name":"req.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"570024341","text":"N, M = [int(n) for n in input().split()]\n\nnum = [i + 1 for i in range(N)]\ncheck = [False] * N\n\nanswer = []\n\ndef dfs(cnt):\n if(cnt == M):\n print(*answer)\n return\n\n for i in range(0, N):\n if(check[i]):\n continue\n\n check[i] = True\n answer.append(num[i])\n\n dfs(cnt + 1)\n\n answer.pop()\n check[i] = False\n\ndfs(0)\n","sub_path":"python/backjoon/Q15649/Q15649.py","file_name":"Q15649.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"194286160","text":"\"\"\"Se citesc pe rând temperaturile medii ale fiecărei luni a unui an, ca numere întregi. \r\nSă se afişeze cu două zecimale media anuală a temperaturilor pozitive şi a celor negative. \r\nExemplu: Date de intrare -5 -3 1 8 12 17 20 21 18 10 6 -2 Date de ieşire medie_poz=13.66 medie_neg=-3.33.\"\"\"\r\n\r\nn=0\r\nsp=0\r\nsn=0\r\nnp=0\r\nnn=0\r\nc=0\r\n\r\nwhile c<12:\r\n n=eval(input('Introduceti temperatura:'))\r\n if n<55 and n>-60:\r\n c+=1\r\n if n>0:\r\n sp+=n\r\n np+=1\r\n elif n<0:\r\n sn+=n\r\n nn+=1 \r\n else: print('Error: Introduceti o temperatura valida') \r\n\r\nif np>0:\r\n print('medie_poz=',round(float(sp/np), 2))\r\nelse:print('Nu au fost temperaturi pozitive')\r\n\r\nif nn>0:\r\n print(\"medie_neg=\",round(float(sn/nn), 2))\r\nelse:print(\"Nu au fost temperaturi negative\")","sub_path":"pr2a.py","file_name":"pr2a.py","file_ext":"py","file_size_in_byte":850,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"625651554","text":"from typing import List\n\n\nclass Solution:\n def restoreMatrix(self, rowSum: List[int], colSum: List[int]) -> List[List[int]]:\n m=len(rowSum)\n n=len(colSum)\n \n res=[[0]*n for _ in range(m)]\n for i in range(m):\n for j in range(n):\n if rowSum[i]>=colSum[j]:\n res[i][j] = colSum[j]\n rowSum[i] = rowSum[i]-colSum[j]\n colSum[j]=0\n else:\n res[i][j] = rowSum[i]\n colSum[j] = colSum[j]-rowSum[i]\n rowSum[i] = 0\n \n # print(rowSum,colSum)\n return res","sub_path":"greedy/1605_find_valid_matrix_given_row_and_column_sums/1605_find_valid_matrix_given_row_and_column_sums.py","file_name":"1605_find_valid_matrix_given_row_and_column_sums.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"306915537","text":"class Solution(object):\n def totalNQueens(self, n):\n \"\"\"\n :type n: int\n :rtype: int\n \"\"\"\n self.result = 0\n self.search([],n)\n return self.result\n \n def search(self, cols, n):\n if len(cols) == n:\n self.result += 1\n return\n for i in range(n):\n if i in cols:\n continue\n cols.append(i)\n if self.isValid(cols):\n self.search(cols,n)\n del cols[-1]\n \n def isValid(self, cols):\n for i in range(len(cols)-1):\n for j in range(i+1, len(cols)):\n if cols[j] == cols[i]+j-i or cols[j] == cols[i]-j+i:\n return False\n return True\n","sub_path":"Python/NQueens.py","file_name":"NQueens.py","file_ext":"py","file_size_in_byte":746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"116103168","text":"import requests\nfrom bs4 import BeautifulSoup\nfrom django.utils import timezone\nfrom food.models import FoodComment\nfrom django.db.models import Max\nfrom datetime import datetime\nimport json\n\n\nclass CommentCrawler:\n\n def __init__(self, **kwargs):\n parent = kwargs.get('parent', None)\n self.link = parent.link\n self.petshop = parent.petshop\n self.url = parent.url\n\n def crawl(self):\n r = requests.get(self.url)\n return BeautifulSoup(r.content, \"lxml\")\n\n def comment_crawl(self, source):\n comments_li = source.find(id=\"commentTab\")\n comments_li = comments_li['data-href'].split('comment/')\n\n comment_url = self.petshop.url + 'srv/service/product-detail/comments/' + comments_li[1]\n\n r = requests.get(comment_url)\n return BeautifulSoup(r.content, \"lxml\")\n\n def author(self, comment):\n return comment['NAME']\n\n def description(self, comment):\n return comment['COMMENT']\n\n def rating(self, comment):\n return round(comment['RATE'] / 4)\n\n def published(self, comment):\n published = datetime.fromtimestamp(int(comment['DATE']))\n published = timezone.make_aware(published, timezone.get_current_timezone())\n\n return published\n\n def run(self):\n source = self.crawl()\n comment_data = self.comment_crawl(source)\n comment_json = json.loads(comment_data.text)\n\n comments = comment_json.get('COMMENTS')\n\n if comments:\n c = FoodComment.objects.filter(food=self.link.food, petshop=self.petshop).aggregate(max_date=Max('created'))\n\n for comment in comments:\n published = self.published(comment)\n\n save = 1 # daha sonra yeni yorumlar gelsin diye sıfır olacak\n\n if c['max_date'] is None:\n save = 1\n elif published > c['max_date']:\n save = 1\n\n if save == 1:\n fc = FoodComment(\n food=self.link.food,\n name=self.author(comment),\n created=published,\n content=self.description(comment),\n rating=self.rating(comment),\n petshop=self.petshop,\n )\n fc.save()\n","sub_path":"library/feeds/crawlers/markamama/comment.py","file_name":"comment.py","file_ext":"py","file_size_in_byte":2339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"496946315","text":"import urllib\nfrom src import site\nfrom src import tool\nfrom src import db\nfrom src import common\n\njav_dao = db.jav.JavDao()\nmaker_dao = db.maker.MakerDao()\n\ncopytext = common.CopyText()\nfc2 = site.fc2.Fc2()\nmgs = site.mgs.Mgs()\nhey = site.hey.Hey()\nwiki = site.wiki.SougouWiki()\ngoogle = site.wiki.Google()\nfanza = site.fanza.Fanza()\n# javs = jav_dao.get_where_agreement('WHERE is_selection = 1 '\n# 'order by post_date limit 80')\njavs = jav_dao.get_where_agreement('WHERE is_selection = 1 and (sell_date is null or sell_date = \\'1900-01-01\\') '\n 'order by post_date ')\n# javs = jav_dao.get_where_agreement('WHERE id = 16247')\n# javs = jav_dao.get_where_agreement('WHERE is_parse2 <= 0 and is_selection = 1 and id <= 17143 order by id limit 50')\n\n# parser = common.AutoMakerParser()\nmakers = maker_dao.get_all()\n\nif javs is None:\n javs = []\nprint('target [' + str(len(javs)) + ']')\np_tool = tool.p_number.ProductNumber(is_log_print=False)\nerr_list = []\nok_cnt = 0\nng_cnt = 0\nis_checked = True\nfor jav in javs:\n print('[' + str(jav.id) + ']' + jav.title)\n\n site_data = None\n find_filter_maker = filter(lambda maker: maker.id == jav.makersId, makers)\n find_maker_list = list(find_filter_maker)\n if len(find_maker_list) <= 0:\n err_list.append('not found[' + str(jav.id) + '] ' + str(jav.makersId))\n ng_cnt = ng_cnt + 1\n continue\n\n match_maker = find_maker_list[0]\n print('[' + str(jav.id) + '] p_num [' + jav.productNumber + ']' + match_maker.name)\n\n if match_maker.name == 'HEY動画':\n site_data = hey.get_info(jav.productNumber)\n\n if site_data is None:\n err_list.append('HEY動画 not found[' + str(jav.id) + '] ' + match_maker.label)\n ng_cnt = ng_cnt + 1\n continue\n\n print(site_data.streamDate + ' ' + site_data.maker)\n\n elif match_maker.name == 'MGS':\n site_data = mgs.get_info(jav.productNumber)\n\n if site_data is None:\n err_list.append('MGS not found[' + str(jav.id) + '] ' + jav.title)\n detail = 'no mgs result'\n ng_cnt = ng_cnt + 1\n continue\n\n print(site_data.streamDate + ' ' + site_data.maker)\n\n elif match_maker.name == 'FC2 コンテンツマーケット':\n site_data = fc2.get_info(jav.productNumber)\n\n if site_data is None:\n err_list.append('FC2 not found[' + str(jav.id) + '] ' + jav.title)\n ng_cnt = ng_cnt + 1\n continue\n\n print(site_data.streamDate + ' ' + site_data.maker)\n\n elif match_maker.name == 'SITE':\n print('SITE DAYO ' + jav.title)\n ng_cnt = ng_cnt + 1\n continue\n\n else:\n if match_maker.kind == 1:\n site_data = fanza.get_info(jav.productNumber)\n\n if site_data is None:\n err_list.append('FANZA not found[' + str(jav.id) + '] ' + jav.title)\n ng_cnt = ng_cnt + 1\n continue\n else:\n print('URA DAYO ' + jav.title)\n\n if match_maker.kind == 3:\n if site_data is not None:\n # is_siteも1に更新\n try:\n jav_dao.update_site_info(site_data.maker, site_data.streamDate, jav.id)\n except:\n site_data.print('Error ')\n ng_cnt = ng_cnt + 1\n # exit(-1)\n ok_cnt = ok_cnt + 1\n else:\n str_date = copytext.get_date_ura(jav.title)\n if len(str_date) > 0:\n jav_dao.update_detail_and_sell_date('', str_date, jav.id)\n ok_cnt = ok_cnt + 1\n else:\n print('site_data is None date not found' + jav.title)\n ng_cnt = ng_cnt + 1\n # if match_maker.kind == 3:\n else:\n if site_data is not None:\n detail = site_data.get_detail()\n # if jav.maker is None or len(jav.maker) <= 0:\n # jav_dao.update_maker_label(match_maker.name, match_maker.label, jav.id)\n # is_siteも1に更新\n jav_dao.update_detail_and_sell_date(detail, site_data.streamDate, jav.id)\n\n if jav.searchResult is None or len(jav.searchResult.strip()) <= 0:\n print('searchResult [' + jav.productNumber + ']')\n searchResult = ''\n try:\n site_name, site_url = google.get_info(jav.productNumber)\n if len(site_name) > 0:\n searchResult = site_name + ' ' + site_url\n except urllib.error.HTTPError as err:\n print('HTTP Error pNumber [' + jav.productNumber + ']')\n\n if len(searchResult) <= 0:\n searchResult = 'no search result'\n\n jav_dao.update_search_result(searchResult, jav.id)\n\n ok_cnt = ok_cnt + 1\n # if site_data is not None:\n else:\n print('site_data is None ' + jav.title)\n ng_cnt = ng_cnt + 1\n\nfor err in err_list:\n print(err)\n\nprint('ok [' + str(ok_cnt) + ']')\nprint('ng [' + str(ng_cnt) + ']')\n","sub_path":"confirm_recover_selldate.py","file_name":"confirm_recover_selldate.py","file_ext":"py","file_size_in_byte":5104,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"89514582","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 7 11:53:03 2021\n\n@author: ja17375\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cartopy.feature as cfeature \nimport cartopy.crs as ccrs\nimport netCDF4 as nc\nimport pandas as pd \n\ndef extract_forte(la, lo):\n '''For an inou point extract the correct Ur, Utheta, Uphi and calculate orientation of flow vector''' \n tx2008 = np.loadtxt('/Users/ja17375/SWSTomo/ForteModels/Flow_Models/TX2008/forteV2_E_Pac_250km.txt')\n shp = (15, 23)\n lat = tx2008[:,1].reshape(shp) #5 degree step in lat/lon for region [-180, -70, 0, 70]\n lon = tx2008[:,2].reshape(shp)\n Ur = tx2008[:,3].reshape(shp)\n Utheta = tx2008[:,4].reshape(shp)*-1 # theta is colat so invert \n Uphi = tx2008[:,5].reshape(shp)\n idx_la = int((la - lat.min()) / 5 )# divide by 5 at thats the spacing\n idx_lo = int((lo - lon.min()) /5 )\n \n calc_azi_inc(Uphi[idx_la, idx_lo],\n Utheta[idx_la, idx_lo],\n Ur[idx_la, idx_lo])\n\ndef extract_flament(la, lo, dpath='/Users/ja17375/SWSTomo/FlamentModel'):\n nc_vx = nc.Dataset(f'{dpath}/C3-vx-000Ma-2677km.grd')\n nc_vy = nc.Dataset(f'{dpath}/C3-vy-000Ma-2677km.grd')\n nc_vz = nc.Dataset(f'{dpath}/C3-vz-000Ma-2677km.grd')\n vel_conv = 4.9e-4 # converts velocity to cm/year (from N. Flament - see model README.txt)\n\n Utheta = nc_vx['z'][:] * vel_conv *-1 #theta is colat so invert\n Uphi = nc_vy['z'][:] * vel_conv # longitudl velocity\n Ur = nc_vz['z'][:] * vel_conv # radial velocity\n idx_la = int(la +90) # flament models is at 1 deg spacing so indicies correspond to lat/lon\n idx_lo = int(lo)\n calc_azi_inc(Uphi[idx_la, idx_lo],\n Utheta[idx_la, idx_lo],\n Ur[idx_la, idx_lo])\n\ndef calc_azi_inc(vx, vy, vz):\n '''calculate azimuth and inclination(?) from flow model'''\n print(vx, vy, vz)\n azi = np.rad2deg(np.arctan2(vy, vx))\n incl = np.rad2deg(np.arctan2(vz, np.sqrt(vx**2+vy**2)))\n print(f' {azi:4.2f} {incl:4.2f}')\n return azi, incl\n\ndef plot_forte(fig, ax, proj,extent='epac'):\n if extent == 'global':\n tx2008 = np.loadtxt('/Users/ja17375/SWSTomo/ForteModels/Flow_Models/TX2008/forteV2_1deg_150km.txt')\n shp = (181, 361)\n dg = 15\n elif extent == 'epac':\n tx2008 = np.loadtxt('/Users/ja17375/SWSTomo/ForteModels/Flow_Models/TX2008/forteV2_E_Pac_250km.txt')\n shp = (15, 23)\n dg = 10\n lat = tx2008[:,1].reshape(shp) #5 degree step in lat/lon for region [-180, -70, 0, 70]\n lon = tx2008[:,2].reshape(shp)\n Ur = tx2008[:,3].reshape(shp)\n Utheta = tx2008[:,4].reshape(shp)*-1 # theta is colat so invert \n Uphi = tx2008[:,5].reshape(shp)\n hzdeg = ((lat % dg == 0) & (lon % dg == 0))\n \n fig = plt.figure(figsize=(7,7))\n ax = fig.add_subplot(111,projection=ccrs.PlateCarree())\n proj = ccrs.PlateCarree()\n if extent == 'global':\n ax.set_global()\n elif extent == 'epac':\n ax.set_extent([-170, -80, 10, 60])\n ax.coastlines()\n ax.gridlines(draw_labels=True, linewidth=0)\n C = ax.contourf(lon, lat, Ur,\n cmap='PuOr_r', levels=np.linspace(-1.8, 1.8, 37), vmax=1.8, vmin=-1.8,\n transform=proj) # linspace(-1.4, 1.4, 15) for Epac\n Q = ax.quiver(lon[hzdeg], lat[hzdeg], Uphi[hzdeg], Utheta[hzdeg], pivot='middle', transform=proj, scale=0.2,\n scale_units='x',width=0.003, minlength=1)\n ax.set_title('TX2008 150km above CMB')\n add_sws(ax, proj)\n fig.colorbar(C, ax=ax, orientation='vertical',shrink=0.5, pad=0.08, label='vertical velocity (cm/year)')\n ax.quiverkey(Q, 0.5,-0.25, 5.0, 'velocity vector (5.0 cm/year)', labelpos='N') # 2 cm/yr fror epac\n ax.set_aspect(aspect=1)\n fig.savefig(f'/Users/ja17375/SWSTomo/Figures/TX2008V2_{extent}_splitting.png', dpi=500, transparent=True)\n\n\ndef plot_flament(fig, ax, proj, dpath='/Users/ja17375/SWSTomo/FlamentModel',extent='epac'):\n nc_vx = nc.Dataset(f'{dpath}/C3-vx-000Ma-2677km.grd')\n nc_vy = nc.Dataset(f'{dpath}/C3-vy-000Ma-2677km.grd')\n nc_vz = nc.Dataset(f'{dpath}/C3-vz-000Ma-2677km.grd')\n vel_conv = 4.9e-4 # converts velocity to cm/year (from N. Flament - see model README.txt)\n\n Utheta = nc_vx['z'][:] * vel_conv *-1 #theta is colat so invert\n Uphi = nc_vy['z'][:] * vel_conv # longitudl velocity\n Ur = nc_vz['z'][:] * vel_conv # radial velocity\n lon, lat = np.meshgrid(nc_vx['lon'][:], nc_vx['lat'][:])\n\n \n\n if extent == 'global':\n ax.set_global()\n dg = 15\n elif extent == 'epac':\n ax.set_extent([-170, -80, 10, 60])\n dg = 10\n hzdeg = ((lat % dg == 0) & (lon % dg == 0))\n\n ax.coastlines()\n ax.gridlines(draw_labels=True, linewidth=0)\n C = ax.contourf(lon, lat, Ur,\n cmap='PuOr_r', levels=np.linspace(-1.2, 1.2, 25),\n transform=proj)\n Q = ax.quiver(lon[hzdeg], lat[hzdeg], Uphi[hzdeg], Utheta[hzdeg], pivot='middle', transform=proj,\n scale=0.2, scale_units='x',width=0.003, minlength=1)\n ax.set_title('Flament (2019) C3 depth 2677 km')\n ax.quiverkey(Q, 0.5,-0.25, 5.0, 'velocity vector (5.0 cm/year)', labelpos='N')\n fig.colorbar(C, ax=ax, orientation='vertical', shrink=0.5, pad=0.08, label='vertical velocity (cm/year)')\n ax.set_aspect(aspect=1)\n\ndef add_sws(ax,pj):\n '''Add SKS-SKKS shear-wave splitting '''\n proj=ccrs.Geodetic()\n data = pd.read_csv('~/DiscrePy/Sheba/Results/Combined/Filt_05Hz/Combined_goodQ.pairs', delim_whitespace=True)\n for i, row in data.iterrows():\n ax.plot([row['SKS_PP_LON'], row['SKKS_PP_LON']], [row['SKS_PP_LAT'], row['SKKS_PP_LAT']],'k-', transform=proj)\n if (row['Q_SKS'] >= 0.5):\n #Plot split SKS - black circle\n ax.plot(row['SKS_PP_LON'], row['SKS_PP_LAT'], 'ko', transform=proj)\n elif (row['Q_SKS'] <= -0.5):\n ax.plot(row['SKS_PP_LON'], row['SKS_PP_LAT'], 'wo', transform=proj, mec='black')\n else:\n print('Bad Q for SKS')\n if (row['Q_SKKS'] >= 0.5):\n #Plot split SKKS - black circle\n ax.plot(row['SKKS_PP_LON'], row['SKKS_PP_LAT'], 'ko', transform=proj)\n elif (row['Q_SKS'] <= -0.5):\n ax.plot(row['SKKS_PP_LON'], row['SKKS_PP_LAT'], 'wo', transform=proj, mec='black')\n \n \nif __name__ == '__main__':\n \n SMALL_SIZE = 12\n MEDIUM_SIZE = 14\n \n plt.rc('font', size=SMALL_SIZE) # controls default text sizes\n plt.rc('axes', titlesize=MEDIUM_SIZE) # fontsize of the axes title\n plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels\n plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels\n plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels\n plt.rc('figure', titlesize=MEDIUM_SIZE) # fontsize of the figure title\n \n fig = plt.figure(figsize=(7,7))\n ax = fig.add_subplot(111,projection=ccrs.PlateCarree())\n proj = ccrs.PlateCarree()\n plot_forte(fig, ax, proj, extent='global')\n # extract_forte(45,220)\n # extract_flament(45, 220)\n # fig = plot_flament(fig, ax, proj, extent='global')\n # fig.savefig(f'/Users/ja17375/SWSTomo/Figures/Flament_C3_{extent}.png', dpi=500, transparent=True)\n \n # fig.savefig(f'/Users/ja17375/SWSTomo/Figures/TX2008V2_{extent}.png', dpi=500, transparent=True)\n","sub_path":"map_flow_models.py","file_name":"map_flow_models.py","file_ext":"py","file_size_in_byte":7379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"486197223","text":"import numpy as np\nfrom scipy import integrate\nfrom my_module import my_sens_func as st\nimport matplotlib.pyplot as plt\n\nGHz = 1e9\np = 1e-12\nTera=1e12\nW = 0.08883*p\nu = 1e-6\nm = 1e-3\ne_0 = 8.85418782*10**(-12)\nh = 6.62607004*10**(-34)\nk_B = 1.38064852*10**(-23)\nc = 299792458\n\ndef equation(f):\n return (1.0/k_B)*((h*f)/(T_cmb*(np.exp((h*f)/(k_B*T_cmb))-1.0)))**2 * np.exp((h*f)/(k_B*T_cmb))\n\n\n\n#f = [40,50,60,68,78,89,100,119,140]\nfreq = np.array([40,50,60,68,78,89,100,119,140,166,195,235,280,337,402])*GHz\nT_cmb = 2.725\nINTEGRATE = []\nfor i in range(len(freq)):\n #print(freq[i])\n WL = c/freq[i]\n F = freq[i]\n BW = st.width(F)\n #print(BW)\n\n INTEGRATE.append(integrate.quad(equation, F-BW, F+BW)[0])\nINTEGRATE = np.array(INTEGRATE)\nINTEGRATE\n\nprint(freq[8], INTEGRATE[8])\n\nratio = INTEGRATE[8]/INTEGRATE\nprint(ratio)\nplt.figure()\nplt.grid()\nplt.title(\"dP(f)/dT_CMB\")\nplt.xlabel(\"Frequency[GHz]\")\nplt.ylabel(\"P/T\")\nplt.plot(freq*1e-9, INTEGRATE, \"o\")\n\nplt.figure()\nplt.grid()\nplt.title(\"[dP(140GHz)/dT_CMB]/[dP(f)/dT_CMB]\")\nplt.xlabel(\"Frequency[GHz]\")\nplt.ylabel(\"Ratio\")\nplt.plot(freq*1e-9, ratio, \"o\")\n","sub_path":"eq17_inP28.py","file_name":"eq17_inP28.py","file_ext":"py","file_size_in_byte":1126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"389543807","text":"from telethon import TelegramClient\nfrom telethon.tl.types import PeerChannel\nimport local\nimport requests\nfrom bs4 import BeautifulSoup\n\nclass BinanceListing:\n\n def run(self):\n # send messages related binance listing\n client = TelegramClient('session_name', local.api_id, local.api_hash)\n client.start()\n channel = PeerChannel(channel_id=1347367599)\n\n last_message = client.get_messages(channel, limit=1)[0]\n\n resp = requests.get(local.listing_page)\n\n soup = BeautifulSoup(resp.text, \"html.parser\")\n links = soup.find_all('li', class_='article-list-item')\n\n my_list = list()\n for link in links:\n if last_message.message != link.text.strip():\n my_list.append(link.text.strip())\n else:\n break\n my_list.reverse()\n for l in my_list:\n client.send_message(channel, l)\n\nif __name__ == \"__main__\":\n binance = BinanceListing()\n binance.run()","sub_path":"binance_listing.py","file_name":"binance_listing.py","file_ext":"py","file_size_in_byte":989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"155577073","text":"#!/usr/bin/env python\n\nfrom __future__ import print_function\nfrom sys import stdin\nfrom signal import signal, SIGPIPE, SIG_DFL\n\n\ndef main():\n signal(SIGPIPE, SIG_DFL)\n\n char_stats = [float('Inf'), -float('Inf'), 0, 0] # Min, Max, Avg, N\n word_stats = [float('Inf'), -float('Inf'), 0, 0] # Min, Max, Avg, N\n\n for line in stdin:\n char_count = len(line.strip())\n word_count = len(line.split())\n\n if char_count < char_stats[0]:\n char_stats[0] = char_count\n char_min_line = line\n if char_count > char_stats[1]:\n char_stats[1] = char_count\n char_max_line = line\n char_stats[2] += char_count\n char_stats[3] += 1\n\n if word_count < word_stats[0]:\n word_stats[0] = word_count\n word_min_line = line\n if word_count > word_stats[1]:\n word_stats[1] = word_count\n word_max_line = line\n word_stats[2] += word_count\n word_stats[3] += 1\n\n print(\"Char - Min: {}, Max: {}, Avg: {}, N: {}\".format(\n char_stats[0], char_stats[1], char_stats[2] / char_stats[3], char_stats[3]))\n print(char_min_line.strip())\n print(char_max_line.strip())\n print()\n print(\"Word - Min: {}, Max: {}, Avg: {}, N: {}\".format(\n word_stats[0], word_stats[1], word_stats[2] / word_stats[3], word_stats[3]))\n print(word_min_line.strip())\n print(word_max_line.strip())\n\n \nif __name__ == \"__main__\":\n main()\n","sub_path":"stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":1474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"317829249","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*- #\nfrom __future__ import unicode_literals\nimport os\n\nAUTHOR = u'Christian Long'\nSITENAME = u\"Christian Long's Blog\"\nSITEURL = ''\n\nPATH = 'content'\n\nTIMEZONE = 'America/Chicago'\n\nDEFAULT_LANG = u'en'\n\n# Feed generation is usually not desired when developing\nFEED_ALL_ATOM = None\nCATEGORY_FEED_ATOM = None\nTRANSLATION_FEED_ATOM = None\n\n\n# Blogroll\nLINKS = (('Archives', 'archives.html'),\n )\n\n# Social widget\nSOCIAL = (('twitter', 'http://twitter.com/christianmlong'),\n )\n\nGITHUB_URL = 'https://github.com/christianmlong'\n\nDEFAULT_PAGINATION = 10\nDEFAULT_DATE = 'fs'\n\n_THEME_BASE = 'themes'\n_THEME_NAME ='blue-penguin'\nTHEME = os.path.join(_THEME_BASE, _THEME_NAME)\n\n# Uncomment following line if you want document-relative URLs when developing\n#RELATIVE_URLS = True\n\nDEFAULT_CATEGORY = ('Articles')\n#COPYRIGHT = 'Copyright Christian Long 2014'\n\nSTATIC_PATHS = ['images',\n 'pages',\n 'pdfs',\n #'extra',\n 'extra/robots.txt',\n ]\nPAGES_PATHS = ['pages']\n\nDISPLAY_PAGES_ON_MENU = True\nDISPLAY_CATEGORIES_ON_MENU = True\nTYPOGRIFY = True\nSLUGIFY_SOURCE = 'title'\n\n# path-specific metadata\nEXTRA_PATH_METADATA = {\n 'extra/robots.txt': {'path': 'robots.txt'},\n }\n\n# Don't regenerate unmodified content. Just use\n# the cached version\nLOAD_CONTENT_CACHE = True\n#LOAD_CONTENT_CACHE = False\n\n# Development server version vs published version\n# This is used in my theme\nIS_DEVELOPMENT_VERSION = True\n\n# Markdown extensions\nMD_EXTENSIONS = [# Default extensions\n 'codehilite(css_class=highlight)',\n 'extra',\n # Extensions I have added\n \"toc(permalink=True)\",\n ]\n\n\n#\n","sub_path":"blog/pelicanconf.py","file_name":"pelicanconf.py","file_ext":"py","file_size_in_byte":1768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"557018332","text":"from typing import TYPE_CHECKING\n\nfrom .hkObject import hkObject\nfrom ...binary import BinaryReader, BinaryWriter\nfrom ...binary.types import UInt32\n\nif TYPE_CHECKING:\n from ...hkfile import HKFile\n from ...container.util.hkobject import HKObject\n\n\nclass hkcdStaticMeshTreeBaseSectionSharedVertices(hkObject):\n data: UInt32\n\n def deserialize(self, hkFile: \"HKFile\", br: BinaryReader, obj: \"HKObject\"):\n self.data = br.read_uint32()\n\n def serialize(self, hkFile: \"HKFile\", bw: BinaryWriter, obj: \"HKObject\"):\n bw.write_uint32(UInt32(self.data))\n\n def as_dict(self):\n return {\"data\": self.data}\n\n @classmethod\n def from_dict(cls, d: dict):\n inst = cls()\n\n inst.data = d[\"data\"]\n\n return inst\n","sub_path":"botw_havok/classes/common/hkcdStaticMeshTreeBaseSectionSharedVertices.py","file_name":"hkcdStaticMeshTreeBaseSectionSharedVertices.py","file_ext":"py","file_size_in_byte":756,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"26925237","text":"import importlib_resources\n\nimport z3\nfrom z3 import And, Or, Not, Implies, If\nfrom z3 import IsMember, IsSubset, SetUnion, SetIntersect, SetComplement, EmptySet, SetAdd\n\nfrom naturalproofs.prover import NPSolver\nfrom naturalproofs.uct import fgsort, fgsetsort, intsort, intsetsort, boolsort, min_intsort, max_intsort\nfrom naturalproofs.decl_api import Const, Consts, Var, Vars, Function, RecFunction, AddRecDefinition, AddAxiom\n\nfrom lemsynth.lemsynth_engine import solveProblem\n\n# Declarations\nx = Var('x', fgsort)\nnil = Const('nil', fgsort)\nk = Const('k', intsort)\nkey = Function('key', fgsort, intsort)\nkeys = Function('keys', fgsort, intsetsort)\nlft = Function('lft', fgsort, fgsort)\nrght = Function('rght', fgsort, fgsort)\nminr = Function('minr', fgsort, intsort)\nmaxr = Function('maxr', fgsort, intsort)\nbst = RecFunction('bst', fgsort, boolsort)\nAddRecDefinition(minr, x, If(x == nil, 100, min_intsort(key(x), minr(lft(x)), minr(rght(x)))))\nAddRecDefinition(maxr, x, If(x == nil, -1, max_intsort(key(x), maxr(lft(x)), maxr(rght(x)))))\nAddRecDefinition(bst, x, If(x == nil, True,\n And(0 < key(x),\n And(key(x) < 100,\n And(bst(lft(x)),\n And(bst(rght(x)),\n And(maxr(lft(x)) <= key(x),\n key(x) <= minr(rght(x)))))))))\nAddRecDefinition(keys, x, If(x == nil, fgsetsort.lattice_bottom,\n SetAdd(SetUnion(keys(lft(x)), keys(rght(x))), key(x))))\nAddAxiom((), lft(nil) == nil)\nAddAxiom((), rght(nil) == nil)\n\n# Problem parameters\ngoal = Implies(bst(x), Implies(And(IsMember(k, keys(x)), k < key(x)), IsMember(k, keys(lft(x)))))\n\n# hardcoded lemma\nlemma_params = (x,k)\nlemma_body = Implies(bst(x), Implies(IsMember(k, keys(x)),\n And(minr(x) <= k, k <= maxr(x))))\nlemmas = {(lemma_params, lemma_body)}\n\n# check validity with natural proof solver\nnp_solver = NPSolver()\nsolution = np_solver.solve(goal, lemmas)\nif not solution.if_sat:\n print('goal is valid')\nelse:\n print('goal is invalid')\n\n","sub_path":"benchmark-suite/bst-left.py","file_name":"bst-left.py","file_ext":"py","file_size_in_byte":2177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"480302411","text":"from dateutil.parser import parse\npath = \"../../Files/authors.txt\"\n\ndef bday_or_death(path):\n file = open(path, \"r\")\n\n datal = []\n for line in file:\n if \"birthday\" in line or \"death\" in line:\n datal.append(line.replace(\"\\n\", \"\"))\n\n day_b = [line for line in datal if \"birthday\" in line]\n day_d = [line for line in datal if \"death\" in line]\n\n dict_b = {}\n for item in day_b:\n itempars = item.split('-')[0].strip()\n key = item.split('-')[1].split(\"birthday\")[0].strip()\n value = parse(itempars).strftime('%Y/%m/%d')\n dict_b.update({key: value})\n\n dict_d = {}\n for item in day_d:\n itempars = item.split('-')[0].strip()\n key = item.split('-')[1].split(\"death\")[0].strip()\n try:\n value = parse(itempars).strftime('%Y/%m/%d')\n except:\n value = '1962/07/06'\n dict_d.update({key: value})\n\n dict_back = [{\"Name\": key, \"B_day\": dict_b.get(key), \"D_day\": dict_d.get(key)} for key in dict_b.keys() | dict_d.keys()]\n\n return dict_back\nprint(bday_or_death(path))","sub_path":"OLD_HOME_WORK/HW_9/HW_-_9.4.py","file_name":"HW_-_9.4.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"548862046","text":"import persistence\nfrom data_model.team import Team\nfrom data_model.user import User\n\n\ndef test_valid_data():\n persistence.load_data()\n persistence._all_users = {}\n persistence._all_teams = {}\n\n\ndef test_save_data(tmpdir):\n\n user1 = User(1, \"https://randomuser.me/api/portraits/lego/8.jpg\", \"Elijah\", \"Ku\")\n amazon_team = Team(1, \"IFS\", [user1])\n\n persistence.add_user(user1)\n persistence.add_team(amazon_team)\n\n\n persistence.save_data(f\"{tmpdir}/bla.json\")\n\n\n","sub_path":"backend/tests/test_persistence.py","file_name":"test_persistence.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"568308125","text":"import cv2\r\nimport xlwt\r\nimport numpy as np\r\nimport pandas as pd\r\nimport math\r\nimport matplotlib.pyplot as plt\r\n\r\n#Saving Brightness\r\nimage = cv2.imread(\"C:/DalMasterCourses/Medical Image Analysis/Xray Grades/program for X-ray images/APJointCentre/1-1R AP.jpg\",1)\r\nimage = cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)\r\ndf=pd.DataFrame(image)\r\nB = np.array(df).transpose()\r\n#print(B)\r\n\r\n#Kernel Calculation\r\nk = 9\r\n\r\nx = []\r\ny = []\r\n\r\nfor val in range(k):\r\n x.append([[-((k-1)/2-val)]*k])\r\n y.append([[-((k-1)/2-val)]*k])\r\nx = np.array(x)\r\ny = np.array(y).transpose()\r\n#print(y)\r\n\r\nX = np.zeros((6,k*k))\r\n#print(x[0]**2)\r\n\r\nfor val in range(k):\r\n X[0][((val)*k):((val+1)*k)]=(x[val])**2\r\n X[1][((val)*k):((val+1)*k)]=(x[val])*y[val]\r\n X[2][((val)*k):((val+1)*k)]=(y[val])**2\r\n X[3][((val)*k):((val+1)*k)]=(x[val])\r\n X[4][((val)*k):((val+1)*k)]=(y[val])\r\n X[5][((val)*k):((val+1)*k)]=1\r\n#print(X)\r\nX = X.transpose()\r\nKernel = np.dot(np.linalg.inv(np.dot(X.transpose(), X)), X.transpose())\r\n#print(Kernel)\r\n\r\n#EigenValues/Vectors Calculation\r\n#print(df.shape[0])\r\nValue1 = np.zeros((df.shape[0]-k+1,df.shape[1]-k+1))\r\nValue2 = np.zeros((df.shape[0]-k+1,df.shape[1]-k+1))\r\n\r\nVector1 = np.zeros((df.shape[0]-k+1,df.shape[1]-k+1,2))\r\nVector2 = np.zeros((df.shape[0]-k+1,df.shape[1]-k+1,2))\r\n\r\nH = np.zeros((df.shape[0]-k+1,df.shape[1]-k+1,4))\r\n#print(Vector2)\r\n\r\nD = np.zeros((2,2))\r\nfor i in range(df.shape[0]-k+1):\r\n for j in range(df.shape[1]-k+1):\r\n Y = []\r\n for h in range(k):\r\n Y += B[j+h][i:(i+k)].tolist()\r\n Beta = np.dot(Kernel, Y)\r\n #print(Beta)\r\n D[0][0]=Beta[0]\r\n D[0][1]=Beta[1]/2\r\n D[1][0]=Beta[1]/2\r\n D[1][1]=Beta[2]\r\n Ev = np.linalg.eig(np.array(D))\r\n #print(Ev)\r\n if Ev[0][1] > Ev[0][0]:\r\n Value1[i][j] = Ev[0][1]\r\n Value2[i][j] = Ev[0][0]\r\n Vector1[i][j] = Ev[1][1]\r\n Vector2[i][j] = Ev[1][0]\r\n else:\r\n Value1[i][j] = Ev[0][0]\r\n Value2[i][j] = Ev[0][1]\r\n Vector1[i][j] = Ev[1][0]\r\n Vector2[i][j] = Ev[1][1]\r\n H[i][j] = D.flatten()\r\nDiff = Value1 - Value2\r\n#print(Y)\r\n#print(Beta)\r\nvalue1_new=pd.DataFrame(Value1)\r\nvalue1_new.to_csv('20201018/Value1.csv',index=False,header=False)\r\nvalue2_new=pd.DataFrame(Value2)\r\nvalue2_new.to_csv('20201018/Value2.csv',index=False,header=False)\r\n#vector1_new=pd.DataFrame(Vector1.reshape((df.shape[0]-k+1,(df.shape[1]-k+1)*2)))\r\n#vector1_new.to_csv('20200502/Vector1.csv',index=False,header=False)\r\n#vector2_new=pd.DataFrame(Vector2.reshape((df.shape[0]-k+1,(df.shape[1]-k+1)*2)))\r\n#vector2_new.to_csv('20200502/Vector2.csv',index=False,header=False)\r\n\r\nplt.figure(num=None, figsize=(120, 120), dpi=80, facecolor='w', edgecolor='k')\r\nax = plt.axes()\r\ntimes = 10\r\nax.set_xlim(120)\r\nax.set_ylim(120)\r\nfor i in range(120):\r\n for j in range(120):\r\n if i%2 == 0 and j%2 == 0:\r\n ax.annotate(\"\", xy=(i-Vector2[i][j][1]*Value2[i][j]*times, j-Vector2[i][j][0]*Value2[i][j]*times), xytext=(i, j),arrowprops=dict(arrowstyle=\"->\"))\r\n #ax.annotate(\"\", xy=(i-Vector1[i][j][1]*times, j-Vector1[i][j][0]*times), xytext=(i, j),arrowprops=dict(arrowstyle=\"->\"))\r\n#plt.arrow(0, 0, 0.5, 0.5)\r\n\r\nplt.show()\r\n","sub_path":"Plot_Vectors.py","file_name":"Plot_Vectors.py","file_ext":"py","file_size_in_byte":3277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"449634787","text":"import re\n\nfrom features.commands.command import Command\nfrom features.commands.command_context import CommandContext\nfrom features.snowflake_settings.settings import SnowflakeSettings\n\nmeta = {\n 'class': 'SFGet',\n 'description': \"Get a snowflake setting\",\n 'usage': '',\n 'pack': 'snowflake',\n 'type': \"config\",\n 'permissions': ['bot_admin']\n}\n\n\nclass SFGet(Command):\n\n async def on_command(self, context: CommandContext):\n if len(context.args) < 2:\n raise CommandContext.MissingArgsException(context)\n\n settings: SnowflakeSettings = self.command_manager.feature_manager.get_feature('SnowflakeSettings')\n context.args[0] = context.args[0].lower()\n if context.args[0] == 'guild':\n context.args[0] = context.guild.id\n elif context.args[0] == 'channel':\n context.args[0] = context.channel.id\n elif context.args[0] == '@me' or context.args[0] == 'me':\n context.args[0] = context.author.id\n else:\n context.args[0] = re.sub(\"[^0-9]\", '', context.args[0])\n\n snowflake = context.args[0]\n key = context.args[1]\n\n try:\n value = settings.get_setting(int(snowflake), key)\n except ValueError:\n await context.error('Invalid snowflake')\n return\n\n await context.ok('**{}** for __{}__ is set to `{}`'.format(key, snowflake, value))\n","sub_path":"src/features/snowflake_settings/cmds/sfget.py","file_name":"sfget.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"73295444","text":"import numpy as np\n\ndef soft_update(target, source, tau=1e-3):\n for target_param, param in zip(target.parameters(), source.parameters()):\n target_param.data.copy_(target_param.data * (1.0 - tau) + param.data * tau)\n\n\nclass OrnsteinUhlenbeckProcess():\n def __init__(self, args, dt=1e-4, x0=None, sigma_min=None, n_steps_annealing=1000):\n theta = args.theta\n mu = args.mu\n sigma = args.sigma\n size = args.action_dim\n\n self.mu = mu\n self.sigma = sigma\n self.n_steps = 0\n\n if sigma_min is not None:\n self.m = -float(sigma-sigma_min)/float(n_steps_annealing)\n self.c = sigma\n self.sigma_min = sigma_min\n else:\n self.m = 0.\n self.c = sigma\n self.sigma_min = sigma\n\n self.theta = theta\n self.dt = dt\n self.x0 = x0\n self.size = size\n self.reset_states()\n\n def sample(self):\n x = self.x_prev + self.theta * (self.mu - self.x_prev) * self.dt + self.current_sigma * np.sqrt(self.dt) * np.random.normal(size=self.size)\n self.x_prev = x\n self.n_steps += 1\n return x\n\n def reset_states(self):\n self.x_prev = self.x0 if self.x0 is not None else np.zeros(self.size)\n\n @property\n def current_sigma(self):\n sigma = max(self.sigma_min, self.m*float(self.n_steps)+self.c)\n return sigma\n","sub_path":"DDPG/HG/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"70382554","text":"import csv\n\n\n# 转换前\n# [['case', 'xx', 'yy', 'expectedResults', 'comments', 'remarks'],\n# ['case1', 'x1', 'y1', 'x1,y1', 'com1', 'rem1'],\n# ['case2', 'x2', 'None', 'x2,y2', 'com2', 'rem2']]\n\n# 转换成纵列之后\n# { 'case': ['case1', 'case2'],\n# 'xx': ['x1', 'x2'],\n# 'yy': ['y1', None],\n# 'expectedResults': ['x1,y1', 'x2,y2'],\n# 'comments': ['com1', 'com2'],\n# 'remarks': ['rem1', 'rem2'] }\n\ndef csvToList(path):\n csv_reader = csv.reader(open(path, encoding='utf-8'))\n\n # 转化csv为dictionary, 列的第一项为key，第二项至最后一项为value(list)\n fileData = list(csv_reader)\n resultDict = {}\n noneList = [\"None\", \"none\", \"Null\", \"null\"]\n falseList = [\"False\", \"false\"]\n trueList = [\"True\", \"true\"]\n for i in range(0, len(fileData[0])):\n tmpList = []\n for j in range(0, len(fileData)):\n if j != 0:\n if fileData[j][i].strip() in noneList:\n tmpList.append(\"\")\n elif fileData[j][i].strip() in falseList:\n tmpList.append(False)\n elif fileData[j][i].strip() in trueList:\n tmpList.append(True)\n else:\n tmpList.append(fileData[j][i])\n resultDict[fileData[0][i]] = tmpList\n return resultDict\n","sub_path":"PythonTools/ReadCSVTool/readCSVTool.py","file_name":"readCSVTool.py","file_ext":"py","file_size_in_byte":1310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"609959365","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /users/payno/.local/share/virtualenvs/tomwer_venc/lib/python3.7/site-packages/tomwer/synctools/axis.py\n# Compiled at: 2019-12-11 09:05:53\n# Size of source mod 2**32: 1978 bytes\n__authors__ = [\n 'H.Payno']\n__license__ = 'MIT'\n__date__ = '03/05/2019'\nfrom silx.gui import qt\nfrom tomwer.core.process.reconstruction.axis.params import AxisRP\nfrom tomwer.core.log import TomwerLogger\nlogger = TomwerLogger(__name__)\n\nclass QAxisRP(AxisRP, qt.QObject):\n sigChanged = qt.Signal()\n sigAxisUrlChanged = qt.Signal()\n\n def __init__(self):\n qt.QObject.__init__(self)\n AxisRP.__init__(self)\n\n def changed(self):\n self.sigChanged.emit()\n\n def axis_urls_changed(self):\n self.sigAxisUrlChanged.emit()","sub_path":"pycfiles/tomwer-0.4.0.linux-x86_64.tar/axis.cpython-37.py","file_name":"axis.cpython-37.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"615014404","text":"from IPython.display import clear_output\n\ndef display_board(board):\n clear_output()\n print ( board[7]+\"|\"+board[8]+\"|\"+board[9])\n print ( '-----')\n print ( board[4]+\"|\"+board[5]+\"|\"+board[6])\n print ( '-----')\n print ( board[1]+\"|\"+board[2]+\"|\"+board[3])\n \ndef player_input():\n marker = ''\n \n while not ( marker == 'X' or marker == 'O'):\n marker = input('Player 1 do you want to be X or O?').upper()\n \n if marker == 'X':\n return ('X','O')\n else:\n return ('O','X')\n \ndef place_marker(board,marker,position):\n board[position] = marker\n \ndef win_check(board,mark):\n return ((board[7] == board[8] == board[9] == mark) or\n (board[4] == board[5] == board[6] == mark) or\n (board[1] == board[2] == board[3] == mark) or\n (board[7] == board[5] == board[3] == mark) or\n (board[9] == board[5] == board[1] == mark) or\n (board[8] == board[5] == board[2] == mark) or\n (board[7] == board[4] == board[1] == mark) or\n (board[9] == board[6] == board[3] == mark))\n\nimport random\n\ndef choose_first():\n if random.randint(0,1) == 0:\n return 'Player2'\n else:\n return 'Player1'\n \ndef space_check(board, position):\n return board [ position ] == ' '\n\ndef full_board_check(board):\n for i in range(1,10):\n if space_check(board, i):\n return False\n else:\n return True\n \ndef player_choice(board):\n position = 0\n \n while position not in [1,2,3,4,5,6,7,8,9] or not space_check(board, position):\n position = int(input(\"Choose your next position (1-9):\"))\n return position\n\ndef replay():\n play_again = str(input(\"Would you like to play again? Y or N? \")).upper()\n return play_again == 'Y'\n\n\nprint('WELCEME TO TIC TAC TOE')\n\n\nwhile True:\n \n #first reset to an empty board. In our functions board is a list\n theboard = [' ']*10\n player1_marker, player2_marker = player_input()\n turn = choose_first()\n print(turn + ' will go first.')\n \n start_game = str(input('Would you like to start the game. Select Y or N: '))\n if start_game.upper() == 'Y':\n game_on = True\n else:\n game_on = False\n \n \n while game_on:\n if turn == 'Player1':\n \n #Player 1 Turn\n \n display_board(theboard)\n position = player_choice(theboard)\n place_marker(theboard, player1_marker, position)\n \n if win_check(theboard, player1_marker):\n display_board(theboard)\n print('Player1 has won the game!')\n game_on = False\n else:\n if full_board_check(theboard):\n display_board(theboard)\n print('The game is a draw')\n break\n else:\n turn = 'Player2'\n \n else: \n #Player 2 Turn\n display_board(theboard)\n position = player_choice(theboard)\n place_marker(theboard, player2_marker, position)\n\n if win_check(theboard, player2_marker):\n display_board(theboard)\n print('Player2 has won the game!')\n game_on = False\n else:\n if full_board_check(theboard):\n display_board(theboard)\n print('The game is a draw')\n break\n else:\n turn = 'Player1'\n if not replay():\n break","sub_path":"tictactoe.py","file_name":"tictactoe.py","file_ext":"py","file_size_in_byte":3596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"81205129","text":"# !/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# -------------------------------------------#\n# author: sean lee #\n# email: xmlee97@gmail.com #\n#--------------------------------------------#\n\nfrom __future__ import absolute_import, unicode_literals\nimport io\nimport os\nimport sys\nfrom math import log, exp\nfrom collections import defaultdict\nfrom ..module import Module\nfrom ..utils import safe_input\nfrom ..postag import seg\n\nif sys.version_info[0] == 2:\n reload(sys)\n sys.setdefaultencoding('utf8')\n\n\nclass NBayes(Module):\n __notsave__ = []\n __onlysave__ = ['counter', 'corpus', 'total']\n\n def __init__(self):\n self.corpus = {}\n self.counter = {}\n self.total = 0\n\n def process_data(self, data):\n \"\"\"process train data\"\"\"\n\n for d in data:\n label = d[0]\n doc = d[1]\n if label not in self.corpus:\n self.corpus[label] = defaultdict(int)\n self.counter[label] = 0\n for word in doc:\n self.counter[label] += 1\n self.corpus[label][word] += 1\n self.total = sum(self.counter.values())\n\n def calc_score(self, sent):\n \"\"\"calculate sentiment score\"\"\"\n\n tmp = {}\n for k in self.corpus:\n tmp[k] = log(self.counter[k]) - log(self.total)\n for word in sent:\n x = float(self.corpus[k].get(word, 1)) / self.counter[k]\n tmp[k] += log(x)\n\n ret, prob = 0, 0\n for k in self.corpus:\n curr = 0\n try:\n for kk in self.corpus:\n curr += exp(tmp[kk] - tmp[k])\n curr = 1.0 / curr\n except OverflowError:\n curr = 0.0\n if curr > prob:\n ret, prob = k, curr\n return (ret, prob)\n\nclass Sentiment(NBayes):\n def filter_stopword(self, words, stopword=None):\n \"\"\"filter stopwords\"\"\"\n\n if stopword is None:\n return words\n ret = [word for word in words if word not in stopword]\n for word in words:\n if word not in stopword:\n ret.append(word)\n return ret\n\n def load_data(self, posfname, negfname):\n \"\"\"load dataset from file\"\"\"\n\n def get_file(path):\n if os.path.isdir(path):\n for root, dirs, files in os.walk(path):\n if not dirs:\n for f in files:\n yield os.sep.join([root, f])\n else:\n yield path\n\n pos_docs, neg_docs = [], []\n for fname in get_file(posfname):\n with io.open(fname, 'r', encoding='utf-8') as f:\n for line in f:\n line = safe_input(line)\n pos_docs.append(seg(line))\n for fname in get_file(negfname):\n with io.open(fname, 'r', encoding='utf-8') as f:\n for line in f:\n line = safe_input(line)\n neg_docs.append(seg(line))\n\n return pos_docs, neg_docs\n\n def train(self, posfname, negfname, stopword=None):\n \"\"\"train sentiment model\"\"\"\n\n pos_docs, neg_docs = self.load_data(posfname, negfname)\n data = []\n for sent in neg_docs:\n data.append(('neg', self.filter_stopword(sent, stopword=stopword)))\n for sent in pos_docs:\n data.append(('pos', self.filter_stopword(sent, stopword=stopword)))\n\n self.process_data(data)\n\n def predict(self, doc, stopword=None):\n \"\"\"predict sentiment score\"\"\"\n\n sent = seg(doc)\n ret, prob = self.calc_score(self.filter_stopword(sent, stopword=stopword))\n if ret == 'pos':\n return prob\n return 1 - prob\n","sub_path":"xmnlp/sentiment/sentiment.py","file_name":"sentiment.py","file_ext":"py","file_size_in_byte":3806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"445901141","text":"import re\r\n\r\nimport requests\r\nimport urllib3\r\nfrom diskcache import Cache\r\nfrom flask import Flask, make_response\r\n\r\nurllib3.disable_warnings()\r\n\r\n_cache: Cache = None\r\n_proxies = None\r\napp = Flask(__name__)\r\n\r\n\r\ndef quad_key_to_tileXY(quadKey):\r\n tileX = tileY = 0\r\n levelOfDetail = len(quadKey)\r\n for i in range(levelOfDetail, 0, -1):\r\n mask = 1 << (i - 1)\r\n t = quadKey[levelOfDetail - i]\r\n if t == '1':\r\n tileX |= mask\r\n if t == '2':\r\n tileY |= mask\r\n if t == '3':\r\n tileX |= mask\r\n tileY |= mask\r\n return tileX, tileY, levelOfDetail\r\n\r\n\r\n@app.route(\"/health\")\r\ndef health():\r\n return \"alive\"\r\n\r\n\r\n@app.route(\"/tiles/akh\")\r\ndef tiles(path):\r\n quadkey = re.findall(r\"(\\d+).jpeg\", path)[0]\r\n tileX, tileY, levelOfDetail = quad_key_to_tileXY(quadkey)\r\n\r\n url = f\"https://mt1.google.com/vt/lyrs=s&x={tileX}&y={tileY}&z={levelOfDetail}\"\r\n\r\n cache_key = f\"{levelOfDetail}{tileX}{tileY}\"\r\n content = _cache.get(cache_key)\r\n if content is None:\r\n print(\"Downloading from:\", url, _proxies)\r\n content = requests.get(\r\n url, proxies=_proxies, timeout=30).content\r\n\r\n _cache.set(cache_key, content)\r\n else:\r\n print(\"Use cached:\", url)\r\n\r\n response = make_response(content)\r\n headers = {\"Content-Type\": \"image/jpeg\", \"Last-Modified\": \"Sat, 24 Oct 2020 06:48:56 GMT\", \"ETag\": \"9580\", \"Server\": \"Microsoft-IIS/10.0\", \"X-VE-TFE\": \"BN00004E85\", \"X-VE-AZTBE\": \"BN000033DA\", \"X-VE-AC\": \"5035\",\r\n \"X-VE-ID\": \"4862_136744347\",\r\n \"X-VE-TILEMETA-CaptureDatesRang\": \"1/1/1999-12/31/2003\",\r\n \"X-VE-TILEMETA-CaptureDateMaxYY\": \"0312\",\r\n \"X-VE-TILEMETA-Product-IDs\": \"209\"}\r\n for k, v in headers.items():\r\n response.headers[k] = v\r\n\r\n return response\r\n\r\n\r\ndef run_server(cache_size, proxies):\r\n global _cache, _proxies\r\n _cache = Cache(\r\n \"./cache\", size_limit=int(cache_size) * 1024 * 1024 * 1024, shards=10)\r\n _proxies = proxies\r\n\r\n app.run(port=8000, host=\"0.0.0.0\", threaded=True)\r\n","sub_path":"src/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2117,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"207281668","text":"#encoding:utf-8\nimport pymysql\nconn = pymysql.connect(host='10.107.10.110', port=3306, user='root', passwd='111111', db='ghtorrent_2017_5',\n charset='utf8mb4')\nprint(conn)\n\n#1.现根据url查找repo_id\n#2.根据id查找一年内每三个月的commits的增量，用commits表，没考虑fork信息\n\n\ntime_list = ['2016-5','2016-8','2016-11','2017-02','2017-05']\n\ndef getRepoUrlList():\n cur = conn.cursor()\n select_sql = \"select id,url from projects_ossean\"\n cur.execute(select_sql % ()) #\n prj_tuples = cur.fetchall()\n print(\"repo length : %d\" % len(prj_tuples))\n cur.close()\n return prj_tuples\n\ndef MineWatchersIncre():\n cur = conn.cursor()\n\n select_sql = \"select count(*) from watchers where repo_id = %d and created_at<= '%s-01 00:00:00' and created_at > '%s-01 00:00:00'\" ;\n insert_sql = \"insert into ossean_watchs_incre_patch(id, s1, s2, s3, s4) VALUES (%d,%d,%d,%d,%d)\"\n\n src_repo_list = getRepoUrlList()\n\n repo_count = 0\n for prj in src_repo_list:\n\n repo_id = prj[0]\n\n season_dict = dict()\n for time_index in range(4):\n cur.execute(select_sql % (repo_id,time_list[time_index+1],time_list[time_index]))\n commits_num = cur.fetchone()[0]\n season_dict[time_index + 1] = commits_num\n\n value = (prj[0],season_dict[1],season_dict[2],season_dict[3],season_dict[4])\n cur.execute(insert_sql % value)\n\n repo_count += 1\n if repo_count % 50 == 0:\n conn.commit()\n print(\"handled %d project\" % repo_count)\n conn.commit()\n print(\"handled %d project\" % repo_count)\n cur.close()\n\nimport sys\nif __name__ == '__main__':\n MineWatchersIncre()\n conn.close()\n","sub_path":"ossean_crawler/gh_patch/watchesIncre.py","file_name":"watchesIncre.py","file_ext":"py","file_size_in_byte":1723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"87038110","text":"import config\r\nimport timefuncs\r\nfrom speech import speak\r\nimport sound_lib\r\nfrom sound_lib import output\r\nfrom sound_lib import stream\r\nfrom sound_lib.effects import effect\r\nfrom sound_lib.effects import tempo\r\nclass status(object):\r\n\tdef __init__(self):\r\n\t\tself.loaded=False\r\n\t\tself.streaming=False\r\n\t\tself.songindex=0\r\n\t\tself.songs=[]\r\n\t\tself.orig_stream=0\r\n\t\tself.stream=0\r\n\r\nfilename=\"\"\r\np=status()\r\no=output.Output()\r\n\r\ndef open_file(filen=\"\",fn=\"\",position=0):\r\n\tconfig.appconfig['general']['lastsong']=filen\r\n\tif p.stream!=0:\r\n\t\tdel p.stream\r\n\tif p.orig_stream!=0:\r\n\t\tdel p.orig_stream\r\n\tp.orig_stream =stream.FileStream(file=filen,decode=True)\r\n\tfilename=fn\r\n\tp.streaming=False\r\n\tp.loaded=True\r\n\tp.stream=tempo.Tempo(p.orig_stream)\r\n\tp.stream.volume=config.appconfig['general']['volume']\r\n\r\ndef add_file(filen=\"\",fn=\"\"):\r\n\tp.songs.append(filen)\r\n\r\ndef open_playlist(filename,path):\r\n\tf=open(filename,\"r\")\r\n\tdata=f.read()\r\n\tf.close()\r\n\tsongs=data.split(\"\\n\")\r\n\tfor i in range(0,len(songs)):\r\n\t\tsongs[i]=songs[i].replace(\"\\r\",\"\")\r\n\t\tif \"http://\" in songs[i]==False:\r\n\t\t\ttry:\r\n\t\t\t\tf=open(songs[i],\"r\")\r\n\t\t\t\tf.close()\r\n\t\t\texcept:\r\n\t\t\t\tsongs[i]=path+songs[i]\r\n\tp.songs=songs\r\n\tswitchtrack(0)\r\n\r\ndef open_stream(filen=\"\"):\r\n\tp.stream =stream.URLStream(url=filename)\r\n\tp.streaming=True\r\n\tp.loaded=True\r\n\tp.songindex=0\r\n\tplay()\r\n\r\ndef play():\r\n\tif not p.stream==0:\r\n\t\tp.stream.play()\r\n\r\ndef stop():\r\n\tif not p.stream==0:\r\n\t\tp.stream.stop()\r\n\t\tp.stream.set_position(0)\r\ndef pause():\r\n\tif not p.stream==0:\r\n\t\tp.stream.pause()\r\n\r\ndef seek(step):\r\n\tpos=p.stream.get_position()\r\n\tpos=p.stream.bytes_to_seconds(pos)\r\n\tpos+=step\r\n\tpos=p.stream.seconds_to_bytes(pos)\r\n\tif pos<0:\r\n\t\tpos=0\r\n\tp.stream.set_position(pos)\r\n\r\ndef speak_remaining_time():\r\n\tpos=p.stream.get_position()\r\n\tpos2=p.stream.get_length()\r\n\tpos=p.stream.bytes_to_seconds(pos)\r\n\tpos2=p.stream.bytes_to_seconds(pos2)\r\n\tspeak(timefuncs.grt((pos2*1000)-(pos*1000)))\r\n\r\ndef speak_elapsed_time():\r\n\tpos=p.stream.get_position()\r\n\tpos=p.stream.bytes_to_seconds(pos)\r\n\tspeak(timefuncs.grt(pos*1000))\r\n\r\ndef speak_length():\r\n\tpos=p.stream.get_length()\r\n\tpos=p.stream.bytes_to_seconds(pos)\r\n\tspeak(timefuncs.grt(pos*1000))\r\n\r\ndef switchtrack(step):\r\n\tp.songindex+=step\r\n\tif p.songindex<0 or p.songindex>=len(p.songs):\r\n\t\tp.songindex=0\r\n\ts=p.songs[p.songindex].split(\":\")\r\n\ttry:\r\n\t\tplaying=p.stream.is_playing\r\n\texcept:\r\n\t\tplaying=False\r\n\r\n\tif s[0]==\"http\":\r\n\t\topen_stream(p.songs[p.songindex])\r\n\telse:\r\n\t\topen_file(p.songs[p.songindex])\r\n\tif playing==True:\r\n\t\tplay()\r\n\r\ndef save_playlist(path, filename):\r\n\tfiles=\"\"\r\n\tf=open(path+filename,\"w\")\r\n\tfor i in range(0,len(p.songs)):\r\n\t\tp.songs[i]=p.songs[i].replace(path,\"\")\r\n\t\tfiles+=p.songs[i]+\"\\n\"\r\n\tf.write(files)\r\n\tf.close()","sub_path":"player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":2724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"498520455","text":"from frame_pair import Frame_pair\nfrom matching_finder import Max_matching_finder\n\n#class Frame_pair:\n# def __init__( self, a_frame_type, b_frame_type, a_frame_inst, b_frame_inst):\n# self.a_frame_type = a_frame_type\n# self.b_frame_type = b_frame_type\n# self.a_frame_inst = a_frame_inst\n# self.b_frame_inst = b_frame_inst\n\n\nclass Linker:\n def __init__( self):\n self.log_file = None\n #self.matching_method = greedy_MWB_matching\n #self.matching_method = try_all_matching\n self.matching_finder = Max_matching_finder()\n\n def set_log_file( self, log_file):\n self.log_file = log_file\n\n def build_score_table( self, a_frame_insts, b_frame_insts, _):\n score_table = [ [ 0 ] * len( b_frame_insts) ] * len( a_frame_insts)\n min_len = min( len(a_frame_insts), len( b_frame_insts))\n for i in range( min_len):\n score_table[ i ][ i ] = 1\n return score_table\n\n # def build_score_table( self, a_frame_insts, b_frame_insts, word_alignments):\n # score_table = []\n # for _ in a_frame_insts:\n # table_row = []\n # for __ in b_frame_insts:\n # score = 1\n # table_row.append( score)\n # score_table.append( table_row)\n # return score_table\n\n def find_frame_pairs( self, a_frame_insts, b_frame_insts, word_alignments):\n score_table = self.build_score_table( a_frame_insts, b_frame_insts,\n word_alignments)\n\n chosen_pairs, opt_val = self.matching_finder.find_matching(\n len( a_frame_insts), len( b_frame_insts),\n score_table)\n\n frame_pairs = []\n for a_index, b_index in chosen_pairs:\n a_frame_inst = a_frame_insts[ a_index ]\n b_frame_inst = b_frame_insts[ b_index ]\n frame_pair = Frame_pair( a_frame_inst, b_frame_inst)\n frame_pairs.append( frame_pair)\n return frame_pairs\n\n def print_stats( self):\n pass\n","sub_path":"udapi-python/udapi/block/valency/linker.py","file_name":"linker.py","file_ext":"py","file_size_in_byte":2044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"588564424","text":"class Auto:\n def __init__(self, marka, model):\n self.marka = marka\n self.model = model\n\n# zakaz grzebania w starym kodzie\npolonez = Auto('Fso', 'Polonez')\nmaluch = Auto('Fso', 'Fiat 126 p')\n\n#klinet chce dodtyac jeszcze kolor\n\n\n","sub_path":"Zajecia_7/2func_param_domyslne/zad1.py","file_name":"zad1.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"145931754","text":"# coding=utf-8\n'''\nGiven an unsorted array of integers, find the length of longest increasing subsequence.\n\nFor example,\nGiven [10, 9, 2, 5, 3, 7, 101, 18],\nThe longest increasing subsequence is [2, 3, 7, 101], therefore the length is 4. \nNote that there may be more than one LIS combination, it is only necessary for you to return the length.\n\nYour algorithm should run in O(n2) complexity.\n\nFollow up: Could you improve it to O(n log n) time complexity?\n'''\nimport collections\nclass TreeNode(object):\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\nclass ListNode(object):\n def __init__(self, x):\n self.val = x\n self.next = None\n\n'''\nhttp://segmentfault.com/a/1190000003819886\n\nhttp://bookshadow.com/weblog/2015/11/03/leetcode-longest-increasing-subsequence/\n\n由于这个最长上升序列不一定是连续的，对于每一个新加入的数，都有可能跟前面的序列构成一个较长的上升序列，或者跟后面的序列构成一个较长的上升序列。\n\n比如1,3,5,2,8,4,6，对于6来说，可以构成1,3,5,6，也可以构成2,4,6。\n\n因为前面那个序列长为4，后面的长为3，所以我们更愿意6组成那个长为4的序列，所以对于6来说，它组成序列的长度，实际上是之前最长一个升序序列长度加1，\n\n注意这个最长的序列的末尾是要小于6的，不然我们就把1,3,5,8,6这样的序列给算进来了。这样，我们的递推关系就隐约出来了，假设dp[i]代表加入第i个数能构成的最长升序序列长度，\n\n我们就是要在dp[0]到dp[i-1]中找到一个最长的升序序列长度，又保证序列尾值nums[j]小于nums[i]，然后把这个长度加上1就行了。同时，我们还要及时更新最大长度。\n\n（二叉搜索没整明白，回头看）\n\n'''\n\n# O(n*n)\nclass Solution(object):\n def lengthOfLIS(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: int\n \"\"\"\n size = len(nums)\n dp = [1] * size\n for x in range(size):\n for y in range(x):\n if nums[x] > nums[y]:\n dp[x] = max(dp[x], dp[y] + 1)\n return max(dp) if dp else 0\n","sub_path":"python/Binary Search/300.Longest Increasing Subsequence.py","file_name":"300.Longest Increasing Subsequence.py","file_ext":"py","file_size_in_byte":2217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"643174435","text":"# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport random\n\nimport six\n\nfrom rally.common.i18n import _, _LE\nfrom rally.common import log as logging\nfrom rally import consts\nfrom rally import exceptions\nfrom rally import osclients\nfrom rally.task import context\n\nLOG = logging.getLogger(__name__)\n\n\n@context.configure(name=\"api_versions\", order=150)\nclass OpenStackAPIVersions(context.Context):\n \"\"\"Context for specifying OpenStack clients versions and service types.\"\"\"\n\n CONFIG_SCHEMA = {\n \"type\": \"object\",\n \"$schema\": consts.JSON_SCHEMA,\n \"patternProperties\": {\n \"^[a-z]+$\": {\n \"type\": \"object\",\n \"properties\": {\n \"version\": {\n \"anyOf\": [{\"type\": \"string\"}, {\"type\": \"number\"}]\n },\n \"service_name\": {\n \"type\": \"string\"\n },\n \"service_type\": {\n \"type\": \"string\"\n }\n },\n \"additionalProperties\": False\n }\n },\n \"additionalProperties\": False\n }\n\n def setup(self):\n # FIXME(andreykurilin): move all checks to validate method.\n\n # do not use admin, if we have users...\n user = random.choice(self.context.get(\"users\",\n [self.context[\"admin\"]]))\n clients = osclients.Clients(user[\"endpoint\"])\n services = clients.services()\n for client_name, conf in six.iteritems(self.config):\n if \"service_type\" in conf and conf[\"service_type\"] not in services:\n raise exceptions.ValidationError(_(\n \"There is no service with '%s' type in your environment.\")\n % conf[\"service_type\"])\n elif \"service_name\" in conf:\n if conf[\"service_name\"] not in services.values():\n raise exceptions.ValidationError(\n _(\"There is no '%s' service in your environment\") %\n conf[\"service_name\"])\n\n service_types = [\n key for key in services\n if services[key] == conf[\"service_name\"]]\n\n if len(service_types) > 1:\n # NOTE(andreykurilin): does it possible??\n raise exceptions.ValidationError(\n _(\"There are several services with name '%s'. Try to \"\n \"specify service_type property instead.\") %\n conf[\"service_name\"])\n self.context[\"config\"][\"api_versions\"][client_name][\n \"service_type\"] = service_types[0]\n\n def cleanup(self):\n # nothing to do here\n pass\n\n @classmethod\n def validate(cls, config, non_hidden=False):\n super(OpenStackAPIVersions, cls).validate(config,\n non_hidden=non_hidden)\n for client in config:\n client_cls = osclients.OSClient.get(client)\n if (\"service_type\" in config[client] and\n \"service_name\" in config[client]):\n raise exceptions.ValidationError(_LE(\n \"Setting both 'service_type' and 'service_name' properties\"\n \" is restricted.\"))\n try:\n if (\"service_type\" in config[client] or\n \"service_name\" in config[client]):\n client_cls.is_service_type_configurable()\n\n if \"version\" in config[client]:\n client_cls.validate_version(config[client][\"version\"])\n\n except exceptions.RallyException as e:\n raise exceptions.ValidationError(\n _LE(\"Invalid settings for '%(client)s': %(error)s\") % {\n \"client\": client,\n \"error\": e.format_message()})\n","sub_path":"rally/plugins/openstack/context/keystone/api_versions.py","file_name":"api_versions.py","file_ext":"py","file_size_in_byte":4481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"411045476","text":"#imports\r\nimport pandas as pd\r\nimport joblib\r\nfrom flask import Flask, render_template, session,Markup, redirect, url_for\r\nfrom flask_bootstrap import Bootstrap\r\nimport os\r\n\r\n#Production server\r\n#from waitress import serve\r\n\r\n#Form validator\r\nfrom flask_wtf import FlaskForm\r\nfrom wtforms import FloatField, SubmitField\r\nfrom wtforms.validators import NumberRange,InputRequired\r\n\r\n###############CODE#####################\r\nloaded_model = joblib.load(\"./obj/knn_model.pkl\")\r\n\r\n#Let's open the file that contains the plotly div\r\nplotly_file = open(\"static/div_html.txt\", \"r\")\r\ndiv = plotly_file.read()\r\nplotly_file.close()\r\n\r\n#Flask app\r\napp = Flask(__name__)\r\napp.config.from_mapping(SECRET_KEY = \"DontTellAnyone\")\r\n\r\nwant_to_validate = \"NO\"\r\nif want_to_validate ==\"YES\":\r\n Bootstrap(app)\r\n index_template = 'index_complex.html'\r\nelse:\r\n index_template = 'index.html'\r\n\r\nclass LoginForm(FlaskForm):\r\n a = FloatField('SepalLengthCm', default=1, validators=[InputRequired(),\r\n NumberRange(min=0, max=1000, message='SepalLengthCm must be between 0 and 1000')])\r\n b = FloatField('SepalWidthCm', default=1, validators=[InputRequired(),\r\n NumberRange(min=0, max=1000, message='SepalWidthCm must be between 0 and 1000')])\r\n c = FloatField('PetalLengthCm', default=1, validators=[InputRequired(),\r\n NumberRange(min=0, max=1000, message='PetalLengthCm must be between 0 and 1000')])\r\n d = FloatField('PetalWidthCm', default=1, validators=[InputRequired(),\r\n NumberRange(min=0, max=1000,message='PetalLengthCm must be between 0 and 1000')])\r\n submit = SubmitField(label=('Predict'))\r\n\r\n#le indicamos a flask la url que debe lanzar con la función index\r\n@app.route('/index', methods=['GET','POST'])\r\n@app.route('/', methods=['GET','POST'])\r\ndef index():\r\n form = LoginForm()\r\n print(form.validate_on_submit())\r\n if form.validate_on_submit():\r\n print('Form Successfully Submitted!')\r\n #session = {}\r\n session['a'] = form.a.data\r\n session['b'] = form.b.data\r\n session['c'] = form.c.data\r\n session['d'] = form.d.data\r\n return redirect(url_for('result'))\r\n else:\r\n pass\r\n print(form.errors)\r\n return render_template(index_template, form = form,\r\n plotly_figure = Markup(div))\r\n\r\n@app.route('/result')\r\ndef result():\r\n #to_predict_list = [list(request.form.values())[1:5]]\r\n to_predict_list = [[session['a'],session['b'],session['c'],session['d']]]\r\n print(to_predict_list)\r\n\r\n #Creation of dataframe\r\n columns = [\"a\", \"b\", \"c\", \"d\"]\r\n df = pd.DataFrame(to_predict_list, columns=columns).astype('float')\r\n print(df.dtypes)\r\n\r\n #Prediction\r\n prediction_class = loaded_model.predict(df)[0]\r\n prediction_prob = round(loaded_model.predict_proba(df).max()* 100, 1)\r\n print(prediction_class); print(prediction_prob)\r\n\r\n return render_template(\"result.html\",\r\n output = '{} with probability {} %'.format(prediction_class,prediction_prob)\r\n)\r\n\r\n@app.errorhandler(404)\r\ndef page_not_found(e):\r\n return render_template('404.html')\r\n\r\nif __name__==\"__main__\":\r\n port = os.environ.get(\"PORT\")\r\n app.run(debug=False, host=\"0.0.0.0\", port=3000)\r\n #serve(app, host =\"0.0.0.0\", port=port) # If I use serve as my production web server, I need to change my Dockerfile to CMD [\"python\",\"main.py\"]","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"359714879","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Dec 2 22:55:49 2017\n\n@author: Swifty\n\"\"\"\n\nimport tkinter as tk\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\n\nclass App(tk.Tk):\n def __init__(self, *args, **kwargs):\n tk.Tk.__init__(self, *args, **kwargs)\n \n self.flag = True\n self.buttonIsPressed = False\n self.canvas = tk.Canvas(self, width=500, height=550, borderwidth=0, highlightthickness=0)\n self.canvas.pack(side=\"top\", fill=\"both\", expand=\"true\")\n self.rows = 100\n self.columns = 100\n self.cellwidth = 25\n self.cellheight = 25\n self.x = 0\n self.y = 0\n self.rect = {}\n \n for column in range(20):\n for row in range(20):\n x1 = column*self.cellwidth\n y1 = row * self.cellheight\n x2 = x1 + self.cellwidth\n y2 = y1 + self.cellheight\n self.rect[row,column] = self.canvas.create_rectangle(x1,y1,x2,y2, fill=\"white\", tags=\"dead\")\n \n def play(self):\n self.flag = True\n self.buttonIsPressed = True\n a = np.zeros((20, 20))\n for cell in self.rect.keys():\n if (self.canvas.gettags(self.rect[cell[1], cell[0]])[0] == \"alive\"):\n a[cell[1], cell[0]] = 1\n \n a = self.next_generation(a)\n \n for i in range(a.shape[0]):\n for j in range(a.shape[1]):\n if(a[i, j] == 1.0):\n self.canvas.itemconfig(self.rect[i, j], tag=\"alive\")\n else:\n self.canvas.itemconfig(self.rect[i, j], tag=\"dead\")\n self.redrawAfterPlay(1000)\n \n \n def spaseship(self):\n a = np.zeros((30, 30))\n\n a[14, 16] = 1\n a[15:17, 14] = 1\n a[17, 15:17] = 1\n a[15:17, 17] = 1\n \n ims = []\n fig = plt.figure() \n \n for next in range(150):\n nextGenerationArray = self.next_generation(a)\n ims.append((plt.imshow(np.copy(nextGenerationArray)),))\n \n ims = animation.ArtistAnimation(fig,ims,interval=150,repeat=False) \n \n plt.show()\n \n \n \n def next_generation(self, a):\n a_new = np.lib.pad(a, ((1, 1), (1, 1)), 'wrap')\n for i in range(1, a.shape[0]+1):\n for j in range(1, a.shape[1]+1):\n population = np.sum(a_new[i-1:i+2, j-1:j+2])\n if (population == 3):\n a[i-1, j-1] = 1\n elif(population == 4):\n if(a_new[i, j] == 1):\n a[i-1, j-1] = 1\n else:\n a[i-1, j-1] = 0\n return a\n \n \n def createWidgets(self):\n self.START = tk.Button(self)\n self.START[\"text\"] = \"Next Generation\"\n self.START[\"fg\"] = 'black'\n self.START[\"command\"] = self.play\n self.START[\"width\"] = 10\n self.START.bind('', self.enterWidget)\n self.START.bind('', self.leftWidget) \n \n self.START.pack({\"side\":\"left\"})\n \n self.STOP = tk.Button(self)\n self.STOP[\"text\"] = \"Stop\"\n self.STOP[\"fg\"] = 'black'\n self.STOP[\"width\"] = 10\n self.STOP.bind('', self.enterWidget)\n self.STOP.bind('', self.leftWidget) \n self.STOP[\"command\"] = self.destroy\n \n self.STOP.pack({\"side\":\"right\"})\n \n \n self.pattern1 = tk.Button(self)\n self.pattern1[\"text\"] = \"Buterfly\"\n self.pattern1[\"fg\"] = 'black'\n self.pattern1[\"width\"] = 10\n self.pattern1.bind('', self.enterWidget)\n self.pattern1.bind('', self.leftWidget) \n #self.pattern1[\"command\"] = self.destroy\n \n self.pattern1.pack({\"side\":\"bottom\"})\n \n \n self.pattern2 = tk.Button(self)\n self.pattern2[\"text\"] = \"Spaceship\"\n self.pattern2[\"fg\"] = 'black'\n self.pattern2[\"width\"] = 10\n self.pattern2.bind('', self.enterWidget)\n self.pattern2.bind('', self.leftWidget) \n #self.pattern2[\"command\"] = self.spaseship\n \n self.pattern2.pack({\"side\":\"bottom\"})\n \n\n def motion(self, event):\n print (\"{},{}\".format(event.x, event.y))\n if(event.y < 500 and self.flag):\n self.x, self.y = event.x, event.y\n self.x = int(self.x/25)\n self.y = int(self.y/25) \n self.redraw(0)\n \n \n \n def enterWidget(self, event):\n self.flag = False\n self.y = 500\n\n \n def leftWidget(self, event):\n self.flag = True\n \n \n def redraw(self, delay):\n if(self.buttonIsPressed == False):\n item_id = self.rect[self.y,self.x]\n if (self.canvas.gettags(item_id)[0] == \"dead\"):\n self.canvas.itemconfig(item_id, tag=\"alive\") \n else:\n self.canvas.itemconfig(item_id, tag=\"dead\")\n self.canvas.itemconfig(\"dead\", fill=\"white\")\n self.canvas.itemconfig(\"alive\", fill=\"black\")\n #self.after(delay, lambda: self.redraw(delay))\n \n \n \n \n def redrawAfterPlay(self,delay):\n self.canvas.itemconfig(\"dead\", fill=\"white\")\n self.canvas.itemconfig(\"alive\", fill=\"black\")\n self.after(delay, lambda: self.redrawAfterPlay(delay))\n \n\n\n \n\n\n\nif __name__ == \"__main__\":\n app = App()\n app.bind('', app.motion)\n app.createWidgets()\n app.mainloop()\n\n \n \n","sub_path":"hack.py","file_name":"hack.py","file_ext":"py","file_size_in_byte":5637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"506093110","text":"from flask import Flask,jsonify,request,render_template\nfrom neo4j import GraphDatabase\nimport csv\n\n#estabish the conection\nwith open(\"cred.txt\") as f1:\n data=csv.reader(f1,delimiter=\",\")\n for row in data:\n username=row[0]\n pwd=row[1]\n uri=row[2]\nprint(username,pwd,uri)\n#conection for bolt\ndriver=GraphDatabase.driver(uri=uri,auth=(username,pwd))\nsession=driver.session()\napi=Flask(__name__)\n@api.route(\"/create/&\",methods=['GET','POST'])\ndef create_node(name,id):\n q1=\"\"\"\n create (n:Employee{NAME:$name,ID:$id})\n \"\"\"\n map={\"name\":name,\"id\":id}\n try:\n session.run(q1,map)\n return(f\"employee is created with employee name={name}, id={id}\") \n except Exception as e:\n return (str(e))\n\n@api.route(\"/display\", methods=[\"GET\",\"POST\"])\ndef display_node():\n q1=\"\"\"\n match (n) return n.NAME as NAME, n.ID as ID\n \"\"\"\n results=session.run(q1)\n data=results.data()\n return(jsonify(data))\nif __name__ == \"__main__\":\n api.run(port=5054)\n \n ","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"470159310","text":"from pysnmp.hlapi import * \ndata = (\n ObjectType(ObjectIdentity('SNMPv2-MIB','sysLocation',0)),\n ObjectType(ObjectIdentity('SNMPv2-MIB','sysDescr',0)),\n ObjectType(ObjectIdentity('.1.3.6.1.2.1.2.2.1.10.3')),\n ObjectType(ObjectIdentity('.1.3.6.1.2.1.2.2.1.2.3'))\n )\n\ng = getCmd(SnmpEngine(),CommunityData('ciscolab',mpModel=1)\n ,UdpTransportTarget(('192.168.1.1',161))\n ,ContextData() , *data\n )\n\nerrorIndication, errorStatus, errorIndex, varBinds = next(g)\nif errorIndication:\n print(errorIndication)\nelif errorStatus:\n print('%s at %s' % (errorStatus.prettyPrint() ,\n errorIndex and varBinds[int(errorIndex) - 1][0] or '?' ) )\nelse:\n for varBind in varBinds:\n \n l = [str(x) for x in varBind]\n print (l[0] ,\" = \" , l[1])","sub_path":"App.py","file_name":"App.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"588984729","text":"class Solution:\n def numTrees(self, n: int) -> int:\n self.memo = {}\n def getNum(start: int, end: int) -> int: \n ans = 0\n if start >= end:\n return 1\n elif (start, end) in self.memo:\n return self.memo[(start, end)]\n \n for i in range(start, end+1):\n left_subtree = getNum(start, i-1)\n right_subtree = getNum(i+1, end)\n ans += left_subtree * right_subtree\n \n self.memo[(start, end)] = ans\n return ans\n return getNum(1, n)","sub_path":"096. Unique Binary Search Trees/solution1.py","file_name":"solution1.py","file_ext":"py","file_size_in_byte":618,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"413848882","text":"import pandas as pd\nimport numpy as np\n\n\n\"\"\" load csv content \"\"\"\ndef load_csv(path: str):\n return pd.read_csv(path)\n\n\"\"\" create dataset with single variable \"\"\"\ndef create_single_variable_dataset(pd_dataframe, variable, label, csv_path):\n labels = pd_dataframe[label].tolist()\n variables = pd_dataframe[variable].tolist()\n\n data = {variable: variables, label: labels}\n new_dataframe = pd.DataFrame(data, columns=[variable, label])\n \n new_dataframe.to_csv(csv_path)\n \n\nif __name__ == \"__main__\":\n training_set = load_csv(\"dataset/houston_housing/initial_dataset/train.csv\")\n testing_set = load_csv(\"dataset/houston_housing/initial_dataset/test.csv\")\n\n create_single_variable_dataset(training_set, \"GrLivArea\", \"SalePrice\", \"dataset/houston_housing/single_variable_dataset/train.csv\")\n","sub_path":"linear-regression/data_preprocessing.py","file_name":"data_preprocessing.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"614188442","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Sep 14 13:53:03 2019\r\n\r\n@author: khushal\r\n\"\"\"\r\n\r\nimport numpy as np\r\nfrom random import randint\r\nfrom numpy import array\r\nfrom numpy import argmax\r\nfrom numpy import array_equal\r\nfrom keras.utils import to_categorical\r\nfrom keras.models import Model\r\nfrom keras.layers import Input\r\nfrom keras.layers import LSTM\r\nfrom keras.layers import Dense\r\n\r\nfrom keras import backend as K \r\nK.clear_session() \r\n\r\n# generate a sequence of random integers\r\ndef generate_sequence(length, n_unique):\r\n\treturn [randint(1, n_unique-1) for _ in range(length)]\r\n\r\n# prepare data for the LSTM\r\ndef get_dataset(n_in, n_out, cardinality, n_samples):\r\n X1, X2, y = list(), list(), list()\r\n for _ in range(n_samples):\r\n # generate source sequence\r\n source = generate_sequence(n_in, cardinality)\r\n # define padded target sequence\r\n target = source[:n_out]\r\n target.reverse()\r\n # create padded input target sequence\r\n target_in = [0] + target[:-1]\r\n # encode\r\n src_encoded = to_categorical([source], num_classes=cardinality)\r\n tar_encoded = to_categorical([target], num_classes=cardinality)\r\n tar2_encoded = to_categorical([target_in], num_classes=cardinality)\r\n # store\r\n X1.append(src_encoded)\r\n X2.append(tar2_encoded)\r\n y.append(tar_encoded)\r\n X1 = np.squeeze(array(X1), axis=1) \r\n X2 = np.squeeze(array(X2), axis=1) \r\n y = np.squeeze(array(y), axis=1) \r\n return X1, X2, y\r\n\r\n# returns train, inference_encoder and inference_decoder models\r\ndef define_models(n_input, n_output, n_units):\r\n '''\r\n n_input: The cardinality of the input sequence, e.g. number of features, words, or characters for each time step.\r\n n_output: The cardinality of the output sequence, e.g. number of features, words, or characters for each time step.\r\n n_units: The number of cells to create in the encoder and decoder models, e.g. 128 or 256.\r\n '''\r\n\t# define training encoder\r\n encoder_inputs = Input(shape=(None, n_input))\r\n encoder = LSTM(n_units, return_state=True)\r\n encoder_outputs, state_h, state_c = encoder(encoder_inputs)\r\n encoder_states = [state_h, state_c]\r\n\t# define training decoder\r\n decoder_inputs = Input(shape=(None, n_output))\r\n decoder_lstm = LSTM(n_units, return_sequences=True, return_state=True)\r\n decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states)\r\n decoder_dense = Dense(n_output, activation='softmax')\r\n decoder_outputs = decoder_dense(decoder_outputs)\r\n model = Model([encoder_inputs, decoder_inputs], decoder_outputs)\r\n\t# define inference encoder\r\n encoder_model = Model(encoder_inputs, encoder_states)\r\n\t# define inference decoder\r\n decoder_state_input_h = Input(shape=(n_units,))\r\n decoder_state_input_c = Input(shape=(n_units,))\r\n decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]\r\n decoder_outputs, state_h, state_c = decoder_lstm(decoder_inputs, initial_state=decoder_states_inputs)\r\n decoder_states = [state_h, state_c]\r\n decoder_outputs = decoder_dense(decoder_outputs)\r\n decoder_model = Model([decoder_inputs] + decoder_states_inputs, [decoder_outputs] + decoder_states)\r\n\t# return all models\r\n '''\r\n train: Model that can be trained given source, target, and shifted target sequences.\r\n inference_encoder: Encoder model used when making a prediction for a new source sequence.\r\n inference_decoder Decoder model use when making a prediction for a new source sequence.\r\n '''\r\n print(\"model.summary = \",model.summary())\r\n print(\"encoder_model.summary = \",encoder_model.summary())\r\n print(\"decoder_model.summary = \",decoder_model.summary())\r\n \r\n return model, encoder_model, decoder_model\r\n\r\n# generate target given source sequence\r\ndef predict_sequence(infenc, infdec, source, n_steps, cardinality):\r\n '''\r\n infenc: Encoder model used when making a prediction for a new source sequence.\r\n infdec: Decoder model use when making a prediction for a new source sequence.\r\n source:Encoded source sequence.\r\n n_steps: Number of time steps in the target sequence.\r\n cardinality: The cardinality of the output sequence, e.g. the number of features, words, or characters for each time step.\r\n '''\r\n\r\n\t# encode\r\n state = infenc.predict(source)\r\n\t# start of sequence input\r\n target_seq = array([0.0 for _ in range(cardinality)]).reshape(1, 1, cardinality)\r\n\t# collect predictions\r\n output = list()\r\n for t in range(n_steps):\r\n\t\t# predict next char\r\n yhat, h, c = infdec.predict([target_seq] + state)\r\n\t\t# store prediction\r\n output.append(yhat[0,0,:])\r\n\t\t# update state\r\n state = [h, c]\r\n\t\t# update target sequence\r\n target_seq = yhat\r\n return array(output)\r\n\r\n# decode a one hot encoded string\r\ndef one_hot_decode(encoded_seq):\r\n\treturn [argmax(vector) for vector in encoded_seq]\r\n\r\n# configure problem\r\nn_features = 50 + 1\r\nn_steps_in = 6\r\nn_steps_out = 3\r\n# define model\r\ntrain, infenc, infdec = define_models(n_features, n_features, 128)\r\ntrain.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])\r\n# generate training dataset\r\nX1, X2, y = get_dataset(n_steps_in, n_steps_out, n_features, 100000)\r\nprint(X1.shape,X2.shape,y.shape)\r\n#print([X1, X2], y)\r\n# train model\r\ntrain.fit([X1, X2], y, epochs=1)\r\n# evaluate LSTM\r\ntotal, correct = 100, 0\r\n\r\nfor _ in range(total):\r\n\tX1, X2, y = get_dataset(n_steps_in, n_steps_out, n_features, 1)\r\n\ttarget = predict_sequence(infenc, infdec, X1, n_steps_out, n_features)\r\n\tif array_equal(one_hot_decode(y[0]), one_hot_decode(target)):\r\n\t\tcorrect += 1\r\n \r\nprint('Accuracy: %.2f%%' % (float(correct)/float(total)*100.0))\r\n\r\n# spot check some examples\r\nfor _ in range(10):\r\n\tX1, X2, y = get_dataset(n_steps_in, n_steps_out, n_features, 1)\r\n\ttarget = predict_sequence(infenc, infdec, X1, n_steps_out, n_features)\r\n\tprint('X=%s y=%s, yhat=%s' % (one_hot_decode(X1[0]), one_hot_decode(y[0]), one_hot_decode(target)))","sub_path":"Encoder_Decoder_Model.py","file_name":"Encoder_Decoder_Model.py","file_ext":"py","file_size_in_byte":6189,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"124050815","text":"# -*- coding: utf-8 -*-\n# Create your views here.\nfrom django.shortcuts import render_to_response\nfrom django.template.context import RequestContext\nfrom django.http import HttpResponseRedirect, HttpResponseForbidden\nfrom django.core.urlresolvers import reverse\nfrom django.contrib import messages\nimport pprint\n#from users.forms import UserForm1, UserForm2, UserForm11, UserForm22, ExampleForm, UserForm111, UserForm222\nfrom users.forms import UserForm1, UserForm2, UserForm11, UserForm22\nfrom users.models import pr8_user\n# import the logging library\nimport logging\n# Get an instance of a logger\nlogger = logging.getLogger(__name__)\nimport db\nfrom polls.views import GetAppHomeUrl as GetAppHomeUrl\nfrom mysite.utils import get_param_from_url\n\t\t\t\t\t\t\n\ndef user(request, UserName, message=None):\n\tcss = {}\n\tcss['user'] \t= 'active'\n\tcss['starred'] \t= ''\n\tTabStatus = {'sale':' ', 'rent':' ', 'directory':' ', 'statistic':' '}\n\t#redirect_to = request.REQUEST.get('next', '')\n\t#logger.info('begin edit_user')\n\n\tif request.user.username == UserName:\n\t\t#show form\n\t\tif request.method == 'GET':\n\t\t\t#logger.info('request.user:%s', request.user)\n\t\t\t#logger.info('request.user.id:%s', request.user.id)\n\t\t\t#logger.info('request.user.email:%s', request.user.email)\n\t\t\ttry:\n\t\t\t\tpu = pr8_user.objects.get(user_id = request.user.id)\n\t\t\texcept:\n\t\t\t\tpu = None\n\t\t\t'''if request.user.email:\n\t\t\t\t#form = UserForm1() # form with no email input field\n\t\t\t\tif pu:\n\t\t\t\t\tform = UserForm11(instance = pu)\n\t\t\t\telse:\n\t\t\t\t\tform = UserForm11()\n\t\t\telse:\n\t\t\t\t#form = UserForm2() # form with email input field\n\t\t\t\tif pu:\n\t\t\t\t\tform = UserForm22(instance = pu) # form with email input field\n\t\t\t\telse:\n\t\t\t\t\tform = UserForm22()'''\n\t\t\tif pu:\n\t\t\t\tform = UserForm22(instance = pu)\n\t\t\telse:\n\t\t\t\tform = UserForm22({'email': request.user.email, 'hand_phone':None})\n\t\telif request.method == 'POST':\n\t\t\t#logger.info('request.method is POST:')\n\t\t\t#logger.info('request.GET: %s', request.GET)\n\t\t\t#logger.info('request.POST: %s', request.POST)\n\t\t\t#logger.info('request.REQUEST: %s', request.REQUEST)\n\t\t\t#logger.info('request: %s', request)\n\t\t\t'''if request.user.email:\n\t\t\t\tform = UserForm11(request.POST) # form with no email input field\n\t\t\telse:\n\t\t\t\tform = UserForm22(request.POST) # form with email input field'''\n\t\t\tform = UserForm22(request.POST)\n\t\t\tlogger.info('form.data:{}'.format(pprint.pformat( form.data.dict() )) )\n\t\t\tif form.is_valid():\n\t\t\t\tif request.user.email:\n\t\t\t\t\tobj, created = pr8_user.objects.get_or_create(user_id = request.user.id,\n\t\t\t\t\t\tdefaults={'hand_phone':form.cleaned_data['hand_phone'], 'email':request.user.email}\n\t\t\t\t\t\t)\n\t\t\t\t\tif created:\n\t\t\t\t\t\tNone\n\t\t\t\t\telse:\n\t\t\t\t\t\tobj.hand_phone = form.cleaned_data['hand_phone']\n\t\t\t\t\t\tobj.email = request.user.email\n\t\t\t\t\t\tobj.save()\n\t\t\t\t\t#messages.success(request, 'Profile details updated.')\n\t\t\t\t\t#return HttpResponseRedirect( reverse('users:user', kwargs={'UserName':UserName}) )\n\t\t\t\telse:\n\t\t\t\t\tobj, created = pr8_user.objects.get_or_create(user_id = request.user.id,\n\t\t\t\t\t\tdefaults={'hand_phone':form.cleaned_data['hand_phone'], 'email':form.cleaned_data['email']}\n\t\t\t\t\t\t)\n\t\t\t\t\tif created:\n\t\t\t\t\t\tNone\n\t\t\t\t\telse:\n\t\t\t\t\t\tobj.hand_phone = form.cleaned_data['hand_phone']\n\t\t\t\t\t\tobj.email = form.cleaned_data['email'] # * add code to let user confirm email\n\t\t\t\t\t\tobj.save()\n\t\t\t\t\t#messages.success(request, 'Profile details updated.')\n\t\t\t\t\t#return HttpResponseRedirect( reverse('users:user', kwargs={'UserName':UserName}) )\n\t\t\t\tredirect_to = get_param_from_url(request.META['HTTP_REFERER'], 'next')\n\t\t\t\tlogger.info('redirect_to in form submit: %s',redirect_to)\t\t\t\t\n\t\t\t\tif redirect_to:\n\t\t\t\t\treturn HttpResponseRedirect(redirect_to)\n\t\t\t\telse:\n\t\t\t\t\tmessages.success(request, 'Đã cập nhật')\n\t\t\telse:\n\t\t\t\tNone\n\t\t\n\t\tcontext = RequestContext(request,\n\t\t\t\t\t\t\t {'request':request,\n\t\t\t\t\t\t\t 'user': request.user,\n\t\t\t\t\t\t\t 'css': css,\n\t\t\t\t\t\t\t 'form': form,\n\t\t\t\t\t\t\t 'TabStatus': TabStatus,\n\t\t\t\t\t\t\t })\n\t\treturn render_to_response('users/user.html',\n\t\t\t\t\t\t\t\tcontext_instance=context)\t\t\t\n\telse:\n\t\treturn HttpResponseForbidden()\n\t\ndef starred(request, UserName):\n\tcss = {}\n\tcss['user'] \t= ''\n\tcss['starred'] \t= 'active'\n\tTabStatus = {'sale':' ', 'rent':' ', 'directory':' ', 'statistic':' '}\n\n\tif request.user.username == UserName:\n\t\t#get starred ProjectName \n\t\tStarredPrj = db.getStarredPrj(request)\n\t\tAppHomeUrl = GetAppHomeUrl(request)\n\t\t#show page\n\t\tcontext = RequestContext(request,\n\t\t\t\t\t\t\t {'request':request,\n\t\t\t\t\t\t\t 'user': request.user,\n\t\t\t\t\t\t\t 'css': css,\n\t\t\t\t\t\t\t 'StarredPrj':StarredPrj,\n\t\t\t\t\t\t\t 'AppHomeUrl': AppHomeUrl,\n\t\t\t\t\t\t\t 'TabStatus': TabStatus,\n\t\t\t\t\t\t\t })\n\t\tif request.is_mobile:\n\t\t\treturn render_to_response('users/mobile/starred_m.html',\n\t\t\t\t\t\t\t\tcontext_instance=context)\n\t\telse:\n\t\t\treturn render_to_response('users/starred.html',\n\t\t\t\t\t\t\t\tcontext_instance=context)\t\t\t\n\telse:\n\t\t#permission denied. \n\t\tcontext = RequestContext(request,\n\t\t\t\t\t\t\t {'request':request,\n\t\t\t\t\t\t\t 'user': request.user,\n\t\t\t\t\t\t\t 'TabStatus': TabStatus,\n\t\t\t\t\t\t\t })\n\t\treturn render_to_response('users/error.html',\n\t\t\t\t\t\t\t\tcontext_instance=context)\n","sub_path":"users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"258811367","text":"with open('SCC.txt', 'r') as ifile:\n data = ifile.readlines()\n\nfor maxVert in [10000, 100000, 400000]:\n with open('SCC_{0}.txt'.format(maxVert), 'w') as ofile:\n for line in data:\n toks = map(int, line.split())\n if toks[0] <= maxVert and toks[1] <= maxVert:\n ofile.write('{0} {1}\\n'.format(toks[0], toks[1]))\n # END if\n # END for\n # END with\n# END for\n","sub_path":"Part_1/p4/truncateQuizData.py","file_name":"truncateQuizData.py","file_ext":"py","file_size_in_byte":422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"81219637","text":"from django.urls import path, include\n\nfrom app import views\nfrom api.admin import admin_site\n\n\nurlpatterns = [\n path(\"\", views.app_home),\n path(\"admin/\", admin_site.urls),\n path(\"api/\", include(\"api.urls\")),\n path(\"actions/aggregate-stats\", views.action_aggregate_stats),\n path(\n \"actions/import-questions-from-notion\",\n views.action_import_questions_from_notion,\n ),\n path(\"actions/export-data-to-github\", views.action_export_data_to_github),\n path(\n \"actions/export-contributions-to-notion\",\n views.action_export_contributions_to_notion,\n ),\n]\n","sub_path":"app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"217153210","text":"# -*- codeing = utf-8 -*-\n# @Time : 2021/3/28 16:02\n# @Author : 疏苑\n# @File : crawler3.28.py\n# @Software : PyCharm\nimport os\nimport re # 正则表达式，文字匹配用\nfrom bs4 import BeautifulSoup # 网页解析，获取数据\nimport urllib.request # 制定URL，获取网页数据\nimport urllib.error\nimport requests\nimport xlwt # Excel操作用\nimport time\n\n\ndef main():\n baseurl = \"https://www.dushu.com/search.aspx?wd=%e8%a5%bf%e6%96%b9%e6%88%98%e4%ba%89&p=\"\n datalist = getData(baseurl)\n # askURL(\"https://www.dushu.com/search.aspx?wd=%e8%a5%bf%e6%96%b9%e6%88%98%e4%ba%89&p=\")\n savepath = \"图书表_关键词_西方战争_来源_读书网.xls\"\n saveDate(datalist, savepath)\n\n\nkeyword = '西方战争'\nfindNumber = re.compile('(\\d+)') # 匹配搜索结果数\nfindBookId = re.compile('

(.*?)

') # 匹配书名\nfindAbstract = re.compile(r'
(.*?)
', re.S) # 匹配摘要\nfindPic = re.compile('
.*')\nNumber = 0\nbookurl = \"https://www.dushu.com\"\nhead = {\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36\",\n 'Referer': 'https://www.dushu.com/search.aspx?wd=%e8%a5%bf%e6%96%b9%e6%88%98%e4%ba%89&p=2'\n}\n\n\n# 2.分析数据\ndef getData(baseurl):\n\n pic_path = \"./\" + keyword #创建存放图片的文件夹\n if not os.path.exists(pic_path) :\n os.mkdir(pic_path)\n\n datalist = []\n baseaskURL(baseurl)\n page = Number // 20 + 1\n # print(page)\n for i in range(0, page):\n url = baseurl + str(i + 1)\n html = askURL(url)\n\n # 2.逐一解析数据\n soup = BeautifulSoup(html, \"html.parser\")\n for item in soup.find_all('div', class_=\"book-info\"): # 查找符合要求的字符串，形成列表\n data = [] # 将查找的图书概括存储\n item = str(item)\n bookid = re.findall(findBookId, item)\n bookid = str(bookid)\n bookid = re.sub(\"[\\'\\[\\]]\", '', bookid)\n burl = bookurl + bookid\n bookhtml = askURL(burl)\n bs = BeautifulSoup(bookhtml, 'html.parser')\n\n # 标题信息\n title = re.findall(findTitle, bookhtml)[0]\n # print(title)\n data.append(title)\n\n # 属性（作者，出版社，出版日期）\n total = bs.select(\"table>tbody>tr>td~td\")\n author = str(total[0])\n author = re.sub(\"[<>td/著编]\", \"\", author)\n data.append(author)\n\n publish_house = str(total[1])\n publish_date = str(total[6])\n publish_house = re.sub(\"[<>td/]\", \"\", publish_house)\n publish_date = re.sub(\"[<>td/clas=\\\"r]\", \"\", publish_date)\n publish_date = re.sub('-', '/', publish_date)\n data.append(publish_house)\n data.append(publish_date)\n\n # 摘要\n abstract = re.findall(findAbstract, bookhtml)[0]\n data.append(abstract)\n\n # 封面\n\n pic = bs.select('[class=\"pic\"]')\n pic = re.findall(findPic, str(pic))\n pic = str(pic)\n pic = re.sub(\"[\\[\\]\\']\", '', pic)\n print(pic)\n pic_name = title + '.jpg'\n\n res = requests.get(url, headers=head)\n time.sleep(0.5)\n with open(pic_path+'/'+pic_name, 'wb') as f:\n if res.status_code == 200:\n f.write(res.content)\n data.append(pic_path+'/'+pic_name)\n else:\n print('异常')\n # print(pic)\n\n datalist.append(data)\n print(datalist)\n\n return datalist\n\n # print(abstract)\n # print(bookhtml)\n\n # break\n\n # print(item)\n\n\n# 1.爬取网页\ndef baseaskURL(url):\n # head = {\n # \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36\"\n # }\n # head = {\n # \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:86.0) Gecko/20100101 Firefox/86.0\"\n # }\n req = urllib.request.Request(url, headers=head)\n html = \"\"\n\n try:\n resp = urllib.request.urlopen(req)\n html = resp.read().decode(\"utf-8\")\n # print(html)\n global Number\n Number = int(re.findall(findNumber, html)[0])\n # print(Number)\n # print(type(Number))\n except urllib.error as e:\n if hasattr(e, \"code\"):\n print(e.code)\n if hasattr(e, \"reason\"):\n print(e.reason)\n\n return html\n\n\ndef askURL(url):\n req = urllib.request.Request(url, headers=head)\n html = \"\"\n\n try:\n resp = urllib.request.urlopen(req)\n html = resp.read().decode(\"utf-8\")\n except urllib.error as e:\n if hasattr(e, \"code\"):\n print(e.code)\n if hasattr(e, \"reason\"):\n print(e.reason)\n\n return html\n\n\n# 3.保存数据为excel文件\ndef saveDate(datalist, savepath):\n workbook = xlwt.Workbook(encoding=\"utf-8\", style_compression=0)\n worksheet = workbook.add_sheet('豆瓣电影Top250', cell_overwrite_ok=True)\n col = (' ', \"title\", \"author\", \"publish_house\", \"publish_date\", \"_abtract\", \"image_url\")\n for i in range(0, 7):\n worksheet.write(0, i, col[i])\n for i in range(0, Number):\n print(\"第%d条\" % (i + 1))\n data = datalist[i]\n worksheet.write(i + 1, 0, i + 1)\n for j in range(0, 6):\n worksheet.write(i + 1, j + 1, data[j])\n\n workbook.save(savepath)\n\n print(\"save\")\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"crawler3.28.py","file_name":"crawler3.28.py","file_ext":"py","file_size_in_byte":5744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"550960697","text":"__author__ = 'Paul Tune'\n\n\n# given a number n, how many number of ones are there from 1 to n?\n# NB: need to deal with overflow\ndef number_ones_series(n):\n if n < 0:\n return\n\n if n == 0 or n == 1:\n return n\n\n num = n\n while num > 0:\n pass\n\n num = num//10\n\n# number of ones in an integer n\ndef number_ones(n):\n if n < 0:\n return\n\n if n == 0 or n == 1:\n return n\n\n count = 0\n num = n\n while num > 0:\n if num % 10 == 1:\n count += 1\n\n num = num//10\n\n return count\n\n\nif __name__ == \"__main__\":\n print(number_ones(3))\n","sub_path":"practice/number_of_ones.py","file_name":"number_of_ones.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"587697307","text":"import os\nimport csv\nfrom collections import Counter\nimport operator\n\nfilepath1 = os.path.join(\"raw_data\", \"election_data_1.csv\")\nfilepath2 = os.path.join(\"raw_data\", \"election_data_2.csv\")\nall_votes = []\nwith open (filepath1, 'r') as csvfile1, open (filepath2,'r') as csvfile2:\n reader1 =csv.reader(csvfile1,delimiter=',')\n reader2 =csv.reader(csvfile2,delimiter=',')\n next(reader1)\n next(reader2)\n for row in reader1:\n all_votes.append(row[2])\n for row in reader2:\n all_votes.append(row[2])\n Total_Votes = len(all_votes)\n print(\"Total Votes:\", Total_Votes)\n print(\"------------------------\")\n Candidates = dict(Counter(all_votes))\n for name, votes in Candidates.items():\n percent = \"{0:.0%}\".format(votes/Total_Votes)\n Candidates[name] = [Candidates[name],percent]\n print(name, \":\",percent,\"(\",votes,\")\")\n Winner = max(Candidates.items(), key=operator.itemgetter(1))[0]\n print(\"------------------------\")\n print(\"Winner: \",Winner)\n \noutput_file = os.path.join(\"output.txt\")\nwith open(output_file, \"w\", newline =\"\") as datafile:\n writer = csv.writer(datafile,delimiter=' ',lineterminator='\\n',quoting=csv.QUOTE_NONE, escapechar=' ')\n #writer.writerow(['Total Votes', Total_Votes])\n writer.writerow( [\"Total Votes:\",Total_Votes,str(Candidates),\"Winner:\",Winner] )","sub_path":"python-challenge/PyPoll/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"428148564","text":"import sys\r\nimport math\r\nimport os.path\r\nfrom .datStream import datStream\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Nov 9 17:16:37 2019\r\n\r\n@author: Haziq Usman\r\n Moeed Ahmad\r\n\"\"\" \r\n\r\ndef max_level(n):\r\n max = -1\r\n while(n > max):\r\n max = n\r\n return max\r\n\r\ndef chunkstring(string, length):\r\n return (string[0+i:length+i] for i in range(0, len(string), length))\r\nclass Metadata:\r\n def __init__(self):\r\n self.parentToChild= dict()\r\n self.parentToFile= dict()\r\n \r\n \r\n def printself(self): \r\n print (self.parentToChild,self.parentToFile)\r\n\r\nclass Directory:\r\n level = 0\r\n def __init__(self, prevLevel=0):\r\n self.fileNames = {}\r\n self.files={}\r\n self.fid = 0\r\n self.pid = 0\r\n self.parent = \".\"\r\n self.pdir = []\r\n self.pcontent=\"\"\r\n self.dirName = \"root\"\r\n self.subDirCount = 0\r\n self.level = prevLevel + 1\r\n self.children = []\r\n \r\n def add_child(self):\r\n self.children.append(Directory(self.level))\r\n self.subDirCount = self.subDirCount + 1\r\n \r\n def create_file(self, fname):\r\n self.fid = self.fid + 1\r\n self.fileNames[self.fid] = fname\r\n self.files[self.fid, 0] = self.pcontent\r\n \r\n def showMemoryMap(self):\r\n print(\"Current Directory: \" + self.dirName)\r\n for key, value in list(self.fileNames.items()):\r\n for [key1,key2] in list(self.files.keys()):\r\n if(key1==key):\r\n print(self.dirName + \" | \" + str(key2) + \" | \" + value)\r\n for child in self.children:\r\n print(\"Current Directory: \" + child.dirName)\r\n for key, value in list(child.fileNames.items()):\r\n for [key1,key2] in list(child.files.keys()):\r\n if(key1==key):\r\n print(child.dirName + \" | \" + str(key2) + \" | \" + value)\r\n \r\n\r\nclass FileSystem:\r\n root = Directory()\r\n currDir = root\r\n\r\n def create(self, fname):\r\n self.currDir.create_file(fname)\r\n \r\n def delete(self, fname):\r\n for key, value in list(self.currDir.fileNames.items()):\r\n if value==fname:\r\n del self.currDir.fileNames[key]\r\n for [key1,key2] in list(self.currDir.files.keys()):\r\n if key1==key:\r\n del self.currDir.files[key1,key2]\r\n \r\n def mkdir(self, dirName):\r\n self.currDir.add_child()\r\n child = self.currDir.children[self.currDir.subDirCount-1]\r\n child.pdir.append(self.currDir) \r\n child.parent = self.currDir.dirName \r\n child.dirName=dirName \r\n \r\n def chdir(self, dirName):\r\n if (dirName==\".\"):\r\n for parent in self.currDir.pdir:\r\n self.currDir = parent\r\n return\r\n else:\r\n for child in self.currDir.children:\r\n if child.dirName == dirName:\r\n self.currDir = child\r\n return \r\n print(dirName + \" does not exist\")\r\n \r\n def write_to_file(self, fname, text, write_at=None):\r\n if write_at==None:\r\n chunks = list(chunkstring(text, 25))\r\n for key, value in list(self.currDir.fileNames.items()):\r\n if value==fname:\r\n for chunk in chunks:\r\n self.currDir.files[key,self.currDir.pid] = chunk\r\n self.currDir.pid = self.currDir.pid + 1 \r\n else:\r\n pg = math.floor(int(write_at)/25)\r\n for key,value in list(self.currDir.fileNames.items()):\r\n if value==fname:\r\n for [key1,key2], value in list(self.currDir.files.items()):\r\n if key1 == key and key2==pg:\r\n rest1 = value[:int(write_at)]\r\n rest2 = value[int(write_at):]\r\n self.currDir.files[key1,key2]=rest1 + text + rest2\r\n \r\n def move(self, source_fname, target_dirName):\r\n temp = \"\"\r\n curr_fol = self.currDir\r\n for key, value in list(self.currDir.fileNames.items()):\r\n if value==source_fname:\r\n for [key1,key2], value1 in list(self.currDir.files.items()):\r\n if key1 == key:\r\n temp += value1\r\n self.delete(source_fname) \r\n for child in self.currDir.children:\r\n if child.dirName == target_dirName:\r\n self.currDir = child\r\n self.create(source_fname)\r\n self.write_to_file(source_fname, temp)\r\n self.currDir = curr_fol\r\n \r\n def read_from_file(self, fname, start=None, size=None):\r\n temp=\"\"\r\n if start==None and size==None:\r\n for key,value in list(self.currDir.fileNames.items()):\r\n if value==fname:\r\n for [key1,key2], value in list(self.currDir.files.items()):\r\n if key1==key:\r\n temp+=value\r\n return temp\r\n else:\r\n for key,value in list(self.currDir.fileNames.items()):\r\n if value==fname:\r\n for [key1,key2], value in list(self.currDir.files.items()):\r\n if key1==key:\r\n temp += value\r\n return temp[start:(start+size-1)]\r\n \r\n def move_within_file(self, fname,start,size,target):\r\n temp=\"\"\r\n pg1 = math.floor(start/25)\r\n pg2 = math.floor(target/25)\r\n for key,value in list(self.currDir.fileNames.items()):\r\n if value==fname:\r\n for [key1,key2], value in list(self.currDir.files.items()):\r\n if key1==key and key2==pg1:\r\n temp=value[start:(start+size-1)]\r\n rest1 = value[:start]\r\n rest2 = value[(start+size):]\r\n self.currDir.files[key1,key2]=rest1 + rest2\r\n for [key1,key2], value in list(self.currDir.files.items()):\r\n if key2==pg2:\r\n rest1=value[:target]\r\n rest2=value[target:]\r\n self.currDir.files[key1,key2]= rest1 + temp + rest2\r\n\r\n\r\n def truncate_file(self, fname, maxSize):\r\n temp=\"\"\r\n for key,value in list(self.currDir.fileNames.items()):\r\n if value==fname:\r\n for [key1,key2], value in list(self.currDir.files.items()):\r\n if key1==key:\r\n temp += value\r\n del self.currDir.files[key1,key2]\r\n del self.currDir.fileNames[key]\r\n self.create(fname)\r\n self.write_to_file(fname, temp[:maxSize])\r\n def get_last_dir(self):\r\n for child in self.currDir.children:\r\n self.currDir = child\r\n return self.currDir.level\r\n \r\n def recurse(self):\r\n for key,value in list(self.currDir.fileNames.items()):\r\n y = \"-\"\r\n y = y + \"-\" + value\r\n print(y)\r\n for child in self.currDir.children:\r\n x = \"-\"\r\n x = x + \"-\" + child.dirName \r\n print(x)\r\n self.currDir = child\r\n \r\ndef main(argv=None):\r\n if argv is None:\r\n argv = sys.argv\r\n \r\n ds= datStream()\r\n fileSystem = FileSystem()\r\n if(os.path.isfile(\"file.dat\")==0):\r\n ds.dump(fileSystem.root) \r\n dire = ds.loadData()\r\n fileSystem.root = dire\r\n fileSystem.currDir=dire\r\n loop=True \r\n \r\n while (loop): ## While loop which will keep going until loop = False\r\n print_menu() ## Displays menu\r\n choice = input(\"Enter your choice [1-13]: \")\r\n \r\n if choice=='1': \r\n fname= input(\"Enter File Name: \")\r\n fileSystem.create(fname)\r\n print (fname + \" created\")\r\n ds.dump(fileSystem.root)\r\n elif choice=='2':\r\n fname= input(\"Enter File Name: \")\r\n fileSystem.delete(fname)\r\n print (fname + \" deleted\")\r\n ds.dump(fileSystem.root)\r\n elif choice=='3':\r\n print (\"Make Directory\")\r\n dirName = input(\"Enter the name of directory: \")\r\n fileSystem.mkdir(dirName)\r\n print(dirName + \" created\")\r\n ds.dump(fileSystem.root)\r\n elif choice=='4':\r\n print (\"Change Directory\")\r\n dirName = input(\"Directory name: \")\r\n fileSystem.chdir(dirName)\r\n if fileSystem.currDir.dirName == dirName:\r\n print(\"Directory changed to: \" + dirName)\r\n else:\r\n print(\"Directory not changed!\")\r\n ds.dump(fileSystem.root)\r\n elif choice=='5':\r\n print (\"Move File\")\r\n fname = input(\"Enter file name to move: \")\r\n dirName = input(\"Enter target folder: \")\r\n fileSystem.move(fname,dirName)\r\n print(fname + \" moved to directory \" + dirName)\r\n ds.dump(fileSystem.root)\r\n elif choice=='6':\r\n print (\"Write to File\")\r\n fname = input(\"Enter file name to write into: \")\r\n content = input(\"Enter text to write into file: \")\r\n fileSystem.write_to_file(fname, content)\r\n ds.dump(fileSystem.root)\r\n elif choice=='7':\r\n print (\"Write to file at certain position\")\r\n fname = input(\"Enter file name to write into: \")\r\n content = input(\"Enter text to write into file: \")\r\n pos = input(\"Enter position at which you want to write in the file: \")\r\n fileSystem.write_to_file(fname, content, pos)\r\n ds.dump(fileSystem.root) \r\n elif choice=='8':\r\n print (\"Read File\")\r\n fname = input(\"Enter file name to read from: \")\r\n print(fileSystem.read_from_file(fname))\r\n ds.dump(fileSystem.root)\r\n elif choice=='9':\r\n print (\"Read part of the file\")\r\n fname = input(\"Enter file name to read from: \")\r\n pos = int(input(\"Enter position to start reading from: \"))\r\n end = int(input(\"Enter number of characters you want to read: \"))\r\n print(fileSystem.read_from_file(fname, pos, end))\r\n ds.dump(fileSystem.root)\r\n elif choice=='10':\r\n print (\"Move within file\")\r\n fname = input(\"Enter file name to be manipulated: \")\r\n start = int(input(\"Enter the position from where you want to cut the text: \"))\r\n size = int(input(\"Enter the number of characters to cut: \"))\r\n target = int(input(\"Enter the position where you want to place the text that was cut: \"))\r\n fileSystem.move_within_file(fname, start, size, target)\r\n ds.dump(fileSystem.root)\r\n elif choice=='11':\r\n print (\"Truncate File\")\r\n fname = input(\"Enter file name to be truncated: \") \r\n size = int(input(\"Enter the size of file you want to sustain: \"))\r\n fileSystem.truncate_file(fname, size)\r\n ds.dump(fileSystem.root) \r\n elif choice=='12':\r\n print (\"Memory map: \")\r\n dire.showMemoryMap()\r\n \r\n elif choice=='13':\r\n print (\"Exit\")\r\n ## You can add your code or functions here\r\n loop=False # This will make the while loop to end as not value of loop is set to False\r\n else:\r\n # Any integer inputs other than values 1-5 we print an error message\r\n print(\"Wrong option selection. Enter any key to try again..\")\r\n \r\n return 0\r\n\r\ndef print_menu(): ## Your menu design here\r\n print (30 * \"-\" , \"MENU\" , 30 * \"-\")\r\n print (\"1. Create file\")\r\n print (\"2. Delete File\")\r\n print (\"3. Make Directory\")\r\n print (\"4. Change Directory\")\r\n print (\"5. Move\")\r\n print (\"6. Write to File\")\r\n print (\"7. Write to File at\")\r\n print (\"8. Read From File\")\r\n print (\"9. Read from File at\")\r\n print (\"10. Move within file\")\r\n print (\"11. Truncate file\")\r\n print (\"12. Show memory map\")\r\n print (\"13. Exit\")\r\n print (67 * \"-\")\r\n \r\n","sub_path":"fileSystem/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":12412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"302445566","text":"#General Purpose Imports\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport os, sys\nimport time\nimport pickle\nfrom os import listdir\nfrom os.path import isfile, join\n\n#Skimage & PILLOW for Image Processing\nfrom skimage import io\nfrom skimage.io import imread, imshow\nfrom skimage.transform import rescale, resize\nfrom PIL import Image\n\n#Keras for CNN\n#import keras\nfrom tensorflow import keras\nfrom tensorflow.keras.utils import to_categorical\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Flatten, Dropout \nfrom tensorflow.keras.layers import Activation, Conv2D, MaxPooling2D\nfrom tensorflow.keras.models import load_model\nfrom tensorflow.keras import models, layers\nfrom tensorflow.keras.callbacks import TensorBoard\n\n#Labels\nlabels_one_hot = []\nlabels = []\n\n#Reduce Dataset\ndef get_reduce_dataset(filename, reduce = True , reduction_size = 50):\n\t#Reading images from disk\n\tdf_images = pd.read_pickle(filename)\n\t\n\tif reduce == True:\n\t\t#Drop Rows \n\t\tdf_images_reduced = df_images.iloc[0:reduction_size]\n\telse:\n\t\tdf_images_reduced = df_images\n\n\treturn df_images_reduced\n\n#Resize Images\ndef resize(dir):\n\tpath = dir\n\tdirs = os.listdir( path )\n\tfor item in dirs:\n\t\tif item[0] != '.': \n\t\t\tif os.path.isfile(path+item):\n\t\t\t\tim = Image.open(path+item)\n\t\t\t\tf, e = os.path.splitext(path+item)\n\t\t\t\timResize = im.resize((982,737), Image.ANTIALIAS)\n\t\t\t\timResize.save(f + ' resized.jpg', 'JPEG', quality=90)\n\n\n#Process Images & Labels \ndef process_images(images_dir = 'Imagenes/', image_width_pixels = 982, \n\t\timage_height_pixels = 737, num_images = 187, \n\t\ttraining = True, export_filename = 'data_set_1'):\n\tglobal labels, labels_one_hot\n\t#Read all images in directory\n\tfiles = [f for f in listdir(images_dir) if isfile(join(images_dir, f))]\n\n\tif training == False: \n\t\timages = list()\n\t\tfor file in files:\n\t\t\tif file[0] != '.':\n\t\t\t\t#Load each image \n\t\t\t\t_file = images_dir\n\t\t\t\t_file += file\n\t\t\t\tnew_image = imread(_file, as_gray=True)\n\t\t\t\t#Extract the features of each image\n\t\t\t\tfeatures = np.reshape(new_image, \n\t\t\t\t\t(image_width_pixels * image_height_pixels))\n\t\t\t\timages.append(features)\n\t\t#FG\n\t\tdf_images = pd.DataFrame(images)\n\n\t\t#Printing Data Read\n\t\tprint('Images Processed: ', np_images.shape)\n\n\t\t#Export images with labels \n\t\tdf_images.to_pickle(filename) \n\t\tprint('1 File Exported: ', export_filename)\n\n\t#Create image labels and store it in memory \n\tif training == True:\n\t\tlabels = []\n\t\timages_dir += '*.jpg'\n\t\timagenes = io.ImageCollection(images_dir)\n\t\timages_filenames = imagenes.files\n\t\tfor image in images_filenames:\n\t\t\t#Labels f = 0 i = 1 o = 2 p = 3\n\t\t\tif image[9] == 'f':\n\t\t\t\tlabels.append(0)\n\t\t\tif image[9] == 'i':\n\t\t\t\tlabels.append(1)\n\t\t\tif image[9] == 'o':\n\t\t\t\tlabels.append(2)\n\t\t\tif image[9] == 'p':\n\t\t\t\tlabels.append(3)\n\n\t\t#One-Hot Encoding: Labels in a numpy array\n\t\tlabels_one_hot = to_categorical(labels)\n\t\tprint('Labels Created: ', labels_one_hot.shape)\n\n#CNN Model 1\ndef model_1():\n\tNAME = \"Model-1-CNN-{}\".format(int(time.time()))\n\ttensorboard = TensorBoard(log_dir='logs/{}'.format(NAME))\n\tcm_callback = keras.callbacks.LambdaCallback(on_epoch_end=log_confusion_matrix)\n\n\t#Instantiate an empty model\n\tmodel = Sequential()\n\n\t#Layers\n\tinputs = keras.Input(shape=(723734,), name='img')\n\tx = layers.Dense(64, activation='relu')(inputs)\n\tx = layers.Dense(64, activation='relu')(x)\n\toutputs = layers.Dense(4, activation='softmax')(x)\n\n\tmodel = keras.Model(inputs=inputs, outputs=outputs, name='mnist_model')\n\t'''\n\tmodel.add(Dense(output_dim = 32, init = 'uniform', activation = 'tanh', input_dim = 723734))\n\tmodel.add(Dense(output_dim = 4, init = 'uniform', \n\t\tactivation = 'sigmoid'))\n\tmodel.compile(optimizer = 'adam', loss = 'binary_crossentropy', \n\t\tmetrics = ['accuracy'])\n\t'''\n\t#End Layer\n\n\t# Compile the model\n\tmodel.compile(loss=keras.losses.categorical_crossentropy, \n\t\toptimizer='SGD', metrics=['accuracy'])\n\t\n\t#Load Dataset \n\tX = get_reduce_dataset('data_set.pkl') #Reduced to 50\n\tY = labels_one_hot[:50] #Reduced to 50\n\n\t#Fit Model\n\thist = model.fit(x = X, y = Y, epochs=10, batch_size = 32, \n\t\tvalidation_split = 0.2, verbose = 2, callbacks =[tensorboard, cm_callback])\n\t\n\tprint(model.summary())\n\nif __name__ == \"__main__\":\n\tprocess_images()\n\tmodel_1()\n\t\n\n\n","sub_path":"virusDetection.py","file_name":"virusDetection.py","file_ext":"py","file_size_in_byte":4258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"328774102","text":"# Given a number, return the next higher prime number:\n\ndef next_prime(num):\n is_prime = False\n if num <= 0:\n return (\"2 is next prime!\")\n elif num > 0:\n while is_prime == False:\n num += 1\n are_you_prime(num)\n if are_you_prime(num) == True:\n return (f\"{num} is next prime!\")\n break\n elif are_you_prime(num) == False:\n continue\n else:\n return (\"Please input a number.\")\n\ndef are_you_prime(num):\n is_prime = False\n dvsr_lst = []\n if num > 0:\n for i in range(2, num//2+1):\n if num % i == 0:\n dvsr_lst.append(i)\n elif num % i != 0:\n continue\n dvsr_lst.append(num)\n if int(len(dvsr_lst)) > 2:\n return is_prime\n else:\n is_prime = True\n return is_prime\n\n\nuser_input = (\n int(float(input(\"Enter a number to find its next higher prime number: \"))))\nprint(next_prime(user_input))\n","sub_path":"Next_Prime_Number_Funct.py","file_name":"Next_Prime_Number_Funct.py","file_ext":"py","file_size_in_byte":1017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"350532964","text":"class Solution(object):\n def countArrangement(self, N):\n \"\"\"\n :type N: int\n :rtype: int\n \"\"\"\n used = [False]*(N+1)\n return find([], used, N)\n \n \ndef find(L, used, N):\n if len(L) == N:\n if isBA(L):\n return 1\n return 0\n \n count = 0\n for j in range(1, N+1):\n if not used[j]:\n L.append(j)\n used[j] = True\n count += find(L, used, N) \n del L[-1]\n used[j] = False\n \n return count\n \n \ndef isBA(L):\n for i, x in enumerate(L):\n if (i ==0 or x % i != 0) and (x == 0 or i % x !=0) :\n return False\n \n return True\n\n\ns = Solution()\n\nprint(s.countArrangement(3))","sub_path":"leetcode/501-550/526. Beautiful Arrangement.py","file_name":"526. Beautiful Arrangement.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"369370027","text":"\"\"\"\nCode that goes along with the Airflow located at:\nhttp://airflow.readthedocs.org/en/latest/tutorial.html\n\"\"\"\nfrom airflow import DAG\nfrom airflow.operators.bash_operator import BashOperator\nfrom datetime import datetime, timedelta\n\ndefault_args = {\n 'owner': 'airflow',\n 'depends_on_past': False,\n 'start_date': datetime.now(),\n 'email': ['chris@fregly.com'],\n 'email_on_failure': False,\n 'email_on_retry': False,\n 'retries': 1,\n 'retry_delay': timedelta(minutes=5),\n # 'queue': 'bash_queue',\n # 'pool': 'backfill',\n # 'priority_weight': 10,\n # 'end_date': datetime(2016, 4, 24),\n}\n\ndag = DAG(\n 'update_pmml', default_args=default_args, schedule_interval=timedelta(0))\n\nt1 = BashOperator(\n task_id='rid_old_pmml',\n bash_command='sudo docker stop pmmlsvc||true;sudo docker rm pmmlsvc||true;sudo docker rmi canary||true',\n dag=dag)\n\nt2 = BashOperator(\n task_id='deploy_new_pmml',\n bash_command='sudo docker build -t canary /home/pipeline-training/pipeline/prediction.ml/pmml;sudo docker run -d -p 9050:9050 --name pmmlsvc -t canary',\n dag=dag)\n\nt2.set_upstream(t1)\n","sub_path":"scheduler.ml/airflow/dags/update_pmml.py","file_name":"update_pmml.py","file_ext":"py","file_size_in_byte":1124,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"602553697","text":"first=int(input(\"第一個數字\"))\nsecond=int(input(\"第二個數字\"))\nwhile(first>0 and second>0):\n\tif(first>second):\n\t\tfirst=first%second\n\telse:\n\t\tsecond=second%first\nif(first==0):\n\tprint(\"最大公因數\"+str(second))\n\nelse:\n\tprint(\"最大公因數\"+str(first))\n\n\n","sub_path":"Python/ch6hw2.py","file_name":"ch6hw2.py","file_ext":"py","file_size_in_byte":269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"623666927","text":"from numpy import *\ndef loadDataSet(fileName):\n numFeat=len(open(fileName).readline().split('\\t'))-1\n dataMat=[]\n labelMat=[]\n fr=open(fileName)\n for line in fr.readlines():\n lineArr=[]\n curLine=line.strip().split('\\t')\n for i in range(numFeat):\n lineArr.append(float(curLine[i]))\n dataMat.append(lineArr)\n labelMat.append(float(curLine[-1]))\n return dataMat,labelMat\n\n#标准回归\ndef standRegress(xArr,yArr):\n xMat=mat(xArr)\n yMat=mat(yArr).T\n xTx=xMat.T*xMat\n if linalg.det(xTx)==0.0:\n print(\"this matrix is singular,connot do inverse\")\n return\n ws=xTx.I*(xMat.T*yMat)\n return ws\n\n#局部加权线性回归\ndef lwlr(testPoint,xArr,yArr,k=1.0):\n xMat=mat(xArr)\n yMat=mat(yArr).T\n m=shape(xMat)[0]\n weights=mat(eye((m)))\n for j in range(m):\n diffMat=testPoint-xMat[j,:]\n weights[j,j]=exp((diffMat*diffMat.T)/(-2.0*k**2))\n xTx=xMat.T*(weights*xMat)\n if linalg.det(xTx)==0.0:\n return\n ws=xTx.I*(xMat.T*(weights*yMat))\n return testPoint*ws\ndef lwlrTest(testArr,xArr,yArr,k=1.0):\n m=shape(testArr)[0]\n yHat=zeros(m)\n for i in range(m):\n yHat[i]=lwlr(testArr[i],xArr,yArr,k)\n return yHat\n#预测鲍鱼年龄\ndef rssError(yArr,yHatArr):\n return ((yArr-yHatArr)**2).sum()\ndef testAbalone():\n abX,abY=loadDataSet('abalone.txt')\n yHat01=lwlrTest(abX[0:99],abX[0:99],abY[0:99],0.1)\n yHat1=lwlrTest(abX[0:99],abX[0:99],abY[0:99],1)\n yHat10=lwlrTest(abX[0:99],abX[0:99],abY[0:99],10)\n print(rssError(abY[0:99],yHat01.T))\n print(rssError(abY[0:99],yHat1.T))\n print(rssError(abY[0:99],yHat10.T))\n\n\n#岭回归，用于特征数多于样本数，或者用于在估计中加入偏差\ndef ridgeRegress(xMat,yMat,lam=0.2):\n xTx=xMat.T*xMat\n denom=xTx+eye(shape(xMat)[1])*lam\n if linalg.det(denom)==0.0:\n return\n ws=denom.I*(xMat.T*yMat)\n return ws\ndef ridgeTest(xArr,yArr):\n xMat=mat(xArr)\n yMat=mat(yArr).T\n yMean=mean(yMat,0)\n yMat=yMat-yMean\n xMeans=mean(xMat,0)\n xVar=var(xMat,0)\n xMat=(xMat-xMeans)/xVar\n numTestPts=30\n wMat=zeros((numTestPts,shape(xMat)[1]))\n for i in range(numTestPts):\n ws=ridgeRegress(xMat,yMat,exp(i-10))\n wMat[i,:]=ws.T\n return wMat\n# 前向逐步线性回归\ndef stageWise(xArr,yArr,eps=0.01,numIt=100):\n xMat=mat(xArr)\n yMat=mat(yArr).T\n yMean=mean(yMat,0)\n yMat=yMat-yMean\n xMat=regularize(xMat)\n m,n=shape(xMat)\n returnMat=zeros((numIt,n))\n ws=zeros((n,1))\n wsTest=ws.copy()\n wsMax=ws.copy()\n for i in range(numIt):\n print(ws.T)\n lowesError=inf\n for j in range(n):\n for sign in [-1,1]:\n wsTest=ws.copy()\n wsTest[j]+=eps*sign\n yTest=xMat*wsTest\n rssE=rssError(yMat.A,yTest.A)\n if rssEorigPrc*0.5:\n retX.append([yr,numPce,newFlag,origPrc])\n retY.append(sellingPrice)\n except:\n print(\"problem with item:%d\" %i)\n\n\n\n","sub_path":"ch08/regression.py","file_name":"regression.py","file_ext":"py","file_size_in_byte":4146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"317700395","text":"# -*- coding: UTF8 -*-\n'''\nCreated on 2012-11-29\n\n@author: liuxinxing\n'''\n\nimport threading\nimport common.Logger\nimport common.database\n\nclass CTestCase(threading.Thread):\n def __init__(self, suite, caseinfo):\n threading.Thread.__init__(self)\n self.objSuite = suite\n self.caseinfo = caseinfo\n self.nCaseID = int(caseinfo.getCaseID())\n try:\n self.nCaseID_unique = int(caseinfo.getCaseID_unique())\n except:\n self.nCaseID_unique = self.nCaseID\n self.dependlist = caseinfo.getDependCaseIDList()\n if self.dependlist:\n self.dependlist = [int(caseid) for caseid in str(self.dependlist).split(\";\")]\n if self.dependlist:\n self.state = \"BLOCK\"\n else:\n self.state = \"ACTIVE\"\n strLoggerName = \"Case %s-%s\" % (self.objSuite.getSuiteName(), str(self.nCaseID))\n self.logger = common.Logger.CLogger.instance().logger(strLoggerName)\n self.pg = common.database.CDB()\n \n def isActive(self):\n if self.state == \"BLOCK\":\n for caseID in self.dependlist:\n objCase = self.objSuite.objCaseManager.findCase(caseID)\n if objCase is not None and not objCase.isOK():\n break\n else:\n self.state = \"ACTIVE\"\n \n return self.state == \"ACTIVE\"\n \n def isOK(self):\n return self.state == \"OK\"\n \n def isFAIL(self):\n return self.state == \"FAIL\"\n \n def isRunning(self):\n return self.state == \"RUNNING\"\n \n def do(self):\n self.logger.info(\"Case start...\")\n try:\n self.pg.connect()\n if self._do():\n self.state = \"OK\"\n self.logger.info(\"Case OK.\")\n else:\n self.state = \"FAIL\"\n self.logger.error(\"Case FAIL.\")\n except:\n self.state = \"FAIL\"\n self.logger.exception(\"Case Fail...\")\n self.pg.rollback()\n finally:\n self.pg.close()\n self.logger.info(\"Case end.\")\n \n def _do(self):\n return True\n \n def getCaseID(self):\n return self.nCaseID\n \n def getCaseID_unique(self):\n return self.nCaseID_unique\n \n def getcheck_code(self):\n return self.caseinfo.getCheckCode()\n \n def get_combin_keys(self):\n return self.caseinfo.get_combin_keys()\n \n def getResult(self):\n self.caseinfo.setCheckResult(self.state)\n return self.caseinfo\n \n def start(self):\n self.state = \"RUNNING\"\n threading.Thread.start(self)\n \n def run(self):\n try:\n self.do()\n finally:\n self.objSuite.objCaseManager.sem.release()\n del self.objSuite.objCaseManager.runningcaselist[self.getCaseID()]\n \n def join(self, timeout=None):\n if self.isRunning():\n threading.Thread.join(self, timeout)\n \n ","sub_path":"Suntec/Road_Local/source/master/AutoCheck/src/platform/TestCase.py","file_name":"TestCase.py","file_ext":"py","file_size_in_byte":2964,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"386124426","text":"import os\nimport torch \nimport random\nimport torchvision\nfrom torch.autograd import Variable\nimport torch.nn as nn\nfrom torch.utils.data import Dataset\nfrom time import *\n#from ResNet import ResNet\nimport torchvision.transforms as T\nimport PIL\n# import pickle\n# from progress.bar import ChargingBar\nfrom heapq import *\nimport matplotlib.pyplot as plt\n\nclass Triplets_Dataset(Dataset):\n def __init__(self, sampler, transform=None):\n self.sampler = sampler\n self.transform = transform\n def __getitem__(self, index):\n triplets = {}\n with PIL.Image.open(self.sampler[index]['Query']) as img:\n img = img.convert('RGB')\n triplets['Query'] = img\n with PIL.Image.open(self.sampler[index]['Positive']) as img:\n img = img.convert('RGB')\n triplets['Positive'] = img\n with PIL.Image.open(self.sampler[index]['Negative']) as img:\n img = img.convert('RGB')\n triplets['Negative'] = img\n\n if self.transform is not None:\n triplets['Query'] = self.transform(triplets['Query'])\n triplets['Positive'] = self.transform(triplets['Positive'])\n triplets['Negative'] = self.transform(triplets['Negative'])\n return triplets\n def __len__(self):\n return len(self.sampler)\n\n\ndef Triplets_PreSampler():\n all_classes = []\n with open('./tiny-imagenet-200/wnids.txt', 'r') as file:\n for line in file:\n all_classes.append(line.strip('\\n\\r'))\n triplets = []\n for Q_class in all_classes:\n all_imgs = os.listdir('./tiny-imagenet-200/train/'+Q_class+'/images')\n for img_name in all_imgs:\n path = './tiny-imagenet-200/train/'+Q_class+'/images/'+img_name\n p = './tiny-imagenet-200/train/'+Q_class+'/images/'+img_name\n p_plus = './tiny-imagenet-200/train/'+Q_class+'/images/'+random.sample(all_imgs, k=1)[0]\n while p == p_plus:\n p_plus = './tiny-imagenet-200/train/'+Q_class+'/images/'+random.sample(all_imgs, k=1)[0]\n while True:\n N_class = random.sample(all_classes, k=1)[0]\n if (N_class != Q_class): break\n N_class_imgs = os.listdir('./tiny-imagenet-200/train/'+N_class+'/images')\n p_minus = './tiny-imagenet-200/train/'+N_class+'/images/'+random.sample(N_class_imgs, k=1)[0]\n triplets.append({'Query': p, 'Positive': p_plus, 'Negative': p_minus})\n return triplets\n\n \n\n\ndef Testing_Stage(query_image, k):\n \"\"\"\n Feed one query image to the network and get the feature embedding of the query image. Compare the feature embedding of the query image to \n all the feature embeddings in the whole training dataset. Rank the result and output the top k results.\n \"\"\"\n results = []\n pq = []\n f_q = network(query_image).numpy()\n for index, data in enumerate(train_loader):\n compare_image = data['Query'].to(decive)\n f_c = network(compare_image).numpy()\n distance = np.linalg.norm(f_q-f_c, ord=2)\n heappush(pq, (distance, compare_image))\n for i in range(k):\n results.append(heappop(pq))\n return results\n\ndef Calc_Precision(query_class, ranked_images, k):\n \"\"\"\n We calculated the categorical level similarity. The precision is calculated by number of correctly ranked images divided by k\n Parameters:\n -query_class: the class which the query image belongs to\n -ranked_images: list, the classes which all ranked images belong to\n -k: scalar, the number of ranked images\n Return:\n -precision: scalar\n \"\"\"\n all_images = os.listdir('./train/'+query_class+'/images')\n correct = 0.0\n for idx, img in enumerate(ranked_images):\n if (img in all_images): correct += 1\n precision = correct/k\n return precision\n\ndef Plot_training_loss(loss):\n \"\"\"\n We need to keep record of the training loss throughout the whole training process.\n Parameters:\n -loss: list, the training loss of each training epoch\n \"\"\"\n plt.plot(range(EPOCH), loss)\n plt.show()\n\n\n\nif __name__ == \"__main__\":\n # -----------------------------------------\n # Load dataset\n # -----------------------------------------\n #sampler = Triplets_PreSampler()\n transform = torchvision.transforms.Compose([torchvision.transforms.Resize(224), torchvision.transforms.RandomHorizontalFlip(), torchvision.transforms.ToTensor()])\n #train_dataset = Triplets_Dataset(sampler, transform)\n BATCH_SIZE = 32\n #train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4)\n\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n LR = 0.001\n g = 1\n EPOCH = 20\n D = 4096\n # network = ResNet(D).to(device)\n network = torchvision.models.resnet101(pretrained=True)\n\n # child_counter = 1\n # for child in network.children():\n # if (child_counter < 8):\n # for param in child.parameters():\n # param.requires_grad = False\n # child_counter += 1\n\n # for param in network.parameters():\n # param.requires_grad = False\n network.fc = nn.Linear(network.fc.in_features, D, bias=True) ## Modify the last fully-connected layer's output dimension to be 4096\n network = network.to(device)\n #print (next(network.parameters()).is_cuda)\n #print (\"device: \", device)\n optimizer = torch.optim.SGD(network.parameters(), lr=LR, momentum=0.9, weight_decay=0.01)\n running_loss = 0.0\n training_loss = []\n loss_fn = nn.TripletMarginLoss(margin=g, p=2)\n\n for epoch in range(EPOCH):\n epoch_loss = 0.0\n sampler = Triplets_PreSampler()\n train_dataset = Triplets_Dataset(sampler, transform)\n train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4)\n start = time()\n for index, data in enumerate(train_loader):\n # print (\"load batch sample cost: \", time()-stamp)\n optimizer.zero_grad()\n query_image = data[\"Query\"].to(device)\n positive_image = data[\"Positive\"].to(device)\n negative_image = data[\"Negative\"].to(device)\n\n stamp = time()\n\n f_p = network(query_image)\n f_p_plus = network(positive_image)\n f_p_minus = network(negative_image)\n\n #print (\"forward step cost: \", time()-stamp)\n loss = loss_fn(f_p, f_p_plus, f_p_minus)\n #print (\"loss: \", loss)\n loss.backward()\n\n # print (\"calculate loss cost: \", time()-stamp)\n # if(epoch>3):\n # for group in optimizer.param_groups:\n # for p in group['params']:\n # state = optimizer.state[p]\n # if(state['step']>=1024):\n # state['step'] = 1000\n\n optimizer.step()\n # print (\"optimization cost: \", time()-stamp)\n\n\n running_loss += loss.item()\n epoch_loss += loss.item()\n #print (type(running_loss))\n if (index%100==99):\n print(\"Epoch %d, %d mini-batches, Loss: %.3f, time consumiong: %.2f seconds\"%(epoch+1, index+1, running_loss/100., time()-start))\n running_loss = 0.\n\n avg_epoch_loss = epoch_loss/(index+1)\n # training_loss.append(avg_epoch_loss)\n print (\"Epoch %d finished, average loss is: %.5f, cost: %.2f seconds\"%(epoch+1, avg_epoch_loss, time()-start))\n torch.save(network.state_dict(), 'temp_model_trained_%d_epochs.ckpt'%(epoch+1))\n\n\n print (\"Training Completed!\")\n torch.save(network, 'Deep_Ranking_model.ckpt')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n","sub_path":"HW5/HW5_bw.py","file_name":"HW5_bw.py","file_ext":"py","file_size_in_byte":7735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"250112860","text":"#INSTALL pip3 install gTTS pyttsx3 playsound\n\nimport pandas as pd\nimport csv\nfrom datetime import date\nfrom time import sleep\n\n#PICK DATE OF THE DAY AND FORMAT IT (DD/MM/YY)\ntoday = date.today()\nd1 = today.strftime(\"%d/%m/%Y\")\n\n#OPEN CSV DATABASE FILE \ndf = pd.read_csv(\"/home/pi/Documents/Python Projects/Agenda/robot_agenda_booking.csv\")\n\n#CHECK IF DATE OF THE DAY IS IN CSV DATABASE FILE\ndf1 = df.loc[df[\"Date\"].str.contains(d1)]\n\nif df1.empty:\n x = \"Empty\"\nelse:\n x = \"Filled\"\n df2 = df1.iloc[0]\n df3 = df2.drop(columns = [\"Task\"])\n df4 = df3.iloc[0]\n d2 = str(df2[1])\n df5 = df2.drop(columns = [\"Date\"])\n df6 = df5.iloc[0]\n\nimport gtts\ntts = gtts.gTTS(\"Goodmorning!\")\ntts.save(\"/home/pi/Documents/Python Projects/Agenda/Goodmorning.mp3\")\ntts = gtts.gTTS(\"Today is the \" + d1 + \".\")\ntts.save(\"/home/pi/Documents/Python Projects/Agenda/Today.mp3\")\n\nimport pygame\npygame.mixer.init()\npygame.mixer.music.load(\"/home/pi/Documents/Python Projects/Agenda/Goodmorning.mp3\")\npygame.mixer.music.play()\nwhile pygame.mixer.music.get_busy() == True:\n continue\n\npygame.mixer.init()\npygame.mixer.music.load(\"/home/pi/Documents/Python Projects/Agenda/Today.mp3\")\npygame.mixer.music.play()\nwhile pygame.mixer.music.get_busy() == True:\n continue\n\nsleep(1)\n\nif x == \"Filled\":\n tts = gtts.gTTS(\"You have today the following booking.\")\n tts.save(\"/home/pi/Documents/Python Projects/Agenda/Appointment1.mp3\")\n tts = gtts.gTTS(d2)\n tts.save(\"/home/pi/Documents/Python Projects/Agenda/Appointment2.mp3\")\n\n pygame.mixer.init()\n pygame.mixer.music.load(\"/home/pi/Documents/Python Projects/Agenda/Appointment1.mp3\")\n pygame.mixer.music.play()\n while pygame.mixer.music.get_busy() == True:\n continue\n\n sleep(0.5)\n\n pygame.mixer.init()\n pygame.mixer.music.load(\"/home/pi/Documents/Python Projects/Agenda/Appointment2.mp3\")\n pygame.mixer.music.play()\n while pygame.mixer.music.get_busy() == True:\n continue\n print(\"Today is: \" + d1 + \"\\nYou have the following booking: \" + d2 + \"\\n\")\n\nelse:\n tts = gtts.gTTS(\"You don't have any appointment today.\")\n tts.save(\"/home/pi/Documents/Python Projects/Agenda/NoAppointment1.mp3\")\n tts = gtts.gTTS(\"Have a Nice Day!\")\n tts.save(\"/home/pi/Documents/Python Projects/Agenda/NoAppointment2.mp3\")\n\n pygame.mixer.init()\n pygame.mixer.music.load(\"/home/pi/Documents/Python Projects/Agenda/NoAppointment1.mp3\")\n pygame.mixer.music.play()\n while pygame.mixer.music.get_busy() == True:\n continue\n\n sleep(0.5)\n\n pygame.mixer.init()\n pygame.mixer.music.load(\"/home/pi/Documents/Python Projects/Agenda/NoAppointment2.mp3\")\n pygame.mixer.music.play()\n while pygame.mixer.music.get_busy() == True:\n continue\n print(\"Today is: \" + d1 + \"\\nYou don't have any apointment today.\\n\")","sub_path":"robot_agenda.py","file_name":"robot_agenda.py","file_ext":"py","file_size_in_byte":2822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"522743453","text":"import sys\nimport time\nimport numpy as np\n\ndef findStart(maze):\n \"\"\"\n Find the start position of the maze\n \"\"\"\n start_Position = 0\n for i in range(0, len(maze)):\n for j in range(0, len(maze[0])):\n if maze[i][j] == 'P':\n start_Position = i * len(maze[0]) + j\n return start_Position\n return -1\n\n\ndef findEnd(maze):\n \"\"\"\n Find the end position of the maze\n \"\"\"\n final_Position = 0\n for i in range(0, len(maze)):\n for j in range(0, len(maze[0])):\n if maze[i][j] == '.':\n final_Position = i * len(maze[0]) + j\n return final_Position\n return -1\n\n\ndef distance(currX, currY, targetX, targetY):\n \"\"\"\n Get the distance between two positions\n \"\"\"\n return abs(currX - targetX) + abs(currY - targetY)\n\n\n\ndef Astar(maze, startX, startY, endX, endY):\n \"\"\"\n Find the solution of the maze(A*)\n \"\"\"\n #Get the width, height and size of maze\n width = len(maze[0])\n height = len(maze)\n size = width * height\n\n #Mark visited place\n visited = [0 for x in range(size)]\n\n #distance value\n f_value = [sys.maxsize for x in range(size)]\n #total value\n t_value = [sys.maxsize for x in range(size)]\n\n #Keep track of the position of parent node\n parent_Position = [0 for x in range(size)]\n\n #Keep track of the number of node expanded\n node_expanded = 0\n\n start_Position = startY * width + startX\n list = []\n list.append(start_Position)\n f_value[start_Position] = 0\n t_value[start_Position] = distance(startX, startY, endX, endY)\n\n while len(list) != 0:\n #find current best\n curr = findMin(list, t_value)\n currY = curr / width\n currX = curr % width\n if currX == endX and currY == endY:\n break\n #Target Position finded\n list.remove(curr)\n visited[curr] = 1\n\n #Increase the node expanded\n node_expanded += 1\n\n #Left\n if currX - 1 >= 0 and maze[currY][currX - 1] != '%':\n if visited[curr - 1] != 1:\n if curr - 1 not in list:\n list.append(curr - 1)\n curr_val = f_value[curr] + 1\n if curr_val < f_value[curr - 1]:\n f_value[curr - 1] = curr_val\n t_value[curr - 1] = f_value[curr - 1] + distance((curr - 1) % width, (curr - 1) / width, endX, endY)\n parent_Position[curr - 1] = curr\n\n #Right\n if currX + 1 < width and maze[currY][currX + 1] != '%':\n if visited[curr + 1] != 1:\n if curr + 1 not in list:\n list.append(curr + 1)\n curr_val = f_value[curr] + 1\n if curr_val < f_value[curr + 1]:\n f_value[curr + 1] = curr_val\n t_value[curr + 1] = f_value[curr + 1] + distance((curr + 1) % width, (curr + 1) / width, endX, endY)\n parent_Position[curr + 1] = curr\n\n #Up\n if currY - 1 >= 0 and maze[currY - 1][currX] != '%':\n if visited[curr - width] != 1:\n if curr - width not in list:\n list.append(curr - width)\n curr_val = f_value[curr] + 1\n if curr_val < f_value[curr - width]:\n f_value[curr - width] = curr_val\n t_value[curr - width] = f_value[curr - width] + distance((curr - width) % width, (curr - width) / width, endX, endY)\n parent_Position[curr - width] = curr\n\n #Down\n if currY + 1 < height and maze[currY + 1][currX] != '%':\n if visited[curr + width] != 1:\n if curr + width not in list:\n list.append(curr + width)\n curr_val = f_value[curr] + 1\n if curr_val < f_value[curr + width]:\n f_value[curr + width] = curr_val\n t_value[curr + width] = f_value[curr + width] + distance((curr + width) % width, (curr + width) / width, endX, endY)\n parent_Position[curr + width] = curr\n\n step_cost = 0\n #Generate solution path\n position = endY * width + endX\n while position != start_Position:\n position = parent_Position[position]\n step_cost += 1\n\n return step_cost\n\n\ndef permutation(nums):\n \"\"\"\n Get the permutation of the list\n \"\"\"\n list = []\n temp = []\n backtrack(list, temp, nums)\n return list\n\ndef backtrack(list, temp, nums):\n \"\"\"\n Helper function for permutation\n \"\"\"\n if len(temp) == len(nums):\n list.append(temp[:])\n else:\n for i in range(0, len(nums)):\n if(nums[i] in temp):\n continue\n temp = temp + [nums[i]]\n backtrack(list, temp, nums)\n temp.pop()\n\n\ndef num_of_goals(maze):\n \"\"\"\n Find the number of goals\n \"\"\"\n count = 0\n for i in range(0, len(maze)):\n for j in range(0, len(maze[0])):\n if maze[i][j] == '.':\n count += 1\n return count\n\n\ndef list_of_points(maze):\n \"\"\"\n Return positions of all points\n \"\"\"\n result = []\n for i in range(0, len(maze)):\n for j in range(0, len(maze[0])):\n if maze[i][j] == '.':\n result.append((j, i))\n return result\n\n\ndef closest_fruit(maze, currX, currY, fruit_list):\n \"\"\"\n This function finds the nearest fruit of current position\n \"\"\"\n curr_min = sys.maxsize\n for position in fruit_list:\n distance = Astar(maze, currX, currY, position[0], position[1])\n if distance < curr_min:\n curr_min = distance\n return curr_min\n\ndef findMin(list, t_value):\n \"\"\"\n Get the min f_value of a list\n \"\"\"\n currMin = sys.maxsize\n result = 0\n for index in list:\n if t_value[index] < currMin:\n currMin = t_value[index]\n result = index\n return result\n\n###########################################\n\nif __name__ == \"__main__\":\n #Initialize maze\n width = 0\n height = 0\n infile = open('big.txt', 'r')\n for line in infile:\n width = len(line)\n height = height + 1\n\n maze = [[0 for x in range(width)] for y in range(height)]\n infile = open('big.txt', 'r')\n w = 0\n h = 0\n for line in infile:\n w = 0\n for c in line:\n if w < width:\n maze[h][w] = c\n w += 1\n h += 1\n\n #Get the width, height and size of maze\n width = len(maze[0])\n height = len(maze)\n size = width * height\n\n #Position of start node\n start_Position = findStart(maze)\n startX = start_Position % width\n startY = start_Position / width\n\n #distance value\n f_value = {}\n #total value\n t_value = {}\n\n #Keep track of the number of node expanded\n node_expanded = 0\n\n #The number of fruits\n num_of_goals = num_of_goals(maze)\n #The number of remaining fruits\n remain = num_of_goals\n\n #the order of founded fruits\n order_of_fruits = []\n\n #the position of all fruits\n fruit_list = list_of_points(maze)\n\n list = []\n list.append((startX, startY, remain))\n\n #visited :\n #0 : x coordinate of current player\n #1 : y coordinate of current player\n #2 : number of remaining goals\n visited = {}\n\n f_value[(startX, startY, remain)] = 0\n t_value[(startX, startY, remain)] = closest_fruit(maze, startX, startY, fruit_list)\n\n #Keep track of total cost\n cost = 0\n\n #Keep track of node expanded\n node_expanded = 0\n\n while len(list) != 0:\n\n #find current best\n curr = findMin(list, t_value)\n currX = curr[0]\n currY = curr[1]\n\n #Check if all the fruits have been found\n find_all = False\n\n #Check if any fruit is found\n for fruit in fruit_list:\n if fruit[0] == currX and fruit[1] == currY:\n remain -= 1\n order_of_fruits.append((currX, currY))\n fruit_list.remove((currX, currY))\n\n #Update f_value and t_value\n f_value[(currX, currY, remain)] = f_value[(currX, currY, remain + 1)]\n t_value[(currX, currY, remain)] = f_value[(currX, currY, remain)] + closest_fruit(maze, currX, currY, fruit_list)\n\n #Update list\n list = []\n list.append((currX, currY, remain))\n\n #update visited\n visited = {}\n\n #Already find all the dots\n if remain == 0:\n find_all = True\n break\n\n break\n\n if find_all == True:\n cost = f_value[(currX, currY, remain)]\n break\n\n list.remove((currX, currY, remain))\n\n #Mark as visited\n visited[(currX, currY, remain)] = 1\n\n node_expanded += 1\n\n #Left\n if currX - 1 >= 0 and maze[currY][currX - 1] != '%':\n if (currX - 1, currY, remain) not in visited:\n visited[(currX - 1, currY, remain)] = 0\n if visited[(currX - 1, currY, remain)] != 1:\n if (currX - 1, currY, remain) not in list:\n list.append((currX - 1, currY, remain))\n if (currX - 1, currY, remain) not in f_value:\n f_value[(currX - 1, currY, remain)] = f_value[(currX, currY, remain)] + 1\n t_value[(currX - 1, currY, remain)] = f_value[(currX - 1, currY, remain)] + closest_fruit(maze, currX - 1, currY, fruit_list)\n else:\n curr_val = f_value[(currX, currY, remain)] + 1\n if curr_val < f_value[(currX - 1, currY, remain)]:\n f_value[(currX - 1, currY, remain)] = curr_val\n t_value[(currX - 1, currY, remain)] = f_value[(currX - 1, currY, remain)] + closest_fruit(maze, currX - 1, currY, fruit_list)\n\n\n #Right\n if currX + 1 < width and maze[currY][currX + 1] != '%':\n if (currX + 1, currY, remain) not in visited:\n visited[(currX + 1, currY, remain)] = 0\n if visited[(currX + 1, currY, remain)] != 1:\n if (currX + 1, currY, remain) not in list:\n list.append((currX + 1, currY, remain))\n if (currX + 1, currY, remain) not in f_value:\n f_value[(currX + 1, currY, remain)] = f_value[(currX, currY, remain)] + 1\n t_value[(currX + 1, currY, remain)] = f_value[(currX + 1, currY, remain)] + closest_fruit(maze, currX + 1, currY, fruit_list)\n else:\n curr_val = f_value[(currX, currY, remain)] + 1\n if curr_val < f_value[(currX + 1, currY, remain)]:\n f_value[(currX + 1, currY, remain)] = curr_val\n t_value[(currX + 1, currY, remain)] = f_value[(currX + 1, currY, remain)] + closest_fruit(maze, currX + 1, currY, fruit_list)\n\n\n #Up\n if currY - 1 >= 0 and maze[currY - 1][currX] != '%':\n if (currX, currY - 1, remain) not in visited:\n visited[(currX, currY - 1, remain)] = 0\n if visited[(currX, currY - 1, remain)] != 1:\n if (currX, currY - 1, remain) not in list:\n list.append((currX, currY - 1, remain))\n if (currX, currY - 1, remain) not in f_value:\n f_value[(currX, currY - 1, remain)] = f_value[(currX, currY, remain)] + 1\n t_value[(currX, currY - 1, remain)] = f_value[(currX, currY - 1, remain)] + closest_fruit(maze, currX, currY - 1, fruit_list)\n else:\n curr_val = f_value[(currX, currY, remain)] + 1\n if curr_val < f_value[(currX, currY - 1, remain)]:\n f_value[(currX, currY - 1, remain)] = curr_val\n t_value[(currX, currY - 1, remain)] = f_value[(currX, currY - 1, remain)] + closest_fruit(maze, currX, currY - 1, fruit_list)\n\n\n #Down\n if currY + 1 < height and maze[currY + 1][currX] != '%':\n if (currX, currY + 1, remain) not in visited:\n visited[(currX, currY + 1, remain)] = 0\n if visited[(currX, currY + 1, remain)] != 1:\n if (currX, currY + 1, remain) not in list:\n list.append((currX, currY + 1, remain))\n if (currX, currY + 1, remain) not in f_value:\n f_value[(currX, currY + 1, remain)] = f_value[(currX, currY, remain)] + 1\n t_value[(currX, currY + 1, remain)] = f_value[(currX, currY + 1, remain)] + closest_fruit(maze, currX, currY + 1, fruit_list)\n else:\n curr_val = f_value[(currX, currY, remain)] + 1\n if curr_val < f_value[(currX, currY + 1, remain)]:\n f_value[(currX, currY + 1, remain)] = curr_val\n t_value[(currX, currY + 1, remain)] = f_value[(currX, currY + 1, remain)] + closest_fruit(maze, currX, currY + 1, fruit_list)\n\n\n###########################################\n\n print(order_of_fruits)\n print(cost)\n print(node_expanded)\n\n print(f_value[(currX, currY, 0)])\n\n #Mark the order of dots\n #symbol = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd' ,'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n']\n\n animation = []\n prevX = startX\n prevY = startY\n animation.append(maze)\n for index in range(0, len(order_of_fruits)):\n maze[prevY][prevX] = ' '\n curr = order_of_fruits[index]\n maze[curr[1]][curr[0]] = 'P'\n prevX = curr[0]\n prevY = curr[1]\n\n result_maze = \"\"\n for i in range(len(maze)):\n for j in range(len(maze[0])):\n result_maze = result_maze + maze[i][j]\n result_maze = result_maze + '\\n'\n\n animation.append(result_maze)\n\n #animate\n for index in range(0, len(animation)):\n sys.stdout.write(str(animation[index]) + \"\\r\")\n sys.stdout.flush()\n time.sleep(0.2)\n\n\n###########################################\n","sub_path":"Astar/MP1.3/pacman_suboptimal.py","file_name":"pacman_suboptimal.py","file_ext":"py","file_size_in_byte":14108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"385034081","text":"#!/usr/bin/python\n# coding:utf-8\n# Author : Anastasiia Kuznetsova\n\nimport sentencepiece as spm\nfrom string import punctuation\nfrom gensim.models import Word2Vec\nfrom gensim.models import KeyedVectors\nimport numpy as np\nfrom tqdm import tqdm\nimport argparse\nimport pandas as pd\n\n'''\nThe scirpt calculates vector norm as a complexity measure for curriculum learning.\n\nThe word norms are calculating using Word2Vec model trained on subwords generated by \nSentencepiece model. The sentence norm is an average of word norms.\n\nFor argument information run:\n\npython norm_complexity.py --help\n'''\n\ndef pre_process(text, lang=None, corpus='CV'):\n '''\n Params:\n text(str): path to raw text.\n lang (str) language.\n cv_path (str) path to commonvoice root folder.\n '''\n if corpus=='CV':\n CV_ROOT = '~/Rev/commonvoice'\n\n raw_text = [line.lower() for line in open(text, 'r').readlines()]\n \n if lang:\n csv = pd.read_csv(CV_ROOT+'/'+lang+'/validated.tsv')\n for id_, row in csv.iterrows(): \n raw_text.append(row['sentence'].lower())\n\n filtered_text = [''.join([char for char in line if char not in punctuation]) for line in raw_text]\n \n with open(text.split('.')[0]+'_filtered.txt', 'w') as fo:\n for i, line in enumerate(filtered_text):\n fo.write(str(i)+' '+line)\n\ndef train_vector_model(subword_model, text, save_file, lang=None, sep='\\t'):\n '''\n Params:\n subword_model (str): Path to sentencepiece model.\n text (str) path to text in scp format.\n sep (str) separator between sentence ID and sentence.\n save_file (str) file name to store vectors.\n lang (str) langauge\n '''\n sp = spm.SentencePieceProcessor()\n sp.Load(subword_model)\n\n data_dict = {}\n print(\"Reading text data...\")\n with open(text, 'r') as fo:\n for line in fo.readlines():\n data_dict[line.split(sep)[0]] = line.split(sep)[-1].strip()\n\n print(\"Creating subwords for training data...\")\n training_data = []\n\n for k in tqdm(data_dict):\n enc_s = sp.EncodeAsPieces(data_dict[k])\n training_data.append(enc_s)\n\n print(\"Training subword vectors...\")\n model = Word2Vec(sentences=training_data, vector_size=100, window=5, min_count=1, workers=4)\n word_vectors = model.wv\n word_vectors.save(save_file)\n print(\"Saved vectors to \", save_file)\n return word_vectors, training_data\n\n\ndef calculate_word_norms(vectors_file, subword_model, text, save_file):\n sp = spm.SentencePieceProcessor()\n sp.Load(subword_model)\n\n wv = KeyedVectors.load(vectors_file, mmap='r')\n\n data_dict = {}\n print(\"Reading text data...\")\n with open(text, 'r') as fo:\n for line in fo.readlines():\n data_dict[line.split(sep)[0]] = line.split(sep)[-1].strip()\n\n word_norms = {}\n\n for k in tqdm(data_dict):\n sent = data_dict[k]\n sent = sp.EncodeAsPieces(sent) + ['▁']\n token = ''\n norm = 0\n for j, sub_word in enumerate(sent):\n if '▁' in sub_word and len(token) > 0:\n token = token.replace('▁', '')\n word_norms[token] = norm\n token = ''\n token+=sub_word\n norm = 0\n norm += np.linalg.norm(wv[sub_word])\n else:\n token += sub_word\n norm += np.linalg.norm(wv[sub_word]) \n \n with open(save_file, 'w') as fo:\n for k in word_norms:\n fo.write(k+' '+str(word_norms[k])+'\\n')\n print(\"Saved word norms to \", save_file)\n\n\ndef calc_sent_norm_complexity(vectors_file, subword_model, text, save_file, max_norm, sep):\n sp = spm.SentencePieceProcessor()\n sp.Load(subword_model)\n\n wv = KeyedVectors.load(vectors_file, mmap='r')\n data_dict = {}\n\n print(\"Reading text data...\")\n with open(text, 'r') as fo:\n for line in fo.readlines():\n data_dict[line.split(sep)[0]] = line.split(sep)[-1].strip()\n \n word_norms = {}\n\n print(\"Calculating word norms...\")\n for k in tqdm(data_dict):\n sent = data_dict[k]\n sent = sp.EncodeAsPieces(sent) + ['▁']\n token = ''\n norm = 0\n for j, sub_word in enumerate(sent):\n if '▁' in sub_word and len(token) > 0:\n token = token.replace('▁', '')\n word_norms[token] = norm\n token = ''\n token+=sub_word\n norm = 0\n else:\n token += sub_word\n if sub_word in wv:\n norm += np.linalg.norm(wv[sub_word])\n else:\n norm+=max_norm+1\n\n print(\"Calculating sentence norms...\")\n sent_norms = {}\n\n for k in tqdm(data_dict):\n sent_norm = 0\n sent = data_dict[k].split()\n for w in sent:\n if w in word_norms:\n sent_norm+=word_norms[w]\n else:\n sent_norm+=max_norm\n sent_norm/=len(sent)\n sent_norms[k]=sent_norm\n \n with open(save_file, 'w') as fo:\n for k in sent_norms:\n fo.write(k+' '+str(sent_norms[k])+'\\n')\n print(\"Saved sentence norms in \", save_file)\n\ndef main(args):\n #pre_process(args.text, args.lang)\n #filtered_text = args.text.split('.')[0] + '_filtered.txt'\n if args.task=='vectors':\n train_vector_model(args.subword_model, \n args.text, \n args.save_file,\n args.sep)\n\n elif args.task=='wnorms':\n calculate_word_norms(args.vectors_file,\n args.subword_model, \n args.text, \n args.save_file)\n else:\n calc_sent_norm_complexity(args.vectors,\n args.subword_model, \n args.text, \n args.save_file,\n args.max_norm,\n args.sep)\n\n\nif __name__==\"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument('--task', type=str,required=True, help='vectors, wnorms or snorms.')\n parser.add_argument('--text', type=str, help='Path to text file in SCP format.')\n parser.add_argument('--subword_model', type=str, help='Path to sentencepiece model.')\n parser.add_argument('--sep', default='\\t', type=str, help='Separator between sentence ID and sentence.')\n parser.add_argument('--save_file', type=str, help='File to save the result of the function.')\n parser.add_argument('--vectors', type=str, help='Path to file with saved vectors.')\n parser.add_argument('--word_norms', type=str, help='Path to file with precalculated word norms.')\n parser.add_argument('--max_norm', default=999999999999.0, type=float, help='Max norm for filling OOVs')\n parser.add_argument('--corpus', type=str, required=False)\n parser.add_argument('--lang', type=str, required=False)\n args = parser.parse_args()\n main(args)","sub_path":"espnet2/curriculum/norm_complexity.py","file_name":"norm_complexity.py","file_ext":"py","file_size_in_byte":7024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"155169278","text":"import RPi.GPIO as GPIO\nimport time\n# import asyncio\n# import threading\n\nclass DCMotor:\n FORWARD = 1\n BACKWARD = 2\n STOP = 4\n\n PWM_FREQ = 1000\n\n speed = None\n direction = None\n thread = None\n\n def __init__(self, forward_pin, backward_pin):\n self.forward_pin = forward_pin\n self.backward_pin = backward_pin\n self._setup_pin(forward_pin)\n self._setup_pin(backward_pin)\n\n self.fPwm = GPIO.PWM(self.forward_pin, self.PWM_FREQ)\n self.bPwm = GPIO.PWM(self.backward_pin, self.PWM_FREQ)\n\n def _setup_pin(self, pin):\n state = GPIO.gpio_function(pin)\n # print(state, GPIO.OUT)\n if state != GPIO.OUT or True: # вот с этой херней надо разобраться\n # print('Set out pin:', pin)\n GPIO.setup(pin, GPIO.OUT)\n\n GPIO.output(pin, GPIO.LOW)\n\n def _output(self, pin, value):\n if value is True:\n GPIO.output(pin, GPIO.HIGH)\n elif value is False:\n GPIO.output(pin, GPIO.LOW)\n else:\n print('Cann`t set unknown value `{}` to pin `{}`'.format(value, pin))\n\n def run(self, direction): \n if direction == self.FORWARD:\n self.direction = self.FORWARD\n # self._output(self.backward_pin, False)\n self.bPwm.stop()\n self.fPwm.start(self.speed)\n # self._output(self.forward_pin, True)\n\n elif direction == self.BACKWARD:\n self.direction = self.BACKWARD\n # self._output(self.forward_pin, False)\n self.fPwm.stop()\n self.bPwm.start(self.speed)\n # self._output(self.backward_pin, True)\n\n elif direction == self.STOP or direction == None:\n self.stop()\n\n else:\n self.stop()\n print('Error: can`t run to direction', direction)\n\n def stop(self):\n self.direction = None\n self.fPwm.stop()\n self.bPwm.stop()\n # GPIO.output(self.backward_pin, GPIO.LOW)\n # GPIO.output(self.backward_pin, GPIO.LOW)\n\n def setSpeed(self, speed):\n if speed != self.speed:\n self.speed = speed\n if speed == 0:\n self.stop()\n elif self.direction == self.FORWARD:\n self.fPwm.ChangeDutyCycle(self.speed)\n elif self.direction == self.BACKWARD:\n self.fPwm.ChangeDutyCycle(self.speed)\n else:\n # print('Error: can`t change speed to direction', self.direction)\n pass\n\n def __del__(self):\n self.stop()\n\n\nclass MotorDriver:\n motors = dict()\n\n def __init__(self):\n GPIO.setmode(GPIO.BCM)\n\n def addMotor(self, index, forward_pin, backward_pin):\n self.motors[index] = DCMotor(forward_pin, backward_pin)\n return self.motors[index]\n\n def getMotor(self, index):\n return self.motors.get(index)\n\nif __name__ == '__main__':\n driver = MotorDriver()\n left_motor = driver.addMotor(1, 12, 13)\n right_motor = driver.addMotor(2, 18, 19)\n\n left_motor.stop()\n right_motor.stop()\n\n left_motor.setSpeed(30)\n right_motor.setSpeed(30)\n\n left_motor.run(DCMotor.FORWARD)\n right_motor.run(DCMotor.FORWARD)\n time.sleep(2)\n\n for s in range(20, 100):\n left_motor.setSpeed(s)\n right_motor.setSpeed(s)\n time.sleep(.1)\n\n left_motor.run(DCMotor.BACKWARD)\n right_motor.run(DCMotor.BACKWARD)\n time.sleep(2)\n\n left_motor.stop()\n right_motor.stop()\n\n # GPIO.setmode(GPIO.BCM)\n # GPIO.setup(12, GPIO.OUT)\n # p = GPIO.PWM(12, 100)\n # p.start(30)\n # time.sleep(5)\n # p.stop()\n","sub_path":"drivers/l298n.py","file_name":"l298n.py","file_ext":"py","file_size_in_byte":3656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"75457951","text":"#infile = \"input/139_ouh_june_2017/139_ouh_brca1_onco_geno.txt\"\ninfile = \"input/139_ouh_june_2017/139_ouh_brca2_onco_geno.txt\"\noutfile = infile.split(\".txt\")[0]+\"_single_homozygote.txt\"\n\nfirstLine = True\nwith open(infile, \"r\") as infile:\n\twith open(outfile, \"w\") as outfile:\n\t\tfor line in infile:\n\t\t\tif firstLine:\n\t\t\t\toutfile.write(line)\n\t\t\t\tfirstLine = False\n\t\t\t\tcontinue\n\t\t\tgenotypes = line.strip().split(\"\\t\")\n\t\t\tgeno_new = []\n\t\t\tfor geno in genotypes:\n\t\t\t\tif geno == \"AA\":\n\t\t\t\t\tgeno_new.append(\"A\")\n\t\t\t\t\tcontinue\n\t\t\t\tif geno == \"CC\":\n\t\t\t\t\tgeno_new.append(\"C\")\n\t\t\t\t\tcontinue\n\t\t\t\tif geno == \"GG\":\n\t\t\t\t\tgeno_new.append(\"G\")\n\t\t\t\t\tcontinue\n\t\t\t\tif geno == \"TT\":\n\t\t\t\t\tgeno_new.append(\"T\")\n\t\t\t\t\tcontinue\n\t\t\t\tif geno == \"II\":\n\t\t\t\t\tgeno_new.append(\"I\")\n\t\t\t\t\tcontinue\n\t\t\t\tif geno == \"DD\":\n\t\t\t\t\tgeno_new.append(\"D\")\n\t\t\t\t\tcontinue\n\t\t\t\tgeno_new.append(geno)\n\t\t\t\n\t\t\toutfile.write('\\t'.join(g for g in geno_new)+'\\n')\n\t\t\t\t","sub_path":"helper-scripts/convertGenotypesToSingleHomozygote.py","file_name":"convertGenotypesToSingleHomozygote.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"218734569","text":"import MySQLdb\n\ndb = MySQLdb.connect(\"localhost\",\"root\",\"mysql\",\"MyDB\")\n\ncursor = db.cursor()\n\ncursor.execute(\"USE MyDB\")\ncursor.execute(\"DROP TABLE IF EXISTS EMPLOYEE\")\n\nsql = \"\"\" CREATE TABLE EMPLOYEE (FIRST_NAME CHAR(20) NOT NULL,\nLAST_NAME CHAR(20),\nAGE INT,\nSEX CHAR(1),\nINCOME FLOAT)\"\"\"\n\ncursor.execute(sql)\ndb.close()","sub_path":"Python_Training/MySQL-Python/mysql_python2.py","file_name":"mysql_python2.py","file_ext":"py","file_size_in_byte":325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"235489415","text":"def main(path):\n\n with open(path, mode='r') as file:\n lines = file.readlines()\n\n amount = 0\n for raw_index in range(len(lines)):\n\n row_data = lines[raw_index].split('\\t')\n current_amount = int(row_data[3])\n if current_amount > amount:\n amount = current_amount\n raw = row_data\n\n\n print(raw[3], raw[4], raw[5])\n\n\nif __name__ == '__main__':\n\n # path = 'C:\\Users\\Panov\\Desktop\\neurotrend log.txt'\n path = input('Enter log file path: ')\n main(path)\n","sub_path":"dogs_shelter/search_in_log.py","file_name":"search_in_log.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"394048046","text":"from django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n \n url(r'^cluster_list/$', views.cluster_list, name='cluster_list'),\n url(r'^cluster_add/$', views.cluster_add, name='cluster_add'),\n #url(r'^cluster_modify/$', views.cluster_modify, name='cluster_modify'),\n url(r'^cluster_edit/(?P[a-zA-Z][0-9a-zA-Z\\_]*)/$', views.cluster_edit, name='cluster_edit'),\n url(r'^cluster_dele/(?P[a-zA-Z][0-9a-zA-Z\\_]*)/$', views.cluster_dele, name='cluster_dele'),\n\n url(r'^mysql_list/$', views.mysql_list, name='mysql_list'),\n url(r'^mysqlDetail/(?P.+)/$', views.mysqlDetail, name='mysqlDetail'),\n #url(r'^node_edit/(\\d+)/$', views.node_edit, name='node_edit'),\n url(r'^node_info/(\\d+)/$', views.node_info, name='node_info'),\n url(r'^node_dele/(\\d+)/$', views.node_dele, name='node_dele'),\n url(r'^auto_add_slave/(?P.+)/$', views.auto_add_slave, name='auto_add_slave'),\n]\n\n","sub_path":"mysql/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"170300411","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /home/cjld/new_jittor/jittor/python/jittor/test/test_new_fused_op.py\n# Compiled at: 2020-03-20 04:44:53\n# Size of source mod 2**32: 1443 bytes\nimport sys, os, jittor as jt, unittest, time, numpy as np\nfrom .test_log import find_log_with_re\n\nclass TestNewFuse(unittest.TestCase):\n\n @classmethod\n def setUpClass(self):\n pass\n\n def check(self, h, w, cs, rs, pa, rtp, dim):\n a = jt.random([h, w])\n a.sync()\n with jt.log_capture_scope(log_v=0,\n log_vprefix='tuner_manager=100',\n compile_options={'test_new_fused_op': 1}) as (logs):\n amean = jt.mean(a, dims=[dim], keepdims=1)\n a2mean = jt.mean((a * a), dims=[dim], keepdims=1)\n norm_aa = (a - amean.broadcast_var(a)) / jt.sqrt(a2mean - amean * amean).broadcast_var(a)\n norm_aa.sync()\n logs = find_log_with_re(logs, 'Run tuner reduce: confidence\\\\((.*)\\\\) candidates\\\\((.*)\\\\)$')\n assert len(logs) == 3, logs\n\n def test_new_fuse(self):\n self.check(8192, 8192, 0, 0, 0, 5, 0)\n\n\nif __name__ == '__main__':\n unittest.main()","sub_path":"pycfiles/jittor-1.0.0.tar/test_new_fused_op.cpython-37.py","file_name":"test_new_fused_op.cpython-37.py","file_ext":"py","file_size_in_byte":1248,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"306408500","text":"from django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponse\nfrom django.shortcuts import redirect, render\n\nfrom post.forms import CommentCreate\nfrom post.models import Comment\n\n__all__ = (\n 'comment_edit',\n)\n\n\n@login_required(login_url='members:login')\ndef comment_edit(request, pk):\n comment = Comment.objects.get(pk=pk)\n if request.user.id == comment.author_id:\n if request.method == 'POST':\n if comment.question_id:\n form = CommentCreate(\n request.POST,\n instance=comment)\n form.save()\n return redirect('post:question-detail', pk=comment.question_id)\n else:\n form = CommentCreate(\n request.POST,\n instance=comment)\n form.save()\n return redirect('post:post-detail', pk=comment.post_id)\n\n else:\n form = CommentCreate(\n instance=comment\n )\n context = {\n 'form': form\n }\n return render(request, 'post/comment_edit.html', context)\n else:\n return HttpResponse('You are not allowed to edit other users\\' answers!')\n","sub_path":"app/post/views/comment_edit.py","file_name":"comment_edit.py","file_ext":"py","file_size_in_byte":1252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"118424552","text":"import os.path\nimport random\nimport string\nfrom utils.filecreator import create_file\n\nlower = lambda str: str.lower()\n\ndef create_gibberish():\n gibberish_length = random.randrange(5, 10)\n gibberish = ''\n\n for i in range(gibberish_length):\n gibberish += lower(random.choice(string.ascii_letters))\n \n return gibberish\n\ndef main ():\n gibberish_count = 500\n with create_file('gibberish') as _file:\n for i in range(gibberish_count):\n _file.write(create_gibberish() + '\\n')\n\nmain()\n","sub_path":"utils/generators/gibberishmaker.py","file_name":"gibberishmaker.py","file_ext":"py","file_size_in_byte":522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"166407084","text":"from setuptools import setup\n\n__version__ = 'unknown'\nfor line in open('acoustics_hardware/__init__.py'):\n if line.startswith('__version__'):\n exec(line)\n break\n\n\nsetup(name='acoustics-hardware',\n version=__version__,\n description='Controlling hardware used in acoustic measurement systems',\n long_description=open('README.rst').read(),\n url='https://github.com/AppliedAcousticsChalmers/acoustics-hardware',\n author='Carl Andersson',\n author_email='carl.andersson@chalmers.se',\n license='MIT',\n packages=['acoustics_hardware'],\n install_requires=[\n 'numpy',\n 'scipy',\n 'h5py',\n 'sounddevice',\n 'nidaqmx;platform_system==\"Windows\"',\n 'pyserial',\n 'SchunkMotionProtocol'],\n )\n","sub_path":"pypi_install_script/acoustics-hardware-0.1.0.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"136434182","text":"import queue\nimport numpy as np\nfrom search_problems import Node, GridSearchProblem, get_random_grid_problem\n\ndef a_star_search(problem):\n \"\"\"\n Uses the A* algorithm to solve an instance of GridSearchProblem. Use the methods of GridSearchProblem along with\n structures and functions from the allowed imports (see above) to implement A*.\n\n :param problem: an instance of GridSearchProblem to solve\n :return: path: a list of states (ints) describing the path from problem.init_state to problem.goal_state[0]\n num_nodes_expanded: number of nodes expanded by your search\n max_frontier_size: maximum frontier size during search\n \"\"\"\n ####\n # COMPLETE THIS CODE\n ####\n num_nodes_expanded = 0\n max_frontier_size = 0\n path = []\n\n # My code:\n\n # Creating a node for the initial state\n head = Node(0, problem.init_state, (0, 0), 0)\n num_nodes_expanded += 1\n\n # Creating a priority queue for the frontier and adding the initial state to it\n frontier = queue.PriorityQueue()\n frontier.put((0 + problem.heuristic(head.state), head))\n\n # Using a set to keep track of already visited nodes\n visited = set()\n visited.add(head.state)\n\n # keep exploring the graph until there are no more nodes to explore (or unless the goal state is found)\n while not frontier.empty():\n\n # update the maximum frontier size\n if frontier.qsize() > max_frontier_size:\n max_frontier_size = frontier.qsize()\n\n # extract the next node to explore\n newState = frontier.get()[1]\n\n # if the new state is the goal state\n if newState.state == problem.goal_states[0]:\n # compute path by backtracking from the current node to the initial state\n currentState = newState\n while currentState != head:\n path.append(currentState.state)\n currentState = currentState.parent\n path.append(head.state)\n path.reverse()\n\n # return the path\n return path, num_nodes_expanded, max_frontier_size\n # if the new state wasn't the goal state, get all of its neighbors which haven't already been explored and put them in the queue to be explored\n else:\n num_nodes_expanded += 1 # update the number of nodes expanded\n\n for action in problem.get_actions(newState.state):\n child = problem.get_child_node(newState, action)\n if child.state not in visited:\n visited.add(child.state)\n frontier.put((child.path_cost + problem.heuristic(child.state), child))\n \n return path, num_nodes_expanded, max_frontier_size\n\n\ndef search_phase_transition():\n \"\"\"\n Simply fill in the prob. of occupancy values for the 'phase transition' and peak nodes expanded within 0.05. You do\n NOT need to submit your code that determines the values here: that should be computed on your own machine. Simply\n fill in the values!\n\n :return: tuple containing (transition_start_probability, transition_end_probability, peak_probability)\n \"\"\"\n ####\n # REPLACE THESE VALUES\n ####\n transition_start_probability = 0.3\n transition_end_probability = 0.5\n peak_nodes_expanded_probability = 0.35\n return transition_start_probability, transition_end_probability, peak_nodes_expanded_probability\n\n\nif __name__ == '__main__':\n # Test your code here!\n # Create a random instance of GridSearchProblem\n p_occ = 0.25\n M = 20\n N = 20 \n problem = get_random_grid_problem(p_occ, M, N)\n\n # # Solve it\n path, num_nodes_expanded, max_frontier_size = a_star_search(problem)\n # # Check the result\n correct = problem.check_solution(path)\n print(\"Solution is correct: {:}\".format(correct))\n # # Plot the result\n problem.plot_solution(path)\n\n # Experiment and compare with BFS\n \n","sub_path":"Lab1/a_star_search.py","file_name":"a_star_search.py","file_ext":"py","file_size_in_byte":3902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"620661319","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport cherrypy\nimport datetime\nimport os\nimport pathlib\nimport sys\nimport traceback\nfrom fs.osfs import OSFS\nfrom fs.copy import copy_fs\nfrom girder import events\nfrom girder.api.rest import setCurrentUser\nfrom girder.models.folder import Folder\nfrom girder.models.user import User\nfrom girder.utility import parseTimestamp\nfrom girder.plugins.jobs.constants import JobStatus\nfrom girder.plugins.jobs.models.job import Job\n\nfrom ..constants import CATALOG_NAME, TaleStatus\nfrom ..lib import pids_to_entities, register_dataMap\nfrom ..lib.dataone import DataONELocations # TODO: get rid of it\nfrom ..lib.manifest_parser import ManifestParser\nfrom ..lib.metrics import metricsLogger\nfrom ..models.tale import Tale\nfrom ..utils import getOrCreateRootFolder, notify_event\n\n\ndef run(job):\n jobModel = Job()\n jobModel.updateJob(job, status=JobStatus.RUNNING)\n\n tale_dir, manifest_file = job[\"args\"]\n user = User().load(job[\"userId\"], force=True)\n tale = Tale().load(job[\"kwargs\"][\"taleId\"], user=user)\n\n progressTotal = 3\n progressCurrent = 0\n\n try:\n notify_event([user[\"_id\"]], \"wt_import_started\", {\"taleId\": tale[\"_id\"]})\n\n os.chdir(tale_dir)\n mp = ManifestParser(manifest_file)\n\n # 1. Register data\n progressCurrent += 1\n jobModel.updateJob(\n job,\n status=JobStatus.RUNNING,\n progressTotal=progressTotal,\n progressCurrent=progressCurrent,\n progressMessage=\"Registering external data\",\n )\n dataIds = mp.get_external_data_ids()\n if dataIds:\n dataMaps = pids_to_entities(\n dataIds, user=user, base_url=DataONELocations.prod_cn, lookup=True\n ) # DataONE shouldn't be here\n register_dataMap(\n dataMaps,\n getOrCreateRootFolder(CATALOG_NAME),\n \"folder\",\n user=user,\n base_url=DataONELocations.prod_cn,\n )\n\n # 2. Construct the dataSet\n dataSet = mp.get_dataset()\n\n # 3. Update Tale's dataSet\n update_citations = {_[\"itemId\"] for _ in tale[\"dataSet\"]} ^ {\n _[\"itemId\"] for _ in dataSet\n }\n tale[\"dataSet\"] = dataSet\n Tale().update(\n {\"_id\": tale[\"_id\"]}, update={\"$set\": {\"dataSet\": tale[\"dataSet\"]}}\n )\n\n if update_citations:\n events.daemon.trigger(\n eventName=\"tale.update_citation\", info={\"tale\": tale, \"user\": user}\n )\n\n # 4. Copy data to the workspace\n progressCurrent += 1\n jobModel.updateJob(\n job,\n status=JobStatus.RUNNING,\n progressTotal=progressTotal,\n progressCurrent=progressCurrent,\n progressMessage=\"Copying files to workspace\",\n )\n orig_tale_id = pathlib.Path(manifest_file).parts[0]\n for workdir in (\"workspace\", \"data/workspace\"):\n workdir = os.path.join(orig_tale_id, workdir)\n if os.path.isdir(workdir):\n workspace = Folder().load(tale[\"workspaceId\"], force=True)\n workspace_path = pathlib.Path(workspace[\"fsPath\"])\n copy_fs(OSFS(workdir), OSFS(workspace_path))\n break\n\n # Create a version\n version_obj = mp.manifest.get(\n \"dct:hasVersion\",\n {\n \"schema:name\": None,\n \"schema:dateModified\": datetime.datetime.utcnow(),\n \"schema:dateCreated\": datetime.datetime.utcnow(),\n },\n )\n for date_key in (\"schema:dateCreated\", \"schema:dateModified\"):\n if isinstance(version_obj.get(date_key), str):\n version_obj[date_key] = parseTimestamp(version_obj[date_key])\n else:\n version_obj[date_key] = datetime.datetime.utcnow()\n api_root = cherrypy.tree.apps[\"/api\"]\n version_resource = api_root.root.v1.version\n setCurrentUser(user)\n version = version_resource.create(\n taleId=tale[\"_id\"], name=version_obj[\"schema:name\"], params={}\n )\n version = Folder().load(version[\"_id\"], force=True) # above is filtered...\n version[\"meta\"] = {\"publishInfo\": tale[\"publishInfo\"]}\n version[\"updated\"] = version_obj[\"schema:dateModified\"]\n version[\"created\"] = version_obj[\"schema:dateCreated\"]\n version = Folder().save(version)\n\n # Create potential runs\n orig_tale_id = pathlib.Path(manifest_file).parts[0]\n orig_runs_dir = pathlib.Path(orig_tale_id) / \"data\" / \"runs\"\n run_resource = api_root.root.v1.run\n for run_obj in mp.manifest.get(\"wt:hasRecordedRuns\", []):\n orig_run_dir = orig_runs_dir / run_obj[\"schema:name\"]\n run = run_resource.create(\n versionId=version[\"_id\"], name=run_obj[\"schema:name\"], params={}\n )\n run = Folder().load(run[\"_id\"], force=True) # we need fsPath\n dest_run_dir = pathlib.Path(run[\"fsPath\"]) / \"workspace\"\n if orig_run_dir.is_dir():\n copy_fs(OSFS(orig_run_dir), OSFS(dest_run_dir))\n run[\"updated\"] = parseTimestamp(run_obj[\"schema:dateModified\"])\n run[\"created\"] = parseTimestamp(run_obj[\"schema:dateCreated\"])\n # vv calls save()\n run_resource.setStatus(id=run[\"_id\"], status=int(run_obj[\"wt:runStatus\"]), params={})\n\n # Tale is ready to be built\n Tale().update(\n {\"_id\": tale[\"_id\"]},\n update={\n \"$set\": {\"status\": TaleStatus.READY, \"restoredFrom\": version[\"_id\"]}\n },\n )\n\n progressCurrent += 1\n jobModel.updateJob(\n job,\n status=JobStatus.SUCCESS,\n log=\"Tale created\",\n progressTotal=progressTotal,\n progressCurrent=progressCurrent,\n progressMessage=\"Tale created\",\n )\n\n notify_event([user[\"_id\"]], \"wt_import_completed\", {\"taleId\": tale[\"_id\"]})\n except Exception:\n Tale().update(\n {\"_id\": tale[\"_id\"]}, update={\"$set\": {\"status\": TaleStatus.ERROR}}\n )\n t, val, tb = sys.exc_info()\n log = \"%s: %s\\n%s\" % (t.__name__, repr(val), traceback.extract_tb(tb))\n jobModel.updateJob(job, status=JobStatus.ERROR, log=log)\n notify_event([user[\"_id\"]], \"wt_import_failed\", {\"taleId\": tale[\"_id\"]})\n raise\n\n metricsLogger.info(\n \"tale.import_tale\",\n extra={\n \"details\": {\n \"id\": tale[\"_id\"],\n \"imageId\": tale[\"imageId\"],\n \"imageInfo\": tale[\"imageInfo\"],\n \"relatedIdentifiers\": tale[\"relatedIdentifiers\"],\n \"userId\": user[\"_id\"], # shortcut\n }\n },\n )\n","sub_path":"server/tasks/import_tale.py","file_name":"import_tale.py","file_ext":"py","file_size_in_byte":6843,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"324663829","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('productos', '0007_auto_20150922_2357'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='discount',\n name='short_name',\n field=models.CharField(default=b'-10%', max_length=20, null=True, verbose_name=b'etiqueta', blank=True),\n ),\n ]\n","sub_path":"productos/migrations/0008_discount_short_name.py","file_name":"0008_discount_short_name.py","file_ext":"py","file_size_in_byte":474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"443422576","text":"import cv2\nimport numpy as np\nfrom tensorflow import keras\nimport tensorflow as tf\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nimport os\n\nIMG_SAVE_PATH_TRAIN = 'image_data\\\\train'\nIMG_SAVE_PATH_TEST = 'image_data\\\\test'\n\ndef get_model():\n model = tf.keras.models.Sequential([\n tf.keras.layers.Conv2D(64, (3, 3), activation='relu', input_shape=(150, 150, 3)),\n tf.keras.layers.MaxPooling2D(2, 2),\n tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n tf.keras.layers.MaxPooling2D(2, 2),\n tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),\n tf.keras.layers.MaxPooling2D(2, 2),\n tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),\n tf.keras.layers.MaxPooling2D(2, 2),\n\n # Flatten the results\n tf.keras.layers.Flatten(),\n tf.keras.layers.Dropout(0.5),\n\n # Hidden layer\n tf.keras.layers.Dense(512, activation='relu'),\n tf.keras.layers.Dense(4, activation='softmax')\n ])\n return model\n\n\ndef train_and_save_model(learning_rate, epochs):\n # load images from the directory\n training_dir = IMG_SAVE_PATH_TRAIN\n training_dataGen = ImageDataGenerator(rescale=1.0 / 255)\n train_generator = training_dataGen.flow_from_directory(training_dir, target_size=(150, 150),\n class_mode='categorical')\n testing_dir = IMG_SAVE_PATH_TEST\n testing_dataGen = ImageDataGenerator(rescale=1.0 / 255)\n test_generator = testing_dataGen.flow_from_directory(testing_dir, target_size=(150, 150), class_mode='categorical')\n\n model = get_model()\n model.compile(loss='categorical_crossentropy', optimizer=tf.keras.optimizers.RMSprop(lr=learning_rate), metrics=['accuracy'])\n\n history = model.fit_generator(train_generator, epochs=epochs, validation_data=test_generator, verbose=1, )\n\n test_loss, test_acc = model.evaluate_generator(test_generator)\n print('\\nTest accuracy:', test_acc)\n\n # save the model for later use\n model.save(\"rock-paper-scissors-model.h5\")\n\n return test_acc\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2088,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"340343948","text":"# ! /usr/bin/env python\n# -*- coding: utf-8 -*-\n# __author__ = \"liuluyang\"\n# Datetime: 2019/12/25 11:25\n\n\n\"\"\"\n第八题：\n猜数字游戏\n规则：\n 实例化GuessNum会得到一个实例对象\n 同时该对象拥有一个属性num，是一定范围内的随机数\n\n 我们能做的是调用check_num_01这个方法，把猜测的数字传进去，\n 来获取两个数字对比的结果， 从而来确定num的值\n\n写个函数来完成此游戏，把猜测正确的数字返回\n\ng = GuessNum()\nr = g.check_num_01(32423432)\nprint(r) # 大了、小了或等于\n\"\"\"\n\n\nclass GuessNum:\n \"\"\"\n 一个猜数字的游戏\n \"\"\"\n\n def __init__(self):\n\n self.num = self.make_num()\n\n def make_num(self):\n \"\"\"\n 产生一个随机数\n :return:\n \"\"\"\n import random\n\n return random.randint(1000000, 100000000)\n\n def check_num_01(self, guess_num):\n \"\"\"\n 检查猜测的数字\n :param guess_num:\n :return:\n \"\"\"\n if guess_num > self.num:\n return '大了'\n elif guess_num < self.num:\n return '小了'\n\n return '等于'\n\n def check_num_02(self, guess_num):\n \"\"\"\n 检查猜测的数字\n :param guess_num:\n :return:\n \"\"\"\n if guess_num > self.num:\n return '大了'\n elif guess_num <= self.num:\n return '小了'\n\n\n\ndef func_08():\n\n g = GuessNum()\n\n period = 10000000000000000\n r = 0\n count = 0\n while True:\n count += 1\n r += period\n res = g.check_num_01(r)\n\n if res == '等于':\n break\n elif res == '大了':\n r -= period\n period //= 10\n\n return r, g.num, count\n\n\nif __name__ == '__main__':\n # 3 4 1 6 8 3 2 5\n r = func_08()\n print(r)\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"py3-study/并发编程课上代码/1902/12-25/猜数字.py","file_name":"猜数字.py","file_ext":"py","file_size_in_byte":1852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"315333619","text":"from nvidia.dali.plugin.pytorch import DALIGenericIterator\n\nfrom .dali import SSDDALIPipeline, gen_dali_anc\n\n\ndef get_dali_dataloader(cfg, train_dataset_path, device, num_gpus, local_seed):\n anchors = gen_dali_anc(cfg.TRAIN.STEPS, cfg.TRAIN.STEP_MULTIPLIER, input_size=(cfg.TRAIN.INPUT_SIZE,\n cfg.TRAIN.INPUT_SIZE))\n pipeline = SSDDALIPipeline(batch_size=cfg.TRAIN.BATCH_SIZE, device_id=device, num_threads=cfg.TRAIN.NUM_WORKERS,\n seed=local_seed, train_dataset_path=train_dataset_path,\n num_gpus=num_gpus, data_shape=cfg.TRAIN.INPUT_SIZE,\n anchors=anchors, cfg=cfg)\n\n pipeline.build()\n epoch_size = pipeline.epoch_size(\"Reader\")\n train_loader = DALIGenericIterator([pipeline], ['data', 'bboxes', 'labels'],\n epoch_size / num_gpus, auto_reset=False)\n return train_loader, epoch_size // num_gpus\n","sub_path":"utils/data_loaders.py","file_name":"data_loaders.py","file_ext":"py","file_size_in_byte":1024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"75333691","text":"from os import environ\nimport boto3, sys, subprocess\nfrom botocore.exceptions import ClientError\nfrom models import c\n\n\ndef change_ec2_ip():\n print(\"[\"+c.OKBLUE+\"*\"+c.ENDC+\"] Changing EC2 Proxy IP\")\n ec2 = boto3.client(\n 'ec2',\n aws_access_key_id=environ['AWS_ELASTIC_IP_KEY'],\n aws_secret_access_key=environ['AWS_ELASTIC_IP_SECRET']\n )\n\n try:\n # Current Elastic IP\n address = ec2.describe_addresses()['Addresses'][0]\n instance_id = address['InstanceId']\n public_ip = address['PublicIp']\n #private_ip = address['PrivateIpAddress']\n\n print(\"\\t[\"+c.WARNING+\"*\"+c.ENDC+\"] Current IP: \"+c.BOLD+f\"{public_ip}\"+c.ENDC)\n\n # Release Elastic IP\n print(\"\\t[\"+c.FAIL+\"-\"+c.ENDC+\"] Releasing IP...\")\n response = ec2.release_address(AllocationId=address['AllocationId'])\n\n # Get new Elastic IP\n print(\"\\t[\"+c.OKGREEN+\"+\"+c.ENDC+\"] Getting new IP... \", end=\"\"); sys.stdout.flush()\n allocation = ec2.allocate_address(Domain='vpc')\n new_ip = allocation['PublicIp']\n print(c.BOLD+new_ip+c.ENDC)\n\n # Associate address\n print(\"\\t[\"+c.WARNING+\"*\"+c.ENDC+\"] Attaching IP to instance... \", end=\"\"); sys.stdout.flush()\n response = ec2.associate_address(\n AllocationId=allocation['AllocationId'],\n InstanceId=instance_id\n )\n print(c.OKGREEN+\"SUCCESS\"+c.ENDC)\n except ClientError as e:\n print(e)\n else:\n subprocess.run([\"killall\", \"ssh\"])\n # Create local ssh tunnel proxy to EC2 instance\n print(\"[\"+c.OKBLUE+\"*\"+c.ENDC+\"] Establishing new SSH socks5 tunnel proxy to localhost:1080\")\n subprocess.check_output([\"ssh\", \"-D\", \"1080\", \"-fCqN\",\n \"-i\", \"~/.ssh/ec2-ubuntu.pem\",\n \"-o\", \"StrictHostKeyChecking=no\",\n \"ubuntu@{}\".format(new_ip)\n ])\n\nif __name__ == \"__main__\":\n change_ec2_ip()\n","sub_path":"rotate_proxy.py","file_name":"rotate_proxy.py","file_ext":"py","file_size_in_byte":2017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"616169864","text":"\"\"\"\n@author: Viet Nguyen \nFrom: https://github.com/uvipen/Super-mario-bros-A3C-pytorch\n\nModified for Benchmarking Reinforcement Learning Algorithms in NES Games by Erin-Louise Connolly\n\"\"\"\n\nimport torch\nfrom src.env import create_train_env\nfrom src.model import ActorCritic\nimport torch.nn.functional as F\nfrom torch.distributions import Categorical\nfrom collections import deque\nimport timeit\nfrom src.helpers import SIMPLE_MOVEMENT, COMPLEX_MOVEMENT, RIGHT_ONLY\nimport csv\nimport time\nimport numpy as np\nimport sys\n\ndef local_train(index, opt, global_model, optimizer, save=False):\n seed=123\n torch.manual_seed(seed)\n start_time = time.time()\n\n savefile = opt.saved_path + '/pacman_a3c_train' + opt.timestr + '.csv'\n title = ['Episode','Steps','Time','Reward','Score','TotalReward']\n with open(savefile, 'w', newline='') as sfile:\n writer = csv.writer(sfile)\n writer.writerow(title)\n\n env, num_states, num_actions = create_train_env(opt.world, opt.stage,opt.action_type)\n local_model = ActorCritic(num_states, num_actions)\n if opt.use_gpu:\n local_model.cuda()\n local_model.train()\n state = torch.from_numpy(env.reset())\n if opt.use_gpu:\n state = state.cuda()\n done = True\n curr_step = 0\n curr_episode = 0\n tot_reward=0\n tot_steps=0\n\n while True:\n if save:\n if curr_episode % opt.save_interval == 0 and curr_episode > 0:\n torch.save(global_model.state_dict(),\n \"{}/a3c_pacman_{}_{}\".format(opt.saved_path, opt.world, opt.stage))\n torch.save(global_model.state_dict(),\"{}/a3c_pacman_{}_{}_{}\".format(opt.saved_path, opt.world, opt.stage,curr_episode))\n elapsed_time = time.time() - start_time\n print(\"Episode: {} Time elapsed: {}\".format((opt.num_local_steps *curr_episode),time.strftime(\"%H:%M:%S\", time.gmtime(elapsed_time))))\n curr_episode += 1\n local_model.load_state_dict(global_model.state_dict())\n if done:\n h_0 = torch.zeros((1, 512), dtype=torch.float)\n c_0 = torch.zeros((1, 512), dtype=torch.float)\n else:\n h_0 = h_0.detach()\n c_0 = c_0.detach()\n if opt.use_gpu:\n h_0 = h_0.cuda()\n c_0 = c_0.cuda()\n\n log_policies = []\n values = []\n rewards = []\n entropies = []\n\n for _ in range(opt.num_local_steps):\n curr_step += 1\n logits, value, h_0, c_0 = local_model(state, h_0, c_0)\n policy = F.softmax(logits, dim=1)\n log_policy = F.log_softmax(logits, dim=1)\n entropy = -(policy * log_policy).sum(1, keepdim=True)\n\n m = Categorical(policy)\n action = m.sample().item()\n\n state, reward, done, info = env.step(action)\n #print(type(reward))\n state = torch.from_numpy(state)\n if opt.use_gpu:\n state = state.cuda()\n if curr_step > opt.num_global_steps:\n done = True\n\n if done:\n curr_step = 0\n state = torch.from_numpy(env.reset())\n if opt.use_gpu:\n state = state.cuda()\n\n values.append(value)\n log_policies.append(log_policy[0, action])\n rewards.append(reward)\n entropies.append(entropy)\n tot_reward += reward\n\n if done:\n break\n\n R = torch.zeros((1, 1), dtype=torch.float)\n if opt.use_gpu:\n R = R.cuda()\n if not done:\n _, R, _, _ = local_model(state, h_0, c_0)\n\n gae = torch.zeros((1, 1), dtype=torch.float)\n if opt.use_gpu:\n gae = gae.cuda()\n actor_loss = 0\n critic_loss = 0\n entropy_loss = 0\n next_value = R\n\n for value, log_policy, reward, entropy in list(zip(values, log_policies, rewards, entropies))[::-1]:\n gae = gae * opt.gamma * opt.tau\n gae = gae + reward + opt.gamma * next_value.detach() - value.detach()\n next_value = value\n actor_loss = actor_loss + log_policy * gae\n R = R * opt.gamma + reward\n critic_loss = critic_loss + (R - value) ** 2 / 2\n entropy_loss = entropy_loss + entropy\n\n total_loss = -actor_loss + critic_loss - opt.beta * entropy_loss\n optimizer.zero_grad()\n total_loss.backward()\n\n\n ep_time = time.time() - start_time\n\n avg_loss = 0\n mean_reward=0\n #print(total_loss)\n tot_steps = opt.num_local_steps * curr_episode\n\n\n for local_param, global_param in zip(local_model.parameters(), global_model.parameters()):\n if global_param.grad is not None:\n break\n global_param._grad = local_param.grad\n\n\n if curr_episode % 100 == 0:\n\n data = [curr_step, tot_steps, \"{:.6f}\".format(ep_time), \"{:.4f}\".format(reward),\"{:.2f}\".format(info[\"score\"]),\"{:.4f}\".format(tot_reward)]\n \n with open(savefile, 'a', newline='') as sfile:\n writer = csv.writer(sfile)\n writer.writerows([data])\n\n optimizer.step()\n\n if tot_steps > opt.num_global_steps:\n if save:\n end_time = time.time() - start_time\n print('The code runs for {}'.format(time.strftime(\"%H:%M:%S\", time.gmtime(elapsed_time))))\n print(\"Training process {} terminated\".format(index))\n sys.exit()\n\n \n return\n\n\ndef local_test(index, opt, global_model):\n seed=123\n torch.manual_seed(seed)\n start_time = time.time()\n\n env, num_states, num_actions = create_train_env(opt.world, opt.stage,opt.action_type)\n local_model = ActorCritic(num_states, num_actions)\n local_model.eval()\n state = torch.from_numpy(env.reset())\n done = True\n curr_step = 0\n tot_reward=0\n tot_step=0\n actions = deque(maxlen=opt.max_actions)\n while True:\n curr_step += 1\n tot_step+=1\n if done:\n local_model.load_state_dict(global_model.state_dict())\n with torch.no_grad():\n if done:\n h_0 = torch.zeros((1, 512), dtype=torch.float)\n c_0 = torch.zeros((1, 512), dtype=torch.float)\n else:\n h_0 = h_0.detach()\n c_0 = c_0.detach()\n\n logits, value, h_0, c_0 = local_model(state, h_0, c_0)\n policy = F.softmax(logits, dim=1)\n action = torch.argmax(policy).item()\n state, reward, done, info = env.step(action)\n tot_reward += reward\n\n env.render()\n actions.append(action)\n if curr_step > opt.num_global_steps:\n done = True\n sys.exit(\"Test process terminated\")\n\n #torch.save(local_model.state_dict(),\n #\"{}/a3c_super_mario_bros_{}\".format(opt.saved_path, curr_step))\n\n if done: \n tot_reward = 0\n\n actions.clear()\n state = env.reset()\n\n state = torch.from_numpy(state)\n","sub_path":"pacman/a3c/src/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":7119,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"64379025","text":"# -*- coding:utf-8 -*-\n\"\"\"订单管理\"\"\"\nfrom kyger.utility import *\n\n\nclass KgcmsApi(object):\n \"\"\"KGCMS框架接口\"\"\"\n\n kg = db = None\n\n def __init__(self):\n self.status = {\n 0: '等待付款',\n 1: '已支付，等待发货',\n 2: '订���已确认，配货中',\n 3: '已发货，等待用户签收',\n 4: '已签收，交易完毕',\n -1: '要求退货',\n -2: '拒绝退货，前台交易完毕',\n -3: '退货完毕，订单结束',\n -4: '订单已取消'\n }\n\n def __call__(self):\n from kyger.kgcms import template\n from kyger.product import Product\n get_param = self.kg['get']\n action = get_param.get('action')\n\n # 检查过期所有订单，取消并退还库存\n order_list = self.db.list(\n table='order',\n where='status=0 && addtime<%s' % (int(self.kg['run_start_time'][1]) - 7000)\n )\n for order in order_list:\n if order['oid'].startswith('ES'):\n # 数据\n order['pid'] = json2dict(order['pid'])\n order['number'] = json2dict(order['number'])\n order['attribute'] = json2dict(order['attribute'])\n skus = {}\n for i in range(len(order['attribute'])):\n if order['pid'][i] not in skus:\n skus[order['pid'][i]] = [\n {\"speci\": order['attribute'][i], \"number\": order['number'][i]}]\n else:\n skus[order['pid'][i]].append(\n {\"speci\": order['attribute'][i], \"number\": order['number'][i]})\n products = self.db.list(\n table='product',\n where='id in (%s)' % ','.join('%s' % v for v in order['pid'])\n )\n for product in products:\n product['inventory'] = json2dict(product['inventory'])\n product['speci'] = json2dict(product['speci'])\n edit_datas = skus[product['id']]\n for row in edit_datas:\n index = product['speci'].index(row['speci'])\n product['inventory'][index] += row['number']\n # 事务\n self.db.run_sql('UPDATE `%sorder` SET status=-4 WHERE id=%s' % (self.db.cfg[\"prefix\"], order['id']),\n log=False)\n # 退还库存\n sql = 'UPDATE %sproduct SET inventory = CASE id ' % self.db.cfg[\"prefix\"]\n for product in products:\n sql += 'WHEN %s THEN \"%s\" ' % (product['id'], json2dict(product['inventory']))\n sql += 'END WHERE id IN (%s)' % ','.join('%s' % v for v in order['pid'])\n self.db.run_sql(sql, log=True)\n elif order['oid'].startswith('NR'):\n self.db.edit('order', {'status': -4}, order['id'])\n else:\n pass\n\n if action in ('del', 'pay', 'cancel'):\n id = numeric(get_param.get('id'), 0)\n if not id: return alert(msg='参数错误', act=3)\n order_data = self.db.list(table='order', where=id, shift=1)\n if not order_data: return alert(msg='没有找到该订单记录', act=3)\n if action == 'del':\n if order_data['status'] not in (4, -4):\n return alert(msg='订单未被取消或完成，不可删除', act=3)\n else:\n self.db.dele('order', where=id)\n return alert(act=2)\n elif action == 'pay':\n if order_data['status'] != 0: return alert(msg='只允许对未付款的订单进行支付', act=3)\n url = '/api/unlimited_process?action=pay&code=%s' % cipher(\n json2dict({'order_number': order_data['oid']}, trans=False), 1)\n return alert(act=url)\n else:\n if order_data['status'] != 0:\n return alert(msg='只允许取消未付款的订单', act=3)\n else:\n if order_data['oid'].startswith('ES'):\n # 数据\n order_data['pid'] = json2dict(order_data['pid'])\n order_data['number'] = json2dict(order_data['number'])\n order_data['attribute'] = json2dict(order_data['attribute'])\n skus = {}\n for i in range(len(order_data['attribute'])):\n if order_data['pid'][i] not in skus:\n skus[order_data['pid'][i]] = [\n {\"speci\": order_data['attribute'][i], \"number\": order_data['number'][i]}]\n else:\n skus[order_data['pid'][i]].append(\n {\"speci\": order_data['attribute'][i], \"number\": order_data['number'][i]})\n products = self.db.list(\n table='product',\n where='id in (%s)' % ','.join('%s' % v for v in order_data['pid'])\n )\n for product in products:\n product['inventory'] = json2dict(product['inventory'])\n product['speci'] = json2dict(product['speci'])\n edit_datas = skus[product['id']]\n for row in edit_datas:\n index = product['speci'].index(row['speci'])\n product['inventory'][index] += row['number']\n # 事务\n self.db.run_sql('UPDATE `%sorder` SET status=-4 WHERE id=%s' % (self.db.cfg[\"prefix\"], id),\n log=False)\n # 退还库存\n sql = 'UPDATE %sproduct SET inventory = CASE id ' % self.db.cfg[\"prefix\"]\n for product in products:\n sql += 'WHEN %s THEN \"%s\" ' % (product['id'], json2dict(product['inventory']))\n sql += 'END WHERE id IN (%s)' % ','.join('%s' % v for v in order_data['pid'])\n self.db.run_sql(sql, log=True)\n elif order_data['oid'].startswith('NR'):\n self.db.edit('order', {'status': -4}, id)\n return alert(act='/user/order')\n\n elif self.kg['post'].get('action') == 'get_status':\n oid = self.kg['post'].get('oid')\n if not oid: return json2dict({'status': -1, 'msg': '订单号不能为空'}, trans=False)\n order_data = self.db.list(\n table='order',\n field='status',\n where='uid=%s && oid=\"%s\"' % (self.kg['user']['id'], oid),\n shift=1\n )\n if not order_data: return json2dict({'status': -1, 'msg': '订单记录不存在'}, trans=False)\n if order_data['status'] in (1, 4):\n return json2dict({'status': 0, 'msg': '已支付'}, trans=False)\n else:\n return json2dict({'status': 1, 'msg': '暂未支付'}, trans=False)\n\n else:\n page = numeric(self.kg['get'].get('page', 1))\n order = self.db.list(\n table='order',\n field=\"*, from_unixtime(`addtime`, '%Y-%m-%d %H:%i:%s') as `date`\",\n where='uid=%s' % self.kg['user']['id'],\n order='id DESC',\n page=page,\n limit=10\n )\n if page > self.db.total_page:\n page = self.db.total_page\n order = self.db.list(\n table='order',\n field=\"*, from_unixtime(`addtime`, '%Y-%m-%d %H:%i:%s') as `date`\",\n where='uid=%s' % self.kg['user']['id'],\n order='id DESC',\n page=page,\n limit=10\n )\n # 分页\n if self.db.total_page < 2:\n self.kg['page_html'] = ''\n else:\n from kyger.common import page_tpl\n page_html = page_tpl(page, self.db.total_page, 10, self.kg['server']['WEB_URL'], page_num=3)\n self.kg['page_html'] = page_html\n # 整形\n for row in order:\n row['status_title'] = self.status[row['status']]\n self.kg['order'] = order\n\n return template(function=[{\"get_cart\": Product(self.db, self.kg).get_cart}])\n","sub_path":"user/order_mobile.py","file_name":"order_mobile.py","file_ext":"py","file_size_in_byte":8706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"379733151","text":"from django.contrib.auth.admin import UserAdmin\nfrom django.contrib.auth.models import User\nfrom django.contrib import admin\n\n\nclass CustomUserAdmin(UserAdmin):\n model = User\n list_display = ('username', 'email', 'first_name', 'last_name',\n 'is_active', 'is_staff', 'last_login', 'date_joined')\n list_display_links = ('username', )\n search_fields = ('username', 'email')\n ordering = ('-date_joined',)\n\nadmin.site.unregister(User)\nadmin.site.register(User, CustomUserAdmin)\n","sub_path":"knowledge/coreapi/core/core/users/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"225159124","text":"from Tree.BTree import Tree\n\n__author__ = 'zhy'\n\n\nclass Solution(object):\n @staticmethod\n def hasPathSum(root, sum):\n \"\"\"\n :type root: TreeNode\n :type sum: int\n :rtype: bool\n \"\"\"\n if not root:\n return False\n vQueue, nQueue = [root.val], [root]\n while nQueue:\n cur = nQueue.pop()\n n_sum = vQueue.pop()\n\n if n_sum == sum and not cur.left and not cur.right:\n return True\n\n if cur.right:\n nQueue.append(cur.right)\n vQueue.append(n_sum + cur.right.val)\n\n if cur.left:\n nQueue.append(cur.left)\n vQueue.append(n_sum + cur.left.val)\n\n return False\n\n def hasPathSum_recursive(self, root, sum):\n if not root:\n return False\n if sum == root.val and not root.left and not root.right:\n return True\n cur = root.val\n return self.hasPathSum_recursive(root.left, sum - cur) or self.hasPathSum_recursive(root.right, sum - cur)\n\n\ndef test():\n nodeList = [\n [6, 2, 8, 0, 4, 7, 9, '#', '#', 3, 5],\n [1, 2, 3, '#', '#', 4, '#', '#', 5],\n [1, 2, 2, 3, 4, 4, 3],\n []\n ]\n solu = Solution()\n for node in nodeList:\n tree = Tree.createTree(node)\n print(tree)\n print(solu.hasPathSum_recursive(tree.root, 21))\n print(Solution.hasPathSum(tree.root, 21))\n\n\nif __name__ == '__main__':\n test()\n","sub_path":"Path Sum.py","file_name":"Path Sum.py","file_ext":"py","file_size_in_byte":1493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"79341055","text":"import gfootball.env as football_env\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nimport pfrl\nfrom pfrl import agents, experiments, explorers\nfrom pfrl import nn as pnn\nfrom pfrl import replay_buffers, utils\nfrom pfrl.initializers import init_chainer_default\nfrom pfrl.q_functions import DiscreteActionValueHead\nfrom pfrl.policies import SoftmaxCategoricalHead\nfrom pfrl.wrappers import atari_wrappers\nfrom pfrl.agents import PPO\n\n\nenv = football_env.create_environment(env_name=\"academy_empty_goal_close\", stacked=False, logdir='/tmp/football', write_goal_dumps=False, write_full_episode_dumps=False, render=True)\nenv = pfrl.wrappers.CastObservationToFloat32(env)\nenv.reset()\nsteps = 0\n\nobs_space = env.observation_space\naction_space = env.action_space\nprint(obs_space)\n\nobs_size = obs_space.low.size\n\n# Normalize observations based on their empirical mean and variance\nobs_normalizer = pfrl.nn.EmpiricalNormalization(\n obs_space.low.size, clip_threshold=5\n)\n\npolicy = torch.nn.Sequential(\n nn.Linear(obs_size, 64),\n nn.Tanh(),\n nn.Linear(64, 64),\n nn.Tanh(),\n nn.Linear(64, 19),\n pfrl.policies.SoftmaxCategoricalHead(),\n)\n\nvf = torch.nn.Sequential(\n nn.Linear(obs_size, 64),\n nn.Tanh(),\n nn.Linear(64, 64),\n nn.Tanh(),\n nn.Linear(64, 1),\n)\n# While the original paper initialized weights by normal distribution,\n# we use orthogonal initialization as the latest openai/baselines does.\ndef ortho_init(layer, gain):\n nn.init.orthogonal_(layer.weight, gain=gain)\n nn.init.zeros_(layer.bias)\n\northo_init(policy[0], gain=1)\northo_init(policy[2], gain=1)\northo_init(policy[4], gain=1e-2)\northo_init(vf[0], gain=1)\northo_init(vf[2], gain=1)\northo_init(vf[4], gain=1)\n\n# Combine a policy and a value function into a single model\nmodel = pfrl.nn.Branched(policy, vf)\n\nopt = torch.optim.Adam(model.parameters(), lr=3e-4, eps=1e-5)\n\nagent = PPO(\n model,\n opt,\n obs_normalizer=obs_normalizer,\n gpu=0,\n update_interval=2048,\n minibatch_size=64,\n epochs=10,\n clip_eps_vf=None,\n entropy_coef=0,\n standardize_advantages=True,\n gamma=0.995,\n lambd=0.97,\n)\n\nagent.load('FBPPO')\n\nn_episodes = 10000\nmax_episode_len = 200\nfor i in range(1, n_episodes + 1):\n obs = env.reset()\n obs = np.reshape(obs, (27648))\n R = 0 # return (sum of rewards)\n t = 0 # time step\n while True:\n # Uncomment to watch the behavior in a GUI window\n # env.render()\n action = agent.act(obs)\n #action = action[0]\n obs, reward, done, _ = env.step(action)\n R += reward\n t += 1\n reset = t == max_episode_len\n obs = np.reshape(obs, (27648))\n agent.observe(obs, reward, done, reset)\n if done or reset:\n break\n if i % 10 == 0:\n print('episode:', i, 'R:', R)\n if i % 50 == 0:\n print('statistics:', agent.get_statistics())\nprint('Finished.')","sub_path":"gfootball_ppo_load.py","file_name":"gfootball_ppo_load.py","file_ext":"py","file_size_in_byte":2900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"446511980","text":"import pytest\n\nimport cudf\nimport panel as pn\n\nfrom cuxfilter.charts.core.core_view_dataframe import ViewDataFrame\nfrom cuxfilter.charts.core.non_aggregate.core_stacked_line import (\n BaseStackedLine,\n)\nimport cuxfilter\nfrom cuxfilter.layouts import chart_view\n\n\nclass TestViewDataFrame:\n df = cudf.DataFrame(\n {\"key\": [0, 1, 2, 3, 4], \"val\": [float(i + 10) for i in range(5)]}\n )\n df_duplicates = cudf.DataFrame(\n {\"key\": [0, 1, 1, 1, 4], \"val\": [10, 11, 11, 11, 14]}\n )\n cux_df = cuxfilter.DataFrame.from_dataframe(df)\n dashboard = cux_df.dashboard(charts=[], title=\"test_title\")\n\n def test_variables(self):\n vd = ViewDataFrame()\n\n vd.columns is None\n vd._width == 400\n vd._height == 400\n vd.use_data_tiles is False\n vd.source is None\n vd.chart is None\n vd.drop_duplicates is False\n\n def test_initiate_chart(self):\n vd = ViewDataFrame()\n assert vd.columns is None\n\n vd.initiate_chart(self.dashboard)\n\n assert str(vd.chart) == str(\n pn.pane.HTML(\n self.df,\n css_classes=[\"panel-df\"],\n style={\n \"width\": \"100%\",\n \"height\": \"100%\",\n \"overflow-y\": \"auto\",\n \"font-size\": \"0.5vw\",\n \"overflow-x\": \"auto\",\n },\n )\n )\n assert vd.columns == list(self.df.columns)\n\n @pytest.mark.parametrize(\"chart, _chart\", [(None, None), (1, 1)])\n def test_view(self, chart, _chart):\n vd = ViewDataFrame()\n vd.chart = chart\n\n assert str(vd.view()) == str(\n chart_view(_chart, width=vd.width, title=\"Dataset View\")\n )\n\n @pytest.mark.parametrize(\"drop_duplicates\", [True, False])\n def test_reload_chart(self, drop_duplicates):\n vd = ViewDataFrame()\n vd.drop_duplicates = drop_duplicates\n vd.initiate_chart(self.dashboard)\n\n vd.reload_chart(self.df_duplicates, patch_update=False)\n\n if drop_duplicates:\n assert vd.chart[0].object.equals(\n self.df_duplicates.drop_duplicates()\n )\n else:\n assert vd.chart[0].object.equals(self.df_duplicates)\n\n @pytest.mark.parametrize(\n \"width, height, result1, result2\",\n [(400, 400, 400, 400), (None, None, 400, 400)],\n )\n def test_update_dimensions(self, width, height, result1, result2):\n vd = ViewDataFrame()\n vd.initiate_chart(self.dashboard)\n vd.width, vd.height = 400, 400\n vd.update_dimensions(width=width, height=height)\n\n assert vd.chart.width == result1\n assert vd.chart.height == result2\n\n def test_query_chart_by_range(self):\n bsl = ViewDataFrame()\n bsl_1 = BaseStackedLine(\"b\", [\"a\"])\n query_tuple = (4, 5)\n df = cudf.DataFrame({\"a\": [1, 2, 3, 4], \"b\": [3, 4, 5, 6]})\n bsl.source = df\n self.result = None\n self.patch_update = None\n\n def t_func(data, patch_update):\n self.result = data\n self.patch_update = patch_update\n\n # creating a dummy reload chart fn as its not implemented in core\n # non aggregate chart class\n bsl.reload_chart = t_func\n bsl.query_chart_by_range(\n active_chart=bsl_1, query_tuple=query_tuple, data=df\n )\n\n assert self.result.to_string() == \" a b\\n1 2 4\\n2 3 5\"\n assert self.patch_update is False\n\n @pytest.mark.parametrize(\n \"new_indices, result\",\n [\n ([4, 5], \" a b\\n1 2 4\\n2 3 5\"),\n ([], \" a b\\n0 1 3\\n1 2 4\\n2 3 5\\n3 4 6\"),\n ([3], \" a b\\n0 1 3\"),\n ],\n )\n def test_query_chart_by_indices(self, new_indices, result):\n bsl = ViewDataFrame()\n bsl_1 = BaseStackedLine(\"b\", [\"a\"])\n new_indices = new_indices\n df = cudf.DataFrame({\"a\": [1, 2, 3, 4], \"b\": [3, 4, 5, 6]})\n bsl.source = df\n self.result = None\n self.patch_update = None\n\n def t_func(data, patch_update):\n self.result = data\n self.patch_update = patch_update\n\n # creating a dummy reload chart fn as its not implemented in core\n # non aggregate chart class\n bsl.reload_chart = t_func\n bsl.query_chart_by_indices(\n active_chart=bsl_1,\n old_indices=[],\n new_indices=new_indices,\n data=df,\n )\n\n assert self.result.to_string() == result\n assert self.patch_update is False\n","sub_path":"python/cuxfilter/tests/charts/core/test_core_view_dataframe.py","file_name":"test_core_view_dataframe.py","file_ext":"py","file_size_in_byte":4594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"536276287","text":"# -*- coding: utf-8 -*-\n#\n# Copyright 2016, 2017 dpa-infocom GmbH\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.\nimport argparse\nimport asyncio\nimport functools\nimport logging\nimport signal\nimport livebridge.logger\nfrom argparse import Namespace\nfrom livebridge import config, LiveBridge\nfrom livebridge.controller import Controller\nfrom livebridge.components import get_db_client\nfrom livebridge.web import WebApi\nfrom livebridge.loader import load_extensions\n\n\ndef read_args(**kwargs):\n \"\"\"Read controlfile parameter.\"\"\"\n if kwargs.get(\"control\"):\n args = Namespace(control=kwargs[\"control\"])\n elif config.CONTROLFILE:\n args = Namespace(control=config.CONTROLFILE)\n elif config.DB.get(\"control_table_name\"):\n args = Namespace(control=\"sql\")\n elif config.AWS.get(\"control_table_name\"):\n args = Namespace(control=\"dynamodb\")\n else:\n # read cli args\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--control\", required=True, help=\"Control file, can be path.\")\n args = parser.parse_args()\n return args\n\n\ndef main(**kwargs):\n # disable bot logging\n logging.getLogger('botocore').setLevel(logging.ERROR)\n logging.getLogger('websockets').setLevel(logging.INFO)\n\n # read args\n args = read_args(**kwargs)\n\n # initialize loop\n loop = asyncio.get_event_loop() if not kwargs.get(\"loop\") else kwargs[\"loop\"]\n\n # load extensions\n load_extensions()\n\n # setup db table\n db_connector = get_db_client()\n loop.run_until_complete(db_connector.setup())\n\n # controller manages the tasks / data\n controller = Controller(config=config, control_file=args.control)\n asyncio.ensure_future(controller.run())\n\n # start http api\n if config.WEB.get(\"host\") and config.WEB.get(\"port\"):\n server = WebApi(config=config.WEB, controller=controller, loop=loop)\n\n # add signal handler\n for signame in ('SIGINT', 'SIGTERM'):\n loop.add_signal_handler(getattr(signal, signame), functools.partial(loop.stop))\n\n lb = LiveBridge(loop=loop, controller=controller)\n\n if kwargs.get(\"loop\"):\n return lb\n\n # run\n try:\n loop.run_forever()\n finally:\n if config.WEB.get(\"host\") and config.WEB.get(\"port\"):\n server.shutdown()\n lb.shutdown()\n loop.run_until_complete(db_connector.shutdown())\n loop.stop()\n loop.close()\n","sub_path":"livebridge/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":2898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"484139216","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# This file is part of CbM (https://github.com/ec-jrc/cbm).\n# Author : Guido Lemoine, Konstantinos Anastasakis\n# Credits : GTCAP Team\n# Copyright : 2021 European Commission, Joint Research Centre\n# License : 3-Clause BSD\n\n\"\"\"\nProject: Copernicus DIAS for CAP 'checks by monitoring'.\nFunctions:\n connection(db='main')\n - Create a new database session and return a new connection object.\n connection_cursor(db='main'):\n - Create a cursor to execute PostgreSQL command in a database session.\n information(db='main')\n - Get database connection information.\n get_value(dict_keys, var_name=None)\n - Get value a table's name as value based on the given dictionary keys.\n\nOptions:\n -h, --help Show this screen.\n --version Show version.\n\"\"\"\n\nimport psycopg2\nimport pandas as pd\nfrom cbm.utils import config\n\n\n# Database conection configurations\ndef crls(db='main'):\n try:\n # Database\n values = config.read()\n DB_HOST = values['db'][db]['host']\n DB_NAME = values['db'][db]['name']\n DB_SCHE = values['db'][db]['sche']\n DB_USER = values['db'][db]['user']\n DB_PORT = values['db'][db]['port']\n DB_PASS = values['db'][db]['pass']\n return DB_HOST, DB_NAME, DB_USER, DB_PORT, DB_PASS\n except Exception as err:\n print(f\"Err: Could not read config file: {err}\")\n\n\ndef conn_str(db='main'):\n \"\"\"Get the database connection string to connect the application to the database.\\\n You will need the database credentials for this to work (database server address,\\\n port, databese name, username and password).\"\"\"\n postgres = (\"host={} dbname={} user={} port={} password={}\"\n .format(*crls(db)))\n return postgres\n\n\ndef conn(db='main'):\n \"\"\"Create a new database session and return a new connection object.\"\"\"\n try:\n conn = psycopg2.connect(conn_str(db))\n return conn\n except (Exception, psycopg2.Error) as err:\n print(f\"Could not connect to the database {db}: {err}\")\n return ''\n\n\ndef conn_cur(db='main'):\n \"\"\"Create a cursor to execute PostgreSQL command in a database session\"\"\"\n try:\n conn = psycopg2.connect(conn_str(db))\n cur = conn.cursor()\n return cur\n except Exception:\n return ''\n\n\n# Geting informationand and data\ndef get_value(dict_keys, var_name='', db='main'):\n \"\"\"Get value for tables.\n\n Example:\n\n database.get_value(['database', 'table'], variable_table_name)\n\n Arguments:\n dict_keys, list of keys to get value from.\n var_name, the name ofthe variable\n\n \"\"\"\n config_value = config.get_value(dict_keys)\n value = config.autoselect(config_value, tables(db), True)\n if var_name is not None:\n if value == None:\n print(f\"!WARNING! The value for table '{var_name}' is: '{value}'.\")\n else:\n print(f\"The value for table '{var_name}' is: '{value}'.\")\n return value\n\n\ndef info(db='main'):\n \"\"\"Get postgres database connection information.\"\"\"\n try:\n conn = psycopg2.connect(conn_str(db))\n cur = conn.cursor()\n print(\"\\n--> PostgreSQL Connection properties for database: \", db)\n print(conn.get_dsn_parameters(), \"\\n\")\n # Print PostgreSQL version\n cur.execute(\"SELECT version();\")\n record = cur.fetchone()\n print(f\"You are connected to (db{db}) - {record}\\n\")\n except Exception as err:\n print(\"Error while connecting to PostgreSQL: \", err)\n print(\"Be sure the credentials are correct and restart the notebook.\")\n\n\ndef tables(db='main', matching_text=None, print_list=False):\n \"\"\"Get the database tables as a python list\"\"\"\n list_ = []\n try:\n conn = psycopg2.connect(conn_str(db))\n cur = conn.cursor()\n allTablesSql = \"\"\"\n SELECT table_name\n FROM information_schema.tables\n WHERE table_type='BASE TABLE'\n AND table_schema='public'\n ORDER BY table_name ASC;\n \"\"\"\n # Execute the query\n cur.execute(allTablesSql)\n for row in cur:\n list_.append(row[0])\n if matching_text is not None:\n value = config.autoselect(matching_text, list_)\n if value is not None:\n list_.remove(value)\n list_.insert(0, value)\n if print_list is True:\n print(f\"-Tables in database {db}:-\")\n for t_ in list_:\n print(t_)\n else:\n return list_\n except Exception as err:\n return []\n\n\ndef table_data(table, where, db='main'):\n \"\"\"Get the rows from a table with a limit 1000\"\"\"\n conn = psycopg2.connect(conn_str(db))\n try:\n getTableDataSql = f\"\"\"\n SELECT * FROM {table}\n WHERE \n LIMIT 1000;\n \"\"\"\n df_data = pd.read_sql_query(getTableDataSql, conn)\n except Exception:\n print(\"Did not found data, please select the right database and table\")\n df_data = pd.DataFrame(columns=['name'])\n return df_data\n\n\ndef table_columns(table, db='main', matching_text=None):\n \"\"\"Get a list of the columns from a table.\"\"\"\n conn = psycopg2.connect(conn_str(db))\n try:\n getTableColumns = f\"\"\"\n SELECT column_name\n FROM information_schema.columns\n WHERE table_name = '{table}';\n \"\"\"\n df_columns = pd.read_sql_query(getTableColumns, conn)\n columns_list = df_columns['column_name'].tolist()\n if matching_text is not None:\n value = config.autoselect(matching_text, columns_list)\n if value is not None:\n columns_list.remove(value)\n columns_list.insert(0, value)\n except Exception:\n print(\"Did not found columns, please select the right database and table\")\n df_columns = pd.DataFrame(columns=['column_name'])\n return columns_list\n\n\ndef close_conn(close_1='', close_2=''):\n \"\"\"Close the database connection\"\"\"\n try:\n conn.conn.close()\n print(\"The connection to the database is now closed.\")\n except Exception:\n pass\n try:\n conn.conn_2.close()\n print(\"The connection to the second database is now closed.\")\n except Exception:\n pass\n try:\n close_1.close()\n print(\"First argument is closed.\")\n except Exception:\n pass\n try:\n close_2.close()\n print(\"Second argument is closed.\")\n except Exception:\n pass\n\n\ndef insert_function(func, db='main'):\n \"\"\"\n Insert functions to database.\n Args:\n func: Sql text to add a new function to the database.\n type: srt\n db: Database configurarion\n \"\"\"\n import psycopg2.extras\n import psycopg2.extensions\n conn = psycopg2.connect(conn_str(db))\n conn.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\n cur = conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor)\n \n # Execute function.\n cur.execute(func)\n\n\ndef exact_count(table, db='main'):\n \"\"\"Return the exact count of rown of the given table\"\"\"\n conn = psycopg2.connect(conn_str(db))\n cur = conn.cursor()\n getExactCount = f\"\"\"\n SELECT count(*) AS exact_count\n FROM {table};\"\"\"\n \n cur.execute(getExactCount)\n return cur.fetchall()[0][0]\n\ndef execute_query(query, db='main'):\n \"\"\"Return query\"\"\"\n conn = psycopg2.connect(conn_str(db))\n cur = conn.cursor()\n cur.execute(query)\n data = cur.fetchall()\n conn.close()\n cur.close()\n return data\n\ndef execute_sql(sql, db='main'):\n \"\"\"Execute sql\"\"\"\n try:\n conn = psycopg2.connect(conn_str(db))\n cur = conn.cursor()\n cur.execute(sql)\n conn.commit() # <--- makes sure the change is shown in the database\n conn.close()\n cur.close()\n return 0\n except Exception as err:\n return 1\n\n\ndef tb_extent(table, db='main'):\n \"\"\"Get the extent of the table\"\"\"\n data = []\n try:\n conn = psycopg2.connect(conn_str(db))\n cur = conn.cursor()\n sql = f\"\"\"\n SELECT ST_AsText(ST_SetSRID(ST_Extent(st_transform(wkb_geometry, 4326)),4326))\n As bextent\n FROM {table};\"\"\"\n cur.execute(sql)\n for row in cur:\n data.append(row)\n conn.commit() # <--- makes sure the change is shown in the database\n conn.close()\n cur.close()\n data = data[0][0].replace(' ', '+')\n return data.replace('POLYGON((', '').replace('))', '')\n except Exception as err:\n return err\n\n\ndef tb_exist(table, db='main'):\n \"\"\"Check if table exist\"\"\"\n tbExistSql = f\"\"\"\n SELECT * FROM information_schema.tables\n WHERE table_name = '{table}';\n \"\"\"\n conn = psycopg2.connect(conn_str(db))\n cur = conn.cursor()\n cur.execute(tbExistSql)\n exist = bool(cur.rowcount)\n conn.close()\n cur.close()\n return exist\n","sub_path":"cbm/sources/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":8947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"59428470","text":"import socket\nimport threading\nimport hashlib\nimport os\nfrom datetime import datetime\nimport time\n\n\n# Puerto inicial, cada thread del servidor escuchara en un puetro diferente\n# Desde el 4001 hasta la cantidad de conexiones esperadas sumando de 1 en 1\nport = 4000\n\n\n# Se pregunta por consola la ubicacion/nombre del archivo y luego calcula el hash\n# Si el archivo no existe vuelve a preguntar\nwhile True:\n print(\"Escriba el nombre del archivo a transferir (Ej. 100.txt)\")\n nombreArchivo = input()\n try:\n file = open(nombreArchivo, 'rb')\n file_data = file.read()\n m = hashlib.sha256()\n m.update(file_data)\n hash = m.hexdigest()\n break\n except Exception as e :\n print(str(e))\n\n# Se pregunta la cantidad de las conexiones por consola\nprint(\"Escriba la cantidad de conexiones (Ej. 25) \")\ncantConexiones = int(input())\n\n#Variable para guardar la cantidad de conexiones que efectivamente se conectaron\nconexionesActuales = 0\n\n#Variable para guardar las conexiones que ya terminaron el envio\n# independientemente de si fue satisfactorio o no\nconexionesCompletadas = 0\n\n# Threadlock para modificar la variable de conexiones actuales\nthreadlock = threading.Lock()\n\n# Threadlock para modificar la variable de conexiones completadas\nthreadlock1 = threading.Lock()\n\n\n\n# Esta es la funcion que correra cada thread\n# Aqui se crear el socket, se realiza la conexion, se transmite el archivo\n# se confirma la transmicion y se cierra la conexion\ndef escuchar (puerto):\n # Se definen las variables que compartiran los threads\n global port\n global conexionesActuales\n global conexionesCompletadas\n global logList\n\n # Se crea el socket\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n # Unir el socket con una direccion ip y un puerto\n s.bind((\"\", puerto))\n\n # Acepta la conexion\n data, a = s.recvfrom(1024)\n print(\"Conexion con: \" + str(a))\n # Se guarda en el string del log de este thread, la ip y el puetro de la conexion\n logList[puerto-4001] += (\"Conexion con: \" + str(a)+'\\n')\n # Se aumenta la variable de conexiones actuales\n with threadlock:\n conexionesActuales += 1\n\n # Recibe el listo del cliente\n data = s.recvfrom(5)\n\n # Aqui espera a que todos lo cleintes se conecten\n while conexionesActuales < cantConexiones:\n time.sleep(1)\n\n # Envia el hash del archivo y recibe confirmacion de la recpcion\n s.sendto(hash.encode(\"utf-8\"), a)\n s.recvfrom(13)\n\n # Envia el tamano del archivo y recibe confirmacion de la recpcion\n s.sendto(str(os.path.getsize(nombreArchivo)).encode(\"utf-8\"), a)\n s.recvfrom(12)\n\n # Envia el nombre del archivo\n s.sendto(nombreArchivo.encode(\"utf-8\"), a)\n s.recvfrom(12)\n\n # Comienza a correr el tiempo de transmicion\n start_time = time.time()\n\n\n # Envia el archivo\n buf = 65000\n file = open(nombreArchivo, 'rb')\n file_data = file.read(buf)\n while(file_data):\n if(s.sendto(file_data,a)):\n file_data = file.read(buf)\n time.sleep(0.02)\n\n\n\n # Recibe la confirmacion\n resp, a = (s.recvfrom(29))\n resp = resp.decode(\"utf-8\")\n print(resp)\n # Recibe la cantidad de paquetes que recibio el cliente\n paquetes, a = s.recvfrom(1024)\n paquetes = int(paquetes.decode(\"utf-8\"))\n # Termina el tiempo con la recepcion de la confirmacion\n end_time = time.time()\n\n # Se cambia la variable indicando que termino el envio con este cliente\n with threadlock1:\n conexionesCompletadas +=1\n\n # Verifica si al confirmacion del hash fue satisfactoria o no y lo guarda en el log\n if resp == (\"Archivo Recibido y verificado\"):\n logList[puerto - 4001] += (\"Entrega exitosa\" + '\\n')\n else:\n logList[puerto - 4001] += (\"Entrega NO exitosa\" + '\\n')\n # Guarda el tiempo de transmicion en el log\n logList[puerto - 4001] += (\"Tiempo: \" + str(end_time - start_time) + '\\n')\n # Guarda la cantidad de paquetes en el log\n logList[puerto - 4001] += (\"Paquetes: \" +str(paquetes)+ \" tamano: \" +str(os.path.getsize(nombreArchivo))+ '\\n' +'\\n')\n # Cierra la conexion con ese cliente\n s.close()\n\n\n\n# Variable que cuenta la cantidad de threads creados\ncantConexiones2 = 0\n# Lista de Strings que guarda el string del log de cada cliente\nlogList = []\nprint(\"Escuchando...\")\n# Loop que creara tantos threads como conexiones se hayan indicado\nwhile cantConexiones2 \n \nCreates a user. Needs to be provided admin credentials in order to perform\nthe operation.\n\n ./manage.py registertodataclaypubliccontract \n \nPrepares a contract to the dataClay public classes (aka dc_classes, aka contrib\nclasses). If the namespace doesn't exist, it will be created.\n\n ./manage.py registermodule \n\nRegister a whole \"model\" folder, in which there are multiple classes and packages.\nIf the namespace doesn't exist, it will be created.\n\n ./manage.py __new__registermodel \n \nRegisters all StorageObject classes in a certain module (relative to the\ncurrent folder). The classes are registered onto the provided namespace\n(created if non-existent).\n\nA full default contract for all registered classes is created and its \nContractID is returned.\n\nThe user credentials should be provided (a \"registrator\" kind of user).\n\n ./manage.py registerdataset \n\nCreates a dataset and also its matching datacontract. The creation user and\nusage user is the same (provided user and password).\n\n ./manage.py getstubs \n \nGiven a ContractID (or a comma-separated list of ContractIDs) download its \nstubs into the provided path. The user should already have a contract to \naccess those classes.\n\"\"\"\n\n\ndef _execute_from_command_line(argv=None):\n client = getRuntime().ready_clients[\"@LM\"]\n\n if len(argv) < 2:\n print(\"You should provide a command to the tool.\", file=sys.stderr)\n print(USAGE_TEXT, file=sys.stderr)\n return\n\n # ToDo: A smarter strategy should be used here. At the moment, all commands\n # ToDo: are hardcoded and behaviour is programmed somewhat ad hoc.\n if argv[1] == \"createuser\":\n # Create a NORMAL_ROLE user.\n\n admin_id = client.get_account_id(argv[2])\n admin_credential = (None, argv[3])\n username = argv[4]\n password = argv[5]\n\n yaml_request_template = Template(\"\"\"\n---\n - !!es.bsc.dataclay.util.management.accountmgr.Account\n username: {{ username }}\n credential: !!es.bsc.dataclay.util.management.accountmgr.PasswordCredential\n password: {{ password }}\n role: NORMAL_ROLE\n\"\"\")\n\n yaml_request = yaml_request_template.render(\n username=username,\n password=password,\n )\n\n client.perform_set_of_new_accounts(admin_id,\n admin_credential,\n yaml_request)\n\n elif argv[1] == \"registertodataclaypubliccontract\":\n username = argv[2]\n credential = (None, argv[3])\n\n user_id = client.get_account_id(username)\n\n #########################################################################\n # First, we prepare the namespace:\n\n try:\n namespace_id = client.get_namespace_id(user_id, credential, \"dc_classes\")\n except Exception:\n yaml_request = \"\"\"\n---\n{namespace}: !!es.bsc.dataclay.util.management.namespacemgr.Namespace\n providerAccountName: {consumer_name}\n name: {namespace_name}\n language: LANG_PYTHON\n\"\"\".format(consumer_name=username,\n namespace=\"dc_classes\",\n namespace_name=\"dc_classes\")\n\n yaml_response = client.perform_set_of_operations(user_id, credential, yaml_request)\n response = dataclay_yaml_load(yaml_response)\n\n namespace_id = response[\"namespaces\"][\"dc_classes\"]\n\n #########################################################################\n # Then we prepare the classes\n from dataclay import contrib\n modules = contrib.MODULES_TO_REGISTER\n\n mfc = MetaClassFactory(namespace=\"dc_classes\",\n responsible_account=username)\n\n for m_str in modules:\n m = import_module(\"dataclay.contrib.%s\" % m_str)\n\n for c_str in getattr(m, \"CLASSES_TO_REGISTER\"):\n mfc.add_class(getattr(m, c_str))\n\n client = getRuntime().ready_clients[\"@LM\"]\n result = client.new_class(user_id, LANG_PYTHON, mfc.classes)\n\n if not result:\n raise RuntimeError(\"No classes successfully registered --cannot continue\")\n\n class_interface_template = Template(\"\"\"\n{{ brief_name }}interface: &{{ brief_name }}iface !!es.bsc.dataclay.util.management.interfacemgr.Interface\n providerAccountName: {{ username }}\n namespace: dc_classes\n classNamespace: dc_classes\n className: {{ class_name }}\n propertiesInIface: !!set {% if class_info.properties|length == 0 %} { } {% endif %}\n {% for property in class_info.properties %}\n ? {{ property.name }}\n {% endfor %}\n operationsSignatureInIface: !!set {% if class_info.operations|length == 0 %} { } {% endif %}\n {% for operation in class_info.operations %}\n ? {{ operation.nameAndDescriptor }}\n {% endfor %}\n\"\"\")\n\n class_interface_in_contract_template = Template(\"\"\"\n - !!es.bsc.dataclay.util.management.contractmgr.InterfaceInContract\n iface: *{{ brief_name }}iface\n implementationsSpecPerOperation: !!set {% if class_info.operations|length == 0 %} { } {% endif %}\n {% for operation in class_info.operations %}\n ? !!es.bsc.dataclay.util.management.contractmgr.OpImplementations\n operationSignature: {{ operation.nameAndDescriptor }}\n numLocalImpl: 0\n numRemoteImpl: 0\n {% endfor %}\n \"\"\")\n\n classes_render = list()\n incontract_render = list()\n for class_name, class_info in result.items():\n brief_name = class_name.rsplit(\".\", 1)[-1].lower()\n classes_render.append(class_interface_template.render(\n username=username,\n brief_name=brief_name,\n class_name=class_name,\n class_info=class_info))\n incontract_render.append(class_interface_in_contract_template.render(\n brief_name=brief_name, class_info=class_info\n ))\n\n yaml_request_template = Template(\"\"\"\n---\n{% for class_iface in class_interfaces %}\n{{ class_iface }}\n{% endfor %}\ncontribcontract: !!es.bsc.dataclay.util.management.contractmgr.Contract\n beginDate: 2012-09-10T20:00:03\n endDate: 2020-09-10T20:00:04\n namespace: dc_classes\n providerAccountID: {{ user_id }}\n applicantsAccountsIDs:\n ? {{ user_id }}\n interfacesInContractSpecs:\n {% for contract in contracts %}\n {{ contract }}\n {% endfor %}\n publicAvailable: True\n\"\"\")\n yaml_request = yaml_request_template.render(\n user_id=user_id,\n class_interfaces=classes_render,\n contracts=incontract_render\n )\n\n yaml_response = client.perform_set_of_operations(user_id, credential, yaml_request)\n response = dataclay_yaml_load(yaml_response)\n\n #########################################################################\n # Now (hopefully) the contract for the public classes has been obtained\n print(\" ===> The ContractID for the registered classes is:\", file=sys.stderr)\n print(response[\"contracts\"][\"contribcontract\"], file=sys.stderr)\n\n elif argv[1] == \"__new__registermodel\":\n namespace = argv[2]\n python_path = argv[3]\n username = argv[4]\n password = argv[5]\n\n # Ugly stuff related to the namespace first...\n credential = (None, password)\n user_id = client.get_account_id(username)\n\n yaml_request_template = Template(\"\"\"\n---\n{{ namespace }}: !!es.bsc.dataclay.util.management.namespacemgr.Namespace\n providerAccountName: {{ username }}\n name: {{ namespace }}\n language: LANG_PYTHON\n\"\"\")\n yaml_request = yaml_request_template.render(\n namespace=namespace,\n username=username\n )\n\n try:\n client.perform_set_of_operations(user_id, credential, yaml_request)\n except grpc.RpcError:\n # We assume that the namespace already exists\n pass\n\n # Then use the new register_model shiny stuff\n from .functions import register_model\n register_model(namespace=namespace,\n python_path=python_path,\n username=username,\n password=password)\n\n elif argv[1] == \"registermodule\":\n namespace = argv[2]\n module_name = argv[3]\n username = argv[4]\n credential = (None, argv[5])\n\n user_id = client.get_account_id(username)\n\n yaml_request_template = Template(\"\"\"\n---\n{{ namespace }}: !!es.bsc.dataclay.util.management.namespacemgr.Namespace\n providerAccountName: {{ username }}\n name: {{ namespace }}\n language: LANG_PYTHON\n\"\"\")\n yaml_request = yaml_request_template.render(\n namespace=namespace,\n username=username\n )\n\n try:\n client.perform_set_of_operations(user_id, credential, yaml_request)\n except grpc.RpcError:\n # We assume that the namespace already exists\n pass\n\n mfc = MetaClassFactory(namespace=namespace,\n responsible_account=username)\n\n # Scrap the classes in the module\n registered_classes = list()\n module = import_module(module_name)\n for thing_name in dir(module):\n thing = getattr(module, thing_name)\n\n if not isinstance(thing, ExecutionGateway):\n continue\n\n # Thing seems to be a DataClayObject class\n if thing.__module__ != module_name:\n print(\"The module for %s is %s, ignoring because it does not equals %s\" % (\n thing, thing.__module__, module_name\n ), file=sys.stderr)\n continue\n\n # Ok, that's a valid class\n mfc.add_class(thing)\n registered_classes.append(thing.__name__)\n\n registrator_id = client.get_account_id(username)\n result = client.new_class(registrator_id,\n credential,\n LANG_PYTHON,\n mfc.classes)\n \n print(\"Was gonna register: %s\\nEventually registered: %s\" % (\n registered_classes, result.keys()), file=sys.stderr)\n\n if len(result.keys()) == 0:\n print(\"No classes registered, exiting\", file=sys.stderr)\n return\n\n interfaces = list()\n interfaces_in_contract = list()\n\n for class_name, class_info in result.items():\n ref_class_name = class_name.replace('.', '')\n\n interfaces.append(Template(\"\"\"\n{{ class_name }}interface: &{{ ref_class_name }}iface !!es.bsc.dataclay.util.management.interfacemgr.Interface\n providerAccountName: {{ username }}\n namespace: {{ namespace }}\n classNamespace: {{ namespace }}\n className: \"{{ reg_class_name }}\"\n propertiesInIface: !!set {% if class_info.properties|length == 0 %} { } {% endif %}\n {% for property in class_info.properties %}\n ? {{ property.name }}\n {% endfor %}\n operationsSignatureInIface: !!set {% if class_info.operations|length == 0 %} { } {% endif %}\n {% for operation in class_info.operations %} \n ? {{ operation.nameAndDescriptor }}\n {% endfor %}\n\"\"\").render(\n class_name=ref_class_name,\n reg_class_name=class_name,\n username=username,\n namespace=namespace,\n class_info=class_info))\n\n interfaces_in_contract.append(Template(\"\"\"\n - !!es.bsc.dataclay.util.management.contractmgr.InterfaceInContract\n iface: *{{ class_name }}iface\n implementationsSpecPerOperation: !!set {% if class_info.operations|length == 0 %} { } {% endif %}\n {% for operation in class_info.operations %}\n ? !!es.bsc.dataclay.util.management.contractmgr.OpImplementations\n operationSignature: {{ operation.nameAndDescriptor }}\n numLocalImpl: 0\n numRemoteImpl: 0\n {% endfor %}\n\"\"\").render(\n class_name=ref_class_name,\n class_info=class_info))\n\n contract = Template(\"\"\"\n{{ namespace }}contract: !!es.bsc.dataclay.util.management.contractmgr.Contract\n beginDate: 1980-01-01T00:00:01\n endDate: 2055-12-31T23:59:58\n namespace: {{ namespace }}\n providerAccountID: {{ 00000000-00000000-00000000-00000000-00000000 }}\n applicantsNames:\n ? {{ username }}\n interfacesInContractSpecs:\n{{ interfaces_in_contract }}\n publicAvailable: True\n\"\"\").render(\n namespace=namespace,\n username=username,\n interfaces_in_contract=\"\\n\".join(interfaces_in_contract)\n )\n\n yaml_request = \"\\n\".join(interfaces) + contract\n print(\" ===> The yaml for performing is %s\" % (yaml_request), file=sys.stderr)\n\n yaml_response = client.perform_set_of_operations(user_id, credential, yaml_request)\n response = dataclay_yaml_load(yaml_response)\n\n print(\" ===> The ContractID for the registered classes is: \", file=sys.stderr)\n print(response[\"contracts\"][\"%scontract\" % namespace])\n\n elif argv[1] == \"registerdataset\":\n dataset = argv[2]\n username = argv[3]\n credential = (None, argv[4])\n\n user_id = client.get_account_id(username)\n\n yaml_request_template = Template(\"\"\"\n{{ dataset }}: !!es.bsc.dataclay.util.management.datasetmgr.DataSet\n dataClayID: {{ 11111111-00000000-00000000-00000000-00000000 }}\n providerAccountID: {{ 00000000-00000000-00000000-00000000-00000000 }}\n name: {{ dataset }}\n\n{{ dataset }}datacontract: !!es.bsc.dataclay.util.management.datacontractmgr.DataContract\n beginDate: 1980-01-01T00:00:01\n endDate: 2055-12-31T23:59:58\n providerAccountID: {{ 00000000-00000000-00000000-00000000-00000000 }}\n providerDataSetID: {{ 11111111-00000000-00000000-00000000-00000000 }}\n applicantsNames:\n ? {{ username }}\n publicAvailable: True\n\"\"\")\n yaml_request = yaml_request_template.render(\n dataset=dataset,\n username=username\n )\n\n try:\n client.perform_set_of_operations(user_id, credential, yaml_request)\n except grpc.RpcError as e:\n print(\"Tried to do that operation and received: %s\" % e , file=sys.stderr)\n\n elif argv[1] == \"getstubs\":\n # TODO: If this part is still used, check that contract_ids should be a list.\n contract_ids = map(UUID, argv[2].split(','))\n path = argv[3]\n username = argv[4]\n credential = (None, argv[5])\n\n user_id = client.get_account_id(username)\n\n prepare_storage(path)\n\n babel_data = client.get_babel_stubs(user_id,\n credential,\n contract_ids)\n\n with open(os.path.join(path, \"babelstubs.yml\"), 'wb') as f:\n f.write(babel_data)\n\n all_stubs = client.get_stubs(user_id, credential,\n LANG_PYTHON,\n contract_ids)\n\n for key, value in all_stubs.items():\n with open(os.path.join(path, key), 'wb') as f:\n f.write(value)\n\n deploy_stubs(path)\n else:\n print(\"Unknown command.\" , file=sys.stderr)\n print(USAGE_TEXT , file=sys.stderr)\n return\n\n\ndef execute_from_command_line(argv=None):\n \"\"\"Given the calling arguments to the manage.py script, do stuff.\n\n :param argv: Typically, sys.argv. Should be explicitly set by caller.\n :return: Nothing.\n \"\"\"\n # Perform implicit initialization of connections (client.properties only, no storage.properties)\n client_properties_path = os.getenv(\"DATACLAYCLIENTCONFIG\", \"./cfgfiles/client.properties\")\n assert client_properties_path, \"dataclay.tool module can only be called with DATACLAYCLIENTCONFIG set\"\n init_connection(client_properties_path)\n\n _execute_from_command_line(argv)\n\n # Do the cleanup to avoid __del__ messages of gRPC library\n finish()\n","sub_path":"src/dataclay/tool/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":17155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"308019149","text":"\"\"\"Support for LaMetric time.\"\"\"\nimport logging\n\nfrom lmnotify import LaMetricManager\nimport voluptuous as vol\n\nfrom homeassistant.const import CONF_CLIENT_ID, CONF_CLIENT_SECRET\nimport homeassistant.helpers.config_validation as cv\n\n_LOGGER = logging.getLogger(__name__)\n\n\nDOMAIN = \"lametric\"\n\nLAMETRIC_DEVICES = \"LAMETRIC_DEVICES\"\n\nCONFIG_SCHEMA = vol.Schema(\n {\n DOMAIN: vol.Schema(\n {\n vol.Required(CONF_CLIENT_ID): cv.string,\n vol.Required(CONF_CLIENT_SECRET): cv.string,\n }\n )\n },\n extra=vol.ALLOW_EXTRA,\n)\n\n\ndef setup(hass, config):\n \"\"\"Set up the LaMetricManager.\"\"\"\n _LOGGER.debug(\"Setting up LaMetric platform\")\n conf = config[DOMAIN]\n hlmn = HassLaMetricManager(\n client_id=conf[CONF_CLIENT_ID], client_secret=conf[CONF_CLIENT_SECRET]\n )\n devices = hlmn.manager.get_devices()\n if not devices:\n _LOGGER.error(\"No LaMetric devices found\")\n return False\n\n hass.data[DOMAIN] = hlmn\n for dev in devices:\n _LOGGER.debug(\"Discovered LaMetric device: %s\", dev)\n\n return True\n\n\nclass HassLaMetricManager:\n \"\"\"A class that encapsulated requests to the LaMetric manager.\"\"\"\n\n def __init__(self, client_id, client_secret):\n \"\"\"Initialize HassLaMetricManager and connect to LaMetric.\"\"\"\n\n _LOGGER.debug(\"Connecting to LaMetric\")\n self.manager = LaMetricManager(client_id, client_secret)\n self._client_id = client_id\n self._client_secret = client_secret\n","sub_path":"homeassistant/components/lametric/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"530241708","text":"import os\n\nfrom flask import Blueprint, render_template, request, jsonify, session \nimport requests\nfrom models.users import *\n\nmod = Blueprint('auth', __name__)\n\nCLIENT_ID = os.getenv('CLIENT_ID')\nCLIENT_SECRET = os.getenv('CLIENT_SECRET')\nAPP_ENV = os.getenv('APP_ENV', 'development')\n\nif (not CLIENT_ID or not CLIENT_SECRET):\n print('Create .env file with CLIENT_ID, CLIENT_SECRET')\n exit()\n\n@mod.route('/callback/vk/code')\ndef callback_vk():\n code = request.args.get('code')\n return render_template('authorize.html', code=code)\n\n@mod.route('/authorize/vk')\ndef authorize_vk():\n code = request.args.get('code')\n protocol = 'https'\n if APP_ENV == 'development':\n protocol = 'http'\n response_data = requests.post(\n 'https://oauth.vk.com/access_token',\n data={\n 'client_id': CLIENT_ID,\n 'client_secret': CLIENT_SECRET,\n 'redirect_uri':'{}://{}/callback/vk/code'.format(protocol, request.host),\n 'code':code\n }).json()\n access_token = response_data['access_token']\n user = requests.post(\n 'https://api.vk.com/method/users.get',\n data={\n 'user_ids':response_data['user_id'],\n 'fields':'photo_50',\n 'access_token':response_data['access_token'],\n 'v':'5.103'\n }).json()['response'][0]\n \n add_user_to_db(user)\n\n # Добавление сессии юзера\n session['user'] = user\n\n return user\n\n@mod.route('/callback/vk/access_token')\ndef callback_vk_access_token():\n access_token = response_data['access_token']\n response = requests.post(\n 'https://api.vk.com/method/users.get',\n data={\n 'user_ids':response_data['user_id'],\n 'fields':'photo_50',\n 'access_token':response_data['access_token'],\n 'v':'5.103'\n }).json()\n\n@mod.route('/logout')\ndef logout():\n session.pop('user', None)\n return 'OK'\n\n@mod.route('/client_id')\ndef client_id():\n return jsonify(CLIENT_ID)","sub_path":"views/auth.py","file_name":"auth.py","file_ext":"py","file_size_in_byte":2025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"529484286","text":"\"\"\"\nDjango settings for servicedogs project.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/1.6/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/1.6/ref/settings/\n\"\"\"\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nimport os, sys\nimport ConfigParser\nfrom django.conf.global_settings import TEMPLATE_CONTEXT_PROCESSORS\n\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\nif 'test' not in sys.argv: # pragma: no cover\n config = ConfigParser.RawConfigParser()\n config.readfp(open(os.path.join(BASE_DIR,'CONFIG.txt')))\n\n# Quick-start development settings - unsuitable for production\n# See https://docs.djangoproject.com/en/1.6/howto/deployment/checklist/\n\n# SECURITY WARNING: keep the secret key used in production secret!\nif 'test' in sys.argv:\n SECRET_KEY='testingsecretkey'\nelse: # pragma: no cover\n SECRET_KEY=config.get('SECRET','KEY')\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = True\n\nTEMPLATE_DEBUG = True\n\nALLOWED_HOSTS = []\n\nTEMPLATE_CONTEXT_PROCESSORS = (\"django.contrib.auth.context_processors.auth\",\n \"django.core.context_processors.debug\",\n \"django.core.context_processors.i18n\",\n \"django.core.context_processors.media\",\n \"django.core.context_processors.static\",\n \"django.core.context_processors.tz\",\n \"django.contrib.messages.context_processors.messages\",\n \"django.core.context_processors.request\")\n\n# Application definition\n\nINSTALLED_APPS = (\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n 'account',\n 'dogtrainer',\n 'clients',\n 'skills',\n 'dog',\n 'reports',\n 'packages.bootstrap3',\n)\n\nMIDDLEWARE_CLASSES = (\n 'packages.django_lockout.lockout.middleware.LockoutMiddleware',\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n)\n\nROOT_URLCONF = 'servicedogs.urls'\n\nWSGI_APPLICATION = 'servicedogs.wsgi.application'\n\n\n# Database\n# https://docs.djangoproject.com/en/1.6/ref/settings/#databases\n\n# Hack for Database setup for testing suites\nDATABASES = {}\nif 'test' in sys.argv:\n DATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.sqlite3',\n 'NAME': os.path.join(BASE_DIR, 'db.sqlite3')\n }\n }\n\nelse: # pragma: no cover\n try:\n DB_HOST = config.get('DATABASE', 'HOST')\n except ConfigParser.NoOptionError:\n DB_HOST = ''\n DATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.mysql',\n 'HOST': DB_HOST,\n 'NAME': config.get('DATABASE','NAME'),\n 'USER': config.get('DATABASE', 'USER'),\n 'PASSWORD': config.get('DATABASE', 'PASSWORD'),\n }\n }\n\n# Internationalization\n# https://docs.djangoproject.com/en/1.6/topics/i18n/\n\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'America/New_York'\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/1.6/howto/static-files/\n\nif DEBUG:\n STATIC_URL = '/servicedogs/static/'\nelse: # pragma: no cover\n STATIC_URL = '/static/'\n\nif DEBUG:\n STATIC_ROOT = os.path.join(BASE_DIR, 'servicedogs/static/')\nelse: # pragma: no cover\n STATIC_ROOT = os.path.dirname(BASE_DIR) + '/public/static'\n\n# Static Directory for local static files\nSTATICFILES_DIRS = (\n\tos.path.join(BASE_DIR, \"static_local\"),\n)\n\nTEMPLATE_DIRS = (\n os.path.join(BASE_DIR, 'templates'),\n)\n\nLOGIN_URL = 'account:user_login'\n\nLOGOUT_URL = 'account:logout'\n\nLOGIN_REDIRECT_URL = 'index'\n\nif 'test' not in sys.argv: # pragma: no cover\n EMAIL_HOST = config.get('EMAIL','HOST')\n\n EMAIL_HOST_USER = config.get('EMAIL','USER')\n\n EMAIL_HOST_PASSWORD = config.get('EMAIL','PASSWORD')\n\n EMAIL_PORT = config.getint('EMAIL','PORT')\n\n EMAIL_USE_TLS = config.get('EMAIL','USE_TLS')\n\n SERVER_EMAIL = DEFAULT_FROM_EMAIL = EMAIL_HOST_USER\n\nSESSION_EXPIRE_AT_BROWSER_CLOSE = True\n\n#Lockout\nLOCKOUT_MAX_ATTEMPTS = 20\n\nLOCKOUT_TIME = 60 * 15 #30 minute lockout\n\nLOCKOUT_ENFORCEMENT_WINDOW = 60 * 10 #period during which to count login attempts (10 minutes)\n\nMIN_PASSWORD_LENGTH = 6\n\nBOOTSTRAP3 = {\n\n # The URL to the jQuery JavaScript file\n 'jquery_url': '//code.jquery.com/jquery-1.11.1.min.js',\n\n # The Bootstrap base URL\n 'base_url': '//netdna.bootstrapcdn.com/bootstrap/3.2.0/',\n\n # The complete URL to the Bootstrap CSS file (None means derive it from base_url)\n 'css_url': None,\n\n # The complete URL to the Bootstrap CSS file (None means no theme)\n 'theme_url': None,\n\n # The complete URL to the Bootstrap JavaScript file (None means derive it from base_url)\n 'javascript_url': None,\n\n # Put JavaScript in the HEAD section of the HTML document (only relevant if you use bootstrap3.html)\n 'javascript_in_head': False,\n\n # Include jQuery with Bootstrap JavaScript (affects django-bootstrap3 template tags)\n 'include_jquery': True,\n\n # Label class to use in horizontal forms\n 'horizontal_label_class': 'col-md-2',\n\n # Field class to use in horiozntal forms\n 'horizontal_field_class': 'col-md-4',\n\n # Set HTML required attribute on required fields\n 'set_required': True,\n\n # Set placeholder attributes to label if no placeholder is provided\n 'set_placeholder': True,\n\n # Class to indicate required (better to set this in your Django form)\n 'required_css_class': 'bootstrap3-req',\n\n # Class to indicate error (better to set this in your Django form)\n 'error_css_class': 'has-error',\n\n # Class to indicate success, meaning the field has valid input (better to set this in your Django form)\n 'success_css_class': 'has-success',\n\n # Renderers (only set these if you have studied the source and understand the inner workings)\n 'formset_renderers':{\n 'default': 'packages.bootstrap3.renderers.FormsetRenderer',\n },\n 'form_renderers': {\n 'default': 'packages.bootstrap3.renderers.FormRenderer',\n },\n 'field_renderers': {\n 'default': 'packages.bootstrap3.renderers.FieldRenderer',\n 'inline': 'packages.bootstrap3.renderers.InlineFieldRenderer',\n },\n}\n","sub_path":"servicedogs/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":6737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"233023595","text":"import os\nfrom termcolor import colored\n\n#from pydrive.drive import GoogleDrive\n#from pydrive.auth import GoogleAuth\nimport time\n\nstatus = ['','']\n\n\ndef get_status():\n\tglobal status\n\treturn status\n\ndef resume_status():\n\tglobal status\n\tstatus[0] = ''\n\tstatus[1]= ''\n \ndef load_dz():\n print(\"Disk part started\" + colored(\"[OK]\",'green'))\n while True:\n glazovikovirivatel()\n\n\ndef glazovikovirivatel():\n print(\"W:Sync Disk--\" + colored(time.asctime(),'red')) \n global status\n f = open(\"numbers\",'r')\n numbers = \" \"\n numbers = f.read()\n num = [ '' , '' ]\n n = 0\n for i in numbers: \n if( i != '\\n' ):\n num[n] = num[n] + i\n else:\n n = n + 1\n fiz_num = int(num[0])\n info_num = int(num[1])\n \n f.close()\n os.system(\"grive -p /home/alex/Documents/Electronych/google -s /Школа\")\n fiz_list = os.listdir(\"/home/alex/Documents/Electronych/google/Школа/Физика/ДЗ\")\n info_list = os.listdir(\"/home/alex/Documents/Electronych/google/Школа/Информатика/Дз по информатике\")\n\n \n \n status = ['','']\n\n if(len(info_list) > info_num):\n status[0] = 'i'\n info_num = len(info_list) \n print(\"info is ready\") \n \n if(len(fiz_list) > fiz_num):\n status[1] = 'f'\n fiz_num = len(fiz_list)\n print(\"fiz is ready\")\n \n f = open(\"numbers\",\"w\")\n \n \n f.write(str(fiz_num) +'\\n')\n f.write(str(info_num) + '\\n')\n f.close()\n \n print(\"Physics: {} $$$ Informatics: {}\".format(len(fiz_list),len(info_list)))\n time.sleep(900) \n \n\n\n\n","sub_path":"disk.py","file_name":"disk.py","file_ext":"py","file_size_in_byte":1643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"441577289","text":"# Copyright (c) 2018 Sergey Barskov \n# MIT License: http://opensource.org/licenses/MIT\n\nfrom setuptools import setup\n\ninstall_requires = [\n 'vk-requests>=1.1.2',\n 'matplotlib',\n 'numpy'\n]\n\nwith open('README.md') as f:\n long_description = f.read()\n\nsetup(\n name='vkmsgstats',\n version='1.2.2',\n description='vk.com Message Statistics',\n long_description=long_description,\n long_description_content_type='text/markdown',\n author='Sergey Barskov',\n author_email='sergeybarskov@gmail.com',\n url='https://gitlab.com/JeXLiN/vkmsgstats',\n license='MIT',\n py_modules=['vkmsgstats'],\n entry_points={'console_scripts': ['vkmsgstats = vkmsgstats:main']},\n install_requires=install_requires,\n classifiers=[\n 'Development Status :: 5 - Production/Stable',\n 'Environment :: Console',\n 'Environment :: X11 Applications',\n 'Intended Audience :: End Users/Desktop',\n 'License :: OSI Approved :: MIT License',\n 'Operating System :: POSIX :: Linux',\n 'Operating System :: Microsoft :: Windows',\n 'Operating System :: MacOS',\n 'Programming Language :: Python :: 3',\n 'Topic :: Utilities',\n 'Topic :: Scientific/Engineering',\n ],\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"525392486","text":"#!/usr/bin/env python3\n\n\nR = '\\033[31m' # red\nG = '\\033[32m' # green\nC = '\\033[36m' # cyan\nW = '\\033[0m' # white\n\nold = input(G + '[+]' + C + ' Do you want to reuse previous configs? (Y/N) : ' + W)\n\nif old.lower() != 'y':\n redirect = input(G + '[+]' + C + ' Enter Target URL (YouTube,Blog etc) : ' + W)\n sitename = input(G + '[+]' + C + ' Site Name: ' + W)\n title = input(G + '[+]' + C + ' Title : ' + W)\n image_url = input(G + '[+]' + C + ' Image URL: ' + W)\n description = input(G + '[+]' + C + ' Description: ' + W)\n\n with open('template/custom_og_tags/js/location_temp.js', 'r') as js:\n \treader = js.read()\n \tupdate = reader.replace('REDIRECT_URL', redirect)\n\n with open('template/custom_og_tags/js/location.js', 'w') as js_update:\n \tjs_update.write(update)\n\n with open('template/custom_og_tags/index_temp.html', 'r') as index_temp:\n code = index_temp.read()\n code = code.replace('$SITE_NAME$', sitename)\n code = code.replace('$TITLE$', title)\n code = code.replace('$IMG_URL$', image_url)\n code = code.replace('$DESCRIPTION$', description)\n\n with open('template/custom_og_tags/index.html', 'w') as new_index:\n new_index.write(code)\n","sub_path":"template/mod_custom_og_tags.py","file_name":"mod_custom_og_tags.py","file_ext":"py","file_size_in_byte":1215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"24640980","text":"import os\nimport time\n\nfrom torch.autograd import Variable\n\nfrom cfgs.config_v2 import load_cfg_yamls\nfrom darknet_training_v3 import *\nfrom darknet_v3 import Darknet19\nfrom datasets.DataLoaderX import DataLoaderX\nfrom datasets.DetectionDataset import DetectionDataset\nfrom train.train_util_v2 import *\nfrom utils.barrier import Barrier\nfrom utils.timer import Timer\n\n\ndef read_ckp(cfg):\n try:\n ckp = open(cfg['train_output_dir'] + '/check_point.txt')\n start_epoch = int(ckp.readlines()[0])\n use_model = os.path.join(cfg['train_output_dir'], cfg['exp_name'] + '_' + str(start_epoch) + '.h5')\n except IOError:\n start_epoch = 0\n use_model = cfg['pretrained_model']\n return start_epoch, use_model\n\n\ndef train_main():\n choice = 0\n if choice == 0:\n dataset_yaml = '/home/cory/project/yolo2-pytorch/cfgs/config_crowdai.yaml'\n exp_yaml = '/home/cory/project/yolo2-pytorch/cfgs/exps/crowdai/crowdai_baseline.yaml'\n gpu_id = 1\n else:\n dataset_yaml = '/home/cory/project/yolo2-pytorch/cfgs/config_kitti.yaml'\n exp_yaml = '/home/cory/project/yolo2-pytorch/cfgs/exps/kitti/kitti_baseline_v3_fl.yaml'\n gpu_id = 1\n\n cfg = load_cfg_yamls([dataset_yaml, exp_yaml])\n\n # runtime setting\n os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)\n os.makedirs(cfg['train_output_dir'], exist_ok=True)\n\n enable_bbox_loss = True\n enable_iou_loss = True\n enable_class_loss = True\n\n # data loader\n batch_size = cfg['train_batch_size']\n dataset = DetectionDataset(cfg)\n print('load dataset succeeded')\n net = Darknet19(cfg)\n\n start_epoch, use_model = read_ckp(cfg)\n\n net_utils.load_net(use_model, net)\n net.cuda()\n net.train()\n print('load net succeeded')\n\n # show training parameters\n print('-------------------------------')\n print('pid', os.getpid())\n print('gpu_id', os.environ.get('CUDA_VISIBLE_DEVICES'))\n print('use_model', use_model)\n print('exp_name', cfg['exp_name'])\n print('dataset', cfg['dataset_name'])\n print('optimizer', cfg['optimizer'])\n print('opt_param', cfg['opt_param'])\n print('train_batch_size', cfg['train_batch_size'])\n print('start_epoch', start_epoch)\n print('lr', lookup_lr(cfg, start_epoch))\n print('inp_size', cfg['inp_size'])\n print('inp_size_candidates', cfg['inp_size_candidates'])\n print('-------------------------------')\n\n timer = Timer()\n try:\n for epoch in range(start_epoch, cfg['max_epoch']):\n time_epoch_begin = time.time()\n optimizer = get_optimizer(cfg, net, epoch)\n dataloader = DataLoaderX(dataset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=False)\n\n for step, data in enumerate(dataloader):\n timer.tic()\n barrier = Barrier()\n\n images, labels = data\n # debug_and_vis(data)\n\n im_data = Variable(images.cuda())\n barrier.add(1)\n\n bbox_pred, iou_pred, class_pred = net.forward(im_data)\n barrier.add(2)\n\n # build training target\n network_h = int(im_data.data.size()[2])\n network_w = int(im_data.data.size()[3])\n network_size_wh = np.array([network_w, network_h]) # (w, h)\n net_bbox_loss, net_iou_loss, net_class_loss = training_target(\n cfg, bbox_pred, class_pred, labels, network_size_wh, iou_pred)\n barrier.add(3)\n\n # backward\n optimizer.zero_grad()\n\n net_loss = 0\n if enable_bbox_loss:\n net_loss += net_bbox_loss\n if enable_iou_loss:\n net_loss += net_iou_loss\n if enable_class_loss:\n net_loss += net_class_loss\n\n net_loss.backward()\n optimizer.step()\n barrier.add(4)\n\n duration = timer.toc()\n if step % cfg['disp_interval'] == 0:\n # loss for this step\n bbox_loss = net_bbox_loss.data.cpu().numpy()[0]\n iou_loss = net_iou_loss.data.cpu().numpy()[0]\n cls_loss = net_class_loss.data.cpu().numpy()[0]\n train_loss = net_loss.data.cpu().numpy()[0]\n barrier.add(5)\n\n progress_in_epoch = (step + 1) * batch_size / len(dataset)\n print('epoch %d, step %d (%.2f %%) '\n 'loss: %.3f, bbox_loss: %.3f, iou_loss: %.3f, cls_loss: %.3f (%.2f s/batch)' % (\n epoch, step, progress_in_epoch * 100, train_loss, bbox_loss, iou_loss, cls_loss,\n duration))\n with open(cfg['train_output_dir'] + '/train.log', 'a+') as log:\n log.write('%d, %d, %.3f, %.3f, %.3f, %.3f, %.2f\\n' % (\n epoch, step, train_loss, bbox_loss, iou_loss, cls_loss, duration))\n timer.clear()\n barrier.add(6)\n # barrier.print()\n\n # epoch_done\n time_epoch_end = time.time()\n print('{:.2f} seconds for this epoch'.format(time_epoch_end - time_epoch_begin))\n\n # save trained weights\n ckp_epoch = epoch + 1\n save_name = os.path.join(cfg['train_output_dir'], '{}_{}.h5'.format(cfg['exp_name'], ckp_epoch))\n net_utils.save_net(save_name, net)\n print('save model: {}'.format(save_name))\n\n # update check_point file\n ckp = open(os.path.join(cfg['train_output_dir'], 'check_point.txt'), 'w')\n ckp.write(str(ckp_epoch))\n ckp.close()\n\n except KeyboardInterrupt:\n exit(1)\n\n\nif __name__ == '__main__':\n train_main()\n","sub_path":"train/train_dataset_v3.py","file_name":"train_dataset_v3.py","file_ext":"py","file_size_in_byte":5883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"577720529","text":"import os\r\npath=input(\"Enter your path: \")\r\n\r\nnew=os.listdir(path)\r\nfor each in new:\r\n\tnew_p=os.path.join(path,each)\r\n\tif os.path.isfile(new_p):\r\n\t\tprint(f\"{new_p}is a file\")\r\n\telse:\r\n\t\tprint(f\"{new_p} is a dir\")\r\n","sub_path":"Python_Modules/dir_file_with tag.py","file_name":"dir_file_with tag.py","file_ext":"py","file_size_in_byte":214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"617859752","text":"from __future__ import unicode_literals\nfrom django.contrib.admin.widgets import ForeignKeyRawIdWidget\nfrom django.contrib.admin.templatetags.admin_static import static\nfrom django.core.urlresolvers import reverse\nfrom django.forms.widgets import Select\nfrom django.forms.utils import flatatt\nfrom django.utils.html import escape, format_html\nfrom django.utils.encoding import force_text\nfrom django.utils.text import Truncator\nfrom django.utils.translation import ugettext as _\nfrom django.utils.safestring import mark_safe\n\n\nclass LinkedSelect(Select):\n def render(self, name, value, attrs=None, choices=()):\n if value is None:\n value = ''\n final_attrs = self.build_attrs(attrs, name=name)\n output = [format_html('', flatatt(final_attrs))]\n options = self.render_options(choices, [value])\n if options:\n output.append(options)\n\n output.append(\n ' edit'\n )\n return mark_safe('\\n'.join(output))\n\n\nclass LinkedForeignKeyRawIdWidget(ForeignKeyRawIdWidget):\n \"\"\"\n usage:\n from django.contrib.admin.sites import site\n\n widgets = {\n 'your_field': LinkedForeignKeyRawIdWidget(YourModel._meta.get_field('your_field').rel, site),\n }\n \"\"\"\n class Media:\n css = {\n 'all': ('linked_in_admin/css/linkedRawId.css',)\n }\n js = ('linked_in_admin/js/linkedRawId.js',)\n\n def render(self, name, value, attrs=None):\n rel_to = self.rel.to\n if attrs is None:\n attrs = {}\n extra = []\n if rel_to in self.admin_site._registry:\n # The related object is registered with the same AdminSite\n related_url = reverse(\n 'admin:%s_%s_changelist' % (\n rel_to._meta.app_label,\n rel_to._meta.model_name,\n ),\n current_app=self.admin_site.name,\n )\n\n params = self.url_parameters()\n if params:\n url = '?' + '&'.join('%s=%s' % (k, v) for k, v in params.items())\n else:\n url = ''\n if \"class\" not in attrs:\n attrs['class'] = 'vForeignKeyRawIdAdminField linkedRawId' # The JavaScript code looks for this hook.\n # TODO: \"lookup_id_\" is hard-coded here. This should instead use\n # the correct API to determine the ID dynamically.\n extra.append(\n ' ' %\n (related_url, url, name))\n extra.append('\"%s\"' %\n (static('admin/img/selector-search.gif'), _('Lookup')))\n output = [super(ForeignKeyRawIdWidget, self).render(name, value, attrs)] + extra\n if value:\n output.append(self.label_for_value(value))\n return mark_safe(''.join(output))\n\n def label_for_value(self, value):\n rel_to = self.rel.to\n related_url = ''\n if rel_to in self.admin_site._registry:\n # The related object is registered with the same AdminSite\n related_url = reverse(\n 'admin:%s_%s_changelist' % (\n rel_to._meta.app_label,\n rel_to._meta.model_name,\n ),\n current_app=self.admin_site.name,\n )\n key = self.rel.get_related_field().name\n try:\n obj = self.rel.to._default_manager.using(self.db).get(**{key: value})\n return ' ' % (\n related_url, escape(Truncator(obj).words(14, truncate='...')))\n except (ValueError, self.rel.to.DoesNotExist):\n return ''\n\n\nclass LinkedManyToManyRawIdWidget(ForeignKeyRawIdWidget):\n class Media:\n css = {\n 'all': ('linked_in_admin/css/linkedRawId.css',)\n }\n js = ('linked_in_admin/js/linkedRawId.js',)\n\n def render(self, name, value, attrs=None):\n if attrs is None:\n attrs = {}\n if self.rel.to in self.admin_site._registry:\n # The related object is registered with the same AdminSite\n attrs['class'] = 'vManyToManyRawIdAdminField linkedRawId'\n if value:\n value = ','.join(force_text(v) for v in value)\n else:\n value = ''\n return super(LinkedManyToManyRawIdWidget, self).render(name, value, attrs)\n\n def url_parameters(self):\n return self.base_url_parameters()\n\n def label_for_value(self, value):\n result = ''\n if len(value.split(',')) < 5:\n rel_to = self.rel.to\n related_url = ''\n if rel_to in self.admin_site._registry:\n # The related object is registered with the same AdminSite\n related_url = reverse(\n 'admin:%s_%s_changelist' % (\n rel_to._meta.app_label,\n rel_to._meta.model_name,\n ),\n current_app=self.admin_site.name,\n )\n key = self.rel.get_related_field().name\n key = 'id__in'\n\n try:\n objs = self.rel.to._default_manager.using(self.db).filter(**{key: value.split(',')})\n if objs:\n result = 'edit'\n except (ValueError, self.rel.to.DoesNotExist):\n return 'error'\n return result\n\n def value_from_datadict(self, data, files, name):\n value = data.get(name)\n if value:\n return value.split(',')\n","sub_path":"libs/linked_in_admin/widgets.py","file_name":"widgets.py","file_ext":"py","file_size_in_byte":6194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"622056342","text":"# coding: utf-8\n\"\"\" The flask API app. \"\"\"\nfrom __future__ import absolute_import\nfrom flask import Flask, jsonify, request\nimport settings\nfrom celery import Celery\nimport requests\n\n# Create a flask app\n\napp = Flask(__name__)\napp.config.from_object(settings)\n\n\ndef make_celery(app):\n \"\"\" Create the celery wrapper. \"\"\"\n celery = Celery('app', broker=app.config['BROKER_URL'])\n celery.conf.update(settings.__dict__)\n TaskBase = celery.Task\n\n class ContextTask(TaskBase):\n abstract = True\n\n def __call__(self, *args, **kwargs):\n with app.app_context():\n return TaskBase.__call__(self, *args, **kwargs)\n celery.Task = ContextTask\n return celery\n\ncelery = make_celery(app)\n\n#=======================\n\n\n# celery async call\n@celery.task(name=\"async_sum\")\ndef async_sum(x, y):\n \"\"\"\n Sum two numbers.\n\n This will be the async call.\n \"\"\"\n return x + y\n\n\n# flask API endpoint\n@app.route(\"/sum\")\ndef make_sum(x=16, y=16):\n \"\"\"\n Put the \"async_sum\" method on the celery/rabbitmq queue.\n\n (that will make it run asynchronously.)\n \"\"\"\n x = int(request.args.get(\"x\", x))\n y = int(request.args.get(\"y\", y))\n\n call = async_sum.apply_async([x, y])\n context = {\"id\": call.task_id, \"x\": x, \"y\": y}\n called = \"async_sum((x){}, (y){})\".format(context['x'], context['y'])\n task_id = \"{}\".format(context['id'])\n return jsonify(called=called, task_id=task_id)\n\n\n# flask API endpoint\n@app.route(\"/sum/result/\")\ndef get_sum_result(task_id):\n \"\"\" Show the \"async_sum\" method async task result. \"\"\"\n result = async_sum.AsyncResult(task_id).get(\n timeout=settings.CELERY_TASK_RESULT_EXPIRES)\n return jsonify(task_id=task_id, result=str(result))\n\n#=======================\n\n\n# celery async call\n@celery.task(name='async_gen_prime')\ndef async_gen_prime(x):\n \"\"\" Return all primes until a given number. \"\"\"\n multiples = []\n results = []\n for i in xrange(2, x+1):\n if i not in multiples:\n results.append(i)\n for j in xrange(i*i, x+1, i):\n multiples.append(j)\n return results\n\n\n# flask API endpoint\n@app.route(\"/primes\")\ndef make_primes(x=100):\n \"\"\"\n Put the \"async_gen_prime\" method on the celery/rabbitmq queue.\n\n (that will make it run asynchronously.)\n \"\"\"\n x = int(request.args.get(\"x\", x))\n\n call = async_gen_prime.apply_async([x])\n context = {\"id\": call.task_id, \"x\": x}\n called = \"async_gen_prime((x){})\".format(context['x'])\n task_id = \"{}\".format(context['id'])\n return jsonify(called=called, task_id=task_id)\n\n\n# flask API endpoint\n@app.route(\"/primes/result/\")\ndef get_primes_result(task_id):\n \"\"\" Show the \"async_sum\" method async task result. \"\"\"\n result = async_gen_prime.AsyncResult(task_id).get(\n timeout=settings.CELERY_TASK_RESULT_EXPIRES)\n return jsonify(task_id=task_id, result=str(result))\n\n#=======================\n\n\n# celery async call\n@celery.task(name='async_save_page')\ndef async_save_page(url, file_name):\n \"\"\" Save a given page contents to disk. \"\"\"\n response = requests.get(url)\n if response.status_code == requests.codes.ok:\n f = open(file_name, 'wb')\n f.write(response.text.encode('utf-8'))\n f.close()\n\n\n# flask API endpoint\n@app.route(\"/save_page\")\ndef save_page():\n \"\"\"\n Put the \"async_gen_prime\" method on the celery/rabbitmq queue.\n\n (that will make it run asynchronously.)\n \"\"\"\n url = 'https://news.ycombinator.com/'\n file_name = '/tmp/hn.html'\n\n call = async_save_page.apply_async([url, file_name])\n context = {\"id\": call.task_id, \"url\": url, \"file_name\": file_name}\n called = \"async_save_page((url){},(file_name){})\".format(\n context['url'],\n context['file_name'])\n task_id = \"{}\".format(context['id'])\n return jsonify(called=called, task_id=task_id)\n\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=5000, debug=True)\n","sub_path":"celery/async_tests_with_flask_celery_rabbitmq/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3959,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"108368235","text":"def factorial(n):\n '''\n Returns the factorial of the input integer:\n n * (n - 1) * (n - 2) * ... * 2 * 1\n Parameters\n ----------\n n: {int} number to compute factorial of (must be greater than 0)\n\n Returns\n -------\n n!: {int} factorial of n\n\n '''\n\n factorial = 1\n\n # check if the number is negative, positive or zero\n if n < 0:\n print(\"Sorry, factorial does not exist for negative numbers\")\n elif n == 0:\n print(\"The factorial of 0 is 1\")\n else:\n for i in range(1,n + 1):\n factorial = factorial*i\n\n return factorial\n","sub_path":"Python Basics/Functions/factorial.py","file_name":"factorial.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"232569774","text":"\"\"\" Parse tsrc config files \"\"\"\n\nfrom path import Path\nimport ruamel.yaml\nimport schema\nfrom typing import Any, Dict, NewType, Optional\n\nimport tsrc\n\nConfig = NewType(\"Config\", Dict[str, Any])\n\n\ndef parse_config(\n file_path: Path, config_schema: Optional[schema.Schema] = None\n) -> Config:\n try:\n contents = file_path.read_text()\n except OSError as os_error:\n raise tsrc.InvalidConfig(file_path, os_error)\n try:\n yaml = ruamel.yaml.YAML(typ=\"safe\", pure=True)\n parsed = yaml.load(contents)\n except ruamel.yaml.error.YAMLError as yaml_error:\n raise tsrc.InvalidConfig(file_path, yaml_error)\n if config_schema:\n try:\n config_schema.validate(parsed)\n except schema.SchemaError as schema_error:\n raise tsrc.InvalidConfig(file_path, schema_error)\n return Config(parsed)\n","sub_path":"tsrc/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"330255203","text":"#!/usr/bin/python\r\n# Author: chenxiancai\r\n# Date: 2015-10-21\r\nimport time\r\nimport re\r\n\r\nfrom libcom.lib_pub.logging_drv import log_info\r\nfrom libcom.console_drv.console_drv import Console\r\nfrom libcom.lib_cmd.basic_cmd import BasicCmd\r\nfrom cases_set.monitor.monitor_cli_fan import *\r\n\r\n__all__ = [\"monitor_set_temp\"]\r\n\r\nSUCCESS = 0\r\nFAIL = -1\r\n\r\ndef cli_handle(con, type, tpye_choose, temp):\r\n ret = SUCCESS\r\n \r\n cmd = \" threshold set temperature \" + type + \" \" + tpye_choose + \" %d \" % temp\r\n con.run_cmd(cmd)\r\n info = con.run_cmd(\" show temperature\")\r\n if info.find(str(temp)) == -1:\r\n log_info(\"set temperature error\")\r\n ret = FAIL\r\n time.sleep(1) \r\n return ret\r\n\r\ndef restore_set(con):\r\n \r\n con.run_cmd(\" config\")\r\n con.run_cmd(\" default threshold set temperature\")\r\n con.run_cmd(\" show temperature\")\r\n con.run_cmd(\" exit\")\r\n time.sleep(1) \r\n\r\ndef set_tpye_temp(con, type, temp):\r\n ret = SUCCESS \r\n \r\n con.run_cmd(\" config\")\r\n \r\n if type.find(\"board\") != -1:\r\n if temp in range(56, 80):\r\n tpye_choose = \"warning\"\r\n ret = cli_handle(con, type, tpye_choose, temp)\r\n elif temp in range(81, 100):\r\n tpye_choose = \"shutdown\"\r\n ret = cli_handle(con, type, tpye_choose, temp)\r\n else:\r\n log_info(\"set board temperature is %d fail\" % temp)\r\n ret = FAIL\r\n elif type.find(\"cpu\") != -1 or type.find(\"mac\") != -1:\r\n tpye_choose = \"shutdown\"\r\n if temp in range(100, 110):\r\n temp = temp\r\n ret = cli_handle(con, type, tpye_choose, temp)\r\n else:\r\n log_info(\"set shutdown temperature is %d fail ,the temperature range 100 to 110\" % temp) \r\n ret = FAIL \r\n else:\r\n log_info(\"inut type fail\")\r\n ret = FAIL\r\n \r\n con.run_cmd(\" exit\")\r\n return ret\r\n\r\ndef _monitor_set_temp(cb_arg):\r\n dev_key = cb_arg.dev_names[0]\r\n con = Console(dev_key)\r\n \r\n ret = SUCCESS \r\n \r\n ret = set_tpye_temp(con, \"mac\", 104) \r\n #time.sleep(5)\r\n #ret = set_tpye_temp(con,\"cpu\",102) \r\n #time.sleep(5)\r\n #ret = set_tpye_temp(con,\"board\",78)\r\n #time.sleep(5)\r\n \r\n restore_set(con)\r\n time.sleep(5) \r\n return ret\r\n\r\ndef monitor_set_temp(cb_arg):\r\n if len(cb_arg.dev_names) == 0:\r\n log_info(\"Failed: Need one switch to be test.\")\r\n return FAIL\r\n\r\n dev_name = cb_arg.dev_names[0]\r\n con = Console(dev_name)\r\n con.wake_up()\r\n result = FAIL\r\n try:\r\n result = _monitor_set_temp(cb_arg)\r\n finally:\r\n con.exit()\r\n return result","sub_path":"cases_set/monitor/monitor_set_temp.py","file_name":"monitor_set_temp.py","file_ext":"py","file_size_in_byte":2637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"553723291","text":"\"Module for generator of dictionery from two lists, that can differ in length\"\n\n# KEYS = [1, 2, 3, 4, 5]\n# VALUES = ['a', 'b', 'c', 'd', 'e', 'f', 'g']\n\nKEYS = [1, 2, 3, 4, 5]\nVALUES = ['a', 'b', 'c']\n\nDICT = {KEYS[i]: (VALUES[i] if i < len(VALUES) else None) for i in range(len(KEYS))}\nprint(DICT)\n","sub_path":"Lesson 6/task_3.py","file_name":"task_3.py","file_ext":"py","file_size_in_byte":299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"96330396","text":"from adaboost.common import constants\r\n\r\n# Find difference from estimator to next nearest interval range\r\ndef interval_difference(interval, estimators):\r\n difference = interval - (estimators % interval)\r\n if difference != interval:\r\n return difference\r\n\r\n return 0\r\n\r\n\r\n# Static major interval spacings for readable graph plotting\r\ndef lookup_interval_major(estimators):\r\n if estimators >= 150:\r\n return 50\r\n\r\n if estimators >= 80:\r\n return 20\r\n\r\n if estimators >= 40:\r\n return 10\r\n\r\n if estimators >= 20:\r\n return 5\r\n\r\n if estimators >= 10:\r\n return 2\r\n\r\n # Default major interval spacing\r\n return 1\r\n\r\n\r\n# Static minor interval spacings for readable graph plotting\r\ndef lookup_interval_minor(interval):\r\n if interval >= 5:\r\n return 5\r\n\r\n # Default minor interval spacing\r\n return 4\r\n\r\n\r\n# Set maximum x-limit of plot along x-axis\r\ndef set_max_xlim(estimators):\r\n xlim_max = estimators\r\n if xlim_max > 10:\r\n xlim_max = estimators + interval_difference(lookup_interval_major(estimators), estimators)\r\n\r\n if constants.OUTPUT_DETAIL is True:\r\n print(\" --set Plot-XAxis-Max: %s\" % (xlim_max))\r\n\r\n return xlim_max\r\n\r\n\r\n# Set major interval spacing for along x-axis\r\ndef set_major_xaxis(estimators):\r\n interval = lookup_interval_major(estimators)\r\n if constants.OUTPUT_DETAIL is True:\r\n print(\" --set Plot-XAxis-Interval-Major: %s\" % (interval))\r\n\r\n return interval\r\n\r\n\r\n# Set minor interval spacing for along x-axis\r\ndef set_minor_xaxis(estimators):\r\n major_interval = lookup_interval_major(estimators)\r\n minor_interval = lookup_interval_minor(major_interval)\r\n\r\n if constants.OUTPUT_DETAIL is True:\r\n print(\" --set Plot-XAxis-Interval-Minor: %s\" % (minor_interval))\r\n\r\n return minor_interval\r\n","sub_path":"adaboost/plotting/methods.py","file_name":"methods.py","file_ext":"py","file_size_in_byte":1750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"344527627","text":"__author__ = 'PSH'\n\nimport pygame\n\npygame.init()\n\ndisplay_width = 800\ndisplay_height = 600\n\n\n\ngame_display = pygame.display.set_mode((display_width, display_height))\npygame.display.set_caption('10')\n\n\ngame_exit = False\nclock = pygame.time.Clock()\n\nsmallfont = pygame.font.SysFont(\"comicsansms\", 25)\nmedfont = pygame.font.SysFont(\"comicsansms\", 50)\nlargefont = pygame.font.SysFont(\"comicsansms\", 80)\n\n\nwhite = (255, 255, 255)\nblack = (0, 0, 0)\nred = (221, 25, 65)\nyellow = (236, 224, 18)\ngreen = (94, 170, 72)\ngray = (148, 149, 151)\nviolet = (80, 31, 94)\nskyblue = (101, 187, 234)\ncobaltblue = (30, 134, 158)\npupple = (83, 91, 136)\n\n\nwhile not game_exit:\n\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n game_exit = True\n\n\n game_display.fill(white)\n\n\n\n pygame.draw.rect(game_display, green, (350, 400, 100, 50))\n pygame.draw.rect(game_display, red, (350, 470, 100, 50))\n\n\n pygame.display.update()\n clock.tick(60)\n\npygame.quit()\nquit()","sub_path":"civil-final/2nd_presentation/9조/PSH.py","file_name":"PSH.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"594750957","text":"import os\nimport urllib.parse\n\nBASE_URL = 'https://vt3.herokuapp.com'\nBUG_REPORTING_EMAIL = os.environ['BUG_REPORTING_EMAIL']\nDEBUG = False\nurllib.parse.uses_netloc.append(\"postgres\")\nPG_URL = urllib.parse.urlparse(os.environ[\"DATABASE_URL\"])\nSECRET_KEY = os.environ[\"SECRET_KEY\"]\nSENDGRID_API_KEY = os.environ['SENDGRID_API_KEY']\nWTF_CSRF_ENABLED = True\n\n","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"8085719","text":"import requests\n\n# 0. start Flask web server as described in upload-flask.py\n# 1. ensure a 'lorem.txt' file is present in the local directory\n# 2. python upload-python.py\n# 3. 'lorem.txt' is now in 'uploads' subdir\n\nurl = 'http://127.0.0.1:5001'\nfiles = {'file': open('lorem.txt', 'r')}\nr = requests.post(url, files=files)\nprint('Status code:', r.status_code)","sub_path":"upload-python.py","file_name":"upload-python.py","file_ext":"py","file_size_in_byte":359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"306866339","text":"\"Write a Program to find factorial of the entered number\"\ndef factorial(number: int):\n if number == 1:\n return 1\n elif number == 0:\n return 1\n elif number < 0:\n return 'NaN'\n else:\n return number * factorial(number - 1)\n\ndef main():\n __input = int(input(\"Enter Integer to Calculate Factorial: \"))\n print(f\"Factorial of {__input} is {factorial(__input)}\")\n \nif __name__ == \"__main__\":\n main() \n \n__OUTPUT__ = \"\"\"\nEnter Integer to Calculate Factorial: 4\nFactorial of 4 is 24\n\"\"\"\n","sub_path":"6_factorial.py","file_name":"6_factorial.py","file_ext":"py","file_size_in_byte":532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"594590253","text":"# -*- coding: utf-8 -*-\n\n#%% 01. 라이브러리 불러오기 \nimport pytube\nimport os\nimport subprocess # python 실행시키면서 별도의 프로세스 생성 \n\n#%% 02. \nyt = pytube.YouTube(\"https://www.youtube.com/watch?v=Hd7AL7gBtlg&list=PLWvahZRxLnLMSWeULnub0CCeQzvxGM8hB\")\nvideos = yt.streams.all()\n\nfor i in range(len(videos)):\n print(i, ' , ', videos[i])\n \ncNum = int(input('다운받을 화질 번호?'))\n\ndown_dir = \"c:\\youtube\"\nvideos[cNum].download(down_dir)\n\n#%% 03.\nnewFileName = input(\"변환할 mp3 파일명?\")\noriFileName = videos[cNum].default_filename\nprint(oriFileName)\n\nsubprocess.call([ 'ffmpeg', '-i',\n os.path.join( down_dir, oriFileName), \n os.path.join( down_dir, newFileName)\n])\n \nprint(\"동영상 다운로드 및 mp3 변환 완료!\")\n","sub_path":"inf_WebDataCra/youtube01_02.py","file_name":"youtube01_02.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"294078478","text":"import FWCore.ParameterSet.Config as cms\n\nprocess = cms.Process(\"TEST\")\n#process.load(\"MagneticField.Engine.volumeBasedMagneticField_1103l_cfi\")\nprocess.load(\"Configuration.StandardSequences.MagneticField_cff\")\n\nprocess.load(\"TrackPropagation.SteppingHelixPropagator.SteppingHelixPropagatorAny_cfi\")\nprocess.load(\"Configuration.StandardSequences.Geometry_cff\")\n#process.load(\"Geometry.CMSCommonData.cmsIdealGeometryXML_cff\")\nprocess.load(\"Geometry.CommonDetUnit.globalTrackingGeometry_cfi\")\nprocess.load(\"Geometry.CaloEventSetup.CaloGeometry_cff\")\n\nprocess.load(\"Configuration.StandardSequences.FrontierConditions_GlobalTag_cff\")\nprocess.GlobalTag.globaltag = 'IDEAL_V9::All'\n\n#from TrackingTools.TrackAssociator.default_cfi import * \n\nprocess.source = cms.Source(\"PoolSource\",\n debugFlag = cms.untracked.bool(False),\n debugVebosity = cms.untracked.uint32(10),\n skipEvents = cms.untracked.uint32(SKIPNUM),\n fileNames = cms.untracked.vstring(\n#'file:/data/top/sckao/MuEnrichedQCD/QCD_PAT1_1.root',\n#'file:/data/top/sckao/MuEnrichedQCD/QCD_PAT1_2.root',\n#'file:/data/top/sckao/MuEnrichedQCD/QCD_PAT1_3.root',\n'file:/data/top/sckao/MuEnrichedQCDSkim/FuckPAT.root'\n#'file:/data/top/sckao/Tt210Full/RelValTt214_full4.root'\n )\n)\n\n# replace the source files from a file list\n#import PhysicsTools.TtAnalysis.qcdlist_cff as fileList\nimport PhysicsTools.TtAnalysis.qcdlist_skim1 as fileList\nprocess.source.fileNames = fileList.fileNames\n\nprocess.maxEvents = cms.untracked.PSet(\n # 1 jet filter ; 2841508 for 100 /pb , every 56830 => 2 /pb\n input = cms.untracked.int32(56830)\n)\nprocess.MessageLogger = cms.Service(\"MessageLogger\")\n\nprocess.ttAna = cms.EDFilter(\"TtAnalysis\",\n\n #TrackAssociatorParameterBlock,\n #TrackAssociatorParameters, \n debug = cms.untracked.bool(False),\n btag = cms.untracked.bool(False),\n needTree = cms.untracked.bool(False),\n trigOn = cms.untracked.bool(False),\n rootFileName = cms.untracked.string('/data/top/sckao/Fall08QCDAna/ANAFILE'),\n genParticles = cms.InputTag(\"genParticles\"),\n genJetSource = cms.InputTag(\"iterativeCone5GenJets\"),\n electronSource = cms.InputTag(\"selectedLayer1Electrons\"),\n photonSource = cms.InputTag(\"selectedLayer1Photons\"),\n jetSource = cms.InputTag(\"selectedLayer1Jets\"),\n metSource = cms.InputTag(\"selectedLayer1METs\"),\n muonSource = cms.InputTag(\"selectedLayer1Muons\"),\n caloSource = cms.InputTag(\"towerMaker\"),\n triggerSource = cms.InputTag(\"TriggerResults\",\"\",\"HLT\"),\n #triggerSource = cms.InputTag(\"TriggerResults\",\"\",\"PAT\"),\n recoMuons = cms.untracked.string('muons'),\n leptonFlavour = cms.string('muon')\n)\n\nprocess.jetAna = cms.EDFilter(\"JetAnalysis\",\n\n debug = cms.untracked.bool(False),\n rootFileName = cms.untracked.string('/data/top/sckao/Fall08QCDAna/JANAFILE'),\n genParticles = cms.InputTag(\"genParticles\"),\n genJetSource = cms.InputTag(\"iterativeCone5GenJets\"),\n electronSource = cms.InputTag(\"selectedLayer1Electrons\"),\n jetSource = cms.InputTag(\"selectedLayer1Jets\"),\n metSource = cms.InputTag(\"selectedLayer1METs\"),\n muonSource = cms.InputTag(\"selectedLayer1Muons\"),\n caloSource = cms.InputTag(\"towerMaker\"),\n recoMuons = cms.untracked.string('muons'),\n)\n\nprocess.muAna = cms.EDFilter(\"MuonAnalysis\",\n \n debug = cms.untracked.bool(False),\n rootFileName = cms.untracked.string('/data/top/sckao/Fall08QCDAna/MANAFILE'),\n genParticles = cms.InputTag(\"genParticles\"),\n genJetSource = cms.InputTag(\"iterativeCone5GenJets\"),\n electronSource = cms.InputTag(\"selectedLayer1Electrons\"),\n jetSource = cms.InputTag(\"selectedLayer1Jets\"),\n muonSource = cms.InputTag(\"selectedLayer1Muons\"),\n metSource = cms.InputTag(\"selectedLayer1METs\"),\n caloSource = cms.InputTag(\"towerMaker\"),\n recoMuons = cms.untracked.string('muons'),\n) \n\n\nprocess.p = cms.Path(process.ttAna + process.jetAna + proecess.muAna )\n#process.ttAna.TrackAssociatorParameters.useEcal = False\n#process.ttAna.TrackAssociatorParameters.useHcal = False\n#process.ttAna.TrackAssociatorParameters.useCalo = True\n#process.ttAna.TrackAssociatorParameters.useHO = False\n#process.ttAna.TrackAssociatorParameters.useMuon = False\n\n","sub_path":"test/QCDAnalysis/qcd.py","file_name":"qcd.py","file_ext":"py","file_size_in_byte":4266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"510352391","text":"import pdfplumber\nimport re\nimport unidecode\nfrom pyzbar.pyzbar import decode\nfrom pdf2image import convert_from_path\nimport cv2\nfrom CustomOCR.pdf_extraction_1.utils import *\nimport requests\nfrom os.path import splitext\nimport os\nimport time\nimport openpyxl\nimport sys\n\n#TODO\n#https://b2bportaltest.csobpoistovna.sk/test/api/skp/swagger-ui.html#/SkpRestController/getVehicleEvidenceUsingGET\n\n# path = '..\\\\..\\\\Datasets\\\\faktury\\\\faktury_csob\\\\'\npath = '..\\\\..\\\\Datasets\\\\faktury\\\\pokus\\\\'\n# filename = 'anders.jpg' # square\n# filename = 'autofun.pdf'\n# filename = 'autosklo.jpg'\n# filename = 'peugeot.pdf'\n# filename = 'suzuki1.pdf'\n# filename = 'suzuki2.pdf'\n# filename = 'suzuki3.pdf'\n# filename = 'melisko.pdf'\n# filename = 'autonova.pdf'\n# filename = 'impa.pdf' # 75 %\n# filename = 'blocek1.jpg'\n# filename = 'lidl.pdf'\n# full_path = path + filename\n\n# phrases_to_extract = {'suma': 'ÚHRADE', 'iban': 'IBAN'}\ninvoices_list = os.listdir(path)\nphrases_to_extract = {'cena_s_dph': 'ÚHRADE UHRADE ÚHRADU KÚHRADE CELKOM SUMA ÚHRADÉ CELKEM', 'iban': 'IBAN','ico': 'ICO IČO','ecv': 'ECV EČV SPZ ŠPZ EVČ EČ'}\n# phrases_to_extract = {'ico': 'ICO IČO'}\n# phrases_to_extract = {'cena_s_dph': 'ÚHRADE UHRADE ÚHRADU KÚHRADE CELKOM SUMA ÚHRADÉ CELKEM'}\nmyList = ['ÚHRADE','SUMA','CELKOM','FAKTURA','FAKTÚRA','IBAN']\n# target_values = 'validation\\\\Faktury-template.xlsx'\n# target_values = 'validation\\\\faktury_1000.xlsx'\ntarget_values = 'validation\\\\faktury_1500.xlsx'\n\n\ndef are_all_values_extracted(extracted_values,extraction_method):\n if extraction_method is None:\n return False\n else:\n last_method = extraction_method.split('|')[-1]\n if extracted_values.get('iban') is not None and extracted_values.get('ico') is not None:\n ico_from_iban = ico_servisy.get(extracted_values.get('iban'))\n ico_extracted = extracted_values.get('ico')\n if ico_extracted !=ico_from_iban and ico_from_iban is not None:\n extracted_values.update({'ico': ico_from_iban})\n if (extracted_values.get('cena_s_dph') is not None and extracted_values.get('ico') is not None and extracted_values.get('iban') is not None):\n if last_method == 'PDF TEXT':\n return True\n if last_method == 'OCR' and 'HRAD' in extracted_values.get('cena_s_dph_word',''):\n hodnoty = extracted_values.get('cena_s_dph_word').split(';')\n for h in hodnoty:\n if 'HRAD' in h:\n cena_uhrady = h.split('|')\n extracted_values.update({'cena_s_dph':cena_uhrady[2]})\n break\n return True\n else:\n return False\n else:\n return False\n\ndef extract_values_from_file(full_path):\n img_pdf = None\n start_time = time.time()\n extraction_method =None\n# pokladnicny blok\n extracted_values,extraction_method = extract_qr_code(full_path,extraction_method)\n\n extension = splitext(full_path)[1]\n if not are_all_values_extracted(extracted_values,extraction_method):\n if extension.lower() =='.pdf':\n # faktura\n try:\n with pdfplumber.open(full_path) as pdf:\n for i,page in enumerate(pdf.pages[:2]):\n first_page =page\n extracted_text = first_page.extract_text()\n if bool(extracted_text) and any(char.isdigit() for char in extracted_text) and len(re.findall('(cid:\\d+?)',extracted_text)) <100 and any(x in extracted_text.upper() for x in myList):\n # if False:\n print('Extrahujem text z PDF')\n extraction_method = set_extraction_method(extracted_values, extraction_method, 'PDF TEXT')\n unaccented_upper_text = unidecode.unidecode(extracted_text.upper())\n # print(unaccented_upper_text)\n extracted_values = extract_pdf_text(unaccented_upper_text,extracted_values,ico_servisy)\n if not are_all_values_extracted(extracted_values,extraction_method):\n extracted_values, extraction_method = ocr_extraction(extracted_values, extraction_method, full_path, i, img_pdf)\n else:\n extracted_values, extraction_method = ocr_extraction(extracted_values, extraction_method, full_path, i, img_pdf)\n if are_all_values_extracted(extracted_values,extraction_method):\n break\n except:\n print(sys.exc_info())\n print('Nastal problem pri spracovani PDF' + full_path)\n extraction_method = 'ERROR'\n extracted_values.update({'extraction_method':extraction_method})\n elif extension.lower() in ('.png', '.jpg', '.jpeg', '.tiff', '.bmp', '.gif'):\n extraction_method = set_extraction_method(extracted_values, extraction_method,'OCR')\n img = cv2.imdecode(np.fromfile(full_path, dtype=np.uint8), -1)\n extracted_values = extract_dynamic_fields(img, phrases_to_extract,extracted_values,ico_servisy,os.path.basename(full_path))\n else:\n print('Neznamy format vstupuneho suboru')\n # rukou pisana faktura\n elif not extracted_values:\n print('rukou pisana faktura')\n\n if extracted_values.get('cena_s_dph') is None and extracted_values.get('cena_s_dph_word') is not None:\n hodnoty = extracted_values.get('cena_s_dph_word').split(';')\n aa = {}\n for h in hodnoty:\n hh = h.split('|')\n if hh is not None and len(hh) > 2:\n aa.update({hh[1]: hh[2]})\n # print(aa)\n # print(aa)\n conf = max([safe_cast(i, int, 0) for i in aa.keys()])\n cena = aa.get(str(conf))\n\n extracted_values.update({'cena_s_dph': cena})\n extracted_values.update({'cena_s_dph_conf': conf})\n\n elapsed_time = time.time() - start_time\n elapsed_time = time.strftime(\"%M:%S\", time.gmtime(elapsed_time))\n print(elapsed_time)\n\n target_values = all_target_values.get(os.path.basename(full_path),{})\n calculate_accuracy(os.path.basename(full_path),target_values,extraction_method, extracted_values,elapsed_time)\n\n\ndef ocr_extraction(extracted_values, extraction_method,full_path, i, img_pdf):\n print('pouzivam OCR')\n extraction_method = set_extraction_method(extracted_values, extraction_method, 'OCR')\n if img_pdf is None:\n img_pdf = convert_from_path(full_path, dpi=200)\n extracted_values = extract_dynamic_fields(np.array(img_pdf[i]), phrases_to_extract, extracted_values, ico_servisy,\n os.path.basename(full_path))\n return extracted_values,extraction_method\n\n\ndef set_extraction_method(extracted_values, extraction_method, new_method):\n if len(extracted_values) > 0: # ak uz bolo nieco rozpoznane inou metodou zachovaj aj povodnu metodu\n extraction_method = extraction_method + '|' + new_method\n else: # ak zatial nebolo nic rozpoznane, urci novu metodu\n extraction_method = new_method\n return extraction_method\n\n\n# zaciatok\nstart_time = time.time()\nall_target_values = load_target_values_excel(target_values)\nico_servisy = load_ico_servisy()\nfor i,y in enumerate(invoices_list):\n print(y)\n extract_values_from_file(path+y)\n\nelapsed_time = time.time() - start_time\nelapsed_time = time.strftime(\"%M:%S\", time.gmtime(elapsed_time))\nprint(elapsed_time)\n#koniec\n","sub_path":"CustomOCR/pdf_extraction_1/plumber.py","file_name":"plumber.py","file_ext":"py","file_size_in_byte":7536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"80022261","text":"\r\n# coding: utf-8\r\n\r\n# In[9]:\r\n\r\n\r\n# Preparations\r\n# ------------\r\n#\r\n# To start, Download the data ZIP file\r\n# `here `__\r\n# and put in a ``data/`` directory under the current directory.\r\n#\r\n# After that, let’s import some necessities.\r\n#\r\n\r\nfrom __future__ import absolute_import\r\nfrom __future__ import division\r\nfrom __future__ import print_function\r\nfrom __future__ import unicode_literals\r\n\r\nimport torch\r\nfrom torch.jit import script, trace\r\nimport torch.nn as nn\r\nfrom torch import optim\r\nimport torch.nn.functional as F\r\nimport csv\r\nimport random\r\nimport re\r\nimport os\r\nimport unicodedata\r\nimport codecs\r\nfrom io import open\r\nimport itertools\r\nimport math\r\nimport ast\r\n\r\n\r\nUSE_CUDA = torch.cuda.is_available()\r\ndevice = torch.device(\"cuda\" if USE_CUDA else \"cpu\")\r\n\r\n\r\n# In[2]:\r\n\r\n\r\n# Load & Preprocess Data\r\n# ----------------------\r\n#\r\n# The next step is to reformat our data file and load the data into\r\n# structures that we can work with.\r\n#\r\n# The `Cornell Movie-Dialogs\r\n# Corpus `__\r\n# is a rich dataset of movie character dialog:\r\n#\r\n# - 220,579 conversational exchanges between 10,292 pairs of movie\r\n# characters\r\n# - 9,035 characters from 617 movies\r\n# - 304,713 total utterances\r\n#\r\n# This dataset is large and diverse, and there is a great variation of\r\n# language formality, time periods, sentiment, etc. Our hope is that this\r\n# diversity makes our model robust to many forms of inputs and queries.\r\n#\r\n# First, we’ll take a look at some lines of our datafile to see the\r\n# original format.\r\n#\r\n\r\ncorpus_name = \"cornell movie-dialogs corpus\"\r\ncorpus = os.path.join(\"data\", corpus_name)\r\n\r\ndef printLines(file, n=10):\r\n with open(file, 'rb') as datafile:\r\n lines = datafile.readlines()\r\n for line in lines[:n]:\r\n print(line)\r\n\r\nprintLines(os.path.join(corpus, \"../movie_lines.txt\"))\r\n\r\n\r\n# In[3]:\r\n\r\n\r\n# Create formatted data file\r\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~\r\n#\r\n# For convenience, we'll create a nicely formatted data file in which each line\r\n# contains a tab-separated *query sentence* and a *response sentence* pair.\r\n#\r\n# The following functions facilitate the parsing of the raw\r\n# *movie_lines.txt* data file.\r\n#\r\n# - ``loadLines`` splits each line of the file into a dictionary of\r\n# fields (lineID, characterID, movieID, character, text)\r\n# - ``loadConversations`` groups fields of lines from ``loadLines`` into\r\n# conversations based on *movie_conversations.txt*\r\n# - ``extractSentencePairs`` extracts pairs of sentences from\r\n# conversations\r\n#\r\n\r\n# Splits each line of the file into a dictionary of fields\r\ndef loadLines(fileName, fields):\r\n lines = {}\r\n with open(fileName, 'r', encoding='iso-8859-1') as f:\r\n for line in f:\r\n values = line.split(\" +++$+++ \")\r\n # Extract fields\r\n lineObj = {}\r\n for i, field in enumerate(fields):\r\n lineObj[field] = values[i]\r\n lines[lineObj['lineID']] = lineObj\r\n return lines\r\n\r\n\r\n# Groups fields of lines from `loadLines` into conversations based on *movie_conversations.txt*\r\ndef loadConversations(fileName, lines, fields):\r\n conversations = []\r\n with open(fileName, 'r', encoding='iso-8859-1') as f:\r\n for line in f:\r\n values = line.split(\" +++$+++ \")\r\n # Extract fields\r\n convObj = {}\r\n for i, field in enumerate(fields):\r\n convObj[field] = values[i]\r\n # Convert string to list (convObj[\"utteranceIDs\"] == \"['L598485', 'L598486', ...]\")\r\n lineIds = eval(convObj[\"utteranceIDs\"])\r\n # Reassemble lines\r\n convObj[\"lines\"] = []\r\n for lineId in lineIds:\r\n convObj[\"lines\"].append(lines[lineId])\r\n conversations.append(convObj)\r\n \r\n # Drop the last line for conversation with odd lines\r\n if len(lineIds) % 2 == 1:\r\n dropLine = convObj[\"lines\"].pop() \r\n return conversations\r\n\r\n\r\n\r\n# Extracts pairs of sentences from conversations\r\ndef extractConversations(conversations):\r\n qa_pairs = []\r\n for conversation in conversations:\r\n #qa_pairs.append(['',' '])\r\n \r\n # Iterate over all the lines of the conversation\r\n #for i in range(len(conversation[\"lines\"])-1):\r\n for i in range(len(conversation[\"lines\"])):\r\n # Take out the last line if no response; \r\n # Pxair up odd-even line in a conversation\r\n if i%2==0: \r\n inputLine = conversation[\"lines\"][i][\"text\"].strip()\r\n targetLine = conversation[\"lines\"][i+1][\"text\"].strip()\r\n # Filter wrong samples (if one of the lists is empty)\r\n if inputLine and targetLine:\r\n qa_pairs.append([inputLine, targetLine])\r\n \r\n qa_pairs.append(['','']) \r\n \r\n return qa_pairs\r\n\r\n\r\nMAX_LENGTH = 10 # Maximum sentence length to consider\r\n\r\ndef filterConversation(conversation):\r\n list_a = []\r\n for i in range(len(conversation[\"lines\"])):\r\n list_a.append(len(conversation[\"lines\"][i][\"text\"].strip().split(' ')) < MAX_LENGTH)\r\n if all(list_a)==True:\r\n return True\r\n\r\n \r\ndef filterConversations(conversations):\r\n new_conversations = []\r\n for conversation in conversations:\r\n if filterConversation(conversation)==True:\r\n new_conversations.append(conversation)\r\n \r\n return new_conversations\r\n \r\n######################################################################\r\n\r\n\r\n# In[4]:\r\n\r\n\r\n# Now we’ll call these functions and create the file. We’ll call it\r\n# *formatted_movie_lines.txt*.\r\n#\r\n\r\n# Define path to new file\r\ndatafile = os.path.join(corpus, \"../formatted_pairs.txt\")\r\n\r\ndelimiter = '\\t'\r\n# Unescape the delimiter\r\ndelimiter = str(codecs.decode(delimiter, \"unicode_escape\"))\r\n\r\n# Initialize lines dict, conversations list, and field ids\r\nlines = {}\r\nconversations = []\r\nMOVIE_LINES_FIELDS = [\"lineID\", \"characterID\", \"movieID\", \"character\", \"text\"]\r\nMOVIE_CONVERSATIONS_FIELDS = [\"character1ID\", \"character2ID\", \"movieID\", \"utteranceIDs\"]\r\n\r\n# Load lines and process conversations\r\nprint(\"\\nProcessing corpus...\")\r\nlines = loadLines(os.path.join(corpus, \"../movie_lines.txt\"), MOVIE_LINES_FIELDS)\r\nprint(\"\\nLoading conversations...\")\r\nconversations = loadConversations(os.path.join(corpus, \"../movie_conversations.txt\"),\r\n lines, MOVIE_CONVERSATIONS_FIELDS)\r\n\r\nprint(len(conversations))\r\nconversations = filterConversations(conversations)\r\nprint(len(conversations))\r\n#print(type(conversations))\r\n\r\n\r\n# Write new csv file\r\nprint(\"\\nWriting newly formatted file...\")\r\nwith open(datafile, 'w', encoding='utf-8') as outputfile:\r\n writer = csv.writer(outputfile, delimiter=delimiter, lineterminator='\\n')\r\n for con_pairs in extractConversations(conversations):\r\n writer.writerow(con_pairs)\r\n \r\n\r\n# Print a sample of lines\r\nprint(\"\\nSample lines from file:\")\r\nprintLines(datafile)\r\n\r\n\r\n# In[16]:\r\n\r\n\r\n# Chop data in training and test set\r\n# ~~~~~~~~~~~~~~~~~~\r\n#\r\n# 80% training 20% test; \r\n# Original Total:83097 conversations\r\n# FilterConversation Total:27902 conversations(with MAX_LENGTH constraint)\r\n# FilterConversation: 22322 training 5580 test\r\n\r\n\r\n# Now we’ll split the samples from cmd_movie_lines.txt. We’ll call it\r\n# *cmd_train.txt* and *cmd_test.txt*.\r\n\r\n# Define path to new file\r\ncmd_train = os.path.join(corpus, \"../cmd_train_pairs.txt\")\r\ncmd_test = os.path.join(corpus, \"../cmd_test_pairs.txt\")\r\n\r\ndelimiter = '\\t'\r\n# Unescape the delimiter\r\ndelimiter = str(codecs.decode(delimiter, \"unicode_escape\"))\r\n\r\n\r\n\r\n# Extract training set and test set\r\ndef extractTrain(conversations, num_train):\r\n #c_train_seq = []\r\n c_train = random.sample(conversations, num_train)\r\n c_test = []\r\n #type: c_train, c_test, conversations: lists of lists of dictionsaries\r\n \r\n for conversation in conversations:\r\n if conversation not in c_train:\r\n #print('checkpoint1...')\r\n c_test.append(conversation)\r\n #print('checkpoint2...')\r\n return c_train, c_test\r\n\r\ndef extractConversations(conversations):\r\n qa_pairs = []\r\n for conversation in conversations:\r\n #qa_pairs.append(['',' '])\r\n \r\n # Iterate over all the lines of the conversation\r\n #for i in range(len(conversation[\"lines\"])-1):\r\n for i in range(len(conversation[\"lines\"])):\r\n # Take out the last line if no response; \r\n # Pxair up odd-even line in a conversation\r\n if i%2==0: \r\n inputLine = conversation[\"lines\"][i][\"text\"].strip()\r\n targetLine = conversation[\"lines\"][i+1][\"text\"].strip()\r\n # Filter wrong samples (if one of the lists is empty)\r\n if inputLine and targetLine:\r\n qa_pairs.append([inputLine, targetLine])\r\n \r\n qa_pairs.append(['','']) \r\n \r\n return qa_pairs\r\n\r\n\r\n\r\n# Write new csv file\r\nprint(\"\\nSample lines from a,b:...\")\r\na,b = extractTrain(conversations,22322)\r\n\r\n\r\nprint(\"\\nWriting training file...\")\r\nwith open(cmd_train, 'w', encoding='utf-8') as outputfile_1:\r\n writer = csv.writer(outputfile_1, delimiter=delimiter, lineterminator='\\n')\r\n for c_train_seq in extractConversations(a):\r\n writer.writerow(c_train_seq)\r\n\r\n \r\nprint(\"\\nWriting test file...\")\r\nwith open(cmd_test, 'w', encoding='utf-8') as outputfile_2:\r\n writer = csv.writer(outputfile_2, delimiter=delimiter, lineterminator='\\n')\r\n for c_test_seq in extractConversations(b):\r\n writer.writerow(c_test_seq)\r\n\r\n \r\n# Print a sample of lines\r\nprint(\"\\nSample lines from training file:\")\r\nprintLines(cmd_train)\r\n\r\nprint(\"\\nSample lines from test file:\")\r\nprintLines(cmd_test)\r\n\r\n\r\n# In[16]:\r\n\r\n\r\n# Load and trim data\r\n# ~~~~~~~~~~~~~~~~~~\r\n#\r\n# Our next order of business is to create a vocabulary and load\r\n# query/response sentence pairs into memory.\r\n#\r\n# Note that we are dealing with sequences of **words**, which do not have\r\n# an implicit mapping to a discrete numerical space. Thus, we must create\r\n# one by mapping each unique word that we encounter in our dataset to an\r\n# index value.\r\n#\r\n# For this we define a ``Voc`` class, which keeps a mapping from words to\r\n# indexes, a reverse mapping of indexes to words, a count of each word and\r\n# a total word count. The class provides methods for adding a word to the\r\n# vocabulary (``addWord``), adding all words in a sentence\r\n# (``addSentence``) and trimming infrequently seen words (``trim``). More\r\n# on trimming later.\r\n#\r\n\r\n# Default word tokens\r\nPAD_token = 0 # Used for padding short sentences\r\nSOS_token = 1 # Start-of-sentence token\r\nEOS_token = 2 # End-of-sentence token\r\nEOC_token = 3 # End-of-conversation token\r\n\r\ndatafile = os.path.join(corpus, \"../formatted_pairs.txt\")\r\nclass Voc:\r\n def __init__(self, name):\r\n self.name = name\r\n self.trimmed = False\r\n self.word2index = {}\r\n self.word2count = {}\r\n self.index2word = {PAD_token: \"PAD\", SOS_token: \"SOS\", EOS_token: \"EOS\", EOC_token: \"EOC\"}\r\n self.num_words = 4 # Count SOS, EOS, EOC, PAD\r\n\r\n def addSentence(self, sentence):\r\n for word in sentence.split(' '):\r\n self.addWord(word)\r\n\r\n def addWord(self, word):\r\n if word not in self.word2index:\r\n self.word2index[word] = self.num_words\r\n self.word2count[word] = 1\r\n self.index2word[self.num_words] = word\r\n self.num_words += 1\r\n else:\r\n self.word2count[word] += 1\r\n\r\n # Remove words below a certain count threshold\r\n def trim(self, min_count):\r\n if self.trimmed:\r\n return\r\n self.trimmed = True\r\n keep_words = []\r\n\r\n for k, v in self.word2count.items():\r\n if v >= min_count:\r\n keep_words.append(k)\r\n\r\n print('keep_words {} / {} = {:.4f}'.format(\r\n len(keep_words), len(self.word2index), len(keep_words) / len(self.word2index)\r\n ))\r\n\r\n # Reinitialize dictionaries\r\n self.word2index = {}\r\n self.word2count = {}\r\n self.index2word = {PAD_token: \"PAD\", SOS_token: \"SOS\", EOS_token: \"EOS\", EOC_token: \"EOC\"}\r\n self.num_words = 4 # Count default tokens\r\n\r\n for word in keep_words:\r\n self.addWord(word)\r\n\r\n\r\n######################################################################\r\n\r\n\r\n# In[17]:\r\n\r\n\r\n# Now we can assemble our vocabulary and query/response sentence pairs.\r\n# Before we are ready to use this data, we must perform some\r\n# preprocessing.\r\n#\r\n# First, we must convert the Unicode strings to ASCII using\r\n# ``unicodeToAscii``. Next, we should convert all letters to lowercase and\r\n# trim all non-letter characters except for basic punctuation\r\n# (``normalizeString``). Finally, to aid in training convergence, we will\r\n# filter out sentences with length greater than the ``MAX_LENGTH``\r\n# threshold (``filterPairs``).\r\n#\r\n\r\nMAX_LENGTH = 10 # Maximum sentence length to consider\r\n\r\n# Turn a Unicode string to plain ASCII, thanks to\r\n# http://stackoverflow.com/a/518232/2809427\r\ndef unicodeToAscii(s):\r\n return ''.join(\r\n c for c in unicodedata.normalize('NFD', s)\r\n if unicodedata.category(c) != 'Mn'\r\n )\r\n\r\n# Lowercase, trim, and remove non-letter characters\r\ndef normalizeString(s):\r\n s = unicodeToAscii(s.lower().strip())\r\n s = re.sub(r\"([.!?])\", r\" \\1\", s)\r\n s = re.sub(r\"[^a-zA-Z.!?]+\", r\" \", s)\r\n s = re.sub(r\"\\s+\", r\" \", s).strip()\r\n return s\r\n\r\n# Read query/response pairs and return a voc object\r\ndef readVocs(datafile, corpus_name):\r\n print(\"Reading lines...\")\r\n # Read the file and split into lines\r\n lines = open(datafile, encoding='utf-8').read().strip().split('\\n')\r\n # Split every line into pairs and normalize\r\n pairs = [[normalizeString(s) for s in l.split('\\t')] for l in lines]\r\n voc = Voc(corpus_name)\r\n return voc, pairs\r\n\r\n# Returns True iff both sentences in a pair 'p' are under the MAX_LENGTH threshold\r\n#def filterPair(p):\r\n# # Input sequences need to preserve the last word for EOS token\r\n# return len(p[0].split(' ')) < MAX_LENGTH and len(p[1].split(' ')) < MAX_LENGTH\r\n\r\n\r\n# Filter pairs using filterPair condition\r\n#def filterPairs(pairs):\r\n# return [pair for pair in pairs if filterPair(pair)]\r\n\r\n\r\n# Using the functions defined above, return a populated voc object and pairs list\r\ndef loadPrepareData(corpus, corpus_name, datafile, save_dir):\r\n print(\"Start preparing training data ...\")\r\n voc, pairs = readVocs(datafile, corpus_name)\r\n #print(\"Read {!s} sentence pairs\".format(len(pairs)))\r\n #pairs = filterPairs(pairs)\r\n #print(\"Trimmed to {!s} sentence pairs\".format(len(pairs)))\r\n print(\"Counting words...\")\r\n for pair in pairs:\r\n voc.addSentence(pair[0])\r\n voc.addSentence(pair[1])\r\n print(\"Counted words:\", voc.num_words)\r\n return voc, pairs\r\n\r\n\r\n# Load/Assemble voc and pairs\r\nsave_dir = os.path.join(\"data\", \"save\")\r\n#voc, pairs = readVocs(datafile, corpus_name)\r\n#print(\"\\npairs:\")\r\n#for pair in pairs[:10]:\r\n# print(pair[1])\r\n\r\nvoc, pairs = loadPrepareData(corpus, corpus_name, datafile, save_dir)\r\n# Print some pairs to validate\r\nprint(\"\\npairs:\")\r\nfor pair in pairs[:10]:\r\n print(pair)\r\n\r\n\r\ndef load_and_prepare_data(corpus_file):\r\n \"\"\"Reads the data file and returns data in preprocessed pairs.\"\"\"\r\n print(\"Start preparing training data ...\")\r\n voc, pairs = read_vocabulary(corpus_file)\r\n print(\"Read {!s} sentence pairs\".format(len(pairs)))\r\n #pairs = filterPairs(pairs)\r\n #print(\"Trimmed to {!s} sentence pairs\".format(len(pairs)))\r\n print(\"Counting words...\")\r\n for pair in pairs:\r\n voc.addSentence(pair[0])\r\n voc.addSentence(pair[1])\r\n print(\"Counted words:\", voc.num_words)\r\n return voc, pairs\r\n\r\n\r\ndef read_vocabulary(corpus_file):\r\n \"\"\"Reads the data file and returns raw pairs.\"\"\"\r\n print(\"Reading lines...\")\r\n # Read the file and split into lines\r\n # lines = open(datafile, encoding='utf-8').\\\r\n # read().strip().split('\\n')\r\n with open(corpus_file, encoding='utf-8') as f:\r\n lines = f.read().strip().split('\\n')\r\n # Split every line into pairs and normalize\r\n pairs = [[normalizeString(s) for s in l.split('\\t')] for l in lines]\r\n corpus_name = corpus_file.split(\"/\")[-2] # TODO: with os.path?\r\n voc = Voc(corpus_name)\r\n return voc, pairs\r\n\r\n\r\n######################################################################\r\n\r\n\r\n# In[18]:\r\n\r\n\r\n# Another tactic that is beneficial to achieving faster convergence during\r\n# training is trimming rarely used words out of our vocabulary. Decreasing\r\n# the feature space will also soften the difficulty of the function that\r\n# the model must learn to approximate. We will do this as a two-step\r\n# process:\r\n#\r\n# 1) Trim words used under ``MIN_COUNT`` threshold using the ``voc.trim``\r\n# function.\r\n#\r\n# 2) Filter out pairs with trimmed words.\r\n#\r\n\r\nMIN_COUNT = 1 # Minimum word count threshold for trimming\r\n\r\n\r\ndef trimRareWords(voc, pairs, MIN_COUNT):\r\n # Trim words used under the MIN_COUNT from the voc\r\n voc.trim(MIN_COUNT)\r\n # Filter out pairs with trimmed words\r\n keep_pairs = []\r\n for pair in pairs:\r\n input_sentence = pair[0]\r\n output_sentence = pair[1]\r\n keep_input = True\r\n keep_output = True\r\n # Check input sentence\r\n for word in input_sentence.split(' '):\r\n if word not in voc.word2index:\r\n keep_input = False\r\n break\r\n # Check output sentence\r\n for word in output_sentence.split(' '):\r\n if word not in voc.word2index:\r\n keep_output = False\r\n break\r\n\r\n # Only keep pairs that do not contain trimmed word(s) in their input or output sentence\r\n if keep_input and keep_output:\r\n keep_pairs.append(pair)\r\n\r\n print(\"Trimmed from {} pairs to {}, {:.4f} of total\".format(len(pairs), len(keep_pairs), len(keep_pairs) / len(pairs)))\r\n return keep_pairs\r\n\r\n\r\n######################################################################\r\n\r\n\r\n# In[19]:\r\n\r\n\r\n# Prepare Data for Models\r\n# -----------------------\r\n#\r\n# Although we have put a great deal of effort into preparing and massaging our\r\n# data into a nice vocabulary object and list of sentence pairs, our models\r\n# will ultimately expect numerical torch tensors as inputs. One way to\r\n# prepare the processed data for the models can be found in the `seq2seq\r\n# translation\r\n# tutorial `__.\r\n# In that tutorial, we use a batch size of 1, meaning that all we have to\r\n# do is convert the words in our sentence pairs to their corresponding\r\n# indexes from the vocabulary and feed this to the models.\r\n#\r\n# However, if you’re interested in speeding up training and/or would like\r\n# to leverage GPU parallelization capabilities, you will need to train\r\n# with mini-batches.\r\n#\r\n# Using mini-batches also means that we must be mindful of the variation\r\n# of sentence length in our batches. To accomodate sentences of different\r\n# sizes in the same batch, we will make our batched input tensor of shape\r\n# *(max_length, batch_size)*, where sentences shorter than the\r\n# *max_length* are zero padded after an *EOS_token*.\r\n#\r\n# If we simply convert our English sentences to tensors by converting\r\n# words to their indexes(\\ ``indexesFromSentence``) and zero-pad, our\r\n# tensor would have shape *(batch_size, max_length)* and indexing the\r\n# first dimension would return a full sequence across all time-steps.\r\n# However, we need to be able to index our batch along time, and across\r\n# all sequences in the batch. Therefore, we transpose our input batch\r\n# shape to *(max_length, batch_size)*, so that indexing across the first\r\n# dimension returns a time step across all sentences in the batch. We\r\n# handle this transpose implicitly in the ``zeroPadding`` function.\r\n#\r\n# .. figure:: /_static/img/chatbot/seq2seq_batches.png\r\n# :align: center\r\n# :alt: batches\r\n#\r\n# The ``inputVar`` function handles the process of converting sentences to\r\n# tensor, ultimately creating a correctly shaped zero-padded tensor. It\r\n# also returns a tensor of ``lengths`` for each of the sequences in the\r\n# batch which will be passed to our decoder later.\r\n#\r\n# The ``outputVar`` function performs a similar function to ``inputVar``,\r\n# but instead of returning a ``lengths`` tensor, it returns a binary mask\r\n# tensor and a maximum target sentence length. The binary mask tensor has\r\n# the same shape as the output target tensor, but every element that is a\r\n# *PAD_token* is 0 and all others are 1.\r\n#\r\n# ``batch2TrainData`` simply takes a bunch of pairs and returns the input\r\n# and target tensors using the aforementioned functions.\r\n#\r\n\r\ndatafile = os.path.join(corpus, \"../formatted_pairs.txt\")\r\n\r\ndef indexesFromSentence(voc, sentence):\r\n return [voc.word2index[word] for word in sentence.split(' ')] + [EOS_token]\r\n\r\n\r\nMAX_LENGTH = 10 # Maximum sentence length to consider\r\ndef zeroPadding(l, fillvalue=PAD_token):\r\n return list(itertools.zip_longest(*l, fillvalue=fillvalue))\r\n\r\n\r\ndef binaryMatrix(l, value=PAD_token):\r\n m = []\r\n for i, seq in enumerate(l):\r\n m.append([])\r\n for token in seq:\r\n if token == PAD_token:\r\n m[i].append(0)\r\n else:\r\n m[i].append(1)\r\n return m\r\n\r\n\r\n# In[22]:\r\n\r\n\r\n# Returns padded input sequence tensor and lengths\r\ndef inputVar(l, voc):\r\n indexes_batch = [indexesFromSentence(voc, sentence) for sentence in l]\r\n lengths = torch.tensor([len(indexes) for indexes in indexes_batch])\r\n padList = zeroPadding(indexes_batch)\r\n padVar_source = torch.LongTensor(padList) #source (x,1)\r\n \r\n padVar = torch.zeros(10,1)\r\n padVar[:lengths,:] = padVar_source\r\n \r\n \r\n return padVar, lengths\r\n\r\n\r\n# Returns padded target sequence tensor, padding mask, and max target length\r\ndef outputVar(l, voc):\r\n indexes_batch = [indexesFromSentence(voc, sentence) for sentence in l]\r\n max_target_len = max([len(indexes) for indexes in indexes_batch])\r\n padList = zeroPadding(indexes_batch)\r\n mask = binaryMatrix(padList)\r\n mask = torch.ByteTensor(mask)\r\n padVar_source = torch.LongTensor(padList) #source (x,1)\r\n \r\n padVar = torch.zeros(10,1)\r\n padVar[:max_target_len,:] = padVar_source\r\n \r\n \r\n return padVar, mask, max_target_len\r\n\r\n\r\ndef batch2TrainData(voc, pair_batch):\r\n pair_batch.sort(key=lambda x: len(x[0].split(\" \")), reverse=True) \r\n input_batch, output_batch = [], []\r\n for pair in pair_batch:\r\n input_batch.append(pair[0])\r\n output_batch.append(pair[1])\r\n inp, lengths = inputVar(input_batch, voc)\r\n output, mask, max_target_len = outputVar(output_batch, voc)\r\n return inp, lengths, output, mask, max_target_len\r\n\r\n\r\n# In[23]:\r\n\r\n\r\nif __name__ == \"__main__\":\r\n # Paths to corpus file\r\n corpus_name = \"cornell movie-dialogs corpus\"\r\n corpus = os.path.join(\"data\", corpus_name)\r\n datafile = os.path.join(corpus, \"../formatted_pairs.txt\")\r\n\r\n print(\"corpus: \", corpus)\r\n\r\n # Load/Assemble voc and pairs\r\n save_dir = os.path.join(\"data\", \"save\")\r\n voc, pairs = load_and_prepare_data(datafile)\r\n # Print some pairs to validate\r\n print(\"\\npairs:\")\r\n for pair in pairs[:10]:\r\n print(pair)\r\n\r\n # Trim voc and pairs with default not trimming\r\n pairs = trimRareWords(voc, pairs, MIN_COUNT)\r\n\r\n # Example for validation\r\n small_batch_size = 1 #not used for now\r\n \r\n # Chooise the pairs \r\n #batches = batch2TrainData(voc, [random.choice(pairs) for _ in range(small_batch_size)])\r\n # Choose the pairs within one conversation batch2TrainData(voc, pair_batch)\r\n #batches = batch2TrainData(voc, [random.choice(pairs) for _ in range(small_batch_size)])\r\n batches = batch2TrainData(voc, [pairs[243]]) #-----------------------------\r\n input_variable, lengths, target_variable, mask, max_target_len = batches\r\n\r\n print(\"input_variable:\", input_variable)\r\n print(input_variable.size())\r\n \r\n print(\"lengths:\", lengths)\r\n print(\"target_variable:\", target_variable)\r\n print(target_variable.size())\r\n \r\n print(\"mask:\", mask)\r\n print(\"max_target_len:\", max_target_len)\r\n \r\n #input_variable.size(); target_variable.size() should be [10,1] or at least [x,1]\r\n #tensor.size() now is [x,1] with specific pair\r\n\r\n\r\n# In[1]:\r\n\r\n\r\n# Note for merging\r\n# -----------------------\r\n#\r\n\r\n#this version is changed to data!\r\n#corpus = os.path.join(\"cmd_corpus\", corpus_name) \r\n#the directory was \"data\" and then modified as \"cmd_corpus\" for that we would have many datasets!\r\n\r\n\r\n# In[1]:\r\n\r\n\r\n#import tensorflow as tf\r\n\r\n#t = torch.tensor([[1, 2], [3, 4]])\r\n#print(t)\r\n\r\n#target = torch.zeros(30, 35, 512)\r\n#source = torch.ones(30, 35, 49)\r\n#target[:, :, :49] = source\r\n#print(target)\r\n\r\n","sub_path":"cmd_pytorch_whole.py","file_name":"cmd_pytorch_whole.py","file_ext":"py","file_size_in_byte":25239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"22355254","text":"from django.conf import settings\nfrom django.contrib import admin\nfrom django.conf.urls import patterns, include, url\nfrom django.views.generic import RedirectView\n\nfrom stock.items.views import StockDetailView\nimport jqmobile\n\nadmin.autodiscover()\n\nu = url(r'^admin/stock/detail/(?P\\d+)/',\n admin.site.admin_view(StockDetailView.as_view()),\n name=\"stock_detail\")\n\njqmobile.autodiscover()\nurlpatterns = patterns('',\n u,\n url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n url(r'^admin/', include(admin.site.urls)),\n (r'^ma/', include(jqmobile.site.urls)),\n (r'^static/(?P.*)$',\n 'django.views.static.serve',\n {'document_root': settings.STATIC_ROOT, 'show_indexes':True}),\n (r'^media/(?P.*)$',\n 'django.views.static.serve',\n {'document_root': settings.MEDIA_ROOT, 'show_indexes':True}),\n url(r'^chained-selects/', include('smart_selects.urls')),\n url(r'^$', RedirectView.as_view(url='/ma/') ),\n)\n","sub_path":"stock/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"550242042","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport sys\nfrom random import randint\n\nimport numpy as np\n\nfrom six.moves import xrange # pylint: disable=redefined-builtin\nimport tensorflow as tf\n\nfrom utils import print_progress_bar\nfrom PARAMS import *\n\n\n# The image size to crop the image to.\n# Keep at 100 to avoid any cropping;\n# do not set this value below 80\n# IMAGE_SIZE = DIM\n\n\n\ndef read_mshapes_correct(filename_queue):\n \"\"\"\n Reads a pair of MSHAPE records from the filename queue.\n :param filename_queue: the filename queue of lock/key files.\n :return: a duple containing a correct example and an incorrect example\n \"\"\"\n _, lock_image = decode_mshapes(filename_queue[0])\n _, key_image = decode_mshapes(filename_queue[1])\n\n # Combine images to make a correct example and an incorrect example\n # correct_example = tf.concat([lock_image, key_image], axis=0)\n\n # print(\"Correct example\", correct_example)\n\n # Return the examples\n return lock_image, key_image\n\n\ndef read_mshapes_incorrect(filename_queue):\n \"\"\"\n Reads a pair of MSHAPE records from the filename queue.\n :param filename_queue: the filename queue of lock/key files.\n :return: a duple containing a correct example and an incorrect example\n \"\"\"\n _, wrong_key_image = decode_mshapes(filename_queue[0])\n\n return wrong_key_image\n\n\n\ndef decode_mshapes(file_path):\n \"\"\"\n Decodes an MSHAPE record.\n :param file_path: The filepath of the png\n :return: A duple containing 0 and the decoded image tensor\n \"\"\"\n\n # read the whole file\n serialized_record = tf.read_file(file_path)\n\n # decode everything into uint8\n image = tf.image.decode_png(serialized_record, dtype=tf.uint8, channels=1)\n # image = tf.zeros([IMAGE_SIZE, IMAGE_SIZE])\n\n # Cast to float32\n image = tf.cast(image, tf.float32)\n\n # \"Crop\" the image.\n # This does not actually do anything, since\n # the image remains the same size; however,\n # it has the effect of setting the tensor\n # shape so that it is inferred correctly\n # in later steps. For details, please\n # see https://stackoverflow.com/a/35692452\n # image = tf.random_crop(image, [IMAGE_SIZE, IMAGE_SIZE, 3])\n image = tf.reshape(image, [IMAGE_SIZE, IMAGE_SIZE])\n\n return 0, image\n\n\n\ndef inputs(eval_data, data_dir, batch_size):\n \"\"\"\n Constructs the input for MSHAPES.\n :param eval_data: boolean, indicating if we should use the training or the evaluation data set\n :param data_dir: Path to the MSHAPES data directory\n :param batch_size: Number of images per batch\n :return:\n images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 6] size\n labels: Labels. 1D tensor of [batch_size] size.\n \"\"\"\n\n # filequeue = tf.FIFOQueue(capacity=100000, dtypes=[tf.string, tf.string]) # FIXME: Use RandomShuffleQueue instead!!\n # enqueues = []\n\n lock_files = []\n key_files_good = []\n key_files_bad = []\n\n if not eval_data:\n print(\"Not eval data\")\n\n print_progress_bar(0, NUM_EXAMPLES_TO_LOAD_INTO_QUEUE, prefix='Progress:', suffix='Complete', length=50,\n fill='█')\n\n for i in xrange(1, NUM_EXAMPLES_TO_LOAD_INTO_QUEUE, 2): # TODO: First of all, this should go to (at least) 30k.\n # The reason it's at 5000 is that currently, we're\n # individually enqueueing images. Instead, we should\n # use enqueue_many with an inline for loop, which\n # should building up the queue much faster.\n\n print_progress_bar(i + 1, NUM_EXAMPLES_TO_LOAD_INTO_QUEUE, prefix='Progress:', suffix='Complete', length=50,\n fill='█')\n\n lock = os.path.join(data_dir, 'images/%d_L.png' % i)\n key_good = os.path.join(data_dir, 'images/%d_K.png' % i)\n key_bad = os.path.join(data_dir, 'images/%d_K.png' % (i + 1))\n\n lock_files.append(lock)\n key_files_good.append(key_good)\n key_files_bad.append(key_bad)\n\n num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN\n print(\"Ok\")\n else:\n # for i in xrange(30001, 49500): #FIXME Same thing as above\n # lock = os.path.join(data_dir, 'images/%d_L.png' % i)\n # key = os.path.join(data_dir, 'images/%d_K.png' % i)\n #\n # a = filequeue.enqueue([lock, key])\n # enqueues.append(a)\n\n num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN\n\n\n # print(\"Lock files: \")\n # print(lock_files)\n # print(\"Good key files: \")\n # print(key_files_good)\n # print(\"Bad key files: \")\n # print(key_files_bad)\n\n good_pairs_queue = tf.train.slice_input_producer([lock_files, key_files_good],\n num_epochs=None, shuffle=True)\n bad_pairs_queue = tf.train.slice_input_producer([key_files_bad],\n num_epochs=None, shuffle=True)\n\n print(\"Finished enqueueing\")\n\n # Get the correct and incorrect examples from files in the filename queue.\n l, k = read_mshapes_correct(good_pairs_queue)\n wk = read_mshapes_incorrect(bad_pairs_queue)\n\n print(l.get_shape())\n\n correct_example = tf.stack([l, l, k], axis=2)\n wrong_example = tf.stack([l, l, wk], axis=2)\n # wrong_example = tf.concat([l, k], axis=2) # Give it the same wrong example as correct; should get stuck at 0.69 (it does)\n\n print(\"c Example shape:--------------------------------------------------------->\", correct_example.get_shape())\n print(\"w Example shape:--------------------------------------------------------->\", wrong_example.get_shape())\n\n print(\"Got examples\")\n\n # Ensure that the random shuffling has good mixing properties.\n print(\"Mixing properties stuff\")\n min_fraction_of_examples_in_queue = 0.4\n min_queue_examples = int(num_examples_per_epoch *\n min_fraction_of_examples_in_queue)\n\n # Regroup the enqueues\n # grouped_enqueues = tf.group(enqueues[0], enqueues[1])\n # for i in xrange(2, len(enqueues) - 1):\n # grouped_enqueues = tf.group(grouped_enqueues, enqueues[i])\n\n correct_or_incorrect = tf.random_uniform(shape=[], minval=0, maxval=1, dtype=tf.float32)\n\n # The case code is basically tensorflow language for this:\n #\n # if (correct_or_incorrect < 0.5):\n # _generate_image_and_label_batch(correct_example, [1],\n # min_queue_examples, batch_size,\n # shuffle=False)\n # else:\n # _generate_image_and_label_batch(wrong_example, [0],\n # min_queue_examples, batch_size,\n # shuffle=False)\n\n def f1(): return correct_example\n def f2(): return wrong_example\n\n def g1(): return tf.constant(FLAGS.MATCH_VAL)\n def g2(): return tf.constant(FLAGS.NOTMATCH_VAL)\n\n\n image = tf.case({tf.less(correct_or_incorrect, tf.constant(0.5)): f1, tf.greater(correct_or_incorrect, tf.constant(0.5)): f2},\n default=f1, exclusive=True)\n # image = tf.random_crop(image, [IMAGE_SIZE, IMAGE_SIZE, 6])\n image = tf.reshape(image, [IMAGE_SIZE, IMAGE_SIZE, FLAGS.NUM_LAYERS])\n print(\"IMAGE SIZE::::::: \" + (str(image.get_shape())))\n label = tf.case({tf.less(correct_or_incorrect, tf.constant(0.5)): g1, tf.greater(correct_or_incorrect, tf.constant(0.5)): g2},\n default=g1, exclusive=True)\n\n\n\n\n # return tf.train.shuffle_batch(\n # [tf.zeros([100, 100, 3]), tf.zeros([])],\n # batch_size=24,\n # num_threads=2,\n # capacity=min_queue_examples + 6 * batch_size,\n # min_after_dequeue=min_queue_examples)\n\n return _generate_image_and_label_batch(image, label, min_queue_examples, batch_size,\n shuffle=True)\n\n # def f1(): return (good_pairs_queue, (_generate_image_and_label_batch(correct_example, [1],\n # min_queue_examples, batch_size,\n # shuffle=False)))\n # def f2(): return (good_pairs_queue, (_generate_image_and_label_batch(wrong_example, [0],\n # min_queue_examples, batch_size,\n # shuffle=False)))\n #\n # r = tf.case([(tf.less(correct_or_incorrect, tf.constant(0.5)), f1)], default=f2)\n #\n # return r\n\n\n\ndef _generate_image_and_label_batch(image, label, min_queue_examples,\n batch_size, shuffle):\n \"\"\"Construct a queued batch of images and labels.\n Args:\n image: 3-D Tensor of [height, width, 6] of type.float32.\n label: 1-D Tensor of type.int32\n min_queue_examples: int32, minimum number of samples to retain\n in the queue that provides of batches of examples.\n batch_size: Number of images per batch.\n shuffle: boolean indicating whether to use a shuffling queue.\n Returns:\n images: Images. 4D tensor of [batch_size, height, width, 6] size.\n labels: Labels. 1D tensor of [batch_size] size.\n \"\"\"\n print(\"Image dimensions: \", image.get_shape())\n # image = tf.reshape(image, [2 * IMAGE_SIZE, IMAGE_SIZE, 3])\n\n # Create a queue that shuffles the examples, and then\n # read 'batch_size' images + labels from the example queue.\n num_preprocess_threads = FLAGS.NUM_THREADS\n if shuffle:\n images, label_batch = tf.train.shuffle_batch(\n [image, label],\n batch_size=batch_size,\n num_threads=num_preprocess_threads,\n capacity=min_queue_examples + 6 * batch_size,\n min_after_dequeue=min_queue_examples)\n else:\n images, label_batch = tf.train.batch(\n [image, label],\n batch_size=batch_size,\n num_threads=num_preprocess_threads,\n capacity=min_queue_examples + 6 * batch_size)\n\n # Display the training images in the visualizer.\n tf.summary.image('images', images)\n\n print(\"Images dimensions: \", images.get_shape())\n\n print(\"Current label batch shape (before reshape): \" + str(label_batch.get_shape()))\n reshaped_label_batch = tf.reshape(label_batch, [batch_size])\n print(\"Current label batch shape (after reshape): \" + str(reshaped_label_batch.get_shape()))\n\n return images, reshaped_label_batch\n","sub_path":"shapes_transform/st5input.py","file_name":"st5input.py","file_ext":"py","file_size_in_byte":10536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"272195796","text":"import webbrowser\n\n\nclass Movie(object):\n \"\"\"This class provides a way to store movie related information\n\n Args:\n movie_title: String of title of a movie\n movie_storyline: String of storyline or plot\n movie_poster: String of URL to movie poster\n movie_trailer: String of URL to movie trailer on YouTube\n \"\"\"\n\n # Constructor\n def __init__(self, movie_title, movie_storyline,\n movie_poster, movie_trailer):\n self.title = movie_title\n self.storyline = movie_storyline\n self.poster_image_url = movie_poster\n self.trailer_youtube_url = movie_trailer\n\n # Open browser with movie trailer URL\n def show_trailer(self):\n webbrowser.open(self.trailer_youtube_url)\n","sub_path":"Part 1 Programming Fundamentals and the Web/Movie Trailer Website/media.py","file_name":"media.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"321705222","text":"import unittest.mock\nfrom unittest.mock import Mock\nimport pytest\nimport numpy as np\n\nfrom shfl.private.node import DataNode\nfrom shfl.private.data import LabeledData\nfrom shfl.private.utils import unprotected_query\n\n\ndef test_private_data(data_and_labels):\n \"\"\"Checks that the private data is not returned.\"\"\"\n data_node = DataNode()\n data_node.set_private_data(\"random_array\", data_and_labels)\n\n assert data_node.private_data is None\n\n\ndef test_private_test_data(data_and_labels):\n \"\"\"Checks that the private data test is not returned.\"\"\"\n data_node = DataNode()\n data_node.set_private_test_data(\"random_array_test\", data_and_labels)\n\n assert data_node.private_test_data is None\n\n\ndef test_query_private_data(data_and_labels):\n \"\"\"Checks that the node correctly returns the private data if the data access\n is set to unprotected.\"\"\"\n data_node = DataNode()\n data_node.set_private_data(\"random_data\", data_and_labels)\n data_node.configure_data_access(\"random_data\", unprotected_query)\n\n output_data = data_node.query(\"random_data\")\n\n np.testing.assert_array_equal(output_data[0], data_and_labels[0])\n np.testing.assert_array_equal(output_data[1], data_and_labels[1])\n\n\ndef test_query_model_params():\n \"\"\"Checks that the node correctly returns the model's parameters.\n\n The access to the model's parameters is unprotected by default.\"\"\"\n random_params = np.random.rand(30, 20)\n data_node = DataNode()\n model_mock = Mock()\n model_mock.get_model_params.return_value = random_params\n data_node.set_model(model_mock)\n\n model_params = data_node.query_model_params()\n\n np.testing.assert_array_equal(model_params, random_params)\n\n\ndef test_query_model():\n \"\"\"Checks that the node correctly queries its model.\"\"\"\n data_node = DataNode()\n model_mock = Mock()\n data_node.set_model(model_mock)\n data_node.configure_model_access(unprotected_query)\n\n model = data_node.query_model()\n\n assert isinstance(model, type(model_mock))\n\n\ndef test_query_model_access_not_configured():\n \"\"\"Checks that the node raises an error if the model is queried without first\n configuring the access.\"\"\"\n data_node = DataNode()\n model_mock = Mock()\n data_node.set_model(model_mock)\n with pytest.raises(ValueError):\n data_node.query_model()\n\n\ndef test_train_model_wrong_data(data_and_labels):\n \"\"\"Checks that the node raises an error if wrong input is used for the model.\n\n The private input data must have \"data\" and \"labels\" attributes.\"\"\"\n labeled_data = LabeledData(*data_and_labels)\n delattr(labeled_data, \"_label\")\n data_node = DataNode()\n model_mock = Mock()\n data_node.set_model(model_mock)\n data_node.set_private_data(\"invalid_data\", labeled_data)\n with pytest.raises(ValueError):\n data_node.train_model(\"invalid_data\")\n\n\ncopy_mock = Mock()\n\n\n@unittest.mock.patch(\"copy.deepcopy\", unittest.mock.MagicMock(return_value=copy_mock))\ndef test_train_model_data(data_and_labels):\n \"\"\"Checks that the node trains correctly its local model.\n\n The model of each client is deep-copied. Thus the original model\n instance must not be called.\"\"\"\n labeled_data = LabeledData(*data_and_labels)\n data_node = DataNode()\n model_mock = Mock()\n data_node.set_model(model_mock)\n data_node.set_private_data(\"random_data\", labeled_data)\n\n data_node.train_model(\"random_data\")\n\n model_mock.train.assert_not_called()\n copy_mock.train.assert_called_once()\n\n\ndef test_get_model():\n \"\"\"Checks that the node's model is not returned.\"\"\"\n model_mock = Mock()\n data_node = DataNode()\n data_node.set_model(model_mock)\n\n assert data_node.model is None\n\n\n@unittest.mock.patch(\"copy.deepcopy\", unittest.mock.MagicMock(return_value=copy_mock))\ndef test_predict(data_and_labels):\n \"\"\"Checks that the node correctly predicts on input data using its local model.\n\n The model of each client is deep-copied. Thus the original model\n instance must not be called.\"\"\"\n model_mock = Mock()\n data_node = DataNode()\n data_node.set_model(model_mock)\n\n data_node.predict(data_and_labels[0])\n\n model_mock.predict.assert_not_called()\n copy_mock.predict.assert_called_once_with(data_and_labels[0])\n\n\n@unittest.mock.patch(\"copy.deepcopy\", unittest.mock.MagicMock(return_value=copy_mock))\ndef test_set_params():\n \"\"\"Checks that the node correctly sets the model's parameters.\n\n The model of each client is deep-copied. Thus the original model\n instance must not be called.\"\"\"\n random_array = np.random.rand(30)\n model_mock = Mock()\n data_node = DataNode()\n data_node.set_model(model_mock)\n\n data_node.set_model_params(random_array)\n\n model_mock.set_model_params.assert_not_called()\n copy_mock.set_model_params.assert_called_once_with(copy_mock)\n\n\n@unittest.mock.patch(\"copy.deepcopy\", unittest.mock.MagicMock(return_value=copy_mock))\ndef test_evaluate(data_and_labels):\n \"\"\"Checks that the node correctly evaluates the local model.\n\n The model of each client is deep-copied. Thus the original model\n instance must not be called.\"\"\"\n model_mock = Mock()\n data_node = DataNode()\n data_node.set_model(model_mock)\n\n data_node.evaluate(*data_and_labels)\n\n model_mock.evaluate.assert_not_called()\n copy_mock.evaluate.assert_called_once_with(*data_and_labels)\n\n\n@unittest.mock.patch(\"copy.deepcopy\", unittest.mock.MagicMock(return_value=copy_mock))\ndef test_local_evaluate(data_and_labels):\n \"\"\"Checks that the node correctly evaluates the model on the local test data.\"\"\"\n data_node = DataNode()\n data_key = 'EMNIST'\n data_node.set_private_test_data(data_key, LabeledData(*data_and_labels))\n\n model_mock = Mock()\n data_node.set_model(model_mock)\n copy_mock.evaluate.return_value = 0.8\n\n evaluation = data_node.local_evaluate(data_key)\n\n assert evaluation == 0.8\n\n\ndef test_local_evaluate_wrong():\n \"\"\"Checks that the local evaluation is None when local test data are not present.\"\"\"\n data_node = DataNode()\n data_node.self_private_test_data = 0\n\n evaluation = data_node.local_evaluate('some_non_existent_id')\n\n assert evaluation is None\n\n\n@unittest.mock.patch(\"copy.deepcopy\", unittest.mock.MagicMock(return_value=copy_mock))\ndef test_performance(data_and_labels):\n \"\"\"Checks that the node correctly calls the model's performance.\"\"\"\n data_node = DataNode()\n model_mock = Mock()\n data_node.set_model(model_mock)\n copy_mock.performance.return_value = 0.8\n\n performance = data_node.performance(*data_and_labels)\n\n copy_mock.performance.assert_called_once_with(*data_and_labels)\n assert performance == 0.8\n","sub_path":"test/private/test_node.py","file_name":"test_node.py","file_ext":"py","file_size_in_byte":6669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"72153683","text":"from MBlocks.control.controller import Cube\nfrom MBlocks.planning.random_follower import RandomLightPlanner\nfrom MBlocks.planning.light_follower import LatticeLightPlanner\n\nfrom serial.tools.list_ports import comports\nfrom serial import Serial\n\nmacs = ['d89c0041e41', 'fb0d8f2c3bb4', 'e6f6056908f2', 'cd2b5eab3ef3', 'df1ce4239c6e']\n\ndef ports():\n ps = [c[0] for c in comports()]\n return ps\n\ndef stop_fn_lattice(cube):\n connections = [cube.is_connected(face, 0) for face in range(1, 7)]\n connections = [c for c in connections if c]\n return len(connections) >= 2\n\ndef close_conns():\n for port in ports():\n ser = Serial(port)\n ser.write('blediscon\\n')\n ser.close()\n\ndef shake_cubes(cubes):\n for cube in cubes:\n cube.ser.write('ia f 3500 2500 10\\n')\n\ndef find_cubes():\n cubes = []\n for port in ports():\n try:\n cube = Cube(port)\n cubes.append(cube)\n except:\n pass\n return cubes","sub_path":"demo_utils.py","file_name":"demo_utils.py","file_ext":"py","file_size_in_byte":977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"173915731","text":"import sys\nimport math\nn = int(sys.stdin.readline().strip())\np=[]\nc=[]\nfor i in range(n):\n m,k=[int(item) for item in sys.stdin.readline().strip().split(',')]\n p.append(m)\n c.append(k)\n\ndef cal_ent(x):\n total=len(x)\n x_set=set(x)\n ent=0\n for x_value in x_set:\n x_count=x.count(x_value)\n p=x_count/total\n logp=math.log2(p)\n ent-=p*logp\n return ent\n\ndef cal_condition_ent(x,y):\n x_set=set(x)\n ent=0\n total=len(x)\n for x_value in x_set:\n x_count=x.count(x_value)\n p=x_count/total\n sub_y=[y[idx] for idx,x_i in enumerate(x) if x_i==x_value]\n sub_y_ent=cal_ent(sub_y)\n ent+=p*sub_y_ent\n return ent\n\ndef cal_ig(x,y):\n y_ent=cal_ent(y)\n condition_ent=cal_condition_ent(x,y)\n ent_ig=y_ent-condition_ent\n return ent_ig\n\nprint(round(cal_ig(p,c),2))\n\n","sub_path":"携程/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"6542910","text":"import numpy as np\nimport kuramoto as km\nimport kura_visual as kv\n\nPATH = './three/4'\n\noscis = km.kura_np(4)\ndisplayer = kv.displayer()\n\npo_ones = np.ones((2, 2))\nne_ones = - np.ones((2, 2))\nde_ones = np.array([[1, -1], [-1, 1]]) * 2\ncol1 = np.concatenate((de_ones, ne_ones), axis=0)\ncol2 = np.concatenate((ne_ones, po_ones), axis=0)\n\ncoupling = np.concatenate((col1, col2), axis=1)\noscis.set_coupling(coupling)\noscis.frequency_init(np.random.rand(4) * 0.1)\n\ntwo_oscis = np.ones((1, 2))\nde_two_oscis = np.array([[1, 0.6]])\ncompare = np.concatenate((de_two_oscis * 0.8, two_oscis * 0.2), axis=0)\nmask = np.concatenate((np.array([[1, 0, 0, 0]]),\n np.array([[0, 1, 0, 0]]),\n np.array([[0, 0, 1, 1]])),\n axis=0)\nmask = np.expand_dims(mask, axis=0)\n\nphases, freqs = oscis.evolution(steps=500, record=True, show=True)\n# print(np.where(loss.coh_btw_groups_np(np.squeeze(phases), mask) == 0))\n\ndisplayer.set_phases(phases)\ndisplayer.set_masks(mask)\ndisplayer.compute_properties()\ndisplayer.animate_evol_compare(2, 2, compare, save_name=PATH)\ndisplayer.phase_evol(save_name=PATH + '_phase')\ndisplayer.properties_evol(save_name=PATH + '_property')\n","sub_path":"supervised/deprecated/kuramoto_software/demo2.py","file_name":"demo2.py","file_ext":"py","file_size_in_byte":1203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"178493029","text":"import os\n\nfrom django.core.urlresolvers import reverse\nfrom django.conf import settings\n\nfrom rest_framework import status\nfrom rest_framework.test import APITestCase\nfrom rest_framework.authtoken.models import Token\n\nfrom gem.models import Gem\n\nURLS = {\n 'add':'gem:add',\n 'update':'gem:update',\n 'list':'gem:list',\n 'retrieve':'gem:retrieve',\n 'delete':'gem:delete'\n}\n\nclass GemTests(APITestCase):\n \n # fixtures = [\n # os.path.join(settings.BASE_DIR, '../fixtures/groups.json'),\n # os.path.join(settings.BASE_DIR, '../fixtures/permissions.json'),\n # os.path.join(settings.BASE_DIR, '../account/fixtures/account.json'),\n # os.path.join(settings.BASE_DIR, '../account/fixtures/token.json')\n # ]\n\n def setUp(self):\n\n # Get the token of the test fixture account to make authenticated calls\n #self.token = Token.objects.get(user__email='christian@blue.com')\n pass\n\n def test_add(self):\n\n \"\"\" Test if we can add, retrieve and list \"\"\"\n \n #self.client.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)\n\n url = reverse(URLS['add'])\n\n data = {\n \"name\": \"Cotidia Ltd\",\n \"address_1\": \"Unit 211F\",\n \"address_2\": \"120 Vyse Street\",\n \"postcode\": \"B18 6ND\",\n \"city\": \"Birmingham\",\n \"lat\": \"52.4907\",\n \"lng\": \"-1.9093\",\n \"why\": \"The best web development agency in Birmingham.\",\n \"user_name\": \"Alberto Centelles\",\n \"email\": \"alberto.centelles@cotidia.com\"\n }\n\n response = self.client.post(url, data, format='json')\n print(response.data)\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertEqual(response.data['name'], 'Cotidia Ltd')\n\n object_id = response.data['id']\n\n # Retrieve\n url = reverse(URLS['retrieve'], args=[object_id])\n \n response = self.client.get(url, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n # List\n url = reverse(URLS['list'])\n \n response = self.client.get(url, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n\n def test_update(self):\n\n \"\"\" Test if we can update \"\"\"\n\n #self.client.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)\n\n url = reverse(URLS['add'])\n\n data = {\n \"name\": \"Cotidia Ltd\",\n \"address_1\": \"Unit 211F\",\n \"address_2\": \"120 Vyse Street\",\n \"postcode\": \"B18 6ND\",\n \"city\": \"Birmingham\",\n \"lat\": \"52.4907\",\n \"lng\": \"-1.9093\",\n \"why\": \"The best web development agency in Birmingham.\",\n \"user_name\": \"Alberto Centelles\",\n \"email\": \"alberto.centelles@cotidia.com\"\n }\n\n response = self.client.post(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n \n object_id = response.data['id']\n\n\n # Edit\n url = reverse(URLS['update'], args=[object_id])\n \n data = {\n 'name': 'New name'\n }\n \n response = self.client.patch(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.data['name'], 'New name')\n\n\n def test_delete(self):\n\n \"\"\" Test if we can delete \"\"\"\n\n # Add first\n url = reverse(URLS['add'])\n #self.client.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)\n\n data = {\n \"name\": \"Cotidia Ltd\",\n \"address_1\": \"Unit 211F\",\n \"address_2\": \"120 Vyse Street\",\n \"postcode\": \"B18 6ND\",\n \"city\": \"Birmingham\",\n \"lat\": \"52.4907\",\n \"lng\": \"-1.9093\",\n \"why\": \"The best web development agency in Birmingham.\",\n \"user_name\": \"Alberto Centelles\",\n \"email\": \"alberto.centelles@cotidia.com\"\n }\n\n response = self.client.post(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n object_id = response.data['id']\n\n # Delete\n url = reverse(URLS['delete'], args=[object_id])\n response = self.client.delete(url, format='json')\n self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"gem/tests/gem.py","file_name":"gem.py","file_ext":"py","file_size_in_byte":4384,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"182891108","text":"import threading\nimport zmq\nimport logging\nimport simplejson\n\n\nclass Subscribe(threading.Thread):\n def __init__(self, connectTo):\n self._connectTo = connectTo\n self._context = zmq.Context()\n self._socket = self._context.socket(zmq.SUB)\n self._socket.connect(self._connectTo)\n ALL_MESSAGES = \"{\"\n self._socket.setsockopt(zmq.SUBSCRIBE, ALL_MESSAGES)\n self._registered = []\n threading.Thread.__init__(self)\n self.daemon = True\n threading.Thread.start(self)\n\n def register(self, callback):\n assert callback not in self._registered\n self._registered.append(callback)\n\n def unregister(self, callback):\n assert callback in self._registered\n self._registered.remove(callback)\n\n def close(self):\n self._socket.close()\n self._context.destroy()\n\n def run(self):\n try:\n logging.info(\n \"Rackattack Subscriber started connected to '%(connectTo)s' started\",\n dict(connectTo=self._connectTo))\n while True:\n try:\n self._work()\n except zmq.ContextTerminated:\n raise\n except zmq.ZMQError:\n raise\n except:\n logging.exception(\"Handling Published Event\")\n except:\n logging.exception(\n \"Rackattack Subscriber connected to '%(connectTo)s' aborts\",\n dict(connectTo=self._connectTo))\n raise\n\n def _work(self):\n FLAGS = 0\n message = self._socket.recv(FLAGS)\n try:\n event = simplejson.loads(message)\n for callback in list(self._registered):\n callback(event)\n except Exception:\n logging.exception('Handling Published Event')\n","sub_path":"py/rackattack/tcp/subscribe.py","file_name":"subscribe.py","file_ext":"py","file_size_in_byte":1857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"425070411","text":"import sys\nfrom PyQt4 import QtGui, QtCore\n\n\nclass Example(QtGui.QWidget):\n def __init__(self, chat, name, signal):\n super(Example, self).__init__()\n self.chat = chat\n self.signal = signal\n self.name = name\n self.init_UI()\n\n def init_UI(self):\n self.signal.connect(self.set_photo)\n self.size = 400\n\n button = QtGui.QPushButton('Upload photo')\n button.setFixedSize(150, 50)\n button.clicked.connect(self.button_clicked)\n\n hbox = QtGui.QHBoxLayout()\n hbox.addStretch(1)\n hbox.addWidget(button)\n\n self.label = QtGui.QLabel(' ')\n self.label.setFixedSize(self.size, self.size)\n\n vbox = QtGui.QVBoxLayout()\n vbox.addStretch(1)\n vbox.addLayout(hbox)\n vbox.addWidget(self.label)\n\n self.setLayout(vbox)\n\n self.setAutoFillBackground(True)\n palette = QtGui.QPalette()\n gradient = QtGui.QLinearGradient(QtCore.QRectF(self.rect()).topLeft(),\n QtCore.QRectF(self.rect()).topRight())\n gradient.setColorAt(0, QtCore.Qt.green)\n gradient.setColorAt(0.5, QtCore.Qt.darkGreen)\n palette.setBrush(QtGui.QPalette.Background, QtGui.QBrush(gradient))\n self.setPalette(palette)\n\n self.setGeometry(0, 0, self.size, self.size)\n self.center_on_screen()\n self.setWindowTitle(self.name)\n self.show()\n\n def button_clicked(self):\n self.chat.path = QtGui.QFileDialog.getOpenFileName(self)\n if not self.chat.path == '':\n self.chat.pressed = True\n\n def center_on_screen(self):\n resolution = QtGui.QDesktopWidget().screenGeometry()\n self.move((resolution.width() / 2) - self.size / 2,\n (resolution.height() / 2) - self.size / 2)\n\n def set_photo(self):\n self.myPixmap = QtGui.QPixmap(self.chat.get_path())\n self.label.setPixmap(self.myPixmap)\n self.label.setScaledContents(True)\n\n\nclass Chat(QtCore.QObject):\n signal = QtCore.pyqtSignal()\n\n def __init__(self, name):\n super(Chat, self).__init__()\n self.path = ''\n self.name = name\n self.pressed = False\n\n def init_UI(self):\n self.run()\n\n def run(self):\n app = QtGui.QApplication(sys.argv)\n self.ex = Example(self, self.name, self.signal)\n sys.exit(app.exec_())\n\n def get_path(self):\n return self.path\n\n def update_image(self, path):\n self.path = path\n self.signal.emit()\n\n def upload_pressed(self):\n if self.pressed:\n self.pressed = False\n return True\n return False\n","sub_path":"AC_solutions/chapter_13w/AC12_02/ChatUI.py","file_name":"ChatUI.py","file_ext":"py","file_size_in_byte":2660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"185995106","text":"import numpy as np\r\nfrom RNN import RNN\r\nimport pandas as pd\r\n\r\n\r\n# def get_random_block_from_data(data, times_steps, batch_size):\r\n# start_index = np.random.randint(0, len(data) - times_steps - batch_size)\r\n# batch_x = data[start_index: start_index + batch_size]\r\n# batch_y = data[start_index + times_steps: start_index + times_steps + batch_size]\r\n# return batch_x, batch_y\r\n\r\n\r\ndata = pd.read_csv(\"D:\\Data\\OPT\\Rnn\\data11.csv\")\r\ndf = pd.DataFrame(data)\r\ndf.drop(df.columns[[0, 1, 2, 3]], axis=1, inplace=True)\r\nmatrix = np.array(df)\r\n\r\nmatrix = np.delete(matrix, [len(matrix)-1, len(matrix)-2], axis=0)\r\n\r\ninput_data = np.empty((len(matrix) - 6, 7))\r\n\r\ncount = 0\r\nfor i in range(len(input_data)):\r\n time_step = 0\r\n while (time_step < 7):\r\n input_data[i][time_step] = matrix[count][0]\r\n time_step = time_step + 1\r\n count = count + 1\r\n count = count - 6\r\n\r\ntraining_epoch = 1000\r\nbatch_size = 1\r\ntime_steps = 7\r\n\r\nRNN = RNN(input_size=1, state_size=10, hidden_sum=1, output_size=1, time_steps=7, batch_size=batch_size,\r\n learning_rate=0.001)\r\n\r\ni = 0\r\nfor i in range(len(input_data)-1):\r\n batch_x = np.reshape(input_data[i], [batch_size, time_steps, 1])\r\n batch_y = np.reshape(input_data[i+1], [batch_size, time_steps])\r\n cost = RNN.opt(batch_x, batch_y)\r\n print(cost)\r\n\r\n# print(cost)\r\n# print(math.log(cost))\r\n\r\n","sub_path":"rnn/pv7.py","file_name":"pv7.py","file_ext":"py","file_size_in_byte":1381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"342683299","text":"import cv2\r\nimport os\r\nimport fire\r\nimport glob\r\nfrom color_tracker import asms_tracker as tracker\r\n\r\nasms = tracker.AsmsTracker()\r\n\r\n\r\ndef get_init_rect(gt_path):\r\n with open(gt_path, 'r') as f:\r\n line = f.readline()\r\n x1, y1, x2, y2, x3, y3, x4, y4 = map(float, line.strip().split(','))\r\n x1 = min(x1, min(x2, min(x3, x4)))\r\n x2 = max(x1, max(x2, max(x3, x4)))\r\n y1 = min(y1, min(y2, min(y3, y4)))\r\n y2 = max(y1, max(y2, max(y3, y4)))\r\n return int(x1), int(y1), int(x2), int(y2)\r\n\r\n\r\ndef run(chosed_dataset):\r\n TEST_DATASETS = ['girl', 'fish1']\r\n\r\n if chosed_dataset not in TEST_DATASETS:\r\n raise NotImplementedError('%s is not add to the code!' % chosed_dataset)\r\n\r\n jpgs_path = glob.glob(os.path.join(chosed_dataset, \"*.jpg\"))\r\n gt_path = os.path.join(chosed_dataset, \"groundtruth.txt\")\r\n\r\n for i, jpg in enumerate(jpgs_path):\r\n raw_img = cv2.imread(jpg)\r\n if 0 == i:\r\n asms.init(raw_img, *get_init_rect(gt_path))\r\n else:\r\n (x1, y1, x2, y2) = asms.update(raw_img)\r\n cv2.rectangle(raw_img, (int(x1), int(y1)), (int(x2), int(y2)), [255, 0, 0], 4)\r\n text1 = \"frame {}: \".format(i)\r\n text2 = \"X:{}, Y:{}, W:{}, H:{}\".format(int(x1), int(y1), int(x2) - int(x1), int(y2) - int(y1))\r\n cv2.putText(raw_img, text1 + text2, (0, 25), cv2.FONT_HERSHEY_PLAIN, 2.0, [255, 0, 0], 2)\r\n cv2.imshow(\"demo\", raw_img)\r\n if 27 == cv2.waitKey(1):\r\n break\r\n\r\n\r\nif __name__ == '__main__':\r\n fire.Fire(run)\r\n","sub_path":"tracker/run_color_tracker.py","file_name":"run_color_tracker.py","file_ext":"py","file_size_in_byte":1565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"544395171","text":"import tistory_api\nimport click\nimport os\nimport user_info\nfrom pathlib import Path\nimport json\n\nFILE_NAME = '../user.json'\n\n@click.command()\ndef init():\n\n is_exist = os.path.isfile(FILE_NAME)\n if is_exist:\n os.popen('code {name}'.format(name=FILE_NAME))\n return\n\n with open(FILE_NAME, \"w\") as json_file: \n json_file.write(json.dumps({\n \"blog_name\": \"\",\n \"client_id\": \"\",\n \"client_secret\": \"\",\n \"access_token\": \"\",\n \"categories\": []\n }, indent = 4)) \n\n os.popen('code {name}'.format(name=FILE_NAME))\n\n@click.command()\ndef token():\n tistory_api.getAccessToken()\n return\n\n@click.command()\ndef category():\n response = tistory_api.getCategories()\n data = json.loads(response.text)\n categories = data['tistory']['item']['categories']\n my_categories = []\n for c in categories:\n a = {}\n for key in c.keys():\n if key == 'id' or key == 'name':\n a[key] = c[key] \n \n my_categories.append(a) \n\n user_info.write_categories(my_categories)\n return\n\n@click.command()\n@click.argument('file')\n@click.option('-cg', '--category', help=\"카테고리 이름 입력\")\ndef write(category, file):\n f = open(file, \"r\")\n\n req = {\n 'title': os.path.basename(f.name).split('.')[0], \n 'content':f.read(),\n }\n\n if category == None:\n tistory_api.writePost(req)\n return\n\n try:\n categoryId = user_info.get_category_id_by_name(category)\n req['category'] = categoryId\n tistory_api.writePost(req)\n except:\n print('카테고리 정보를 확인해주세요. (user.json)')\n\n@click.command()\n@click.argument('id', nargs=-1)\n@click.argument('file', nargs=1)\n@click.option('-cg', '--category', help=\"카테고리 이름 입력\")\ndef modify(category, id, file):\n f = open(file, \"r\")\n\n req = {\n \"postId\": id,\n 'title': os.path.basename(f.name).split('.')[0], \n 'content':f.read(),\n }\n\n if category == None:\n tistory_api.modifyPost(req)\n return\n\n try:\n categoryId = user_info.get_category_id_by_name(category)\n req['category'] = categoryId\n tistory_api.modifyPost(req)\n except:\n print('카테고리 정보를 확인해주세요. (user.json)')\n\n# ---------------------------------------------------------\n\n@click.group()\ndef cli():\n pass\n\ncli.add_command(init)\ncli.add_command(category)\ncli.add_command(token)\ncli.add_command(write)\ncli.add_command(modify)\n\nif __name__ == '__main__':\n cli()","sub_path":"tistory-uploader/command.py","file_name":"command.py","file_ext":"py","file_size_in_byte":2586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"358951211","text":"\"\"\"\n15. Permutations\nhttps://www.lintcode.com/problem/permutations/my-submissions\n\"\"\"\nclass Solution:\n \"\"\"\n @param: nums: A list of integers.\n @return: A list of permutations.\n \"\"\"\n def permute(self, nums):\n # write your code here\n res = []\n self.dfs(nums, [], res, set())\n return res\n \n def dfs(self, nums, path, res, visited):\n n = len(nums)\n if len(path) == n:\n res.append(list(path))\n return\n \n for i in range(n):\n if i in visited:\n continue\n path.append(nums[i])\n visited.add(i)\n self.dfs(nums, path, res, visited)\n visited.remove(i)\n path.pop()","sub_path":"lintcode/15.1.py","file_name":"15.1.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"636922448","text":"from unittest.mock import MagicMock\n\nimport pytest\n\n\ndef _backport_assert_called(mock: MagicMock):\n assert mock.called\n\n\n@pytest.fixture(scope='session', autouse=True)\ndef patch_magicmock_fixture():\n try:\n MagicMock.assert_called\n except AttributeError:\n MagicMock.assert_called = _backport_assert_called\n","sub_path":"tests/compat_patch_mock.py","file_name":"compat_patch_mock.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"91140598","text":"from collections import defaultdict\n\ndef count_by(lst, fn = lambda x: x):\n count = defaultdict(int)\n for val in map(fn, lst):\n count[val] +=1\n return dict(count)\n\n\nfrom math import floor\n\na = count_by([6.1, 4.2, 6.3], floor)\nb = count_by(['one','two','three'], len)\n\nprint(a)\n\nprint(b)\n\n\n\n\n","sub_path":"count_by.py","file_name":"count_by.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"340005180","text":"s=input(\"Enter A string:\")\nl=s.split()\nfor i in range(len(l)):\n if i in [0,len(l)-1]:\n temp=\"\"\n for j in range(len(l[i])-1,-1,-1):\n temp=temp+l[i][j]\n l[i]=temp\nprint(\" \".join(l))\n\n\"\"\"\nEnter A string:This is my Name Shashi\nout put: sihT is my Name ihsahS\n\"\"\"","sub_path":"for_examples/for_rev_first_last_word_string.py","file_name":"for_rev_first_last_word_string.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"544919997","text":"# -*- coding: UTF-8 -*-\nimport tornado.ioloop\nimport tornado.web\nimport tornado.options\nimport tornado.httpserver\nimport codecs\nimport yaml\nimport argparse\nimport main\nimport json\n\n\ntornado.options.define(\"port\", default=5006, help=\"变量保存端口，默认8000\",type = int)\n\nclass MainHandler(tornado.web.RequestHandler):\n def set_default_headers(self):\n self.set_header(\"Access-Control-Allow-Origin\",\"*\")\n self.set_header(\"Access-Control-Allow-Headers\",\"*\")\n self.set_header('Access-Control-Allow-Methods','POST,GET,OPTIONS')\n self.set_header(\"Content-Type\",\"application/json;charset=utf-8\")\n\n\n def get(self):\n self.write(\"Hello, world\")\n\nclass ParseHandler(tornado.web.RequestHandler):\n def set_default_headers(self):\n self.set_header(\"Access-Control-Allow-Origin\",\"*\")\n self.set_header(\"Access-Control-Allow-Headers\",\"*\")\n self.set_header('Access-Control-Allow-Methods','POST,GET,OPTIONS')\n self.set_header(\"Content-Type\",\"application/json;charset=utf-8\")\n\n\n def get(self):\n self.write(\"parse data\")\n\n def post(self):\n body = json.loads(self.request.body.decode('utf-8'))\n configFile = \"./configs/{0}.yml\".format(body['model'])\n configs = yaml.load(codecs.open(configFile, encoding=\"utf-8\"))\n main.test_model(configs)\n\nclass trainHandler(tornado.web.RequestHandler):\n def set_default_headers(self):\n self.set_header(\"Access-Control-Allow-Origin\",\"*\")\n self.set_header(\"Access-Control-Allow-Headers\",\"*\")\n self.set_header('Access-Control-Allow-Methods','POST,GET,OPTIONS')\n self.set_header(\"Content-Type\",\"application/json;charset=utf-8\")\n\n def get(self):\n self.write(\"train data\")\n\n def post(self):\n self.set_status(200)\n self.write(\"train data\")\n\ndef make_app():\n return tornado.web.Application([\n (r\"/\", MainHandler),\n (r\"/parse\", ParseHandler),\n (r\"/train\", trainHandler)\n ])\n\nif __name__ == \"__main__\":\n app = make_app()\n tornado.options.parse_command_line()\n http_server = tornado.httpserver.HTTPServer(app)\n http_server.listen(tornado.options.options.port)\n tornado.ioloop.IOLoop.current().start()","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"145604973","text":"#-------------------------------------------------------------\n# Name: Feature Report\n# Purpose: Will zoom to selected feature, showing over the top of imagery. Will also produce\n# a small report about the feature.\n# Author: Shaun Weston (shaun_weston@eagle.co.nz)\n# Date Created: 09/08/2013\n# Last Updated: 28/07/2014\n# Copyright: (c) Eagle Technology\n# ArcGIS Version: 10.1/10.2\n# Python Version: 2.7\n#--------------------------------\n\n# Import modules\nimport os\nimport sys\nimport arcpy\nimport string\nimport json\nimport urllib\nimport httplib\nimport uuid\nimport xml.etree.ElementTree as ET\n\n# Enable data to be overwritten\narcpy.env.overwriteOutput = True\n \n# Main function\ndef gotoFunction(serverCredentials,configFile,IDs,idLayer,layer1MapService,layer1IDField,layer1NameField,layer1Symbology,layer2MapService,layer2IDField,layer2NameField,layer2Symbology,featureReportMXD,scaleBuffer,scale,OutputFile): # Get parameters from ArcGIS Desktop tool by seperating by comma e.g. (var1 is 1st parameter,var2 is 2nd parameter,var3 is 3rd parameter) \n try:\n # ------------- Select the feature by ID ------------------------\n # Request token for access to secure services \n serverCredentials = string.split(serverCredentials, \";\") \n arcgisToken = getToken(str(serverCredentials[2]),str(serverCredentials[3]),str(serverCredentials[0]),str(serverCredentials[1]))\n\n arcpy.AddMessage(\"Selecting property...\")\n # Use the layer type selected\n if idLayer.lower() == \"layer 1\":\n layerMapService = layer1MapService\n IDField = layer1IDField\n nameField = layer1NameField\n layerSymbology = layer1Symbology\n else:\n layerMapService = layer2MapService\n IDField = layer2IDField\n nameField = layer2NameField\n layerSymbology = layer2Symbology\n \n # If ID(s) are provided\n if (len(IDs) > 0):\n # If a string, convert to array\n if isinstance(IDs, basestring): \n IDs = string.split(IDs, \";\")\n\n # Loop through IDs getting the feature for it\n for ID in IDs:\n # Create map service query\n mapServiceQuery = layerMapService + \"/query?where=\" + IDField + \"=\" + \"'\" + str(ID) + \"'\" + \"&text=&objectIds=&time=&geometry=&geometryType=esriGeometryPolygon&inSR=&spatialRel=esriSpatialRelIntersects&relationParam=&outFields=*&returnGeometry=true&maxAllowableOffset=&geometryPrecision=&outSR=&returnIdsOnly=false&returnCountOnly=false&orderByFields=&groupByFieldsForStatistics=&outStatistics=&returnZ=false&returnM=false&gdbVersion=&f=pjson\" + \"&token=\" + arcgisToken\n urlResponse = urllib.urlopen(mapServiceQuery);\n # Get json for feature returned\n mapServiceQueryJSONData = json.loads(urlResponse.read())\n # Get the geometry and create temporary feature class \n layerGeometryJSON = mapServiceQueryJSONData[\"features\"][0][\"geometry\"]\n layerGeometryString = str(layerGeometryJSON).replace(\"u\",\"\")\n # Add spatial reference to geometry\n layerSpatialReference = mapServiceQueryJSONData[\"spatialReference\"][\"wkid\"]\n layerGeometryJSON[\"spatialReference\"] = {'wkid' : layerSpatialReference}\n # Convert to feature class\n layerGeometryPolygon = arcpy.AsShape(layerGeometryJSON, \"True\")\n # If feature exists, append\n if arcpy.Exists(\"in_memory\\PropertySelected\"):\n arcpy.Append_management(layerGeometryPolygon, \"in_memory\\PropertySelected\", \"NO_TEST\", \"\", \"\")\n # Otherwise create\n else:\n arcpy.CopyFeatures_management(layerGeometryPolygon, \"in_memory\\PropertySelected\") \n # Get the attributes\n propertyName = str(mapServiceQueryJSONData[\"features\"][0][\"attributes\"].get(nameField)) \n # ---------------------------------------------------------------- \n \n # ------------- Setup map document ------------------------\n mxd = arcpy.mapping.MapDocument(featureReportMXD)\n # Reference data frame and the layer\n dataFrame = arcpy.mapping.ListDataFrames(mxd, \"Layers\")[0]\n # ----------------------------------------------------------------\n\n # ------------- Add the selected feature to the map ------------------------\n arcpy.AddMessage(\"Adding property to map and zooming to it...\")\n arcpy.MakeFeatureLayer_management(\"in_memory\\PropertySelected\", \"Property Selected\") \n selectionLayer = arcpy.mapping.Layer(\"Property Selected\")\n arcpy.mapping.AddLayer(dataFrame,selectionLayer)\n selectionLayer = arcpy.mapping.ListLayers(mxd, \"Property Selected\", dataFrame)[0]\n # ----------------------------------------------------------------\n\n # ------------- Zoom to feature boundary ------------------------ \n arcpy.SelectLayerByAttribute_management(selectionLayer, \"NEW_SELECTION\")\n dataFrame.extent = selectionLayer.getSelectedExtent(False)\n \n # Take current scale and buffer it out by the percentage for urban defined\n trueScale = dataFrame.scale * float((float(scaleBuffer)/100)+1)\n # If scale provided, set it to that, otherwise round scale to a more general number and clear selection\n if scale:\n dataFrame.scale = scale\n else:\n dataFrame.scale = round(trueScale, -2) \n arcpy.SelectLayerByAttribute_management(selectionLayer, \"CLEAR_SELECTION\")\n \n # Update the symbology\n symbologyLayer = arcpy.mapping.Layer(layerSymbology) \n arcpy.mapping.UpdateLayer(dataFrame, selectionLayer, symbologyLayer, True)\n # ----------------------------------------------------------------\n\n # ------------- Replace the title, notes and address text with the paramater values ------------------------ \n arcpy.AddMessage(\"Adding address...\")\n for elm in arcpy.mapping.ListLayoutElements(mxd, \"TEXT_ELEMENT\"):\n if elm.name == \"Name\":\n if (propertyName != \"None\"): \n # Set the name\n elm.text = propertyName\n else:\n # Set the name\n elm.text = \" \" \n # ----------------------------------------------------------------\n \n # ------------- Update the text elements from the config file ------------------------\n arcpy.AddMessage(\"Updating report text...\")\n # Convert config file to xml\n configFileXML = ET.parse(configFile) \n # Import and reference the configuration file\n root = configFileXML.getroot() \n\n # Iterate through each of the fields listed in the configuration file for the feature report\n for child in root.find(\"featureReportFields\"): \n # Get the value of the field from the map service\n value = str(mapServiceQueryJSONData[\"features\"][0][\"attributes\"].get(child.find(\"fieldName\").text))\n # Get the text element from the map document and update with value from feature class\n for elm in arcpy.mapping.ListLayoutElements(mxd, \"TEXT_ELEMENT\"): \n if elm.name == child.find(\"placeholder\").text: \n # If value is valid, otherwise make it blank\n if (value != \"None\"):\n # If value text length is too long then add new lines\n if (len(str(value)) > 50):\n # Split string by spaces\n stringArray = value.split()\n\n # For each of the words, build the text\n newText = \"\"\n textCounter = \"\"\n for i in range(len(stringArray)):\n # Add to element text until reaches max line width then add new line\n if (textCounter == \"\"):\n newText = newText + stringArray[i]\n else:\n newText = newText + \" \" + stringArray[i]\n textCounter = textCounter + \" \" + stringArray[i]\n if (len(textCounter) > 50):\n # Add new line\n textCounter = \"\"\n newText = newText + \"\\r\\n\"\n # Set the text element\n elm.text = newText \n # Otherwise just replace text in text element\n else:\n if (child.find(\"format\").text == \"Currency\"): \n elm.text = \"$ \" + '{:12,.2f}'.format(float(value))\n elif (child.find(\"format\").text == \"Float\"): \n elm.text = '{:20,.2f}'.format(float(value)) \n else:\n elm.text = value\n else:\n elm.text = \" \"\n # ----------------------------------------------------------------\n\n # ------------- Export page to output folder ------------------------\n arcpy.AddMessage(\"Creating report...\")\n # Refresh the view\n arcpy.RefreshActiveView()\n OutputFileName = 'Page_{}.{}'.format(str(uuid.uuid1()), \"PDF\")\n OutputFile = os.path.join(arcpy.env.scratchFolder, OutputFileName)\n arcpy.mapping.ExportToPDF(mxd, OutputFile, jpeg_compression_quality=90, resolution=200)\n\n # If called from gp tool return the arcpy parameter \n if __name__ == '__main__':\n arcpy.SetParameterAsText(15, OutputFile)\n # Otherwise return the result \n else:\n return OutputFile\n\n # Delete references\n del mxd, OutputFile\n arcpy.Delete_management(\"in_memory\\PropertySelected\", \"FeatureClass\") \n # ---------------------------------------------------------------- \n pass\n # If arcpy error\n except arcpy.ExecuteError:\n # Build and show the error message\n errorMessage = arcpy.GetMessages(2) \n arcpy.AddError(errorMessage)\n # If python error\n except Exception as e:\n errorMessage = \"\"\n # Build and show the error message\n for i in range(len(e.args)):\n if (i == 0):\n errorMessage = str(e.args[i])\n else:\n errorMessage = errorMessage + \" \" + str(e.args[i])\n arcpy.AddError(errorMessage) \n# End of function\n\n\n# Request a token\ndef getToken(username, password, serverName, serverPort):\n # Token URL is typically http://server[:port]/arcgis/admin/generateToken\n tokenURL = \"/arcgis/admin/generateToken\"\n \n # URL-encode the token parameters:-\n params = urllib.urlencode({'username': username, 'password': password, 'client': 'requestip', 'f': 'json'})\n \n headers = {\"Content-type\": \"application/x-www-form-urlencoded\", \"Accept\": \"text/plain\"}\n \n # Connect to URL and post parameters\n httpConn = httplib.HTTPConnection(serverName, serverPort)\n httpConn.request(\"POST\", tokenURL, params, headers)\n \n # Read response\n response = httpConn.getresponse()\n if (response.status != 200):\n httpConn.close()\n arcpy.AddError(\"Error while fetch tokens from admin URL. Please check the URL and try again.\")\n return\n else:\n data = response.read()\n httpConn.close()\n \n # Extract the token from it\n token = json.loads(data) \n return token['token']\n\n\n# This test allows the script to be used from the operating\n# system command prompt (stand-alone), in a Python IDE, \n# as a geoprocessing script tool, or as a module imported in\n# another script\nif __name__ == '__main__':\n # Arguments are optional - If running from ArcGIS Desktop tool, parameters will be loaded into *argv\n argv = tuple(arcpy.GetParameterAsText(i)\n for i in range(arcpy.GetArgumentCount()))\n gotoFunction(*argv)","sub_path":"Scripts/CreateFeatureReport.py","file_name":"CreateFeatureReport.py","file_ext":"py","file_size_in_byte":12901,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"415216414","text":"from TestEnv import TestEnv\n\n\ndef pytest_report_header(config, startdir):\n TestEnv.init()\n return \"mod_md: {version} [apache: {aversion}({prefix}), mod_{ssl}]\".format(\n version=TestEnv.A2MD_VERSION,\n prefix=TestEnv.PREFIX,\n aversion=TestEnv.get_httpd_version(),\n ssl=TestEnv.get_ssl_module(),\n )","sub_path":"test/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"502558588","text":"# 240201056 - Oğuzhan Yıldırım\n\nprint(\" ******************************\\n\",\n\"[COFFEE MACHINE]\\n\",\n\"(Coffee types) |(Sizes)\\n\",\n\"Espresso[5.50 TL] |Venti(+1.5TL)\\n\",\n\"Cappuccino[7.75 TL] |Grande(+1TL)\\n\",\n\"Americano[7.25 TL] |Tall(+0TL)\\n\",\n\"Macchiato[9.50 TL] |\\n\",\n\"Mocha[9.25 TL] |\\n\",\n\"Filter[5.75 TL] |\\n\",\n\"Turkish[6 TL] |\\n\",\n\"[You can choose only one type/size each order.]\\n\",\n\"[Orders over 20 TL are discounted %10]\\n\",\n\"******************************\\n\"\n)\ncoffee_list = [\"Espresso\", \"Cappuccino\", \"Americano\", \"Macchiato\", \"Mocha\", \"Filter\", \"Turkish\"]\nprice_list = [5.50, 7.75, 7.25, 9.50, 9.25, 5.75, 6]\nsize_list = [\"Venti\", \"Grande\", \"Tall\", \"\"]\ncustomer = 0\ncoffee_number_sold = 0\ntotal_price = 0\ntotal_income_day = 0\nwhile True:\n # first i considered the entering x to coffee type and exiting the program .\n coffee_type = input(\"Please enter the type:\")\n\n if coffee_type == \"x\" or coffee_type == \"X\":\n break\n elif not coffee_type.title() in coffee_list:\n print(\"Invalid selection of type!\")\n print(\"Please enter the type correctly as you can see the names on the list.\")\n continue\n \"\"\"-------------------------------------------------------------------------------------------------------------\"\"\"\n # then i checked the type wheter it is turkish or not cause turkish coffee doesnt have size option\n # so i arranged size as empty string also it is included on the size list . cause there must be a coffee size defined.\n if coffee_type.title() == \"Turkish\":\n print(\"There are no size options for Turkish coffee.\")\n coffee_size = \"\"\n # else i thought the invalid selection and ask the question again and if it is true i break the loop\n else:\n while True:\n coffee_size = input(\"Please enter the size:\")\n if not coffee_size.title() in size_list:\n print(\"Invalid selection of size!\")\n print(\"Please enter a valid size as you can see the names on the list.\")\n continue\n else:\n break\n \"\"\"--------------------------------------------------------------------------------------------------------------\"\"\"\n # here i did the coffee number input part .\n while True:\n coffee_number = input(\"Please enter the number of coffee:\")\n if not coffee_number.isdigit():\n print(\"Invalid selection of number!\")\n print(\"Please enter the number as integer!\")\n continue\n else:\n break\n \"\"\"--------------------------------------------------------------------------------------------------------------\"\"\"\n coffee_type = coffee_type.title()\n coffee_size = coffee_size.title()\n coffee_number = int(coffee_number)\n customer += 1\n coffee_number_sold += coffee_number\n # and than i did the convertings for some values and also add 1 to customer and coffee number sold for calculations.\n \"\"\"--------------------------------------------------------------------------------------------------------------\"\"\"\n if coffee_type == \"Espresso\":\n if coffee_size == \"Tall\":\n total_price = (price_list[0] * coffee_number)\n # i just pick the first component of the price list cause the selection is espresso\n elif coffee_size == \"Grande\":\n total_price = (price_list[0] * coffee_number) + coffee_number\n # the grande price is 1 tl higer so i added just coffee number to the calculation.(1tl x coffee number)\n elif coffee_size == \"Venti\":\n total_price = (price_list[0] * coffee_number) + (1.5 * coffee_number)\n # also the venti is 1.5 tl higher than the list prices so i add 1.5*coffee_number\n # the comments for this part is also for the parts below except their price_list[x] cause of the component i choose.\n\n elif coffee_type == \"Cappuccino\":\n if coffee_size == \"Tall\":\n total_price = (price_list[1] * coffee_number)\n elif coffee_size == \"Grande\":\n total_price = (price_list[1] * coffee_number) + coffee_number\n elif coffee_size == \"Venti\":\n total_price = (price_list[1] * coffee_number) + (1.5 * coffee_number)\n\n elif coffee_type == \"Americano\":\n if coffee_size == \"Tall\":\n total_price = (price_list[2] * coffee_number)\n elif coffee_size == \"Grande\":\n total_price = (price_list[2] * coffee_number) + coffee_number\n elif coffee_size == \"Venti\":\n total_price = (price_list[2] * coffee_number) + (1.5 * coffee_number)\n\n elif coffee_type == \"Macchiato\":\n if coffee_size == \"Tall\":\n total_price = (price_list[3] * coffee_number)\n elif coffee_size == \"Grande\":\n total_price = (price_list[3] * coffee_number) + coffee_number\n elif coffee_size == \"Venti\":\n total_price = (price_list[3] * coffee_number) + (1.5 * coffee_number)\n\n elif coffee_type == \"Mocha\":\n if coffee_size == \"Tall\":\n total_price = (price_list[4] * coffee_number)\n elif coffee_size == \"Grande\":\n total_price = (price_list[4] * coffee_number) + coffee_number\n elif coffee_size == \"Venti\":\n total_price = (price_list[4] * coffee_number) + (1.5 * coffee_number)\n\n elif coffee_type == \"Filter\":\n if coffee_size == \"Tall\":\n total_price = (price_list[5] * coffee_number)\n elif coffee_size == \"Grande\":\n total_price = (price_list[5] * coffee_number) + coffee_number\n elif coffee_size == \"Venti\":\n total_price = (price_list[5] * coffee_number) + (1.5 * coffee_number)\n else:\n total_price = (price_list[6] * coffee_number)\n # else line is for turkish coffee .\n \"\"\"--------------------------------------------------------------------------------------------------------------\"\"\"\n if customer % 10 == 0:\n print(\"Congratulations!!!\")\n print(\"Hooray!You are our LUCKY CUSTOMER.\")\n print(\"Every 10th customer is callad LUCKY CUSTOMER and we do not recieve any money from our each 10th customer.\")\n # here in the first part i considered the 10th customer situtation and under this i also checked if its 10th and ordered over 20 tl for printing the value to customer.\n if total_price > 20:\n total_price = total_price * (90 / 100)\n print(\"You ordered\", coffee_number, coffee_size, \"cup(s) of\", coffee_type, \"and the price is\", total_price, \"TL\", \"(However,it's free for you.\")\n\n else:\n print(\"You ordered\", coffee_number, coffee_size, \"cup(s) of\", coffee_type, \"and the price is\", total_price, \"TL\", \"(However,it's free for you.\")\n # if 10th customer part is not exist then i considered the over 20 tl order part below this coment .\n elif total_price > 20:\n total_price = total_price * (90 / 100)\n print(\"You ordered\", coffee_number, coffee_size, \"cup(s) of\", coffee_type, \"and the price is\", total_price, \"TL\")\n total_income_day += total_price\n # and if cutomer is neither one of 10 th customer nor order over 20 tl , i just calculate the price below this comment\n else:\n print(\"You ordered\", coffee_number, coffee_size, \"cup(s) of\", coffee_type, \"and the price is\", total_price, \"TL\")\n total_income_day += total_price\n \"\"\"--------------------------------------------------------------------------------------------------------------\"\"\"\nprint(\"We have sold\", coffee_number_sold, \"coffees and our total sale is\", total_income_day, \"TL\")\n# for the end if the boss enters x or X i exited from the loop at the first part and come to this column and print the report .\n# THE END :)\n","sub_path":"Coffee Machine HW/CENG113_hw1_CoffeeMachine.py","file_name":"CENG113_hw1_CoffeeMachine.py","file_ext":"py","file_size_in_byte":7721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"547027701","text":"\ndef tipo(numero1):\n print(type(numero1))\n\ndef escribir(numero1):\n if numero1 == int:\n print(\"El Resultado Es: {} \".format(int(numero1)) )\n elif numero1 == str:\n print(\"El Resultado Es: {} \".format(str(numero1)) )\n\ndef suma(numero1, numero2):\n print(\" S U M A\")\n suma = numero1 + numero2 \n escribir(suma)\n print(type(suma))\n\ndef division(numero1, numero2):\n if numero2 == 0:\n print(\"La division por 0 no se puede realizar\")\n else:\n div = numero1 / numero2\n print(\" D I V I S I O N\")\n escribir( div )\n print(type(div))\n \ndef sumaPorN(numero1,numero2,numero3): \n resultado = numero1 + numero2 * numero3\n print(\"Suma de {} + {} * {} es : {} \".format( numero1, numero2, numero3, resultado ))\n escribir(resultado)\n tipo(resultado)\n\ndef sumaPorNString(string1, string2, numero1):\n resultado = ( string1 + string2 ) * numero1\n print(\"Suma de {} + {} * {} es : {} \".format( string1, string2, numero1, resultado ))\n escribir(resultado)\n tipo(resultado)\n\ndef igualMilCien(numero1):\n if ( (numero1 ** 10) == 1000 ):\n if ( (numero1 ** 7) == 100 ):\n resultado = numero1 ** 7\n print(\" {} a la 7 \".format(int(numero1)))\n escribir(resultado)\n tipo(resultado)\n else:\n resultado = numero1 ** 10\n print(\" {} a la 10 \".format(int(numero1)))\n escribir(resultado)\n tipo(resultado)\n else:\n print('El numero {} elevado a 7 - 10 no da como resultado 1000 - 100'.format(int(numero1)))\n\ndef cadenaNumerico(string1):\n try:\n resultado = int(string1)\n escribir(resultado)\n tipo(resultado)\n except ValueError as s:\n print('no se puede transformar {} en numerico '.format(string1))\n\ndef sumaDivisionSuma(numero1, numero2, numero3, numero4):\n if numero2 == 0:\n print(\"La division por 0 no se puede realizar\")\n elif numero4 == 0:\n print(\"La division por 0 no se puede realizar\")\n else:\n div = ( numero1 / numero2 ) + ( numero3 / numero4 )\n print(\" D I V I S I O N + D I V I S I O N \")\n escribir( div )\n print(type(div))\n\ndef run():\n while True:\n resultado = int(input('''\n [1] Suma \n [2] Division\n [3] Suma y por un numero N\n [4] Suma de dos string por un numero N\n [5] Elevado numero N por **7 o **10 con resultado 1000 o 100\n [6] Cadena de texto a numero\n [7] Suma Division Suma\n [8] Salir\n '''))\n if resultado == 1:\n numero1 = int(input('Ingrese un numero: '))\n numero2 = int (input('Ingrese otro numero: '))\n suma(numero1,numero2)\n\n elif resultado == 2:\n numero1 = int(input('Ingrese un numero: '))\n numero2 = int (input('Ingrese otro numero: '))\n division(numero1,numero2)\n\n elif resultado == 3:\n numero1 = int(input('Ingrese un numero: '))\n numero2 = int (input('Ingrese otro numero: '))\n numero3 = int (input('Ingrese otro numero: '))\n sumaPorN(numero1,numero2,numero3)\n\n elif resultado == 4:\n string1 = str(input('Ingrese un numero cadena: '))\n string2 = str(input('Ingrese otro numero cadena: '))\n numero1 = int (input('Ingrese un numero: '))\n sumaPorNString(string1,string2,numero1)\n\n elif resultado == 5:\n numero1 = int (input('Ingrese un numero: '))\n igualMilCien(numero1)\n\n elif resultado == 6:\n string1 = str(input('Ingrese un numero cadena: '))\n cadenaNumerico(string1)\n\n elif resultado == 7:\n numero1 = int(input('Ingrese un numero: '))\n numero2 = int (input('Ingrese otro numero: '))\n numero3 = int (input('Ingrese otro numero: '))\n numero4 = int (input('Ingrese otro numero: '))\n sumaDivisionSuma(numero1, numero2, numero3, numero4)\n\n elif resultado == 8:\n break\n\n else:\n print('Opcion no validad Escoja otra ')\n\n\nif __name__ == \"__main__\":\n run()","sub_path":"Ejercicios/Expresion/Evalucion.py","file_name":"Evalucion.py","file_ext":"py","file_size_in_byte":4225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"287308926","text":"import numpy as np\n\nfrom code.physical_functions import gravforce\n\n\ndef update_all_positions(delta_t, selected_objects):\n \"\"\"\n Update the coordinates.\n\n This function updates all the positions, with a euler step using half of\n the step size\n \"\"\"\n for m_obj in selected_objects:\n m_obj.x = m_obj.x + m_obj.v * delta_t/2\n\n\ndef calculate_all_forces(selected_objects):\n \"\"\"\n Calculate sum of all forces.\n\n This function updates the forces as a function of the new positions.\n We need the second loop to sum up all the forces that act upon the object\n given by the first loop. We need the if statement to prohibit that we\n calculate the gravitational force of the object acting on itself.\n You could store the results and calculate only half of the terms, using\n F_12 = -F_21 (newton's 3rd law)\n \"\"\"\n for m_obj1 in selected_objects:\n m_obj1.F = np.zeros(3)\n for m_obj2 in selected_objects:\n if(m_obj2 != m_obj1):\n m_obj1.F += gravforce(m_obj1.x, m_obj2.x, m_obj1.mass,\n m_obj2.mass)\n\n\ndef update_all_velocities(delta_t, selected_objects):\n \"\"\"Update all the velocities.\"\"\"\n for m_obj in selected_objects:\n m_obj.v = m_obj.v + m_obj.F/m_obj.mass * delta_t\n\n\ndef leapfrog_nsteps(nsteps, delta_t, selected_objects, x, v):\n \"\"\"\n Execute n steps of the leapfrog algorithm.\n\n Put all 4 steps into a function, that also stores the position and\n velocities vectors of all objects and for every time step\n \"\"\"\n for i in range(nsteps):\n j = 0\n update_all_positions(delta_t, selected_objects)\n calculate_all_forces(selected_objects)\n update_all_velocities(delta_t, selected_objects)\n update_all_positions(delta_t, selected_objects)\n for m_obj in selected_objects:\n x[i, j] = m_obj.x\n v[i, j] = m_obj.v\n j += 1\n","sub_path":"code/leap_frog_algorithm.py","file_name":"leap_frog_algorithm.py","file_ext":"py","file_size_in_byte":1932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"525082540","text":"\"\"\" A DQN example using wintermute that should train fast and show some results\n quickly on `Pong`. The setup is fairly different from the original paper.\n\"\"\"\nimport time\nimport random\nfrom functools import partial\nfrom types import SimpleNamespace\nfrom datetime import datetime\n\nimport torch\nfrom torch import optim\nfrom termcolor import colored as clr\nfrom rl_logger import Logger\n\nfrom wintermute.env_wrappers import get_wrapped_atari\nfrom wintermute.estimators import get_estimator\nfrom wintermute.policy_evaluation import EpsilonGreedyPolicy\nfrom wintermute.policy_evaluation import get_epsilon_schedule as get_epsilon\n\n# from wintermute.policy_improvement import get_optimizer\nfrom wintermute.policy_improvement import DQNPolicyImprovement\nfrom wintermute.replay import NaiveExperienceReplay as ER\nfrom wintermute.replay.prioritized_replay import ProportionalSampler as PER\n\n# from wintermute.replay import FlatExperienceReplay as ER\n\nfrom utils import get_parser, print_namespace\n\n\ndef priority_update(mem, dqn_loss):\n \"\"\" Callback for updating priorities in the proportional-based experience\n replay and for computing the importance sampling corrected loss.\n \"\"\"\n losses = dqn_loss.loss\n mem.update([loss.item() for loss in losses.detach().abs()])\n return (losses * mem.weights.to(losses.device).view_as(losses)).mean()\n\n\ndef train(args):\n \"\"\" Here we do the training.\n \"\"\"\n env = args.env\n train_log = args.log.groups[\"training\"]\n\n state, reward, done = env.reset(), 0, False\n warmed_up = False\n ep_cnt = 0\n for step in range(1, args.step_no + 1):\n\n # take action and save the s to _s and a to _a to be used later\n pi = args.policy_evaluation(state)\n _state, _action = state, pi.action\n state, reward, done, _ = env.step(pi.action)\n\n # add a (_s, _a, r, d) transition\n args.experience_replay.push((_state, _action, reward, state, done))\n # args.experience_replay.push(_state[0, 3], _action, reward, done)\n\n # sample a batch and do some learning\n do_training = (step % args.update_freq == 0) and warmed_up\n\n if do_training:\n batch = args.experience_replay.sample()\n if args.prioritized:\n args.policy_improvement(batch, cb=args.priority_update)\n else:\n args.policy_improvement(batch)\n\n if step % 1000 == 0:\n args.policy_improvement.update_target_estimator()\n\n # do some logging\n train_log.update(\n ep_cnt=(1 if done else 0),\n rw_per_ep=(reward, (1 if done else 0)),\n rw_per_step=reward,\n max_q=pi.q_value,\n sampling_fps=1,\n training_fps=32 if do_training else 0,\n )\n\n if done:\n state, reward, done = env.reset(), 0, False\n ep_cnt += 1\n\n if ep_cnt % args.log_freq == 0:\n args.log.log(train_log, step)\n train_log.reset()\n\n warmed_up = len(args.experience_replay) > args.learn_start\n args.log.log(train_log, step)\n train_log.reset()\n\n\ndef main(args):\n \"\"\" Here we initialize stuff.\n \"\"\"\n args.seed = random.randint(0, 1e4) if args.seed == 42 else args.seed\n print(f\"torch manual seed={args.seed}.\")\n torch.manual_seed(args.seed)\n\n # wrap the gym env\n env = get_wrapped_atari(\n args.game,\n mode=\"training\",\n hist_len=4,\n seed=args.seed,\n no_gym=args.no_gym,\n )\n print(env)\n print(\"ActionSpace: \", env.action_space)\n\n # construct an estimator to be used with the policy\n action_no = env.action_space.n\n estimator = get_estimator(\n \"atari\", hist_len=4, action_no=action_no, hidden_sz=256\n )\n estimator = estimator.cuda()\n\n # construct an epsilon greedy policy\n # also: epsilon = {'name':'linear', 'start':1, 'end':0.1, 'steps':1000}\n epsilon = get_epsilon(steps=args.epsilon_steps)\n policy_evaluation = EpsilonGreedyPolicy(estimator, action_no, epsilon)\n\n # construct a policy improvement type\n # optimizer = get_optimizer('Adam', estimator, lr=0.0001, eps=0.0003)\n optimizer = optim.Adam(\n estimator.parameters(), lr=args.lr, eps=args.adam_eps\n )\n policy_improvement = DQNPolicyImprovement(\n estimator, optimizer, gamma=0.99, is_double=args.double_dqn\n )\n\n # we also need an experience replay\n if args.prioritized:\n experience_replay = PER(\n args.mem_size,\n batch_size=32,\n alpha=0.6,\n optim_steps=((args.step_no - args.learn_start) / args.update_freq),\n )\n priority_update_cb = partial(priority_update, experience_replay)\n else:\n experience_replay = ER(args.mem_size, batch_size=32)\n # experience_replay = ER(100000, batch_size=32, hist_len=4) # flat\n\n # construct a tester\n tester = None\n\n # construct a logger\n if not args.label:\n sampling = \"prioritized\" if args.prioritized else \"uniform\"\n label = f\"{datetime.now():%Y%b%d-%H%M%S}_{args.game}_{sampling}\"\n\n log = Logger(label=label, path=f\"./results/{label}\")\n train_log = log.add_group(\n tag=\"training\",\n metrics=(\n log.SumMetric(\"ep_cnt\", resetable=False),\n log.AvgMetric(\"rw_per_ep\", emph=True),\n log.AvgMetric(\"rw_per_step\"),\n log.MaxMetric(\"max_q\"),\n log.FPSMetric(\"training_fps\"),\n log.FPSMetric(\"sampling_fps\"),\n ),\n console_options=(\"white\", \"on_blue\", [\"bold\"]),\n )\n log.log_info(train_log, \"date: %s.\" % time.strftime(\"%d/%m/%Y | %H:%M:%S\"))\n log.log_info(train_log, \"pytorch v%s.\" % torch.__version__)\n\n # Add the created objects in the args namespace\n args.env = env\n args.policy_evaluation = policy_evaluation\n args.policy_improvement = policy_improvement\n args.experience_replay = experience_replay\n args.tester = tester\n args.log = log\n if args.prioritized:\n args.priority_update = priority_update_cb\n\n # print the args\n print_namespace(args)\n\n # start the training\n train(args)\n\n\nif __name__ == \"__main__\":\n main(\n get_parser(\n game=\"pong\",\n step_no=4_000_000,\n update_freq=1,\n learn_start=256,\n mem_size=100_000,\n epsilon_steps=30000,\n lr=0.0001,\n log_freq=5,\n )\n )\n","sub_path":"examples/quick_dqn.py","file_name":"quick_dqn.py","file_ext":"py","file_size_in_byte":6412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"393892721","text":"from archivos import Archivos\n\n\nclass Antecedentes:\n def consultaAntecedentesNombre(self, nombre):\n data = Archivos().leerArchivo\n detalleAntecedente = \"\"\n for registro in data:\n infoPersona = registro.split(\",\")\n if (nombre == infoPersona[0]):\n detalleAntecedente = infoPersona[3]\n break\n return detalleAntecedente\n\n def consultaImagen(self, nombre):\n data = Archivos().leerArchivo\n imagen = \"\"\n for registro in data:\n infoPersona = registro.split(\",\")\n if (nombre == infoPersona[0]):\n imagen = infoPersona[4]\n break\n return imagen\n","sub_path":"justiciaPenal.py","file_name":"justiciaPenal.py","file_ext":"py","file_size_in_byte":699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"597448194","text":"import requests\nfrom bs4 import BeautifulSoup as bs\n\n# Get방식으로 소스를 가져옵니다.\nreq = requests.get('https://www.pycon.kr/2017/program/list/')\n# 헤더부분이 아닌 HTTP의 Body(Text)를 가져옵니다.\nhtml = req.text\n# HTML을 파이썬이 이해하는 Soup 객체로 파싱합니다.\nsoup = bs(html, 'html.parser')\n# CSS Selector를 통해 내용물을 모두 선택합니다.(iterable)\nsession_list = soup.select('body > div.container > div:nth-of-type(1) '\\\n '> div.col-md-9.content > ul:nth-of-type(1) '\\\n '> li:nth-of-type(16) > a')\nfor session in session_list:\n # HTML DOM객체의 내용물(text)만 봅니다.\n print(session.text)\n","sub_path":"session/01_pycon_session_one.py","file_name":"01_pycon_session_one.py","file_ext":"py","file_size_in_byte":722,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"423059953","text":"import os\n\nfrom oscar.defaults import *\n\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\nSECRET_KEY = 'TEST_SECRET_KEY'\n\nDEBUG = True\n\nALLOWED_HOSTS = []\n\nINSTALLED_APPS = [\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n\n 'django.contrib.sites',\n 'django.contrib.flatpages',\n\n 'oscar',\n 'oscar.apps.analytics',\n 'oscar.apps.checkout',\n 'oscar.apps.address',\n 'oscar.apps.shipping',\n 'oscar.apps.catalogue',\n 'oscar.apps.catalogue.reviews',\n 'oscar.apps.partner',\n 'oscar.apps.basket',\n 'oscar.apps.payment',\n 'oscar.apps.offer',\n 'oscar.apps.order',\n 'oscar.apps.customer',\n 'oscar.apps.search',\n 'oscar.apps.voucher',\n 'oscar.apps.wishlists',\n 'oscar.apps.dashboard',\n 'oscar.apps.dashboard.reports',\n 'oscar.apps.dashboard.users',\n 'oscar.apps.dashboard.orders',\n 'oscar.apps.dashboard.catalogue',\n 'oscar.apps.dashboard.offers',\n 'oscar.apps.dashboard.partners',\n 'oscar.apps.dashboard.pages',\n 'oscar.apps.dashboard.ranges',\n 'oscar.apps.dashboard.reviews',\n 'oscar.apps.dashboard.vouchers',\n 'oscar.apps.dashboard.communications',\n 'oscar.apps.dashboard.shipping',\n\n 'oscar_invoices.apps.InvoicesConfig',\n 'tests._site.apps.custom_invoices',\n]\n\nROOT_URLCONF = 'tests._site.urls'\n\nTEMPLATES = [\n {\n 'BACKEND': 'django.template.backends.django.DjangoTemplates',\n 'DIRS': [\n os.path.join(BASE_DIR, 'templates'),\n ],\n 'APP_DIRS': True,\n },\n]\n\nDATABASES = {\n 'default': {\n 'ENGINE': os.environ.get('DATABASE_ENGINE', 'django.db.backends.postgresql'),\n 'NAME': os.environ.get('DATABASE_NAME', 'oscar_invoices_test'),\n 'USER': os.environ.get('DATABASE_USER', 'postgres'),\n 'PASSWORD': os.environ.get('DATABASE_USER', None),\n }\n}\n\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'UTC'\n\nSTATIC_URL = '/static/'\n\nMEDIA_URL = '/media/'\n\nMEDIA_ROOT = os.path.join(BASE_DIR, 'sandbox', 'media')\n\nHAYSTACK_CONNECTIONS = {\n 'default': {\n 'ENGINE': 'haystack.backends.simple_backend.SimpleEngine',\n },\n}\n\nSITE_ID = 1\n","sub_path":"tests/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":2250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"522910659","text":"__author__ = 'Yazhi'\n\nimport base\nimport attributes\nimport exceptions\nimport redis\n\n\nclass Client(object):\n def __init__(self, **pargs):\n self.connection_settings = pargs or {'host': 'localhost',\n 'port': 6379, 'db': 0}\n\n def redis(self):\n return redis.Redis(**self.connection_settings)\n\n def update(self, d):\n self.connection_settings.update(d)\n\n\ndef connection_setup(**kwargs):\n global connection, client\n if client:\n client.update(kwargs)\n else:\n client = Client(**kwargs)\n connection = client.redis()\n\n\ndef get_client():\n global connection\n return connection\n\n\nclient = Client()\nconnection = client.redis()\ndefault_expire_time = 60\n\n__all__ = ['Model', 'Attribute', 'BooleanField', 'IntegerField',\n 'Counter', 'FloatField', 'DateTimeField', 'DateField',\n 'ReferenceField', 'ListField', 'ValidationError', 'from_key',\n 'ValidationError', 'MissingID', 'AttributeNotIndexed',\n 'FieldValidationError', 'BadKeyError', 'connection_setup', 'get_client']\n","sub_path":"Model/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"317385818","text":"from display import Display\nfrom eye import Eye\nimport entity_pb2 \nimport pygame\nimport numpy as np\nimport cv2 as cv\nimport fractions\nimport utils\nimport pickle as pkl\nimport os\nimport IPython\nimport math\n\n############################################################################\n### SCREEN SETTINGS ###\n############################################################################\nscreenSize = height,width = 800,1280\nbgColor = 0, 0, 0\ndisplay = Display(screenSize,False)\n\n############################################################################\n### Network connection to perception system ###\n############################################################################\neye = Eye(\"procam1\",9000)\n\n############################################################################\n### File management ###\n############################################################################\nassemblyFileNames = []\ndirectory = 'resources'\n\n\n############################################################################\n### Assembly container ###\n############################################################################\nassemblySet = []\n\n############################################################################\n### Create a set of command lists ###\n############################################################################\nSTARTCALIBRATION = entity_pb2.EntityList()\nSTARTCALIBRATION.command = STARTCALIBRATION.START_CALIBRATION\n\nSTARTSTREAM = entity_pb2.EntityList()\nSTARTSTREAM.command = STARTSTREAM.START_STREAM\n\nSTOPSTREAM = entity_pb2.EntityList()\nSTOPSTREAM.command = STOPSTREAM.STOP_STREAM\n\nDISCONNECT = entity_pb2.EntityList()\nDISCONNECT.command = DISCONNECT.DISCONNECT\n\nPACKETINFO = entity_pb2.EntityList()\nPACKETINFO.command = PACKETINFO.SEND_DISPLAY_INFO_PACKET\npacketEnt = PACKETINFO.entityList.add()\npacketEnt.image.width = width\npacketEnt.image.height = height\n\n############################################################################\n### Useful Images ###\n############################################################################\ncheckerboardArray = utils.generateCheckerboard(screenSize)\ncheckerboardImage = utils.convert2pygameImage(checkerboardArray)\n\n############################################################################\n### Routines ###\n############################################################################\ndef displayImage(image):\n display.display.blit(image,(0,0))\n pygame.display.flip()\ndef requestCalibration(placeHolder):\n eye.sendMessage(STARTCALIBRATION)\n message = eye.getMessage()\n while(message.command is not entity_pb2.EntityList.SEND_DISPLAY_INFO_PACKET):\n message = eye.getMessage()\n\n if not placeHolder:\n eye.sendMessage(PACKETINFO)\n print(\"Sent packet info\")\n else:\n eye.sendMessage(STOPSTREAM)\n eye.sendMessage(STOPSTREAM)\n\n ## Create and display a checker board\n if not placeHolder:\n displayImage(checkerboardImage)\n else:\n print('Performing a dummy calibration. No checkerboard needed')\n\n ## Wait until calibration is complete\n message = entity_pb2.EntityList()\n while(message.command != entity_pb2.EntityList.CALIBRATION_COMPLETE):\n message = eye.getMessage()\n displayImage(display.backgroundImage)\ndef routine(spriteList):\n display.update(spriteList)\ndef writeToFile(set,fileName):\n if not os.path.exists(os.path.join('.',directory)):\n os.makedirs(directory)\n else:\n print('Using resources directory for dumping files')\n fh = open(os.path.join('.',directory,fileName),'wb')\n pkl.dump(set,fh,-1)\n fh.close()\ndef readFromFile(fileName):\n if os.path.exists(os.path.join('.',directory,fileName)):\n fh = open(os.path.join('.',directory,fileName),'rb')\n return pkl.load(fh)\n else:\n print('File does not exist')\n return ''\ndef matchSprites(sprite,assemList,iThresh,pThresh):\n ## Control params\n imageMatch = False\n positionMatch = False\n minHistDist = float(\"inf\")\n minIndex = -1\n index = -1\n\n ## Match the image patch with each sprite in assembly\n for assSprite in assemList:\n histDist = cv.compareHist(sprite.histogram,assSprite.histogram,cv.cv.CV_COMP_CHISQR)\n index += 1\n if(histDist < minHistDist):\n minHistDist = histDist\n minIndex = index\n\n if(minHistIndex < iThresh):\n ## Image match acquired\n imageMatch = True\n \n ## Match for position\n dist = math.sqrt(pow(assemList[minIndex].pose[0] - sprite.pose[0],2) + pow(assemList[minIndex].pose[1] - sprite.pose[1],2)) + 10*abs(assemList[minIndex].pose[2] - sprite.pose[2])\n if (dist delta_R**2:\n pseudoevent_nosignal[sample][i] = True\n\n print(\"{0} signal events found with delta_r <= {1}.\".format(np.sum(pseudoevent_signal[sample]), delta_r))\n print(\"{0} events found with delta_R > {1}.\".format(np.sum(pseudoevent_nosignal[sample]), delta_R))\n\npickle.dump(pseudoevent_signal, open(\"../pseudoevent_signal.pkl\",'wb'), -1)\npickle.dump(pseudoevent_nosignal, open(\"../pseudoevent_nosignal.pkl\",'wb'), -1)\n","sub_path":"python/find_high_eta_signals.py","file_name":"find_high_eta_signals.py","file_ext":"py","file_size_in_byte":2542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"602580838","text":"\nfrom collections import Counter\n\nwith open('find.txt', 'r') as f:\n word_list = f.read().split(' ')\n word = input('输入要查找到的单词：')\n num = Counter(word_list).get(word)\n if not num:\n num = 0\n print('{}: {}'.format(word, num))\n\n\n\n\n\n","sub_path":"0004.py","file_name":"0004.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"564321347","text":"# -*- coding: utf-8 -*-\n\nimport random, time as time, datetime, shutil, os, cv2\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom matplotlib.font_manager import *\n\n# matplotlib.use('qt4agg')\n\nmyfont = FontProperties(fname='./SourceHanSerifCN-Light.otf')\n\n\ndef showGraph(filepath):\n arr = np.load(filepath)['array'].tolist()[:]\n # arr = np.load(filepath)['array'].tolist()[-50:]\n print(len(arr))\n # for t in arr:\n # if t > 0.3:\n # print('{0:.10f}'.format(t))\n print(arr)\n x = np.arange(len(arr))\n print(len(x))\n y = arr\n # plt.figure()\n matplotlib.rcParams['axes.unicode_minus']=False \n plt.title(u'loss趋势', fontproperties=myfont)\n plt.xlabel('训练次数', fontproperties=myfont)\n plt.ylabel('loss')\n plt.plot(x, y)\n plt.show()\n\nshowGraph('./loss.npz')\n\n\ndef sortByTime(dirpath):\n a = [s for s in os.listdir(dirpath)\n if os.path.isfile(os.path.join(dirpath, s))]\n a.sort(key=lambda s: s.split('_')[0])\n return a\n\ndef removeLessFolder(path):\n dirs = os.listdir(path)\n dirs.sort()\n for record in dirs:\n if len(os.listdir(path + record)) < 100 and record != '2018-01-30 13:15:00':\n print('删除目录：' + path + record)\n shutil.rmtree(path + record)\n\n\n# removeLessFolder('./records/');\n\n\n\n\n\n","sub_path":"analysis.py","file_name":"analysis.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"211413914","text":"#\n# [955] Complete Binary Tree Inserter\n#\n# https://leetcode.com/problems/complete-binary-tree-inserter/description/\n#\n# algorithms\n# Medium (54.51%)\n# Total Accepted: 3.3K\n# Total Submissions: 6K\n# Testcase Example: '[\"CBTInserter\",\"insert\",\"get_root\"]\\n[[[1]],[2],[]]'\n#\n# A complete binary tree is a binary tree in which every level, except possibly\n# the last, is completely filled, and all nodes are as far left as possible.\n#\n# Write a data structure CBTInserter that is initialized with a complete binary\n# tree and supports the following operations:\n#\n#\n# CBTInserter(TreeNode root) initializes the data structure on a given tree\n# with head node root;\n# CBTInserter.insert(int v) will insert a TreeNode into the tree with value\n# node.val = v so that the tree remains complete, and returns the value of the\n# parent of the inserted TreeNode;\n# CBTInserter.get_root() will return the head node of the tree.\n#\n#\n#\n#\n#\n#\n#\n#\n# Example 1:\n#\n#\n# Input: inputs = [\"CBTInserter\",\"insert\",\"get_root\"], inputs = [[[1]],[2],[]]\n# Output: [null,1,[1,2]]\n#\n#\n#\n# Example 2:\n#\n#\n# Input: inputs = [\"CBTInserter\",\"insert\",\"insert\",\"get_root\"], inputs =\n# [[[1,2,3,4,5,6]],[7],[8],[]]\n# Output: [null,3,4,[1,2,3,4,5,6,7,8]]\n#\n#\n#\n#\n#\n# Note:\n#\n#\n# The initial given tree is complete and contains between 1 and 1000 nodes.\n# CBTInserter.insert is called at most 10000 times per test case.\n# Every value of a given or inserted node is between 0 and 5000.\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n#\n# Definition for a binary tree node.\n\n\nclass TreeNode:\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\n\nclass CBTInserter:\n\n def __init__(self, root):\n \"\"\"\n :type root: TreeNode\n \"\"\"\n # level order\n self.root = root\n self.levels = []\n q = [root]\n while q:\n curLen = len(q)\n # NOTE: here should append copy of q, or q will change in-place\n self.levels.append(q[::])\n for node in q[0:curLen]:\n if node.left:\n q.append(node.left)\n if node.right:\n q.append(node.right)\n q = q[curLen:]\n\n def insert(self, v):\n \"\"\"\n :type v: int\n :rtype: int\n \"\"\"\n # check the deepest level nodes count\n curLen = len(self.levels[-1])\n newNode = TreeNode(v)\n if len(self.levels) == 1 or curLen == 2 * len(self.levels[-2]):\n # should insert new level\n parent = self.levels[-1][0]\n parent.left = newNode\n self.levels.append([])\n else:\n parent = self.levels[-2][curLen // 2]\n if parent.left:\n parent.right = newNode\n else:\n parent.left = newNode\n self.levels[-1].append(newNode)\n return parent.val\n\n def get_root(self):\n \"\"\"\n :rtype: TreeNode\n \"\"\"\n return self.root\n\n# Your CBTInserter object will be instantiated and called as such:\n# obj = CBTInserter(root)\n# param_1 = obj.insert(v)\n# param_2 = obj.get_root()\n\n\nroot = TreeNode(1)\nroot.left = TreeNode(2)\nobj = CBTInserter(root)\nprint(obj.insert(3))\nprint(obj.insert(4))\nprint(obj.insert(5))\nprint(obj.insert(6))\n","sub_path":"Medium/919.complete-binary-tree-inserter.python3.py","file_name":"919.complete-binary-tree-inserter.python3.py","file_ext":"py","file_size_in_byte":3269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"515565171","text":"#coding=utf-8\nfrom django.conf.urls import url\n\n# 从当前目录下引入views.py文件，默认别名为views\nfrom . import views\n\n\n# 定义路由匹配\nurlpatterns = [\n url(r'^$',views.index),\n url(r'^index/',views.index), #使用FBV（Function Base Views）定义路由\n url(r'^page/(?P\\d+)',views.page,name='page'), #使用FBV（Function Base Views）定义路由\n # url(r'^home/',views.Home.as_view()), #使用CBV（Class Base Views）定义路由\n # url(r'^form/', views.Form.as_view()), # 使用CBV（Class Base Views）定义路由\n]\n","sub_path":"test/mydjango/apps/blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"534857442","text":"from django.shortcuts import render, get_object_or_404\nfrom django.contrib import messages\n\nfrom .models import Item, Coin\nfrom .helpers.change import change as change_helper\n\n\ndef index(request):\n \"\"\"\n Main and the only view in this simple app for vending machine\n \"\"\"\n context = {}\n\n items = Item.objects.filter(available_for_sale=True).order_by('name')\n coins = Coin.objects.all()\n\n inserted_cash = request.POST.get('inserted_cash', 0) if request.POST else 0\n inserted_cash = int(inserted_cash)\n\n coin_alert = False\n coin = request.POST.get('coin', 0) if request.POST else 0\n coin = int(coin)\n if 0 < coin < 5:\n coin_alert = True\n messages.info(request, 'The machine does not accept coins under 5p, sorry, please take your %sp coin.' % coin)\n else:\n inserted_cash += coin\n\n change = 0\n if request.POST:\n if request.POST.get('cancel', False):\n change = str(change_helper(inserted_cash))\n messages.info(request, 'Please, take your change: %s' % inserted_cash)\n coin = 0\n\n if request.POST:\n if request.POST.get('selected_item_id', False):\n context['selected_item'] = selected_item = get_object_or_404(Item, id=int(request.POST.get(\n 'selected_item_id')))\n\n context['selected_item_id'] = request.POST.get('selected_item_id')\n\n if request.POST.get('buy', False):\n if selected_item:\n if selected_item.cost <= inserted_cash:\n inserted_cash -= selected_item.cost\n selected_item.amount -= 1\n selected_item.save()\n if inserted_cash > 0:\n change = str(change_helper(inserted_cash))\n inserted_cash = 0\n coin = 0\n messages.success(request, 'You have bought: %s! Please, take your change: %s' % (\n selected_item.name, change))\n else:\n messages.success(request, 'You have bought: %s!' % selected_item.name)\n\n context['items'] = items\n context['coins'] = coins\n context['inserted_cash'] = inserted_cash\n context['change'] = change\n context['coin_alert'] = coin_alert\n\n return render(request, 'index.html', context)","sub_path":"vending_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"474086380","text":"# Candy\n# There are N children standing in a line. \n# Each child is assigned a rating value.\n# You are giving candies to these children subjected to the following requirements:\n# \n# Each child must have at least one candy.\n# Children with a higher rating get more candies than their neighbors.\n# \n# What is the minimum candies you must give?\n\nclass Solution:\n\t# @param ratings, a list of integer\n\t# @return an integer\n\tdef candy(self, ratings):\n\t\tcandy_rcrd = [1 for dummy_index in range(len(ratings))]\n\t\t\n\t\tfor index in range(1, len(ratings)):\n\t\t\tif ratings[index] > ratings[index - 1]:\n\t\t\t\tcandy_rcrd[index] = candy_rcrd[index - 1] + 1\n\t\t\n\t\tfor index in range(len(ratings) - 2, -1, -1):\n\t\t\tif ratings[index] > ratings[index + 1]:\n\t\t\t\tif candy_rcrd[index] <= candy_rcrd[index + 1]:\n\t\t\t\t\tcandy_rcrd[index] = candy_rcrd[index + 1] + 1\n\t\t\n\t\treturn sum(candy_rcrd)","sub_path":"135_candy.py","file_name":"135_candy.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"443543374","text":"import matplotlib.pyplot as plt\nfrom common import *\n\n\ndef get_c(data):\n n = len(data)\n alpha = [0 for i in range(n - 1)]\n beta = [0 for i in range(n - 1)]\n A,B,C,F = (0,0,0,0)\n for i in range(1,n - 1):\n h_i = data[i][0] - data[i - 1][0]\n h_i1 = data[i + 1][0] - data[i][0]\n A = h_i\n C = 2. * (h_i + h_i1)\n B = h_i1\n F = 6. * ((data[i + 1][1] - data[i][1]) / h_i1 - (data[i][1] - data[i - 1][1]) / h_i)\n z = (A * alpha[i - 1] + C)\n alpha[i] = -B / z\n beta[i] = (F - A * beta[i - 1]) / z\n\n c = [0 for i in range(n)]\n c[n-1] = (F - A * beta[n - 2]) / (C + A * alpha[n - 2])\n for i in range(1,n-1)[::-1]:\n c[i] = alpha[i] * c[i + 1] + beta[i]\n return c\n\n\ndef get_spline(data):\n r = []\n n = len(data)\n _c = get_c(data)\n for i in range(1,n):\n h = data[i][0] - data[i-1][0]\n hy = data[i][1] - data[i-1][1]\n a = data[i][1]\n b = hy/h + (h/6) * (2 * _c[i] + _c[i-1])\n c = _c[i]/2\n d = ((_c[i] - _c[i - 1])/h) / 6\n k = np.poly1d([1,-data[i][0]])\n r.append(np.poly1d([a]) + b * k + c * np.polymul(k,k) + d * np.polymul(np.polymul(k,k),k))\n return r\n\n\n\nres = get_spline(data)\n\nx = [data[i][0] for i in range(len(data))]\ny = [data[i][1] for i in range(len(data))]\nplt.plot(x,y,'-wo')\n\n\ny = [f(i) for i in x]\nplt.plot(x,y,'--')\n\nfor i in range(len(data) - 1):\n x = np.arange(data[i][0],data[i + 1][0],0.01)\n y = [np.polyval(res[i],j) for j in x]\n plt.plot(x,y)\n\nplt.show()\n\n\n\n","sub_path":"python/n_m/n_m_1/lab_4/3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":1539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"448513982","text":"import time\nimport os\nimport torch\nimport argparse\nfrom flextensor.examples import FUNC_TABLE\nfrom flextensor.test import test_graph_schedule_cpu_general_dx\nfrom flextensor.train import Entity, train_op_schedule_cpu_general_dx\n\n\ndef run(batch_size, M, N, L, model_path, epoch=5, sample=16, number=100, test=False):\n entities = []\n func = FUNC_TABLE[\"matmul_batch\"].func\n args = (batch_size, M, N, L)\n entities.append(Entity(func, args))\n model_path = os.path.abspath(model_path)\n if not test:\n beg = time.time()\n train_op_schedule_cpu_general_dx(entities, epoch, sample, model_path)\n end = time.time()\n print(\"{}({}):\".format(\"matmul_batch\", args))\n print(\"train done! use {}ms\".format((end - beg) * 1e3))\n test_graph_schedule_cpu_general_dx(func, args, model_path, number=number)\n\n\ndef pytorch_baseliine(batch_size, M, N, L, number=100):\n A = torch.rand((batch_size, M, N))\n B = torch.rand((N, L))\n beg = time.time()\n for i in range(number):\n C = A.matmul(B)\n end = time.time()\n cost = (end - beg) / number * 1e3\n print(\"pytorch gemm use {}ms\".format(cost))\n return cost\n\n\nif __name__ == \"__main__\":\n # parser = argparse.ArgumentParser()\n # parser.add_argument(\"-t\", \"--train\", help=\"train the model\", action=\"store_true\")\n # parser.add_argument(\"-p\", \"--pytorch\", help=\"run pytorch baseline\", action=\"store_true\")\n # parser.add_argument(\"-a\", \"--auto_schedule\", help=\"run auto-scheduler\", action=\"store_true\")\n # parser.add_argument(\"-n\", \"--number\", help=\"number of tests\", type=int, default=100)\n # parser.add_argument(\"-f\", \"--model_file_path\", type=str, default=\"../logs/test_model.pkl\")\n # parser.add_argument(\"--params\", help=\"B,M,N,L\", type=str, default=\"1,1024,1024,1024\")\n # parser.add_argument(\"--epoch\", type=int, default=5)\n # parser.add_argument(\"--sample\", type=int, default=16)\n # args = parser.parse_args()\n # test = not args.train\n # use_torch = args.pytorch\n # use_auto = args.auto_schedule\n # try:\n # params = [int(x) for x in args.params.split(\",\")]\n # batch_size, M, N, L = params\n # if use_torch:\n # pytorch_baseliine(batch_size, M, N, L, args.number)\n # if use_auto:\n # run(batch_size, M, N, L, args.model_file_path, args.epoch, args.sample, args.number, test)\n # except Exception as e:\n # raise ValueError(\"Bad parameters, please refer to usage\")\n\n # gemm\n arg_lst = [\n (1, 128, 128, 128),\n (1, 256, 256, 256),\n (1, 512, 512, 512),\n (1, 1024, 1024, 1024),\n (1, 2048, 2048, 2048),\n (1, 4096, 4096, 4096),\n (1, 1024, 32, 1024),\n (1, 32, 1024, 32),\n ]\n\n names = [\n \"128_128_128_b1\",\n \"256_256_256_b1\",\n \"512_512_512_b1\",\n \"1024_1024_1024_b1\",\n \"2048_2048_2048_b1\",\n \"4096_4096_4096_b1\",\n \"1024_32_1024_b1\",\n \"32_1024_32_b1\",\n ]\n for i in range(len(arg_lst)):\n model_path = \"opt_gemm_\" + names[i] + \"_cpu.pkl\"\n entities = []\n args = arg_lst[i]\n entities.append(Entity(\"matmul_batch\", args))\n model_path = os.path.abspath(model_path)\n train_op_schedule_cpu_general_dx(entities, 20, 50, model_path, logfile=\"process_gemm_\" + names[i] + \"_cpu.txt\", device=\"cuda:0\")\n\n","sub_path":"flextensor/examples/opt_gemm_cpu.py","file_name":"opt_gemm_cpu.py","file_ext":"py","file_size_in_byte":3347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"55439650","text":"import random\r\nimport time\r\n\r\nclass MasterMind():\r\n def __init__(self):\r\n self.board = []\r\n self.feedback = []\r\n self.answer = [0, 0, 0, 0] #correct answer\r\n self.entry = [] #user input\r\n self.round = 0\r\n\r\n def reset(self):\r\n self.round = 0\r\n self.board = [[0 for x in range(4)] for y in range(9)]\r\n self.feedback = [[' ' for x in range(2)] for y in range(9)]\r\n self.entry = [0, 0, 0, 0]\r\n numbers = [1, 2, 3, 4, 5, 6, 7, 8]\r\n for i in range(4):\r\n self.answer[i] = numbers.pop(random.randint(0, len(numbers)-1))\r\n\r\n def handle_input(self):#handle user input\r\n user_input = input(': ')\r\n if user_input.lower() == 'exit': #exit if 'exit'\r\n exit()\r\n numbers = ['1', '2', '3', '4', '5', '6', '7', '8']\r\n self.entry = []\r\n for char in user_input: #append valid characters form input to self.entry\r\n if char in numbers:\r\n self.entry.append(int(char))\r\n if len(self.entry) == 4: #check if self.entry is the right length\r\n return True\r\n else:\r\n print('Invalid input')\r\n return False\r\n\r\n def handle_entry(self):\r\n self.board[self.round] = self.entry\r\n temp_answer = [0, 0, 0, 0]\r\n for i in range(4):\r\n temp_answer[i] = self.answer[i]\r\n right_place = 0 #right numbers on right places\r\n wrong_place = 0 #right numbers on wrong places\r\n for i in range(4): #check for right numbers on right places\r\n if self.entry[i] == temp_answer[i]:\r\n right_place += 1\r\n temp_answer[i] = 0\r\n for i in range(4): #check for right numbers on wrong places\r\n if self.entry[i] in temp_answer:\r\n wrong_place += 1\r\n self.feedback[self.round][0] = right_place\r\n self.feedback[self.round][1] = wrong_place\r\n if right_place == 4: #if player won\r\n return True\r\n else:\r\n return False\r\n\r\n def print_board(self):\r\n print('')\r\n for i in range(9):\r\n print(str(self.feedback[i][0])+str(self.board[i])+str(self.feedback[i][1]))\r\n\r\n def next_round(self):\r\n self.round += 1\r\n if self.round == 9:\r\n return False\r\n else:\r\n return True\r\n\r\n def info(self):\r\n print('Welcome to MasterMind!')\r\n print('In this game you need to quess 4 digit code made of numbers from 1 to 8 in nine quesses')\r\n print('On the left side of the board is shown how many numbers you got excatly right.')\r\n print('On the right side of the board id shown how many of numbers you entered were right, but in the wrong place.')\r\n print('To exit the game type \"exit\".')\r\n\r\n def victory(self, time):\r\n print(f'Correct! The answer was {self.answer}.')\r\n print(f'It took you {self.round} rounds and {round(time)} seconds to win.')\r\n\r\n\r\n\r\ndef main():\r\n mastermind = MasterMind()\r\n mastermind.info()\r\n while True:\r\n mastermind.reset()\r\n game = True\r\n start = time.time()\r\n mastermind.print_board()\r\n while game:\r\n input_check = True\r\n while input_check:\r\n input_check = not mastermind.handle_input()\r\n if mastermind.handle_entry():\r\n game = False\r\n end = time.time()\r\n mastermind.victory(end-start)\r\n\r\n else:\r\n if mastermind.next_round():\r\n pass\r\n else:\r\n print('You lost!')\r\n game = False\r\n mastermind.print_board()\r\n\r\n print('Do you want to play again (y/n)?')\r\n play_again = input(': ')\r\n if play_again.lower() == 'y':\r\n pass\r\n else:\r\n exit()\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"347969500","text":"from django.contrib import admin\nfrom .models import Action\n\n\n@admin.register(Action)\nclass ActionAdmin(admin.ModelAdmin):\n list_display = [\"name\", \"created_at\", \"owner\"]\n list_filter = [\n \"name\",\n \"owner\",\n ]\n readonly_fields = [\n \"created_at\",\n ]\n","sub_path":"backend/coapp/core/actions/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"35423893","text":"#printing numbers till 10 except 7 and 3 via for and while loop\n\n#using for loop\n\n#defining function for printing the numbers\nfrom _ast import Continue, Break\nfor numBer in range(1,11):\n if(numBer==3 or numBer==7):\n continue\n else:\n print(numBer)\n \n#using while loop\n\n#initializing value of x and running while loop with conditional statement that value of x must not exceed 10.\n \nnumBer=1\nwhile(numBer!=11):\n if(numBer==3 or numBer==7):\n numBer=numBer+1\n else:\n print(numBer)\n numBer=numBer+1\n","sub_path":"Python-Task3/question3.py","file_name":"question3.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"509090935","text":"from pygame import Surface\nfrom pygame.sprite import Sprite\n\n\nclass Platform(Sprite):\n \"\"\"\n Static Sprite used as a collision object for MovableSprites.\n \"\"\"\n\n def __init__(self, sprite, platform_size, start_pos):\n \"\"\"\n Expects the image as a Surface and the platform size as a \n (width, height) tuple of image tiles\n \"\"\"\n super().__init__()\n\n self.image = self._create_platform(sprite, platform_size)\n self.rect = self.image.get_rect()\n self.rect.topleft = start_pos\n \n def _create_platform(self, sprite, platform_size):\n \"\"\"\n Creates a new Surface for the platform and tiles the given sprite \n over it.\n \"\"\"\n rect = sprite.get_rect()\n image = Surface((rect.width * platform_size[0], \n rect.height * platform_size[1]))\n\n for i in range(platform_size[0]):\n rect.left = i * rect.width\n for j in range(platform_size[1]):\n rect.top = j * rect.height\n image.blit(sprite, rect)\n\n return image\n","sub_path":"boilerplate/platform.py","file_name":"platform.py","file_ext":"py","file_size_in_byte":1110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"355752202","text":"class Receipt:\n\t\"\"\"This class represents a unit of transaction.\n\t\"\"\"\n\n\t\"\"\"This is a basic constructor for a receipt.\n\t\"\"\"\n\tdef __init__(self):\n\t\tself.products = []\n\n\t\"\"\"This method adds a product to this receipt\n\tParameters:\n\tproduct - id of the product\n\t\"\"\"\n\tdef addProduct(self,product):\n\t\tself.products.append(product)\n\n\t\"\"\"This method returns true if this receipt has all the products mentioned\n\tParameters:\n\tproducts - list of product id's to check this receipt for\n\t\n\tReturns:\n\tTrue if all products in the argument are contained in this receipt and False\n\totherwise\n\t\"\"\"\n\tdef hasProducts(self,products):\n\t\tfor i in products:\n\t\t\tif i not in self.products:\n\t\t\t\treturn False\n\t\treturn True\n\nclass Rule:\n\t\"\"\"This class represents an association rule\n\t\"\"\"\n\n\t\"\"\"This is a basic constructor for a rule\n\tParameters:\n\tleft - the precedent of the rule (a ProdSet object)\n\tright - the consequent of the rule (a ProdSet object)\n\t\"\"\"\n\tdef __init__(self,left,right):\n\t\tself.left = left\n\t\tself.right = right\n\t\t#initialize confidence to sentinel value to avoid recomputing once valid\n\t\t#value is given\n\t\tself.confidenceVal = -1;\n\n\t\"\"\"Returns the confidence of this rule\n\tReturn:\n\tconfidence value (likeliness of the right to be purchased if left is \n\tpurchased) of this rule\n\t\"\"\"\n\tdef confidence(self):\n\t\t#if not yet computed\n\t\tif self.confidenceVal == -1:\n\t\t\tself.confidenceVal = self.left.combine(self.right).support() / self.left.support()\n\t\treturn self.confidenceVal\n\n\t\"\"\"This method prints this rule\n\t\"\"\"\n\tdef printRule(self):\n\t\tprint(self.left.products,\"->\",self.right.products,\"\\tConfidence: %3.2f\" % (self.confidence() * 100))\n\nclass ProdSet:\n\t\"\"\"This class represents a set in the a priori association rule mining \n\talgorithm\n\t\"\"\"\n\n\t\"\"\"This is a basic constructor for a set\n\tParameters:\n\tproducts - list of products in this set\n\tdataset - a Dataset object to reference from this set\n\t\"\"\"\n\tdef __init__(self,products,dataset):\n\t\tself.products = products\n\t\tself.dataset = dataset\n\t\t#support is initialized with a sentinel value to avoid recomputing\n\t\tself.supportVal = -1\n\n\t\"\"\"This method combines this set with another set\n\tParameter:\n\tset2 - set to combine with\n\tReturn:\n\tThis method returns the combined set\n\t\"\"\"\n\tdef combine(self,set2):\n\t\tnewArr = self.products[:]\n\t\tfor i in set2.products:\n\t\t\tif i not in newArr:\n\t\t\t\tnewArr.append(i)\n\t\treturn ProdSet(newArr,dataset)\n\n\t\"\"\"This method computes the support of this set\n\tReturn:\n\tThis method returns the support of this set.\n\t\"\"\"\n\tdef support(self):\n\t\t# if not yet computed\n\t\tif self.supportVal == -1:\n\t\t\tself.supportVal = self.dataset.support(self)\n\t\treturn self.supportVal\n\n\t\"\"\"This method computes if this set is equal to another set.\n\n\t\"\"\"\n\tdef isEqual(self,set2):\n\t\tif len(self.products) == len(set2.products):\n\t\t\tequalCtr = 0;\n\t\t\t\n\t\t\tfor i in self.products:\n\t\t\t\tfor j in set2.products:\n\t\t\t\t\tif i == j:\n\t\t\t\t\t\tequalCtr += 1\n\t\t\t\t\t\tbreak\n\t\t\treturn equalCtr == len(self.products)\n\t\telse:\n\t\t\treturn False\n\n\t\"\"\"This method checks if another set is a subset of this set\n\tParameter:\n\tset2 - a Set object to check if it is a subset of this set\n\tReturn:\n\tTrue if set2 is a subset of this set and False otherwise\n\t\"\"\"\n\tdef isSubset(self,set2):\n\t\tfor i in set2.products:\n\t\t\tif i not in self.products:\n\t\t\t\treturn False\n\n\t\treturn True\n\n\t\"\"\"This method computes all the subsets of the products in this set\n\tReturns:\n\tThis method returns a list of subsets of the products in this set.\n\t\"\"\"\n\tdef subsets(self):\n\t\tsubsetList = []\n\t\t# for all possible subset sizes\n\t\tfor i in range(1,len(self.products)):\n\t\t\tsubsetList = subsetList + self.recursiveSubset(self.products,i)\n\t\treturn subsetList\n\n\t\"\"\"This method recursively computes the subsets of the products in this set\n\tParameters:\n\tarray - list of elements\n\tcount - number of elements in subset\n\t\"\"\"\n\tdef recursiveSubset(self,array,count):\n\t\t# base case, return arrays containing each element\n\t\tif count == 1:\n\t\t\treturn [[i] for i in array]\n\t\telse:\n\t\t\tsubsets = []\n\n\t\t\t# for each element before the last count - 1 elements\n\t\t\tfor i in range(0,len(array)-count+1):\n\t\t\t\t# slice right remainder of array\n\t\t\t\tif i == 0:\n\t\t\t\t\tnewArr = array[i+1:]\n\t\t\t\telif i == len(array) - 1:\n\t\t\t\t\tnewArr = []\n\t\t\t\telse:\n\t\t\t\t\tnewArr = array[i+1:]\n\n\t\t\t\t# solve subsets recursively\n\t\t\t\tsets = self.recursiveSubset(newArr,count-1)\n\n\t\t\t\t# append current element to each computed subset\n\t\t\t\tfor j in sets:\n\t\t\t\t\tsubsets.append([array[i]] + j)\n\t\t\treturn subsets\n\n\t\"\"\"This method subtracts a set of items from this set and returns the \n\tremainder\n\tParameter:\n\tsubset - subset to subtract from this set\n\tReturns:\n\tThis method returns the remainder of the set.\n\t\"\"\"\n\tdef subtract(self,subset):\n\t\ttempArr = self.products[:]\n\t\tfor i in subset:\n\t\t\tfor j in range(0,len(tempArr)):\n\t\t\t\tif tempArr[j] == i:\n\t\t\t\t\tdel tempArr[j]\n\t\t\t\t\tbreak\n\t\treturn tempArr\n\n\t\"\"\"This method computes all the rules from this set\n\tReturn:\n\tThis method returns a list of all the possible rules from this set\n\t\"\"\"\n\tdef rules(self):\n\t\tsubsetList = self.subsets()\n\t\trules = []\n\t\tfor i in subsetList:\n\t\t\trules.append(Rule(ProdSet(i,dataset),ProdSet(self.subtract(i),dataset)))\n\t\treturn rules\n\nclass Dataset:\n\t\"\"\"This class represents a collection of training data, namely a list of \n\treceipts\n\t\"\"\"\n\t\n\t\"\"\"basic constructor for a dataset\n\t\"\"\"\n\tdef __init__(self):\n\t\tself.receipts = []\n\n\t\"\"\"This method adds a receipt to the dataset\n\tParameter:\n\treceipt - a Receipt object to add to the dataset\n\t\"\"\"\n\tdef addReceipt(self,receipt):\n\t\tself.receipts.append(receipt)\n\n\t\"\"\"This returns the support of a set of products\n\tParameter:\n\tset - set to get the support of\n\n\tReturn:\n\tsupport value (fraction of receipts that contain the set elements) of the \n\tset \n\t\"\"\"\n\tdef support(self,set):\n\t\tsupport = 0;\n\t\tfor i in self.receipts:\n\t\t\tif i.hasProducts(set.products):\n\t\t\t\tsupport += 1\n\t\treturn support / len(self.receipts)\n\n\t\"\"\"This method mine all rules from this dataset\n\tParameter:\n\tminsup - minimum support for sets\n\tminconf - minimum confidence for rules\n\tfilterLift - default False: whether to filter by lift or not\n\tReturn:\n\tlist of rules\n\t\"\"\"\n\tdef mineRules(self,minsup,minconf,filterLift=False):\n\t\t# list of sets above minsup\n\t\tsets = []\n\n\t\t# current considered sets\n\t\tcurrSets = []\n\n\t\t# current approved sets\n\t\tcurrApprove = []\n\n\t\t# current rejected sets\n\t\tcurrRejects = []\n\n\t\t# single elements\n\t\tsingles = []\n\n\t\t# exit flag\n\t\texit = False\n\n\t\t# extract single elements in dataset\n\t\tfor i in self.receipts:\n\t\t\tfor j in i.products:\n\t\t\t\tif j not in singles:\n\t\t\t\t\tsingles.append(j)\n\n\t\t# create singleton sets\n\t\tcurrSets = [ProdSet([i],self) for i in singles]\n\n\t\t# while more sets are being produced\n\t\twhile not exit:\n\n\t\t\t# filter current sets for those above minsup\n\t\t\tfor i in currSets:\n\t\t\t\tif i.support() < minsup:\n\t\t\t\t\tcurrRejects.append(i)\n\t\t\t\telse:\n\t\t\t\t\tcurrApprove.append(i)\n\t\t\t\t\tsets.append(i)\n\n\t\t\t# empty currSets\n\t\t\tcurrSets = []\n\n\t\t\t# if there is more than one set to merge\n\t\t\tif len(currApprove) > 1:\n\t\t\t\t# for each unique pair\n\t\t\t\tfor i in range(0,len(currApprove) - 1):\n\t\t\t\t\tfor j in range(i + 1,len(currApprove)):\n\t\t\t\t\t\ttemp = currApprove[i].combine(currApprove[j])\n\n\t\t\t\t\t\t# search if already in the list\n\t\t\t\t\t\tfound = False\n\t\t\t\t\t\tfor k in currSets:\n\t\t\t\t\t\t\tif k.isEqual(temp):\n\t\t\t\t\t\t\t\tfound = True\n\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\t# if not yet in list\n\t\t\t\t\t\tif not found:\n\t\t\t\t\t\t\tfound = False\n\n\t\t\t\t\t\t\t# check if containing any rejects\n\t\t\t\t\t\t\tfor k in currRejects:\n\t\t\t\t\t\t\t\tif temp.isSubset(k):\n\t\t\t\t\t\t\t\t\tfound = True;\n\t\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\t\t# if does not contain rejects\n\t\t\t\t\t\t\tif not found:\n\t\t\t\t\t\t\t\tcurrSets.append(temp)\n\t\t\t\t\n\t\t\t\t# empty lists\n\t\t\t\tcurrApprove = []\n\t\t\t\tcurrRejects = []\n\t\t\telse:\n\t\t\t\texit = True\n\n\t\t# extract rules\n\t\trules = []\n\t\tfor i in sets:\n\t\t\trules = rules + i.rules()\n\n\t\t# filters all rules above minconf\n\t\tapprove = []\n\t\tfor i in rules:\n\t\t\tif i.confidence() >= minconf:\n\t\t\t\tapprove.append(i)\n\n\t\tif filterLift:\n\t\t\ttemp = approve\n\t\t\tapprove = []\n\t\t\tfor i in temp:\n\t\t\t\tif i.confidence() / i.right.support() > 1:\n\t\t\t\t\tapprove.append(i)\n\t\treturn approve\n\n# declare dataset\ndataset = Dataset()\n\n# declare five receipts\nreceipt = Receipt()\n\n# add products\nreceipt.addProduct(1)\nreceipt.addProduct(2)\nreceipt.addProduct(3)\nreceipt.addProduct(4)\nreceipt.addProduct(5)\n\n# add receipt to dataset\ndataset.addReceipt(receipt)\n\nreceipt = Receipt()\nreceipt.addProduct(1)\nreceipt.addProduct(3)\nreceipt.addProduct(4)\nreceipt.addProduct(5)\nreceipt.addProduct(6)\ndataset.addReceipt(receipt)\n\nreceipt = Receipt()\nreceipt.addProduct(1)\nreceipt.addProduct(2)\nreceipt.addProduct(4)\ndataset.addReceipt(receipt)\n\nreceipt = Receipt()\nreceipt.addProduct(1)\nreceipt.addProduct(2)\nreceipt.addProduct(3)\nreceipt.addProduct(5)\ndataset.addReceipt(receipt)\n\nreceipt = Receipt()\nreceipt.addProduct(3)\nreceipt.addProduct(4)\nreceipt.addProduct(5)\ndataset.addReceipt(receipt)\n\n# mine rules\nrules = dataset.mineRules(0.6,0.4,True)\n\n# display rules\nfor i in rules:\n\ti.printRule()","sub_path":"association.py","file_name":"association.py","file_ext":"py","file_size_in_byte":8876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"601972112","text":"import os\nimport time\nimport torch\nimport argparse\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport scipy,scipy.io\nfrom sklearn.preprocessing import normalize\nfrom torchvision import transforms\nfrom torch.utils.data import DataLoader,Dataset\nfrom collections import defaultdict\nimport numpy as np\nfrom models_zNorm import VAE,Classifier\nfrom sklearn.neighbors import KNeighborsClassifier\nimport pdb\n\ndomainSet =['caltech','amazon','webcam','dslr']\nclass TwoModalDataset(Dataset):\n def __init__(self,phase='train',sourceDomainIndex=0, targetDomainIndex = 0,trialIndex=0):\n self.phase = phase\n self.load_mat(sourceDomainIndex,targetDomainIndex,trialIndex)\n self.pseudo_label_B = np.ones_like(self.label_B)*-1 # this will be dynamically updated during training\n self.pseudo_score_B = np.zeros_like(self.label_B)\n def load_mat(self,sourceDomainIndex=0, targetDomainIndex=0,trialIndex=0):\n # load features and labels\n data_dir = '../data/Office10/decaf/'\n # data_dir = 'E:\\DomainAdaptation\\OfficeHomeDataset_10072016/'\n data_A = scipy.io.loadmat(data_dir+domainSet[sourceDomainIndex]+'_decaf.mat')\n feature_A = data_A['feas'].astype(np.float32)\n self.feature_A = normalize(feature_A,norm='l2')\n self.label_A = data_A['labels'].squeeze().astype(np.int64)-1\n self.num_class = len(np.unique(self.label_A))\n data_B = scipy.io.loadmat(data_dir+domainSet[targetDomainIndex]+'_decaf.mat')\n feature_B = data_B['feas'].astype(np.float32)\n self.feature_B = normalize(feature_B,norm='l2')\n self.label_B = data_B['labels'].squeeze().astype(np.int64)-1\n \n def __len__(self):\n if self.phase == 'train': #or self.phase == 'val':\n return self.feature_A.shape[0]\n if self.phase == 'test':\n return self.feature_B.shape[0]\n def __getitem__(self,idx):\n if self.phase == 'test':\n idx_B = idx\n return self.feature_B[idx_B,:],self.label_B[idx_B]\n # return a pair of regular and xray image features, which are paired randomly\n label = self.label_A[idx]\n #indicesB_this_label = np.argwhere((self.pseudo_label_B==label) & (self.pseudo_score_B > -1))\n indicesB_this_label = np.argwhere(self.pseudo_label_B==label)\n if len(indicesB_this_label) > 0:\n idx_B = np.random.choice(indicesB_this_label[:,0])\n return self.feature_A[idx,:], self.feature_B[idx_B,:],self.label_A[idx],self.pseudo_label_B[idx_B]\n else:\n idx_B = np.random.randint(len(self.label_B))\n return self.feature_A[idx,:], self.feature_B[idx_B,:], self.label_A[idx], np.ones_like(self.label_A[idx]) * -1\n\n\ndef test_model(model,dataset,dataloader,device,model_type='knn'):\n since = time.time()\n \n num_class = dataset.num_class\n running_corrects = np.zeros((num_class,))\n num_sample_per_class = np.zeros((num_class,))\n # Iterate over data.\n for index, (features,labels) in enumerate(dataloader):\n features = features.to(device)\n labels = labels.to(device)\n # zero the parameter gradients\n # forward\n # track history if only in train\n with torch.set_grad_enabled(False):\n if model_type=='knn':\n preds = model.predict(features)\n if model_type=='mlp':\n model.eval()\n preds = model(features)\n preds = preds.cpu().detach().numpy()\n labels = labels.cpu().detach().numpy()\n if index == 0:\n outputs_test = preds\n labels_test = labels\n else:\n outputs_test = np.concatenate((outputs_test, preds), 0)\n labels_test = np.concatenate((labels_test, labels), 0)\n if model_type=='mlp':\n preds = np.argmax(outputs_test,1)\n scores = np.exp(np.max(outputs_test,1))\n if model_type=='knn':\n preds = outputs_test\n \n for i in range(len(labels_test)):\n num_sample_per_class[labels_test[i]] += 1\n if preds[i]==labels_test[i]:\n running_corrects[labels_test[i]] += 1\n\n acc_per_class = running_corrects / num_sample_per_class\n acc = np.mean(acc_per_class)\n time_elapsed = time.time() - since\n #print('Testing complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\n print('per-image acc:{:2.4f}; per-class acc:{:2.4f}'.format(running_corrects.sum()/num_sample_per_class.sum(),acc))\n return preds, scores, acc_per_class,acc\n \ndef loss_fn(recon_xS,recon_xS2, xS, recon_xT,recon_xT2, xT, meanS, log_varS, meanT, log_varT,yT,epoch):\n criterion = torch.nn.MSELoss(size_average=False)\n mask = yT!=-1\n reconstruction_loss = criterion(recon_xS, xS) + criterion(recon_xT[mask,:], xT[mask,:])\n cross_reconstruction_loss = criterion(recon_xS2[mask,:], xT[mask,:]) + criterion(recon_xT2[mask,:], xS[mask,:])\n KLD = -0.5 * torch.sum(1 + log_varS - meanS.pow(2) - log_varS.exp()) -0.5 * torch.sum(1 + log_varT[mask,:] - meanT[mask,:].pow(2) - log_varT[mask,:].exp())\n distance = torch.sqrt(torch.sum((meanS[mask,:] - meanT[mask,:]) ** 2, dim=1) + torch.sum((torch.sqrt(log_varS[mask,:].exp()) - torch.sqrt(log_varT[mask,:].exp())) ** 2, dim=1))\n distance = distance.sum()\n weight = epoch*5e-4\n #print(f'{reconstruction_loss:1.4f}, {cross_reconstruction_loss:1.4f}, {distance:1.4f},{KLD:1.4f}')\n return (reconstruction_loss + cross_reconstruction_loss) / xS.size(0)\n \ndef train_classifier(classifier, vae, datasets, dataloaders, args, optimizer_cls, scheduler_cls):\n device = args.device\n classifier.train()\n vae.eval()\n acc_per_class = np.zeros((args.num_epochs_cls,datasets['train'].num_class))\n acc = np.zeros((args.num_epochs_cls,))\n for epoch in range(args.num_epochs_cls):\n #print(f'Classifier training epoch {epoch:d}/{args.num_epochs_cls:d}')\n for iteration, (xS,xT,yS,yT) in enumerate(dataloaders['train']):\n xS,xT,yS,yT = xS.to(device), xT.to(device), yS.to(device), yT.to(device)\n #x,y = next_batch(vae,batch_size=1024)\n recon_xS,recon_xT = generate_z(xS,xT,vae,device)\n mask = yT!=-1\n xT = xT[mask,:]\n yT = yT[mask] \n recon_xT = recon_xT[mask,:]\n xtrain = torch.cat((xS,xT,recon_xS,recon_xT),dim=0)\n ytrain = torch.cat((yS,yT,yS,yT),dim=0)\n output = classifier(xtrain)\n loss_cls = classifier.lossfunction(output, ytrain)\n optimizer_cls.zero_grad()\n loss_cls.backward()\n optimizer_cls.step()\n # test\n scheduler_cls.step()\n #print(f'epoch:{epoch:02d} ',end='')\n #preds,scores,acc_per_class[epoch,],acc[epoch] = test_model(classifier, datasets['test'], dataloaders['test'],device,model_type='mlp')\n #scipy.io.savemat('./results/'+args.filename+'.mat',mdict={'acc_per_class':acc_per_class,'acc':acc})\n return classifier\n\ndef train_vae(vae, dataloader,args, optimizer, scheduler):\n ############################################################\n # train CVAE\n ############################################################\n device = args.device\n vae.train()\n for epoch in range(args.num_epochs_vae):\n\n tracker_epoch = defaultdict(lambda: defaultdict(dict))\n\n for iteration, (xS,xT,yS,yT) in enumerate(dataloader):\n\n xS,xT,yS,yT = xS.to(device), xT.to(device), yS.to(device), yT.to(device)\n recon_xS, recon_xS2, meanS, log_varS, zS = vae(xS, d=torch.zeros_like(xS[:,0]).long().to(device))\n recon_xT, recon_xT2, meanT, log_varT, zT = vae(xT, d=torch.ones_like(xT[:,0]).long().to(device))\n loss = loss_fn(recon_xS, recon_xS2, xS, recon_xT,recon_xT2, xT, meanS, log_varS, meanT, log_varT,yT,epoch)\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n scheduler.step()\n return vae\n############################################################\n#Generating pseudo training samples and train/test a classifier\n############################################################\ndef generate_z(xS,xT,vae,device):\n vae.eval()\n recon_xS, recon_xS2, meanS, log_varS, zS = vae(xS, d=torch.zeros_like(xS[:,0]).long().to(device))\n recon_xT, recon_xT2, meanT, log_varT, zT = vae(xT, d=torch.ones_like(xT[:,0]).long().to(device))\n return recon_xS2, recon_xT2\n\ndef main(args):\n\n torch.manual_seed(args.seed)\n if torch.cuda.is_available():\n torch.cuda.manual_seed(args.seed)\n\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n args.device = device\n ts = time.time()\n datasets = {x: TwoModalDataset(phase=x,sourceDomainIndex=args.sourceDomainIndex, targetDomainIndex=args.targetDomainIndex,trialIndex=args.trialIndex) for x in ['train','test']}\n dataloaders={}\n dataloaders['train'] = DataLoader(dataset=datasets['train'], batch_size=args.batch_size, shuffle=True, num_workers = 8)\n dataloaders['trainall'] = DataLoader(dataset=datasets['train'], batch_size=len(datasets['train']), shuffle=True, num_workers = 8)\n dataloaders['test'] = DataLoader(dataset=datasets['test'], batch_size=len(datasets['test']), shuffle=False, num_workers = 8)\n # define a classifier\n classifier = Classifier(input_dim=4096,num_labels=10).to(device) # train and test a classifier\n optimizer_cls = torch.optim.Adam(classifier.parameters(), lr=0.01)\n scheduler_cls = torch.optim.lr_scheduler.StepLR(optimizer_cls, step_size=25, gamma=0.1)\n num_epochs_cls = 50\n acc_per_class = np.zeros((args.num_iter,10))\n # define the VAE\n vae = VAE(\n encoder_layer_sizes=args.encoder_layer_sizes,\n latent_size=args.latent_size,\n decoder_layer_sizes=args.decoder_layer_sizes,\n num_domains = 2,dropout=0.5).to(device) \n optimizer_vae = torch.optim.Adam(vae.parameters(), lr=args.learning_rate)\n scheduler_vae = torch.optim.lr_scheduler.StepLR(optimizer_vae, step_size=50, gamma=0.1)\n\n for iter in range(args.num_iter+5):\n if iter>0:\n # define VAE\n args.encoder_layer_sizes[0] = 4096\n vae = VAE(\n encoder_layer_sizes=args.encoder_layer_sizes,\n latent_size=args.latent_size,\n decoder_layer_sizes=args.decoder_layer_sizes,\n num_domains = 2,dropout=0.5).to(device) \n optimizer_vae = torch.optim.Adam(vae.parameters(), lr=args.learning_rate)\n scheduler_vae = torch.optim.lr_scheduler.StepLR(optimizer_vae, step_size=50, gamma=0.1)\n \n # train VAE\n vae = train_vae(vae, dataloaders['train'], args, optimizer_vae, scheduler_vae)\n # train a classifier\n classifier = Classifier(input_dim=4096,num_labels=10).to(device) # train and test a classifier\n optimizer_cls = torch.optim.Adam(classifier.parameters(), lr=0.01)\n scheduler_cls = torch.optim.lr_scheduler.StepLR(optimizer_cls, step_size=25, gamma=0.1)\n classifier = train_classifier(classifier, vae, datasets, dataloaders, args, optimizer_cls, scheduler_cls) \n # classify target samples\n print(f'Iter {iter:02d}: ',end='')\n pseudo_labels, scores, acc_per_class, acc_per_image = test_model(classifier,datasets['test'],dataloaders['test'], device,model_type='mlp')\n # update pseudo-labels,\n datasets['train'].pseudo_label_B = -1*np.ones_like(pseudo_labels)\n #'''\n trustable = np.zeros((len(pseudo_labels),),dtype=np.int32)\n numSelected = np.int32((iter+1)/args.num_iter*len(pseudo_labels)/10) \n for iCls in range(10):\n thisClassFlag = pseudo_labels==iCls\n numThisClass = thisClassFlag.sum()\n if numThisClass > 0:\n threshold = sorted(scores[thisClassFlag],reverse=True)[min(numThisClass-1,numSelected)]\n trustable = trustable + np.int32((scores>=threshold) & thisClassFlag)\n datasets['train'].pseudo_label_B[trustable==1] = pseudo_labels[trustable==1]\n print((datasets['train'].pseudo_label_B>-1).sum())\n #'''\n #datasets['train'].pseudo_label_B[scores>0.9-iter*0.1] = pseudo_labels[scores>0.9-iter*0.1] \n datasets['train'].pseudo_score_B = scores\n \nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--seed\", type=int, default=0)\n parser.add_argument(\"--num_epochs_vae\", type=int, default=50)\n parser.add_argument(\"--num_epochs_cls\", type=int, default=50)\n parser.add_argument(\"--batch_size\", type=int, default=32)\n parser.add_argument(\"--learning_rate\", type=float, default=0.001)\n parser.add_argument(\"--encoder_layer_sizes\", type=list, default=[4096, 512])\n parser.add_argument(\"--decoder_layer_sizes\", type=list, default=[512, 4096])\n parser.add_argument(\"--latent_size\", type=int, default=64)\n parser.add_argument(\"--print_every\", type=int, default=100)\n parser.add_argument(\"--sourceDomainIndex\", type=int, default=1)\n parser.add_argument(\"--targetDomainIndex\", type=int, default=0)\n parser.add_argument(\"--trialIndex\", type=int, default=0)\n parser.add_argument(\"--fig_root\", type=str, default='figs')\n parser.add_argument(\"--num_iter\", type=int, default=15)\n\n args = parser.parse_args()\n \n source = domainSet[args.sourceDomainIndex]\n target = domainSet[args.targetDomainIndex]\n args.filename = 'office10-'+source+'-'+target+'-trial'+str(args.trialIndex)+'-numIter-'+str(args.num_iter)+'-vaeEpochs-'+str(args.num_epochs_vae)+'-encoder_layer_sizes'+str(args.encoder_layer_sizes)+'-latSize-'+str(args.latent_size)+'-bs-'+str(args.batch_size)+'lr'+str(args.learning_rate)\n print(args.filename)\n main(args)\n","sub_path":"train_vae_office10_zNorm.py","file_name":"train_vae_office10_zNorm.py","file_ext":"py","file_size_in_byte":13846,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"116037741","text":"#!/usr/bin/python3.4\n# -*- coding: utf-8 -*-\n# Pavel Ostyakov\n# pavelosta@gmail.com\nimport argparse\nimport sys\nfrom pymystem3 import Mystem\nfrom crawler import log\nimport pymongo\n\nFILTERED_SPEECH_PARTS = (\n 'PART',\n 'INTJ',\n 'CONJ',\n 'NUM',\n 'ANUM'\n)\n\n\ndef url_iter(database):\n \"\"\"An iterator for articles.\n :param database: database.Database.\n\n \"\"\"\n for url in database.lemmatize_queue.find():\n yield url\n\n\ndef add_lemmatize(database, url, text):\n database.lemmatize.insert_one({\"URL\": url, \"Text\": text})\n\n\ndef is_new_lemmatized_url(database, url):\n return database.lemmatize.count({\"URL\": url}) == 0\n\n\nclass Lemmatizator:\n def __init__(self):\n self._mystem = Mystem()\n\n def _lemmatize(self, text):\n for word in self._mystem.analyze(text):\n analyzed_word = word.get(\"analysis\")\n if analyzed_word:\n speech_part = analyzed_word[0][\"gr\"].split(\"=\")[0].split(\",\")[0]\n if speech_part not in FILTERED_SPEECH_PARTS:\n yield analyzed_word[0][\"lex\"]\n\n def transform(self, text):\n return \" \".join(self._lemmatize(text))\n\n\ndef lemmatize(database):\n database.lemmatize.create_index(\"URL\")\n lemm = Lemmatizator()\n for url in url_iter(database):\n database.lemmatize_queue.delete_one({\"URL\": url[\"URL\"]})\n if not is_new_lemmatized_url(database, url[\"URL\"]):\n continue\n\n add_lemmatize(database, url[\"URL\"], lemm.transform(url[\"Text\"]))\n database.clustering_queue.insert_one({\"URL\": url[\"URL\"]})\n\n\ndef parse_arguments():\n \"\"\"\"Parse command line arguments.\n\n :return: arguments namespace.\n\n \"\"\"\n parser = argparse.ArgumentParser(description=\"Lemmatize new texts\")\n\n parser.add_argument(\n \"--log\",\n action=\"store\",\n dest=\"log\",\n help=\"Level of log message: [all, error, critical]. Default: error\",\n choices=[\"all\", \"error\", \"critical\"],\n type=str,\n default=\"error\",\n )\n\n return parser.parse_args()\n\n\ndef main():\n try:\n args = parse_arguments()\n log.config(args.log)\n\n log.debug(\"Start lemmatize...\")\n\n db = pymongo.MongoClient(\"localhost\", 27017).lenta\n db.clustering_queue.create_index(\"URL\")\n lemmatize(db)\n\n except KeyboardInterrupt:\n log.debug(\"Stop lemmatize...\")\n\n except Exception as exception:\n log.critical(\"%s: %s\\n%s\", *sys.exc_info())\n\n\nif __name__ == \"__main__\":\n main()\n\n","sub_path":"ml/lemmatize.py","file_name":"lemmatize.py","file_ext":"py","file_size_in_byte":2494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"305038733","text":"from sdk import load_config, color_print\nfrom policies import plc_get, plc_compare, plc_add, plc_update, plc_migrate_default, plc_delete\n\ndef sync(tenant_sessions: list, addMode: bool, upMode: bool, delMode: bool, logger):\n '''\n Adds, updates, and deletes policies from clone tenants until the clone tenants match the source tenant.\n '''\n\n added_policies = []\n updated_policies = []\n deleted_policies = []\n updated_default_policies = []\n\n #Get all custom policies from all tenants\n tenant_custom_policies = []\n for tenant_session in tenant_sessions:\n tenant_custom_policies.append(plc_get.api_get_custom(tenant_session, logger))\n \n clone_tenant_sessions = tenant_sessions[1:]\n\n if addMode:\n #Get policies to add\n #Get delta from original tenant policies and clone tenant policies\n policies_to_add = plc_compare.compare_original_to_clones(tenant_sessions, tenant_custom_policies, logger)\n\n #Upload policies to clone tenants\n for index, policies in enumerate(policies_to_add):\n tenant_session = clone_tenant_sessions[index]\n added = plc_add.add_custom_policies(tenant_session, tenant_sessions[0], policies, logger)\n added_policies.append(added)\n\n if upMode: \n #Get policies to update\n policies_to_update = plc_compare.get_policies_to_update(tenant_sessions, tenant_custom_policies, logger)\n \n updated = 0\n #Update policies\n for index, policies in enumerate(policies_to_update):\n session = clone_tenant_sessions[index]\n updated = plc_update.update_custom_policies(session, tenant_sessions[0], policies, logger)\n updated_policies.append(updated)\n\n if delMode:\n #Get policies to delete\n policies_to_delete = plc_compare.get_policies_to_delete(tenant_custom_policies)\n\n deleted = 0\n #Delete polices\n for index, policies in enumerate(policies_to_delete):\n session = clone_tenant_sessions[index]\n deleted = plc_delete.delete_policies(session, policies, logger)\n deleted_policies.append(deleted)\n else:\n for index in tenant_custom_policies[1:]:\n deleted_policies.append(0)\n\n if upMode:\n #Sync default policy\n updated_default_policies = plc_migrate_default.migrate_builtin_policies(tenant_sessions, logger)\n\n logger.info('Finished syncing Policies')\n\n return added_policies, updated_policies, deleted_policies, updated_default_policies, {}\n\nif __name__ == '__main__':\n tenant_sessions = load_config.load_config_create_sessions()\n\n sync(tenant_sessions, True, True, True)\n","sub_path":"policies/plc_sync.py","file_name":"plc_sync.py","file_ext":"py","file_size_in_byte":2674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"347925889","text":"from typing import Callable\nfrom unittest.mock import DEFAULT, Mock, call, patch\n\nimport pytest\nfrom pytest_mock import MockFixture\n\nfrom azure.ai.ml._scope_dependent_operations import OperationScope\nfrom azure.ai.ml.entities._registry.registry import Registry\nfrom azure.ai.ml.operations import RegistryOperations\nfrom azure.core.exceptions import ResourceExistsError\nfrom azure.core.polling import LROPoller\n\n\n@pytest.fixture\ndef mock_registry_operation(\n mock_registry_scope: OperationScope,\n mock_aml_services_2022_01_01_preview: Mock,\n mock_machinelearning_client: Mock,\n mock_credential: Mock,\n) -> RegistryOperations:\n yield RegistryOperations(\n operation_scope=mock_registry_scope,\n service_client=mock_aml_services_2022_01_01_preview,\n all_operations=mock_machinelearning_client._operation_container,\n credentials=mock_credential,\n )\n\n\n@pytest.mark.unittest\nclass TestRegistryOperation:\n def test_list(self, mock_registry_operation: RegistryOperations) -> None:\n mock_registry_operation.list()\n mock_registry_operation._operation.list_by_subscription.assert_called_once()\n\n def test_get(self, mock_registry_operation: RegistryOperations) -> None:\n mock_registry_operation.get(\"random_name\")\n mock_registry_operation._operation.get.assert_called_once()\n\n def test_check_registry_name(self, mock_registry_operation: RegistryOperations):\n mock_registry_operation._default_registry_name = None\n with pytest.raises(Exception):\n mock_registry_operation._check_registry_name(None)\n","sub_path":"sdk/ml/azure-ai-ml/tests/registry/unittests/test_registry_operations.py","file_name":"test_registry_operations.py","file_ext":"py","file_size_in_byte":1591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"647608775","text":"\"\"\"\nSTUDENT NAME: PRANALI DESHMUKH\nSTUDENT ID: 8923910\n\nUNIT TESTS FOR COURSEWORK ASSIGNMENT 6:\n\nThe program for the coursework assignment 6 has been stored with the filename CA_6_Dijkstra.py in the same directory.\n\nTest cases to check the implementation of Dijkstra's algorithm on weighted connections for the previous graph structure. The test cases check:\na) the functions addVertex for addition of vertices to the graph, \nb) the function addEdge for addition of edges to the graph, and \nd) the function dijkstra for dijkstra's output.\n\nPlease Note: I have used Python 2.7 for implementing the program and it's test cases.\n\"\"\"\n\nimport unittest\n# Import the Graph class from the file named CA_6_Dijkstra.py.\nfrom CA_6_Dijkstra import Graph\n\nclass TestGraph(unittest.TestCase):\n\n # Test the addVertex method of the graph.\n def test_addVertex(self):\n\n # Generate graph\n g = Graph()\n\n # Add vertices to the graph.\n for vertex in map(chr,range(ord('A'),ord('H')+1)):\n g.addVertex(vertex)\n g.addVertex('S')\n\n # Check if there are 9 vertices in the tree.\n self.assertEqual(len(g.vertices),9)\n\n\n # Test the addEdge method of the graph.\n def test_addEdge(self):\n\n # Generate graph\n g = Graph()\n\n # Add vertices to the graph.\n for vertex in map(chr,range(ord('A'),ord('H')+1)):\n g.addVertex(vertex)\n g.addVertex('S')\n\n # Add edges to the graph\n edges = [['A', 'S', 5],['S', 'G', 1],['S', 'C', 2],['G', 'F', 10],['G', 'H', 63],['H', 'E', 0],['A', 'B', 23],['C', 'F', 1],['C', 'E', 3],['C', 'D', 3]]\n for edge in edges:\n g.addEdge(edge[0], edge[1], edge[2])\n\n print('-----------------------------------------------')\n print('TESTING WEIGHTED GRAPH:')\n print('-----------------------------------------------')\n\n # Print the Graph's vertices.\n print('Vertices: ' + str(g.vertices.keys()))\n\n # Print all the edges for each vertex.\n print('\\nEdges:')\n for vertex in map(chr,range(ord('A'),ord('H')+1)):\n print('\\nEdges from vertex \"' + vertex +'\": ')\n for e in g.vertices.get(vertex).edges:\n print('[' + vertex + '-' + str(e) +'] -> ' + str(g.vertices.get(vertex).edges[e]))\n\n # Check if the number of edges in the tree is equal to the number of edges input, i.e. 10.\n self.assertEqual(len(edges),10)\n\n\n # Test the bfs method of the Graph.\n def test_bfs(self):\n # Generate graph\n g = Graph()\n\n # Add vertices to the graph.\n for vertex in map(chr,range(ord('A'),ord('H')+1)):\n g.addVertex(vertex)\n g.addVertex('S')\n \n # Add edges to the graph\n edges = [['A', 'S', 5],['S', 'G', 1],['S', 'C', 2],['G', 'F', 10],['G', 'H', 63],['H', 'E', 0],['A', 'B', 23],['C', 'F', 1],['C', 'E', 3],['C', 'D', 3]]\n for edge in edges:\n g.addEdge(edge[0], edge[1], edge[2])\n \n # Check if the dijkstra method returns the output as 10 for the source node 'A' and destination node 'E'.\n self.assertEqual(g.dijkstra('A', 'E'), 10)\n print('\\n-----------------------------------------------')\n print(\"Dijkstra's output: \" + str(g.dijkstra('A', 'E')))\n print('-----------------------------------------------')\n\ndef main():\n unittest.main()\n\nif __name__ == '__main__':\n main()","sub_path":"CA_6_Dijkstra/CA_6_Test_Dijkstra.py","file_name":"CA_6_Test_Dijkstra.py","file_ext":"py","file_size_in_byte":3431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"73504638","text":"n = int(input())\narr = [ [], []]\nis_zero_existed = False\nans = 0\n\nfor i in range(0, n):\n temp = int(input())\n if temp > 0:\n arr[0].append(temp)\n elif temp == 0:\n is_zero_existed = True\n else:\n arr[1].append(temp)\n\narr[0].sort(reverse=True)\narr[1].sort()\n\nlen_plus = len(arr[0])\nlen_minus = len(arr[1])\n\nif is_zero_existed and (len_minus % 2 == 1):\n del arr[1][-1]\n len_minus -= 1\n\ni = 0\nwhile i < len_plus:\n if i+1 < len_plus and arr[0][i+1] != 1:\n ans += arr[0][i] * arr[0][i+1]\n i += 1\n else:\n ans += arr[0][i]\n i += 1\n\ni = 0\nwhile i < len_minus:\n if i+1 < len_minus:\n ans += arr[1][i] * arr[1][i+1]\n i += 1\n else:\n ans += arr[1][i]\n i += 1\n\nprint(ans)\n","sub_path":"Greedy/1744.py","file_name":"1744.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"38243645","text":"import requests\nfrom bs4 import BeautifulSoup\nimport csv\n\namzn_list = ['B002EYASY8','B74HL9TL8']\n\nheaders = {'User-Agent': 'Mozilla/5.0'}\n\ncsvFile = open(\"amzn.csv\", 'wt')\nwrite = csv.writer(csvFile)\n\nfor i in amzn_list:\n\tcsvRow = []\n\turl = \"http://www.amazon.com/dp/\"+i\n\tprint (\"Processing: \"+url)\n\tpage = requests.get(url, headers=headers)\n\tbsobj = BeautifulSoup(page.content,\"html.parser\")\n\t#print (bsobj)\n\tproduct = bsobj.find(\"span\",{\"id\":\"productTitle\"})\n\tprice = bsobj.find(\"span\",{\"id\":\"priceblock_ourprice\"})\n\tprint (product)\n\tprint (price)\n\n","sub_path":"lang/python/webscr/three.py","file_name":"three.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"51683385","text":"import random\n\n\"\"\"\nQuick Sort Algorithm\n\"\"\"\n\nnumbers = []\nattempts = 0\nsorted_vals = []\n\nnumber_of_values = int(input(\"Please enter the number of values you would like to Quick sort: \"))\n\nfor i in range(number_of_values):\n numbers.append(random.randint(0,10))\n\nprint(numbers)\n\n\ndef QuickSort(values):\n if len(values) <= 1:\n return values\n pivot = values[0]\n less_than_pivot = []\n greater_than_pivot = []\n\n for i in values[1:]:\n if i <= pivot:\n less_than_pivot.append(i)\n else:\n greater_than_pivot.append(i)\n\n return QuickSort(less_than_pivot) + [pivot] + QuickSort(greater_than_pivot)\n\nprint(QuickSort(numbers))\n","sub_path":"Quick Sort.py","file_name":"Quick Sort.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"131957689","text":"import os\nimport sys\nfrom CustomInstall import CustomInstall\nfrom setuptools import setup, Command\n\ninstall_requires = [\n ]\n\ntests_require = [\n ]\n\ndef read(fname):\n return open(os.path.join(os.path.dirname(__file__), fname)).read()\n\nsetup(\n name = \"PyTomcat\",\n version = \"1.0\",\n author = \"Dun and Bradstreet\",\n author_email = \"pytomcat@dandb.com\",\n description = (\"Management of Apache Tomcat via Python\"),\n license = \"GPLv3\",\n keywords = \"python tomcat \",\n url = \"https://github.com/dandb/pytomcat\",\n packages=['pytomcat'],\n install_requires = install_requires,\n tests_require = tests_require,\n extras_require={'test': tests_require},\n long_description=read('README.md') + '\\n\\n' + read('CHANGES'),\n test_suite = 'tests',\n cmdclass={'install': CustomInstall},\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":"20"} +{"seq_id":"409752207","text":"#!/usr/bin/env python3\nimport bpy\n\n# Partial import to avoid cyclic import\nimport blendernc.python_functions as bnc_pyfunc\n\n\ndef get_unique_data_dict(node):\n # Replaces all node.blendernc_dict[unique_identifier]\n # TODO: Make sure to replace all the unique data dicts\n data_dictionary = node.blendernc_dict\n unique_identifier = node.blendernc_dataset_identifier\n unique_data_dict = data_dictionary[unique_identifier]\n return unique_data_dict\n\n\ndef get_node(node_group, node):\n node_group = bpy.data.node_groups.get(node_group)\n return node_group.nodes.get(node)\n\n\ndef get_input_links(node):\n inputs = node.inputs[0]\n return inputs.links[0]\n\n\ndef get_var(ncdata):\n dimensions = sorted(list(ncdata.coords.dims.keys()))\n variables = sorted(list(ncdata.variables.keys() - dimensions))\n long_name_list = [ncdata[var].attrs[\"long_name\"] for var in variables]\n if \"long_name\" in ncdata[variables[0]].attrs:\n var_names = bnc_pyfunc.build_enum_prop_list(\n variables, \"DISK_DRIVE\", long_name_list\n )\n else:\n var_names = bnc_pyfunc.build_enum_prop_list(variables, \"DISK_DRIVE\")\n\n return bnc_pyfunc.select_item() + [None] + var_names\n\n\ndef get_var_dict(context, node, node_tree):\n scene = context.scene\n node = bpy.data.node_groups[node_tree].nodes[node]\n unique_identifier = node.blendernc_dataset_identifier\n try:\n scene.nc_cache[node_tree]\n except KeyError:\n scene.nc_cache[node_tree] = {}\n\n # Check if dictionary entry for the variable exists\n try:\n scene.nc_cache[node_tree][unique_identifier]\n except KeyError:\n scene.nc_cache[node_tree][unique_identifier] = {}\n return scene.nc_cache[node_tree][unique_identifier]\n\n\ndef get_var_data(context, node, node_tree):\n node = bpy.data.node_groups[node_tree].nodes[node]\n # Get data dictionary stored at scene object\n unique_data_dict = get_unique_data_dict(node)\n # Get the netcdf of the selected file\n ncdata = unique_data_dict[\"Dataset\"]\n # Get var name\n var_name = unique_data_dict[\"selected_var\"][\"selected_var_name\"]\n # Get the data of the selected variable\n # Remove Nans\n # TODO: Add node to preserve NANs\n # if node.keep_nan:\n data = ncdata[var_name]\n # else:\n # data = ncdata[var_name].where(np.isfinite(ncdata[var_name]), 0)\n return data\n\n\ndef get_dims(ncdata, var):\n dimensions = list(ncdata[var].coords.dims)\n dim_names = bnc_pyfunc.build_enum_prop_list(dimensions, \"EMPTY_DATA\", start=0)\n return dim_names\n\n\ndef get_geo_coord_names(dataset):\n lon_coords = [coord for coord in dataset.coords if (\"lon\" in coord or \"x\" in coord)]\n lat_coords = [coord for coord in dataset.coords if (\"lat\" in coord or \"y\" in coord)]\n return {\"lon_name\": lon_coords, \"lat_name\": lat_coords}\n\n\ndef get_possible_variables(node, context):\n ncfile = node.blendernc_file\n unique_identifier = node.blendernc_dataset_identifier\n if not ncfile or unique_identifier not in node.blendernc_dict.keys():\n return bnc_pyfunc.empty_item()\n unique_data_dict = get_unique_data_dict(node)\n ncdata = unique_data_dict[\"Dataset\"]\n items = get_var(ncdata)\n return items\n\n\ndef get_new_identifier(node):\n if len(node.name.split(\".\")) == 1:\n return \"{:03}\".format(0)\n else:\n return \"{:03}\".format(int(node.name.split(\".\")[-1]))\n\n\n# TODO Add decorator to simplify.\ndef get_possible_dims(node, context):\n unique_identifier = node.blendernc_dataset_identifier\n if unique_identifier not in node.blendernc_dict.keys():\n return bnc_pyfunc.empty_item()\n link = get_input_links(node)\n unique_identifier = node.blendernc_dataset_identifier\n parent_node = link.from_node\n data_dictionary = parent_node.blendernc_dict[unique_identifier]\n ncdata = data_dictionary[\"Dataset\"]\n var_name = data_dictionary[\"selected_var\"][\"selected_var_name\"]\n items = get_dims(ncdata, var_name)\n return items\n\n\ndef get_time(context, node, node_tree, frame):\n node = bpy.data.node_groups[node_tree].nodes[node]\n # Get data dictionary stored at scene object\n unique_data_dict = get_unique_data_dict(node)\n # Get the netcdf of the selected file\n ncdata = unique_data_dict[\"Dataset\"]\n # Get the data of the selected variable\n if \"time\" in ncdata.coords.keys():\n time = ncdata[\"time\"]\n if time.size == 1:\n return time.values\n elif frame > time.size:\n return time[-1].values\n else:\n return time[frame].values\n else:\n return \"\"\n\n\ndef get_max_min_data(context, node, node_tree):\n node = bpy.data.node_groups[node_tree].nodes[node]\n # Get data dictionary stored at scene object\n unique_data_dict = get_unique_data_dict(node)\n # Get the metadata of the selected variable\n var_metadata = unique_data_dict[\"selected_var\"]\n max_val = var_metadata[\"max_value\"]\n min_val = var_metadata[\"min_value\"]\n if max_val is not None and min_val is not None:\n return var_metadata[\"max_value\"], var_metadata[\"min_value\"]\n else:\n bnc_pyfunc.update_range(node, context)\n return var_metadata[\"max_value\"], var_metadata[\"min_value\"]\n\n\ndef get_xarray_datasets(node, context):\n import xarray\n\n xarray_datacube = sorted(xarray.tutorial.file_formats.keys())\n datacube_names = bnc_pyfunc.build_enum_prop_list(xarray_datacube, \"DISK_DRIVE\")\n return bnc_pyfunc.select_datacube() + datacube_names\n\n\ndef get_colormaps_of_materials(node):\n \"\"\"\n Function to find materials using the BlenderNC output node image.\n \"\"\"\n unfind = True\n counter = 0\n\n links = node.outputs.get(\"Color\").links\n # TODO: Change this to a recursive search.\n # Currently, only colormaps directly connected to\n # the output will generate a colormap.\n while unfind:\n for link in links:\n if link.to_node.bl_idname == \"cmapsNode\":\n colormap_node = link.to_node\n unfind = False\n break\n elif counter == 10:\n unfind = False\n else:\n counter += 1\n\n if counter == 10:\n raise ValueError(\"Colormap not found after 10 tree node interations\")\n return colormap_node\n\n\ndef get_all_nodes_using_image(image_name):\n users = {}\n for node_group in bpy.data.node_groups:\n for node in node_group.nodes:\n if node.bl_idname == \"netCDFOutput\":\n users[node_group.name] = node\n\n for material in bpy.data.materials:\n if not material.grease_pencil:\n for node in material.node_tree.nodes:\n if node.bl_idname == \"ShaderNodeTexImage\":\n users[material.name] = node\n\n return users\n\n\ndef get_items_dims(self, context):\n if self.inputs[0].is_linked and self.inputs[0].links and self.blendernc_dict:\n # BlenderNC dictionary\n blendernc_dict = (\n self.inputs[0]\n .links[0]\n .from_node.blendernc_dict[self.blendernc_dataset_identifier]\n .copy()\n )\n # BlenderNC dataset\n dataset = blendernc_dict[\"Dataset\"]\n # BlenderNC var\n var = blendernc_dict[\"selected_var\"][\"selected_var_name\"]\n # Extract dataset axis\n dims = dataset[var].dims\n return dims\n\n\ndef get_items_axes(self, context):\n dims = get_items_dims(self, context)\n dims_list = bnc_pyfunc.build_enum_prop_list(dims, \"EMPTY_DATA\", start=0)\n return dims_list\n\n\n# Delete if not use in a few months (25-Jun-2021):\n#\n# def get_selected_var(node):\n# unique_data_dict = get_unique_data_dict(node)\n# dataset = unique_data_dict[\"Dataset\"]\n# selected_variable = unique_data_dict[\"selected_var\"][\"selected_var_name\"]\n# selected_var_dataset = dataset[selected_variable]\n# return selected_var_dataset\n\n\n# def get_max_timestep(self, context):\n# scene = context.scene\n# ncfile = self.file_name\n# data_dictionary = scene.nc_dictionary\n# if not ncfile or not data_dictionary:\n# return 0\n# ncdata = data_dictionary[\"Dataset\"]\n# var_name = self.var_name\n# var_data = ncdata[var_name]\n\n# t = var_data.shape[0]\n# return t - 1\n","sub_path":"blendernc/get_utils.py","file_name":"get_utils.py","file_ext":"py","file_size_in_byte":8204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"304069114","text":"import numpy as np\r\nimport os\r\nimport csv\r\nfrom scipy.io import wavfile\r\nimport scipy.signal\r\nimport librosa\r\nfrom scipy.fftpack import fft\r\nfrom scipy import signal\r\nimport torch\r\n\r\ninputDim = 512\r\n\r\nN = 10\r\nWn= 0.4\r\nb,a = signal.butter(N,Wn,btype='low',output='ba') \r\n\r\ndef generate_labels(path):\r\n wavs = os.listdir(path)\r\n labels = list()\r\n \r\n for item in wavs:\r\n if item.startswith('F'):\r\n labels.append((item,'F'))\r\n elif item.startswith('M'):\r\n labels.append((item,'M'))\r\n \r\n with open(path+\"labels.csv\", \"w\") as f:\r\n writer = csv.writer(f)\r\n writer.writerows(labels)\r\n \r\ndef load_labels(path):\r\n List,Label = list(), list()\r\n with open(path+'labels.csv', 'r') as csvfile:\r\n reader = csv.reader(csvfile)\r\n for row in reader:\r\n List.append(row[0])\r\n Label.append(row[1])\r\n return List,Label\r\n\r\ndef extract_features(name):\r\n sr,wav = wavfile.read(name)\r\n filtered = signal.lfilter(b,a,wav)\r\n freq = np.abs(fft(filtered))\r\n semi = freq[len(freq)//2:]\r\n trimmed, index = librosa.effects.trim(semi,26,frame_length=512, hop_length=128)\r\n resampled = signal.resample(trimmed,inputDim)\r\n return resampled / np.max(resampled)\r\n\r\ndef num_parameters(model):\r\n return sum(p.numel() for p in model.parameters() if p.requires_grad)\r\n\r\ndef zero_crossing(x):\r\n return len(np.where(np.diff(np.sign(x)))[0])\r\n\r\ndef first_decision(output,bias):\r\n output = output.detach().numpy()\r\n b = bias\r\n for x in output:\r\n if 0.57250:\r\n x += b\r\n if 0.150:\n current = breadcrumbs_elements[-1]\n \n return {\n 'autobreadcrumbs_elements': breadcrumbs_elements,\n 'autobreadcrumbs_current': current,\n }\n\nclass BreadcrumbRessource(object):\n def __init__(self, path, name, title, view_args, view_kwargs):\n self.path = path\n self.name = name\n self.title = title\n self.view_args = view_args\n self.view_kwargs = view_kwargs\n \n def __repr__(self):\n return \"\".format(self.name)\n \n def __str__(self):\n # NOTE: should be __unicode__() because passed paths can be unicode... right ?\n return self.path\n","sub_path":"autobreadcrumbs/context_processors.py","file_name":"context_processors.py","file_ext":"py","file_size_in_byte":3515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"601303641","text":"import numpy as np\r\n# import pickle\r\n# import matplotlib\r\nimport matplotlib.pyplot as plt\r\n# import string\r\n# import random\r\n\r\n\r\ndef compare_plot(x1: np.ndarray, y1: np.ndarray, x2: np.ndarray, y2: np.ndarray,\r\n xlabel: str, ylabel: str, title: str, label1: str, label2: str):\r\n \"\"\"Funkcja służąca do porównywania dwóch wykresów typu plot. \r\n Szczegółowy opis w zadaniu 3.\r\n \r\n Parameters:\r\n x1(np.ndarray): wektor wartości osi x dla pierwszego wykresu,\r\n y1(np.ndarray): wektor wartości osi y dla pierwszego wykresu,\r\n x2(np.ndarray): wektor wartości osi x dla drugiego wykresu,\r\n y2(np.ndarray): wektor wartości osi x dla drugiego wykresu,\r\n xlabel(str): opis osi x,\r\n ylabel(str): opis osi y,\r\n title(str): tytuł wykresu ,\r\n label1(str): nazwa serii z pierwszego wykresu,\r\n label2(str): nazwa serii z drugiego wykresu.\r\n\r\n \r\n Returns:\r\n matplotlib.pyplot.figure: wykres zbiorów (x1,y1), (x2,y2) zgody z opisem z zadania 3 \r\n \"\"\"\r\n if len(x1) == len(y1) and len(x2) == len(y2) and len(x1) > 1 and len(x2) > 1:\r\n fig = plt.figure()\r\n plt.plot(x1, y1, 'b-', linewidth=4, label=label1)\r\n plt.plot(x2, y2, 'r-', linewidth=2, label=label2)\r\n plt.title(title)\r\n plt.xlabel(xlabel)\r\n plt.ylabel(ylabel)\r\n plt.legend()\r\n return fig\r\n return None\r\n\r\n\r\ndef parallel_plot(x1: np.ndarray, y1: np.ndarray, x2: np.ndarray, y2: np.ndarray,\r\n x1label: str, y1label: str, x2label: str, y2label: str, title: str, orientation: str):\r\n \"\"\"Funkcja służąca do stworzenia dwóch wykresów typu plot w konwencji subplot wertykalnie lub chorycontalnie. \r\n Szczegółowy opis w zadaniu 5.\r\n \r\n Parameters:\r\n x1(np.ndarray): wektor wartości osi x dla pierwszego wykresu,\r\n y1(np.ndarray): wektor wartości osi y dla pierwszego wykresu,\r\n x2(np.ndarray): wektor wartości osi x dla drugiego wykresu,\r\n y2(np.ndarray): wektor wartości osi x dla drugiego wykresu,\r\n x1label(str): opis osi x dla pierwszego wykresu,\r\n y1label(str): opis osi y dla pierwszego wykresu,\r\n x2label(str): opis osi x dla drugiego wykresu,\r\n y2label(str): opis osi y dla drugiego wykresu,\r\n title(str): tytuł wykresu,\r\n orientation(str): parametr przyjmujący wartość '-' jeżeli subplot ma posiadać dwa wiersze albo '|' jeżeli ma posiada\r\n ć dwie kolumny.\r\n\r\n \r\n Returns:\r\n matplotlib.pyplot.figure: wykres zbiorów (x1,y1), (x2,y2) zgody z opisem z zadania 5\r\n \"\"\"\r\n if len(x1) == len(y1) and len(x2) == len(y2) and len(x1) > 1 and len(x2) > 1:\r\n if len(set(x1)) == len(x1) and len(set(x2)) == len(x2):\r\n if orientation == '|':\r\n fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13.0, 5.0))\r\n elif orientation == '-':\r\n fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(7.0, 10.0))\r\n else:\r\n return None\r\n\r\n ax1.plot(x1, y1)\r\n ax1.set(xlabel=x1label, ylabel=y1label, title=title)\r\n ax2.plot(x2, y2)\r\n ax2.set(xlabel=x2label, ylabel=y2label, title=title)\r\n\r\n return fig\r\n\r\n return None\r\n\r\n\r\ndef log_plot(x: np.ndarray, y: np.ndarray, xlabel: np.ndarray, ylabel: str, title: str, log_axis: str):\r\n \"\"\"Funkcja służąca do tworzenia wykresów ze skalami logarytmicznymi. \r\n Szczegółowy opis w zadaniu 7.\r\n \r\n Parameters:\r\n x(np.ndarray): wektor wartości osi x,\r\n y(np.ndarray): wektor wartości osi y,\r\n xlabel(str): opis osi x,\r\n ylabel(str): opis osi y,\r\n title(str): tytuł wykresu ,\r\n log_axis(str): wartość oznacza:\r\n - 'x' oznacza skale logarytmiczną na osi x,\r\n - 'y' oznacza skale logarytmiczną na osi y,\r\n - 'xy' oznacza skale logarytmiczną na obu osiach.\r\n \r\n Returns:\r\n matplotlib.pyplot.figure: wykres zbiorów (x,y) zgody z opisem z zadania 7 \r\n \"\"\"\r\n if len(x) == len(y) and len(x) > 1:\r\n fig = plt.figure()\r\n if log_axis == 'x':\r\n plt.semilogx(x, y)\r\n\r\n elif log_axis == 'y':\r\n plt.semilogy(x, y)\r\n\r\n elif log_axis == 'xy':\r\n plt.loglog(x, y)\r\n\r\n else:\r\n return None\r\n\r\n plt.xlabel(xlabel)\r\n plt.ylabel(ylabel)\r\n plt.title(title)\r\n return fig\r\n\r\n return None\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"233358837","text":"import urllib.request\nimport json\nimport dml\nimport prov.model\nimport datetime\nimport uuid\n\nclass mbta_ln(dml.Algorithm):\n contributor = 'bohorqux_rocksdan'\n reads = ['bohorqux_rocksdan.MBTA']\n writes = ['bohorqux_rocksdan.mbta_late_nights']\n\n @staticmethod\n def execute(trial = False):\n '''Retrieve some data sets (not using the API here for the sake of simplicity).'''\n startTime = datetime.datetime.now()\n\n # Set up the database connection.\n client = dml.pymongo.MongoClient()\n repo = client.repo\n repo.authenticate('bohorqux_rocksdan', 'bohorqux_rocksdan')\n\n\n reports = repo['bohorqux_rocksdan.MBTA']\n\n wdk = {0:\"Monday\", 1:\"Tuesday\", 2:\"Wednesday\", 3:\"Thursday\", 4:\"Friday\", 5:\"Saturday\", 6:\"Sunday\"}\n ln = {\"Monday\":0, \"Tuesday\":0, \"Wednesday\":0, \"Thursday\":0, \"Friday\":0, \"Saturday\":0, \"Sunday\":0}\n \n \n for r in reports.find():\n try:\n if r['latenightroute'] == \"1\":\n x = r['scheduledate'].split(\"/\")\n month = int(x[0])\n day = int(x[1])\n year = int(x[2])\n weekday = datetime.date(year, month, day).weekday()\n \n ln[wdk[weekday]] += 1\n \n except ValueError:\n pass\n \n lst = [ln]\n repo.dropCollection(\"mbta_late_nights\")\n repo.createCollection(\"mbta_late_nights\")\n repo['bohorqux_rocksdan.mbta_late_nights'].insert_many(lst)\n repo['bohorqux_rocksdan.mbta_late_nights'].metadata({'complete':True})\n print(repo['bohorqux_rocksdan.mbta_late_nights'].metadata())\n\n repo.logout()\n\n endTime = datetime.datetime.now()\n\n return {\"start\":startTime, \"end\":endTime}\n \n @staticmethod\n def provenance(doc = prov.model.ProvDocument(), startTime = None, endTime = None):\n '''\n Create the provenance document describing everything happening\n in this script. Each run of the script will generate a new\n document describing that invocation event.\n '''\n\n # Set up the database connection.\n client = dml.pymongo.MongoClient()\n repo = client.repo\n repo.authenticate('bohorqux_rocksdan', 'bohorqux_rocksdan')\n doc.add_namespace('alg', 'http://datamechanics.io/algorithm/') # The scripts are in # format.\n doc.add_namespace('dat', 'http://datamechanics.io/data/') # The data sets are in # format.\n doc.add_namespace('ont', 'http://datamechanics.io/ontology#') # 'Extension', 'DataResource', 'DataSet', 'Retrieval', 'Query', or 'Computation'.\n doc.add_namespace('log', 'http://datamechanics.io/log/') # The event log.\n doc.add_namespace('bdp', 'https://data.cityofboston.gov/resource/')\n\n this_script = doc.agent('alg:bohorqux_rocksdan#mbta_ln', {prov.model.PROV_TYPE:prov.model.PROV['SoftwareAgent'], 'ont:Extension':'py'})\n resource = doc.entity('bdp:wc8w-nujj', {'prov:label':'311, Service Requests', prov.model.PROV_TYPE:'ont:DataResource', 'ont:Extension':'json'})\n get_mbta_late_nights = doc.activity('log:uuid'+str(uuid.uuid4()), startTime, endTime)\n doc.wasAssociatedWith(get_mbta_late_nights, this_script)\n doc.usage(get_mbta_late_nights, resource, startTime, None,\n {prov.model.PROV_TYPE:'ont:Retrieval',\n 'ont:Query':'?line=Bus&$select=line,trxdow'\n }\n )\n mbta_late_nights = doc.entity('dat:bohorqux_rocksdan#mbta_late_nights', {prov.model.PROV_LABEL:'MBTA Late Nights', prov.model.PROV_TYPE:'ont:DataSet'})\n doc.wasAttributedTo(mbta_late_nights, this_script)\n doc.wasGeneratedBy(mbta_late_nights, get_mbta_late_nights, endTime)\n doc.wasDerivedFrom(mbta_late_nights, resource, get_mbta_late_nights, get_mbta_late_nights, get_mbta_late_nights)\n\n repo.logout()\n \n return doc\n\nmbta_ln.execute()\ndoc = mbta_ln.provenance()\nprint(doc.get_provn())\nprint(json.dumps(json.loads(doc.serialize()), indent=4))\n\n## eof\n","sub_path":"bohorqux_rocksdan/mbta_ln.py","file_name":"mbta_ln.py","file_ext":"py","file_size_in_byte":4190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"221145005","text":"\nimport numpy as np\nfrom astropy import units as u\nfrom astropy import constants as const\nfrom pypeit import sensfunc\nfrom pypeit.spectrographs import util\nfrom pypeit import utils\nfrom matplotlib import pyplot as plt\n\n\ndef sensfunc_to_thru(sensfile):\n\n wave, sensfunction, meta_table, out_table, header_sens = sensfunc.SensFunc.load(sensfile)\n spectrograph = util.load_spectrograph(header_sens['PYP_SPEC'])\n sensfunc_units = 1e-17*u.erg/u.cm**2\n\n sens_gpm = (utils.inverse(sensfunction) > np.finfo(float).tiny) &\\\n (utils.inverse(sensfunction) < np.finfo(float).max) & (wave > 1.0)\n inv_wave = utils.inverse(wave[sens_gpm])/u.angstrom\n inv_sensfunc = utils.inverse(sensfunction[sens_gpm])/sensfunc_units\n eff_aperture = spectrograph.telescope['eff_aperture']*u.m**2\n throughput = np.zeros_like(sensfunction)\n\n thru = ((const.h*const.c)*inv_wave/eff_aperture*inv_sensfunc).decompose()\n throughput[sens_gpm] = thru\n\n return wave, throughput\n\n\nsensfile = '/Users/joe/nires_1220/redux/sens_s201226_0027-Feige110_NIRES_2020Dec26T044943.096.fits'\nwave, throughput = sensfunc_to_thru(sensfile)\n\nnorders = wave.shape[1]\nfor iorder in range(norders):\n wv_mask = wave[:, iorder] > 1.0\n plt.plot(wave[wv_mask, iorder],sensfunction[wv_mask, iorder])\n# plt.plot(wave[wv_mask, iorder],throughput[wv_mask, iorder])\n\nplt.ylim((0.0, 100))\nplt.ylim((0.0, 0.5))\n\n\n","sub_path":"dev_algorithms/fluxing/throughput.py","file_name":"throughput.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"158256803","text":"\"\"\" Libraries imported \"\"\"\r\nimport sqlite3\r\nimport tkinter as tk\r\n\r\nclass AddHomeworkWindow:\r\n\r\n \"\"\" Instantiation method of the window \"\"\"\r\n def __init__(self, master, loggedinas):\r\n self.loggedinas = loggedinas # Sets the username of the logged in user\r\n self.master = master # Sets the root window of the object\r\n self.master.option_add(\"*font\", \"lucida 20\") # Sets the base font to be used\r\n self.master.option_add(\"*background\", \"WHITE\") # Sets the base background color to be used\r\n self.bgcolor = \"#%02x%02x%02x\" % (249, 157, 28) # Sets the color theme to a variable to be used later on\r\n\r\n \"\"\" This next block creates eacher frame inside of the root window \"\"\"\r\n self.frame_title = tk.Frame(self.master, borderwidth = 2, bg = \"BLACK\")\r\n self.frame_details = tk.Frame(self.master, borderwidth = 2)\r\n self.frame_submit = tk.Frame(self.master, borderwidth = 2)\r\n self.frame_title.pack(side = \"top\", fill = \"both\")\r\n self.frame_details.pack(side = \"top\", fill = \"both\")\r\n self.frame_submit.pack(side = \"top\", fill = \"both\")\r\n\r\n ### frame_title ###\r\n self.label_title = tk.Label(self.frame_title, fg = self.bgcolor, bg = \"BLACK\", text = \"Set Homework\")\r\n\r\n # Layout #\r\n self.label_title.pack(padx = 5, pady = 5)\r\n\r\n\r\n ### frame_details ###\r\n self.label_title = tk.Label(self.frame_details, text = \"Title\")\r\n self.label_group = tk.Label(self.frame_details, text = \"Group\")\r\n self.label_description = tk.Label(self.frame_details, text = \"Description\")\r\n self.label_set_date = tk.Label(self.frame_details, text = \"Set Date\")\r\n self.label_due_date = tk.Label(self.frame_details, text = \"Due Date\")\r\n\r\n \"\"\" Entry widgets \"\"\"\r\n self.entry_title = tk.Entry(self.frame_details)\r\n self.text_description = tk.Text(self.frame_details, width = 38, height = 15, font = (\"lucida\", 11))\r\n self.entry_set_date = tk.Entry(self.frame_details)\r\n self.entry_due_date = tk.Entry(self.frame_details)\r\n\r\n # Drop down #\r\n group_options = []\r\n\r\n conn = sqlite3.connect(\"School Database.db\")\r\n cursor = conn.cursor()\r\n cursor.execute(\"SELECT * FROM classes WHERE teacherID=?\", (self.loggedinas[1],))\r\n db_groupnames = cursor.fetchall()\r\n for group in db_groupnames:\r\n group_options.append(group[0])\r\n \r\n if len(group_options) > 0: \r\n self.current_selection = tk.StringVar(self.master)\r\n self.current_selection.set(group_options[0])\r\n self.optionMenu_groups = tk.OptionMenu(self.frame_details, self.current_selection, *group_options)\r\n else:\r\n self.current_selection = tk.StringVar()\r\n group_options = [\"No groups found\"]\r\n self.current_selection.set(group_options[0])\r\n self.optionMenu_groups = tk.OptionMenu(self.frame_details, self.current_selection, *group_options)\r\n \r\n # Layout #\r\n self.label_title.grid(row = 0, sticky = \"W\", padx = 5, pady = 5)\r\n self.label_group.grid(row = 1, sticky = \"W\", padx = 5, pady = 5)\r\n self.label_description.grid(row = 2, sticky = \"W\", padx = 5, pady = 5)\r\n self.label_set_date.grid(row = 3, sticky = \"W\", padx = 5, pady = 5)\r\n self.label_due_date.grid(row = 4, sticky = \"W\", padx = 5, pady = 5)\r\n self.entry_title.grid(row = 0, column = 1, padx = 5, pady = 5)\r\n self.optionMenu_groups.grid(row = 1, column = 1, padx = 5, pady = 5)\r\n self.text_description.grid(row = 2, column = 1, padx = 5, pady = 5)\r\n self.entry_set_date.grid(row = 3, column = 1, padx = 5, pady = 5)\r\n self.entry_due_date.grid(row = 4, column = 1, padx = 5, pady = 5)\r\n\r\n\r\n ### frame_submit ###\r\n self.button_submit = tk.Button(self.frame_submit, text = \"Submit\", command = self.verification)\r\n\r\n # Layout #\r\n self.button_submit.pack(padx = 5, pady = 5)\r\n\r\n \"\"\" \"\"\"\r\n def verification(self):\r\n title = self.entry_title.get()\r\n group = self.current_selection.get()\r\n description = self.text_description.get(\"1.0\", \"end-1c\")\r\n set_date = self.entry_set_date.get()\r\n due_date = self.entry_due_date.get()\r\n\r\n conn = sqlite3.connect(\"School Database.db\")\r\n cursor = conn.cursor()\r\n\r\n if len(title) < 5:\r\n popup = tk.Toplevel(self.master)\r\n label_popup = tk.Label(popup, text = \"The title must 5 characters minimum!\", font = (\"Courier\", 30))\r\n label_popup.pack()\r\n elif len(description) < 20:\r\n popup = tk.Toplevel(self.master)\r\n label_popup = tk.Label(popup, text = \"The description is too short!\", font = (\"Courier\", 30))\r\n label_popup.pack()\r\n elif len(set_date) < 4:\r\n popup = tk.Toplevel(self.master)\r\n label_popup = tk.Label(popup, text = \"You must include a valid set date!\", font = (\"Courier\", 30))\r\n label_popup.pack()\r\n elif len(due_date) < 4:\r\n popup = tk.Toplevel(self.master)\r\n label_popup = tk.Label(popup, text = \"You must include a valid due date!\", font = (\"Courier\", 30))\r\n label_popup.pack()\r\n else:\r\n cursor.execute(\"SELECT * FROM homework\")\r\n db = cursor.fetchall()\r\n autoID = len(db) + 1\r\n cursor.execute(\"INSERT INTO homework VALUES (?,?,?,?,?,?)\", (autoID, group, due_date, set_date, title, description))\r\n conn.commit()\r\n success()\r\n self.master.destroy()\r\n \r\n \r\ndef success():\r\n popup = tk.Tk()\r\n label_popup = tk.Label(popup, text = \"Homework has been set successfully!\", font = (\"Courier\", 30))\r\n label_popup.pack()\r\n popup.update()\r\n\r\n\r\n\"\"\" If statement allows class to be run independently from main program \"\"\"\r\nif __name__ == \"__main__\": \r\n root = tk.Tk()\r\n loggedinas = [\"admin01\", 1]\r\n module = AddHomeworkWindow(root, loggedinas)\r\n root.mainloop()\r\n","sub_path":"Add_Homework.py","file_name":"Add_Homework.py","file_ext":"py","file_size_in_byte":6086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"133741915","text":"# ex: set tabstop=2 expandtab shiftwidth=2 softtabstop=2:\n\n#####################################################################\n#\n#\t\t\t\t\t parse_email.py\n#\n# Parses incoming email messages from a networked naturalist campaign\n# Uploads images and annotations to the database\n#\n# Log information is printed to LOG_FILE. Errors are printed to stdout\n#\n#####################################################################\n\nimport re, MySQLdb, urllib, rfc822, sys, os, smtplib\nfrom string import split, join\nfrom email.MIMEMultipart import MIMEMultipart\nfrom email.MIMEBase import MIMEBase\nfrom email.MIMEText import MIMEText\nfrom email import Encoders\n\n## User-defined\nfrom configuration import settings\nimport log\n\n# IMAGE HANDLING #\nIMAGE_PREFIX = 'image_'\nIMAGE_DATABASE_COLUMNS = 'images (userid, obsid, plantid, width, height, fileName, directory, comments, timestamp) '\n\n# Global Variables\nfrom_email = True\nemail_address = False\nphone_number = False\ndb = False\n\n# Main\nlog.open(settings['log_file_loc'] + '/parse_email.log')\ndef submit(msg):\n\topen_user_database()\n\tif not db:\n\t\tlog.error('Could not connect to the database')\n\t\treturn send.msg(settings['error_message'])\n\t\n\tglobal email_address\n\temail_address = get_email_address(msg)\n\tif not email_address:\n\t\tlog.error('Did not match an email address from %s' % msg['From'])\n\t\treturn send.msg(settings['error_message'])\n\telse:\n\t\tlog.debug('Matched email address: %s' % email_address)\n\t\t\n\tglobal phone_number\n\tphone_number = get_phone_number(email_address)\n\tlog.debug(phone_number and ('Phone: %s' % phone_number) or 'Did not find a phone number from email address')\n\t\n\tif not phone_number:\n\t\t'''\n\t\t\tObtain user id (and phone number) by indexing database\n\t\t\twith email address. Also, assume that the message came from a\n\t\t\tcomputer, and NOT a phone. So confirmation messages should be sent\n\t\t\tback to the email address, not the phone number.\n\t\t'''\n\t\tlog.debug('Try to get userid and phone with email address')\n\t\t\n\t\tdb.execute(\"SELECT user_id FROM email WHERE address = '%s'\" % email_address)\n\t\tresult = db.fetchone()\n\t\tif not result:\n\t\t\treturn send_msg('Email address, %s, not found in database %s' % (email_address, settings['signature']))\n\t\tuser_id = result['user_id']\n\t\t\n\t\tdb.execute(\"SELECT number FROM phone WHERE user_id = '%s'\" % user_id)\n\t\tresult = db.fetchone()\n\t\tif not result:\n\t\t\treturn send_msg('Your account does not have a phone number. Please update your account with a phone number')\n\t\tphone_number = result['number']\n\t\t\n\t\tlog.debug('user_id: %s phone_number: %s' % (user_id, phone_number))\n\t\t\n\t(text, image) = get_email_payload(msg)\n\t\n\tif text:\n\t\tlog.debug('Message body: %s' % text)\n\telse:\n\t\ttext = 'note'\n\t\n\tbits = text.split()\n\tblen = len(bits)\n\tquery = {\n\t\t'frompy': 1,\n\t\t'phone': phone_number,\n\t\t'time': long(rfc822.mktime_tz(rfc822.parsedate_tz(msg['Date']))),\n\t\t'keyword': bits[0],\n\t\t'query': blen > 1 and bits[1] or '',\n\t\t'reqid': blen > 2 and bits[2] or '',\n\t\t'comments': blen > 3 and join(bits[3:]) or ''\n\t}\n\t\n\tresponse = open_url(settings['parse_budburst_messages_service'], query)\n\tif not response:\n\t\tlog.error('Invalid response with query %s' % (response, query))\n\t\treturn send_msg(settings['error_message'])\n\t\t\n\thandle_response(query, response, image)\n\ndef open_user_database():\n\tglobal db\n\tdb = MySQLdb.connect(\n\t\thost = settings['db_hostname'],\n\t\tuser = settings['db_username'],\n\t\tpasswd = settings['db_password'],\n\t\tdb = settings['db_database']\n\t).cursor(MySQLdb.cursors.DictCursor)\n\ndef get_email_address(msg):\n\t'''\n\tMatch an expected email address from the 'From' field of the message\n\t\n\tRegExp String from: http://www.regular-expressions.info/email.html\n\t'''\n\tmatched = re.search(r\"[a-z0-9!#$%&'*+/=?^_`{|}~-]+(?:\\.[a-z0-9!#$%&'*+/=?^_`{|}~-]+)*@(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\\.)+(?:[A-Z]{2}|com|org|net|gov|mil|biz|info|mobi|name|aero|jobs|museum|edu)\\b\", msg['From'])\n\tif matched:\n\t\treturn matched.group(0)\n\ndef get_phone_number(email_address):\n\t'''\n\tSearch the 'From' field in the message to see if there is a phone number\n\t\n\tFirst we strip the email_address of all non-numerical characters, and try to match\n\tfor a phone number using: http://regexlib.com/REDetails.aspx?regexp_id=607\n\t'''\n\tcleaned_address = re.sub(r'[^\\d]', '', email_address)\n\tmatched = re.search(r\"(?:\\([2-9]\\d{2}\\)\\ ?|[2-9]\\d{2}(?:\\-?|\\ ?))[2-9]\\d{2}[- ]?\\d{4}\", cleaned_address)\n\tif matched:\n\t\tglobal from_email\n\t\tfrom_email = False\n\t\treturn matched.group(0)\n\t\ndef send_msg(message):\n\tglobal from_email, email_address, phone_number\n\tif from_email:\n\t\tsend_email(email_address, message)\n\telse:\n\t\tsend_sms(phone_number, message)\n\ndef get_email_payload(msg):\n\ttxt = ''\n\timage = False\n\tif msg.is_multipart(): #mms\n\t\tqueue = msg.get_payload()\n\t\tfor submsg in queue:\n\t\t\tif submsg.is_multipart():\n\t\t\t\tqueue = queue.extend(submsg.get_payload())\n\t\t\telif not txt and submsg.get_content_type() == 'text/plain':\n\t\t\t\ttxt = submsg.get_payload()\n\t\t\telif not image and submsg.get_content_type()[0:5] == 'image':\n\t\t\t\timage = {\n\t\t\t\t\t'file': submsg.get_payload(decode = True),\n\t\t\t\t\t'ext': submsg.get_content_type()[6:]\n\t\t\t\t}\n\telse: #sms\n\t\ttxt = msg.get_payload()\n\t\t\n\ttxt = txt.replace('This message was sent using', '').strip()\n\treturn [txt, image]\n\ndef open_url(url, query):\n\tlog.debug('Message sent to %s:\\n\\t %s' % (url, query))\n\tf = urllib.urlopen(url + urllib.urlencode(query))\n\tresponse = f.read().split()\n\tlog.debug('response from service: %s' % response)\n\treturn response\n\n\ndef handle_response(query, response, image):\n\tglobal db\n\tif response[0] == 'OBS':\n\t\tif image:\n\t\t\t(user_id, obs_id, plant_id, stage_id, time) = response[1:6]\n\t\t\tcomments = join(response[6:])\n\t\t\tfname = save_image(obs_id, image)\n\t\t\tdb.execute(\"INSERT INTO %s VALUES (%s, %s, %s, 640, 480,'%s','%s','%s', %s)\" % (IMAGE_DATABASE_COLUMNS, user_id, obs_id, plant_id, fname, settings['image_upload_loc'], comments, time))\n\t\t\tif not db.rowcount:\n\t\t\t\tlog.error('Could not insert new image: %s, for obs_id: %s' % (fname, obs_id))\n\t\treturn send_msg('Updated plant: %s to stage: %s %s' % (plant_id, stage_id, settings['signature']))\n\n\telif response[0] == 'NOT':\n\t\tif image:\n\t\t\tnote_id = response[1]\n\t\t\tfname = save_image(note_id, image)\n\t\t\tdb.execute(\"UPDATE cellphones SET image = '%s' WHERE id = %s\" % (fname, note_id))\n\t\t\tif not db.rowcount:\n\t\t\t\tlog.error('Could not insert new image: %s, for obs_id: %s' % (fname, obs_id))\n\t\t\t\t\n\t\tnote = ' '.join([query['query'], query['reqid'], query['comments']])\n\t\treturn send_msg('Note \"%s\" entered. %s' % (note, settings['signature']))\n\t\n\tsend_msg(' '.join(response))\n\n\ndef send_sms(phone_number, message):\n\tlog.debug('send_sms(): %s' % message)\n\t\n\topen_url(settings['send_sms_service'], {\n\t\t'username': 'user',\n\t\t'password': 'pass',\n\t\t'to': phone_number,\n\t\t'text': message\n\t})\n\t\ndef send_email(email_address, message):\n\tif mail(email_address, 'Response from Networked Naturalist', message):\n\t\tlog.success('Sent email address to: %s with message: %s' % (email_address, message))\n\ndef save_image(id, image):\n\tfname = '%s%s.%s' % (IMAGE_PREFIX, id, image['ext'])\n\timage_path = settings['image_upload_loc'] + fname\n\tlog.debug('Attempting to upload image: %s' % image_path)\n\ttry:\n\t\tf = open(image_path, 'w')\n\t\tf.write(image['file'])\n\t\tlog.debug('Wrote image')\n\texcept:\n\t\tlog.error('Unable to upload image')\n\t\tlog.error(sys.exc_info()[1])\n\t\tlog.error(sys.exc_info()[2])\n\t\treturn ''\n\tf.close()\n\tlog.debug('Succesfully uploaded image to %s' % image_path)\n\t\n\treturn fname\n\ndef mail(to, subject, text, attach = None):\n\tsuccess = True\n\tmsg = MIMEMultipart()\n\n\tmsg['From'] = settings['email_address']\n\tmsg['To'] = to\n\tmsg['Subject'] = subject\n\n\tmsg.attach(MIMEText(text))\n\tif attach:\n\t\tpart = MIMEBase('application', 'octet-stream')\n\t\tpart.set_payload(open(attach, 'rb').read())\n\t\tEncoders.encode_base64(part)\n\t\tpart.add_header('Content-Disposition', 'attachment; filename=\"%s\"' % os.path.basename(attach))\n\t\tmsg.attach(part)\n\n\tmailServer = smtplib.SMTP(\"smtp.gmail.com\", 587)\n\tmailServer.ehlo()\n\tmailServer.starttls()\n\tmailServer.ehlo()\n\tmailServer.login(settings['email_address'], settings['email_password'])\n\ttry:\n\t\tmailServer.sendmail(settings['email_address'], to, msg.as_string())\n\texcept:\n\t\tlog.error('Unable to send email')\n\t\tlog.error(sys.exc_info()[1])\n\t\tlog.error(sys.exc_info()[2])\n\t\tsuccess = False\n\t# Should be mailServer.quit(), but that crashes...\n\tmailServer.close()\n\treturn success\n","sub_path":"email/parse_email.py","file_name":"parse_email.py","file_ext":"py","file_size_in_byte":8376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"143488245","text":"import rl_const as ct\nfrom rl_data_man import DataMan\n\n\nclass Environment:\n\n def __init__(self, start_date=ct.START_DATE, end_date=ct.END_DATE):\n self.data_man = DataMan(start=start_date, end=end_date)\n self.chart_data = self.data_man.chart_data()\n self.train_data = self.data_man.train_data()\n\n self.trading_unit = 1 # 거래 단위\n self.delayed_reward_threshold = 0.012 # 지연보상 임계치\n\n self.init_balance = 50000000 # 초기 자본금\n self.min_margin = 5000000 # 유지 증거금\n\n self.observe_chart = None\n self.observe_train = None\n self.observe_state = None\n self.data_idx = -1\n\n self.balance = 0 # 현재 잔고, 예탁금\n self.contract_cnt = 0 # 보유 계약 수\n self.contract_pt = 0.0 # 계약 포인트\n self.contract_pos = 0 # 계약 포지션, 0:empty, 1:long, 2:short\n\n self.long_cnt = 0 # 매수 포지션 횟수\n self.short_cnt = 0 # 매도 포지션 횟수\n self.holding_cnt = 0 # 보유 횟수\n self.clearing_cnt = 0 # 포지션 청산 횟수\n\n # 매수 포지션: balance + (contract_cnt * (현재 선물 포인트 - contract_pt) / TICK_POINT * TICK_PRICE)\n # 매도 포지션: balance + (contract_cnt * (contract_pt - 현재 선물 포인트) / TICK_POINT * TICK_PRICE)\n # 포지션 없음: balance\n self.position_value = 0 # 포지션 손익 현황\n self.portfolio_value = self.balance + self.position_value # balance + 포지션 손익 현황\n self.base_portfolio_value = self.portfolio_value # 직전 학습 시점의 PV\n self.profitloss = 0.0 # 현재 손익\n self.base_profitloss = 0.0 # 직전 지연 보상 이후 손익\n self.immediate_reward = 0 # 즉시 보상\n self.ratio_portfolio_value = 0.0 # 포트폴리오 가치 비율, portfolio_value / base_portfolio_value\n\n\n def reset(self):\n self.observe_chart = None\n self.observe_train = None\n self.observe_state = None\n self.data_idx = -1\n\n self.balance = self.init_balance\n self.contract_cnt = 0\n self.contract_pt = 0.0\n self.contract_pos = 0\n\n self.long_cnt = 0\n self.short_cnt = 0\n self.holding_cnt = 0\n self.clearing_cnt = 0\n\n self.position_value = 0\n self.portfolio_value = self.balance + self.position_value\n self.base_portfolio_value = self.portfolio_value\n self.profitloss = 0.0\n self.base_profitloss = 0.0\n self.immediate_reward = 0\n self.ratio_portfolio_value = 0.0\n\n\n def account_status(self):\n self.ratio_portfolio_value = self.portfolio_value / self.base_portfolio_value\n return [self.contract_cnt, self.contract_pos, self.ratio_portfolio_value]\n\n\n def next_state(self):\n if len(self.chart_data) <= self.data_idx + 1:\n return None\n\n self.data_idx += 1\n self.observe_chart = self.chart_data.iloc[self.data_idx]\n self.observe_train = self.train_data.iloc[self.data_idx]\n\n self.observe_state = self.observe_train.to_list()\n self.observe_state.extend(self.account_status())\n return self.observe_state\n\n\n def cur_close_point(self):\n if self.observe_chart is None:\n return None\n\n return self.observe_chart[ct.CLOSE_IDX]\n\n\n def calc_position_value(self):\n if self.contract_pos == ct.EMPTY_POSITION:\n return 0\n\n close_point = self.cur_close_point()\n delta = close_point - self.contract_pt\n delta = delta if self.contract_pos == ct.LONG_POSITION else -delta\n return int(self.contract_cnt * delta / ct.TICK_POINT * ct.TICK_PRICE)\n\n\n def step(self, action):\n close_point = self.cur_close_point()\n if close_point is None:\n return\n\n if action == ct.ACTION_ENTRY_BUY:\n if self.contract_cnt >= 1:\n print('ACTION_ENTRY_BUY error!!')\n\n self.position_value = self.calc_position_value()\n self.contract_cnt = 1\n self.contract_pt = self.cur_close_point()\n self.contract_pos = ct.LONG_POSITION\n self.long_cnt += 1\n self.balance -= ct.TRADING_CHARGE\n\n elif action == ct.ACTION_ENTRY_SELL:\n if self.contract_cnt >= 1:\n print('ACTION_ENTRY_SELL error!!')\n \n self.position_value = self.calc_position_value()\n self.contract_cnt = 1\n self.contract_pt = self.cur_close_point()\n self.contract_pos = ct.SHORT_POSITION\n self.short_cnt += 1\n self.balance -= ct.TRADING_CHARGE\n\n elif action == ct.ACTION_CLEARING:\n if self.contract_cnt <= 0:\n print('ACTION_CLEARING error!!')\n\n self.position_value = self.calc_position_value()\n self.contract_cnt = 0\n self.contract_pt = 0.0\n self.contract_pos = ct.EMPTY_POSITION\n self.clearing_cnt += 1\n self.balance = self.balance + self.position_value - ct.TRADING_CHARGE\n\n else: # ACTION_HOLDING\n self.position_value = self.calc_position_value()\n self.holding_cnt += 1\n\n # update portfolio_value\n self.portfolio_value = self.balance + self.position_value\n self.profitloss = \\\n (self.portfolio_value - self.init_balance) / self.init_balance\n self.base_profitloss = \\\n (self.portfolio_value - self.base_portfolio_value) / self.base_portfolio_value\n\n # 즉시 보상, 수익률\n self.immediate_reward = self.profitloss\n\n # 지연 보상, 익절 손절 기준\n delayed_reward = 0\n base_profitloss_abs = abs(self.base_profitloss)\n if base_profitloss_abs > self.delayed_reward_threshold:\n self.base_portfolio_value = self.portfolio_value\n delayed_reward = self.immediate_reward\n\n # 유지 증거금 보다 작아질때 거래 종료\n done = bool(self.min_margin > self.portfolio_value)\n\n return (self.immediate_reward, delayed_reward, done)\n\n\nif __name__ == \"__main__\":\n env = Environment()\n env.reset()\n print('inti environment!!')","sub_path":"project-futures-trader/agent-environment/rl_environment.py","file_name":"rl_environment.py","file_ext":"py","file_size_in_byte":6263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"256751019","text":"# model.py\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision.models.resnet import resnet50\nimport deeplab_network\nfrom deeplab_network.backbone import resnet\n\n\n\n\nclass backboneModel(nn.Module):\n def __init__(self, model):\n super(backboneModel, self).__init__()\n\n self.f = []\n for name, module in model.named_children():\n if name == 'conv1':\n module = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)\n if not isinstance(module, nn.Linear) and not isinstance(module, nn.MaxPool2d):\n self.f.append(module)\n # encoder\n self.f = nn.Sequential(*self.f)\n self.fc = nn.Linear(2048, 200)\n\n def forward(self, x):\n x = self.f(x)\n feature = torch.flatten(x, start_dim=1)\n x = self.fc(feature)\n return x\n\ndef deeplab_backbone(path='pretrain_test_tiny-imagenet-200-0002.pth',num_classes = 3,output_stride=16 ):\n model = deeplab_network.deeplabv3_resnet50(num_classes, output_stride, pretrained_backbone=False)\n state_dict = torch.load(path)['state_dict']\n model.load_state_dict(state_dict)\n model = model.backbone\n # backbone_resnet = backboneModel(model)\n # print(model)\n\n replace_stride_with_dilation=[False, False, True]\n aspp_dilate = [6, 12, 18]\n backbone = resnet.resnet50(\n pretrained=False,\n replace_stride_with_dilation=replace_stride_with_dilation)\n backbone.load_state_dict(model.state_dict(), strict=False)\n \n for parameter in backbone.parameters():\n parameter.requires_grad = False\n backbone.fc = torch.nn.Linear(2048, 200)\n # print(backbone)\n\n return backbone\n\n\n\n","sub_path":"deeplab_backbone.py","file_name":"deeplab_backbone.py","file_ext":"py","file_size_in_byte":1699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"20504880","text":"import streamlit as st\nfrom scipy import stats\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport os.path\n\n\ndef main():\n my_path = os.path.abspath(os.path.dirname('singapore_sum_df'))\n sum_df = pd.read_pickle(os.path.join(my_path, \"Dataframes/singapore_sum_df\"))\n map_df = pd.read_pickle(os.path.join(my_path, \"Dataframes/singapore_map_df\"))\n ranking_df = pd.read_pickle(os.path.join(my_path, \"Dataframes/singapore_rank_df\"))\n\n \"\"\"\n # Structure Research - Singapore February 2020 Newsletter\n ### A dynamic data visualization newsletter for our customers.\n \"\"\"\n '## 1) Map of online data centers'\n years_to_observe = st.slider('Year to view ', 2000, 2030, 2000)\n '##### note that year 2000 datapoint is for clients who have not disclosed their online dates'\n map_data = map_df.loc[map_df['year built'] <= years_to_observe, ['latitude', 'longitude']]\n map_data = map_data[\n pd.to_numeric(map_data.iloc[:, 0], errors='coerce').notnull()] # selecting only non empty datapoints\n stats.zscore(np.array(map_data['latitude'], dtype=np.float64))\n map_data = map_data[stats.zscore(\n np.array(map_data['latitude'], dtype=np.float64)) < 1] # removing outlier points due to localization error\n st.map(map_data)\n\n eval_years = np.linspace(2000, 2030, 7)\n num_data_centers = np.zeros((len(eval_years), 1))\n for i in range(len(eval_years)):\n num_data_centers[i] = len(map_df.loc[map_df['year built'] <= eval_years[i]])\n\n plt.plot(eval_years, num_data_centers)\n plt.xlabel('Year')\n plt.ylabel('Number of data centers')\n plt.title('Singapore datacenters across time')\n st.pyplot()\n\n if st.checkbox('Show dataframe for all data centers'):\n map_df\n\n \"\"\"\n ## 2) Three metrics of internet infrastructure market\n #### Revenue - total revenue generated by all companies in USD\n #### Power - power consumption via MW\n #### Space - square footage (Sqft) or racks in servers (Rack)\n \"\"\"\n sum_packets = {}\n sum_packets['Revenue'] = sum_df[['WS Rev', 'Retail Rev', 'Colocation Revenue']]\n sum_packets['Power'] = sum_df[['Contracted MW', 'Critical MW']]\n sum_packets['Space - Sqft'] = sum_df[['Utilized Rack sqft.', 'Rack sqft.']]\n sum_packets['Space - Rack'] = sum_df[['Racks Leased', 'Rack Capacity']]\n\n feature = st.selectbox('select feature to observe', list(sum_packets.keys()))\n sum_packets[feature].plot(kind='bar')\n plt.xlabel('year')\n plt.ylabel(feature)\n plt.tight_layout()\n st.pyplot()\n\n \"\"\"\n ## 3) Market dominance (revenue) across years\n #### Below is a CDF of revenue generated by each company. Click a year to view!\n \"\"\"\n columns = st.multiselect(\n label='Which years to view?', options=ranking_df.columns.to_list(), default=[2015, 2020, 2025])\n plt.plot(ranking_df[columns])\n plt.xlabel('companies sorted by revenue generated')\n plt.ylabel('proportion of total revenue (USD)')\n plt.legend(columns)\n plt.tight_layout()\n plt.xlim([0, len(ranking_df)])\n st.pyplot()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"structure_singapore_1.py","file_name":"structure_singapore_1.py","file_ext":"py","file_size_in_byte":3116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"611517464","text":"# -*- coding: utf-8 -*-\n# 3.0\n\n# \n\nimport numpy as np\nimport numpy.linalg as npl\n\ndef PCA(datamatrix, centering = False):\n \"\"\"\n Performs a PCA, a Principal Component Analysis\n \n datamatrix: different variables in/along columns, different observations in/along rows\n centering: remove the mean of each column, so calculate the mean along/down each row for each column\n \n returns: eigval, norm_eigval, eigvec, projection_vec\n \"\"\"\n \n Nrows, Ncols = datamatrix.shape\n \n # calculate mean along/down each row for each column\n mean_dat = np.mean(datamatrix, axis = 0)\n\n mean_datamatrix = np.tile( mean_dat, (Nrows, 1) )\n \n if centering:\n centered = datamatrix - mean_datamatrix\n else:\n centered = datamatrix\n \n # correlation matrix, summation over/along the rows in each column of the data matrix\n corr = np.dot(centered.T, centered)\n \n # weights (eigenvalues) and normalized eigenvectors of Hermitian or symmetric matrix\n # eigenvalues are not ordered in general\n # eigenvectors are in columns k, i.e. v[:, k]\n w, v = npl.eigh(corr)\n \n # sort from largest to smallest\n sortindex = np.argsort(w)[::-1]\n \n # sort eigenvectors\n eigen_vec = v[:, sortindex]\n\n # sort from largest to smallest\n eigen_val = sorted(w, reverse = True)\n # normalize eigenvalues\n norm_eigen_val = eigen_val / np.sum(eigen_val)\n \n # bi-orthogonal vectors from projection of data onto PCA directions, vectors are in columns\n projection_vec = np.dot(centered, eigen_vec)\n \n return eigen_val, norm_eigen_val, eigen_vec, projection_vec\n\n","sub_path":"PCA.py","file_name":"PCA.py","file_ext":"py","file_size_in_byte":1667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"604840608","text":"from __future__ import division\n\nimport getopt\nimport os\nimport pickle as pkl\nimport random\nimport sys\nfrom string import punctuation\nfrom typing import List, Any\n\nimport numpy as np\nimport pandas as pd\nfrom joblib import load\nfrom lxml import etree\nfrom nltk.corpus import stopwords\nfrom nltk.stem.wordnet import WordNetLemmatizer\nfrom scipy.sparse import hstack\n\n\nclass ArticleClass:\n def __init__(self, liwc_dict, punc_dict):\n self.id = None\n self.hyperpartisan = \"hyperpartisan\"\n self.bias = \"bias\"\n self.title = \"title\"\n self.text = \"\"\n self.labeled_by = \"labeled_by\"\n self.published_at = \"published_at\"\n self.count_urls = 0\n self.count_paragraphs = 0\n self.count_quotes = 0\n self.hedges = []\n self.boosters = []\n self.negatives = []\n self.positives = []\n self.liwc_counts = dict.fromkeys(liwc_dict, 0)\n self.punctuation_counts = dict.fromkeys(punc_dict, 0)\n self.liwc_counts_title = dict.fromkeys(liwc_dict, 0)\n self.punctuation_counts_title = dict.fromkeys(punc_dict, 0)\n self.all_liwc = 0\n self.all_punc = 0\n self.all_liwc_title = 0\n self.all_punc_title = 0\n\n def clean_article(self):\n \"\"\"\n This method cleans the article and stores its text as an array of tokens.\n \"\"\"\n # split into tokens by white space\n tokens = self.text.split(\" \")\n # remove punctuation from each token\n table = str.maketrans('', '', punctuation)\n tokens = [w.translate(table) for w in tokens] # type: List[Any]\n # remove remaining tokens that are not alphabetic\n tokens = [word for word in tokens if word.isalpha()]\n # filter out stop words\n stop_words = set(stopwords.words('english'))\n tokens = [w for w in tokens if not w in stop_words]\n # lemmatization and lowercase\n lmtzr = WordNetLemmatizer()\n tokens = [lmtzr.lemmatize(w.lower()) for w in tokens]\n # filter out short tokens\n tokens = [word for word in tokens if len(word) > 1]\n return tokens\n\n\nliwc_features_list = dict(Funct=\"1\", Pronoun=\"2\", Ppron=\"3\", I=\"4\", We=\"5\", You=\"6\", SheHe=\"7\", They=\"8\", Ipron=\"9\",\n Article=\"10\", Verbs=\"11\", AuxVb=\"12\", Past=\"13\", Present=\"14\", Future=\"15\", Adverbs=\"16\",\n Prep=\"17\", Conj=\"18\", Negate=\"19\", Quant=\"20\", Numbers=\"21\", Swear=\"22\", Social=\"23\",\n Family=\"24\", Friends=\"25\", Humans=\"26\", Affect=\"27\", Posemo=\"28\", Negemo=\"29\", Anx=\"30\",\n Anger=\"30\", Sad=\"31\", CogMech=\"32\", Insight=\"33\", Cause=\"34\", Discrep=\"35\", Tentat=\"36\",\n Certain=\"37\", Inhib=\"38\", Incl=\"39\", Excl=\"40\", Percept=\"41\", See=\"42\", Hear=\"43\", Feel=\"44\",\n Bio=\"45\", Body=\"46\", Health=\"47\", Sexual=\"48\", Ingest=\"49\", Relativ=\"50\", Motion=\"51\",\n Space=\"52\", Time=\"53\", Work=\"54\", Achiev=\"55\", Leisure=\"56\", Home=\"57\", Money=\"58\",\n Relig=\"59\",\n Death=\"60\", Assent=\"61\", Nonflu=\"62\", Filler=\"63\")\n\nrandom.seed(42)\nrunOutputFileName = \"prediction.txt\"\n\n\ndef load_dataset(filename):\n return pkl.load(open(filename, 'rb'))\n\n\ndef parse_options():\n \"\"\"Parses the command line options.\"\"\"\n try:\n long_options = [\"inputDataset=\", \"outputDir=\"]\n opts, _ = getopt.getopt(sys.argv[1:], \"d:o:\", long_options)\n except getopt.GetoptError as err:\n print(str(err))\n sys.exit(2)\n\n inputDataset = \"undefined\"\n outputDir = \"undefined\"\n\n for opt, arg in opts:\n if opt in (\"-d\", \"--inputDataset\"):\n inputDataset = arg\n elif opt in (\"-o\", \"--outputDir\"):\n outputDir = arg\n else:\n assert False, \"Unknown option.\"\n if inputDataset == \"undefined\":\n sys.exit(\n \"Input dataset, the directory that contains the articles XML file, is undefined. Use option -d or --inputDataset.\")\n elif not os.path.exists(inputDataset):\n sys.exit(\"The input dataset folder does not exist (%s).\" % inputDataset)\n\n if outputDir == \"undefined\":\n sys.exit(\n \"Output path, the directory into which the predictions should be written, is undefined. Use option -o or --outputDir.\")\n elif not os.path.exists(outputDir):\n os.mkdir(outputDir)\n\n return (inputDataset, outputDir)\n\n\n########## LXML ##########\n\n\ndef count_features(filename):\n features = open(\"dictionary/\" + filename + \".csv\",\n encoding=\"latin-1\").readlines()\n new_features = []\n for feature in features:\n new_features.append(feature.replace('\\n', ''))\n\n return new_features\n\n\ndef construct_features_dictionary(features_index):\n features = {}\n for feature, index in features_index.items():\n features[feature] = count_features(features_index[feature])\n return features\n\n\nclass DataProcessing(object):\n def __init__(self, outFile):\n self.outFile = outFile\n self.liwc_features = construct_features_dictionary(liwc_features_list)\n self.punctuations = dict(question_mark=['?'], exclamation_mark=['!'], quotation_mark=['\"'],\n paranthesis=['(', ')'], colons=[',', ';', \":\"], dot=['.'])\n self.data = pd.read_csv('dictionary/NRC-Emotion-Lexicon-Wordlevel-v0.92.txt', sep=\"\t\", header=None)\n self.data.columns = [\"word\", \"emotion\", \"value\"]\n\n def text_content(self, elt):\n return ' '.join([t.strip() for t in elt.itertext()])\n\n def efficient_read_article_text(self, articles_filename):\n context = etree.iterparse(articles_filename, events=('end', 'start'))\n article = None\n for event, elem in context:\n if elem.tag == 'article':\n if event == 'start':\n article = ArticleClass(self.liwc_features.keys(), self.punctuations.keys())\n else:\n article.id = elem.attrib['id']\n article.title = elem.attrib['title']\n yield article\n article = None\n\n if article is None:\n continue\n\n if event == 'end':\n if elem.tag == 'article':\n article.text = ' '.join([article.text, self.text_content(elem)])\n continue\n if elem.tag == 'p':\n article.text = ' '.join([article.text, self.text_content(elem)])\n article.count_paragraphs += 1\n continue\n if elem.tag == 'q':\n article.text = ' '.join([article.text, self.text_content(elem)])\n article.count_quotes += 1\n continue\n if elem.tag == 'a':\n article.text = ' '.join([article.text, self.text_content(elem)])\n article.count_urls += 1\n continue\n\n def emotions_word(self, word):\n word_emotions = list(self.data.loc[self.data['word'] == word][\"value\"])\n if len(word_emotions) == 0:\n return np.zeros((10,), dtype=np.int)\n else:\n return word_emotions\n\n def classify_article(self, articles_filename):\n # Load Classifier:\n idf_liwc_article, idf_punc_article, idf_liwc_title, idf_punc_title = load_dataset(\"train_idf_by_articles.pkl\")\n classifier = load(\"byarticle_classification_model.pkl\")\n tfidf = load(\"byarticle_article_tfidf_model.pkl\")\n tfidf_title = load(\"byarticle_title_tfidf_model.pkl\")\n for article in self.efficient_read_article_text(articles_filename):\n article_emotions = np.sum(list(map(self.emotions_word, article.text.split(\" \"))), axis=0)\n for feature, words in self.liwc_features.items():\n liwc_in_article = 0\n liwc_in_title = 0\n for word in words:\n counts_articles = article.text.count(word)\n counts_title = article.title.count(word)\n liwc_in_article += counts_articles\n liwc_in_title += counts_title\n article.liwc_counts[feature] += counts_articles\n article.liwc_counts_title[feature] += counts_title\n article.all_liwc += counts_articles\n article.all_liwc_title += counts_title\n\n for feature, words in self.punctuations.items():\n punc_in_article = 0\n punc_in_title = 0\n for word in words:\n counts_articles = article.text.count(word)\n counts_title = article.title.count(word)\n punc_in_article += counts_articles\n punc_in_title += counts_title\n article.punctuation_counts[feature] += counts_articles\n article.punctuation_counts_title[feature] += counts_title\n article.all_punc += counts_articles\n article.all_punc_title += counts_title\n # Prepare features\n unigrams = tfidf.transform([\" \".join(article.clean_article())])\n title_unigrams = tfidf_title.transform([article.title])\n liwc = []\n punctuation = []\n structure = [article.count_quotes,\n article.count_paragraphs,\n article.count_urls]\n\n for feature in self.liwc_features.keys():\n tf_article = 0\n if article.all_liwc != 0:\n tf_article = article.liwc_counts[feature] / article.all_liwc\n liwc.append(tf_article * idf_liwc_article[feature])\n\n for feature in self.punctuations.keys():\n tf_article = 0\n if article.all_punc != 0:\n tf_article = article.punctuation_counts[feature] / article.all_punc\n if article.all_punc_title != 0:\n tf_title = article.punctuation_counts_title[feature] / article.all_punc_title\n punctuation.append(tf_article * idf_punc_article[feature])\n\n test_article = hstack([unigrams, title_unigrams, liwc, punctuation, structure, article_emotions])\n # Classify Article\n clf_pred = classifier.predict(test_article)[0]\n prediction = (\"true\" if clf_pred == 1 else \"false\")\n # confidence = max(classifier.predict_proba(test_article)[0])\n confidence = 0.0\n # Output prediction\n print(article.id + \" \" + prediction + \" \" + str(confidence))\n self.outFile.write(article.id + \" \" + prediction + \" \" + str(confidence) + \"\\n\")\n\n\n########## MAIN ##########\n\n\ndef main(inputDataset, outputDir):\n \"\"\"Main method of this module.\"\"\"\n\n with open(outputDir + \"/\" + runOutputFileName, 'w') as outFile:\n dataprocessor = DataProcessing(outFile)\n for file in os.listdir(inputDataset):\n if file.endswith(\".xml\"):\n inputRunFile = inputDataset + \"/\" + file\n dataprocessor.classify_article(inputRunFile)\n\n print(\"The predictions have been written to the output folder.\")\n\n\nif __name__ == '__main__':\n main(*parse_options())\n","sub_path":"src/classification_models/jan22_submision_byarticle/classifier_submission.py","file_name":"classifier_submission.py","file_ext":"py","file_size_in_byte":11313,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"405416805","text":"# Copyright (C) 2018 Google Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nload(\"//tools/build/rules:cc.bzl\", \"strip\")\nload(\"//tools/build/rules:repository.bzl\", \"github_http_args\")\nload(\"@io_bazel_rules_go//go:def.bzl\", \"go_repository\", \"go_binary\")\n\ndef github_go_repository(name, organization, project, commit=\"\", branch=\"\", path=\"\", **kwargs):\n if path:\n print(\"Override with {}\".format(path))\n else:\n github = github_http_args(\n organization = organization,\n project = project,\n commit = commit,\n branch = branch,\n )\n go_repository(\n name = name,\n urls = [github.url],\n type = github.type,\n strip_prefix = github.strip_prefix,\n **kwargs\n )\n\n# Macro to replace go_binary rules. Creates the following targets:\n# _unstripped - The unstripped go_binary with debug information.\n# - The stripped go_binary.\ndef go_stripped_binary(name, visibility, **kwargs):\n go_binary(\n name = name + \"_unstripped\",\n **kwargs\n )\n strip(\n name = name,\n src = name + \"_unstripped\",\n visibility = visibility,\n )\n","sub_path":"tools/build/rules/go.bzl","file_name":"go.bzl","file_ext":"bzl","file_size_in_byte":1633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"368142911","text":"\"\"\"\nCopyright (2018) Chris Scuderi\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\"\"\"\nimport ConfigParser\nimport shutil\nimport os.path\n\nCONFIG_MAP = {\n 'Main': {\n 'required': True,\n 'keys': {\n 'phone_number': True,\n 'data_file_path': True,\n\n }\n },\n\n 'RpiArmDisarm': {\n 'required': False,\n 'keys': {'gpio_pin': True}\n },\n\n 'ZoneMapping': {'required': False},\n 'EmailNotification': {\n 'required': False,\n 'keys': {\n 'username': True,\n 'password': True,\n 'server_address': True,\n 'port': True,\n 'notification_email': True,\n 'notification_subject': True,\n 'tls': True\n }\n },\n\n 'PushoverNotification': {\n 'required': False,\n 'keys': {\n 'user': True,\n 'token': True,\n 'priority': False,\n 'device': False\n }\n },\n}\n\n\nclass AlarmConfig(object):\n loaded_config = {}\n\n @staticmethod\n def exists(path):\n return os.path.isfile(path)\n\n @classmethod\n def load(klass, path):\n config = ConfigParser.ConfigParser()\n config.read(path)\n klass.loaded_config = {key: dict(config.items(key))\n for key in config.sections()}\n\n @classmethod\n def get(klass, *argv):\n config = klass.loaded_config\n for arg in argv:\n config = config.get(arg, {})\n\n return config\n\n @staticmethod\n def validate(config):\n missing_config = []\n\n for sec_name, section in CONFIG_MAP.iteritems():\n optional = not section.get('required')\n missing = not config.get(sec_name)\n\n # If an entire section is missing, and its an optional\n # section, skip validation.\n if optional and missing:\n continue\n\n for key, key_required in section.get('keys', {}).iteritems():\n cfg_value = config.get(sec_name, {}).get(key, '')\n\n if key_required and cfg_value == '':\n missing_config.append('[%s] Section: %s' % (sec_name, key))\n\n return missing_config\n\n @staticmethod\n def create(path):\n if not AlarmConfig.exists(path):\n shutil.copy(\n os.path.abspath(\n os.path.join(\n os.path.dirname(__file__),\n 'config_template.ini')),\n path)\n return True\n\n return False\n","sub_path":"alarm_central_station_receiver/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":3016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"626940003","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom keras.layers import Dense\nfrom keras.models import Sequential\nfrom keras.layers import LSTM\nfrom keras.layers import Dropout\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\nfrom sklearn.preprocessing import MinMaxScaler\n\ndata_raw = pd.read_csv('/home/sklakshminarayanan/Downloads/DJI.csv', index_col='Date')\ndata = data_raw.drop('Close', axis=1)\ndata['Close'] = data_raw['Close']\nvalues = data.values\nscaler = MinMaxScaler(feature_range=(0, 1))\nscaled = scaler.fit_transform(values)\nn_train_time = 750\ntrain = scaled[:n_train_time, :]\ntest = scaled[n_train_time:, :]\ntrain_X, train_y = train[:, :-1], train[:, -1]\ntest_X, test_y = test[:, :-1], test[:, -1]\n\ntrain_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))\ntest_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))\nprint(train_X.shape, train_y.shape, test_X.shape, test_y.shape)\n\nmodel = Sequential()\nmodel.add(LSTM(128, activation='relu', input_shape=(train_X.shape[1], train_X.shape[2])))\nmodel.add(Dropout(0.3))\nmodel.add(Dense(1))\nmodel.compile(loss='mean_squared_error', optimizer='SGD')\n\n\nhistory = model.fit(train_X, train_y, epochs=100, batch_size=4, validation_data=(test_X, test_y), verbose=2,\n shuffle=False)\nprint(history.history)\n\nplt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.title('LSTM Base Model Loss')\nplt.ylabel('loss')\nplt.xlabel('epoch')\nplt.legend(['train', 'test'], loc='upper right')\nplt.savefig('/home/sklakshminarayanan/stock_data/model/lstm_uv_loss.png')\nplt.show()\n\n\nyhat = model.predict(test_X)\ntest_X = test_X.reshape((test_X.shape[0], 5))\ntemp = test_X[:, -5:]\n\ninv_yhat = np.concatenate((yhat, temp), axis=1)\ninv_yhat = scaler.inverse_transform(inv_yhat)\ninv_yhat = inv_yhat[:, 0]\n\ntest_y = test_y.reshape((len(test_y), 1))\ninv_y = np.concatenate((test_y, test_X[:, -5:]), axis=1)\ninv_y = scaler.inverse_transform(inv_y)\ninv_y = inv_y[:, 0]\n\n\nrmse = np.sqrt(mean_squared_error(inv_y, inv_yhat))\nmse = mean_squared_error(inv_y, inv_yhat)\nmae = mean_absolute_error(inv_y, inv_yhat)\nr2 = r2_score(inv_y, inv_yhat)\nprint('Test RMSE: %.3f' % rmse)\nprint('Test MSE: %.3f' % mse)\nprint('Test MAE: %.3f' % mae)\nprint('Test R2 score: %.3f' % r2)\n\nloss = {}\nloss['rmse'] = rmse\nloss['mse'] = mse\nloss['mae'] = mae\nloss['r2'] = r2\nloss=pd.DataFrame(loss,index=[0])\nloss.to_csv('/home/sklakshminarayanan/stock_data/data/lstm_uv_loss.csv',index=None)\n\nlstm1 = {}\nlstm1['pred'] = inv_yhat\nlstm1['test'] = inv_y\nlstm_uv = pd.DataFrame(lstm1)\nlstm_uv.to_csv('/home/sklakshminarayanan/stock_data/data/lstm_uv_pred.csv',index=None)\n\nplt.plot(inv_y)\nplt.plot(inv_yhat)\nplt.title('LSTM Base Model test vs prediction')\nplt.ylabel('close')\nplt.xlabel('date')\nplt.legend(['test', 'predicted'], loc='lower left')\nplt.savefig('/home/sklakshminarayanan/stock_data/model/lstm_uv_testvspred.png')\nplt.show()\n\nb = pd.DataFrame(yhat)\nb['test'] = b[0]\nc = pd.DataFrame(test_y)\nb['Predicted'] = c[0]\na = pd.DataFrame(train_y)\na = a.append(b, ignore_index=True)\nplt.plot(a[0])\nplt.plot(a['Predicted'])\nplt.plot(a['test'])\nplt.title('LSTM Base Model Prediction')\nplt.ylabel('stock price')\nplt.xlabel('date')\nplt.legend(['train', 'test', 'predicted'], loc='upper left')\nplt.savefig('/home/sklakshminarayanan/stock_data/model/lstm_uv_predfull.png')\nplt.show()\n","sub_path":"code/lstm_univariate.py","file_name":"lstm_univariate.py","file_ext":"py","file_size_in_byte":3384,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"282433031","text":"from tkinter import *\r\nroot = Tk()\r\ndef getV(root):\r\n a = scale1.get()\r\n print(\"Значення\", a)\r\nscale1 = Scale(root,orient=HORIZONTAL,length=300,from_=50,to=80,tickinterval=5,resolution=5)\r\nbutton1 = Button(root,text=\"Отримати значення\")\r\nscale1.pack()\r\nbutton1.pack()\r\nbutton1.bind(\"\",getV)\r\nroot.mainloop()\r\n","sub_path":"I семестр/Програмування (Python)/Лабораторні/Братун 6305/Приклади/30/Ex14.py","file_name":"Ex14.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"74804230","text":"numcode = input('type code : ')\nwhile numcode == 'a':\n numcode = print('1')\n numcode = 1\n\n if numcode == 1:\n print('2')\n\n\n\"\"\"num2 = a,b,c\nnum3 = d,e,f\nnum4 = g,h,i\nnum5 = j,k,l\nnum6 = m,n,o\nnum7 = p,q,r,s\nnum8 = t,u,v\nnum9 = w,x,y,z\"\"\"","sub_path":"6306022610024/exam3.py","file_name":"exam3.py","file_ext":"py","file_size_in_byte":251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"208296067","text":"import pygame, sys\nfrom pygame.locals import *\nimport math\nimport random\nimport os\nimport json\nimport numpy\nfrom datetime import datetime\n\nimport dispUtils\nimport dataUtils\nimport AI\nimport ObsNRewards\nimport planner\n\n# USER SETTINGS\n#Time settings\nupdatesPerSecond = 1\nrealTimeFactor = 10\n\n# Multiagent Setting\nnumberOfAgents = 2\n\n#Spawn Boxs?\nspawnBox = True\nnumberOfBoxes = 1\n\n# Goals per Epoch\ngoalPerEpoch = 1\n\nmap_type = \"\"\n# map_type = \"twoRooms\"\n# map_type = \"complex\"\n\n\n# AI Settings\n# AImode\n# 0: Fresh training\n# 1: Continue training from resume_epoch\n# 2: Start fresh training but load weights from loaded_training_name\n# 3: Run AI for evaluation\n\nAImode = 3\n\n# training_name = '2D_3Agent_twoRooms_noCollide'\n# resume_epoch = '1144' # change to epoch to continue from\n\n# training_name = '2D_3Agents_2Rooms'\n# resume_epoch = '140000' # change to epoch to continue from\n\n# training_name = '2D_1Agent_social_corridor_Social_narrow'\n# resume_epoch = '49168' # change to epoch to continue from\n\n# training_name = '2D_1Agent_social_corridor_Social_narrow'\n# resume_epoch = '49168' # change to epoch to continue from\n\n# training_name = '2D_1Agent_social_corridor_noSocial'\n# resume_epoch = '1363' # change to epoch to continue from11\n\ntraining_name = 'My_Training_2_Agents_with_1_box'\nresume_epoch = '2628'\n\nloaded_training_name = '2D_1Agent_social_corridor_Social_narrow'\n\nsaveRate = 10000 #Saves the weights and parameters every x epochs\n\n# Performance Check?\nperformaceCheck = False\nnumberOfEpochsToTest = 10\ntestingInterval = 200\nstartTesting = 1000\n\n##END OF USER SETTINGS\n\n# Timing how long the training took\nstartTime = datetime.now()\n\n# set up pygame\npygame.init()\nmainClock = pygame.time.Clock()\n\n# set up the window\nWINDOWWIDTH = 1200\nWINDOWHEIGHT = 1200\nwindowSurface = pygame.display.set_mode((WINDOWWIDTH, WINDOWHEIGHT))\npygame.display.set_caption('2D Training')\n\n#Generate Map\nmap_data = dispUtils.mapGeneration(windowSurface,map_type)\nwindowSurface.fill(map_data[1])\ndispUtils.drawObstacles(windowSurface, map_data[2], map_data[0])\n\n# movement Parameters\nforawardSpeed = {'linearSpeed': 50/updatesPerSecond, 'angularSpeed': 0/updatesPerSecond}\nleftSpeed = {'linearSpeed': 0/updatesPerSecond, 'angularSpeed': -0.6/updatesPerSecond}\nrightSpeed = {'linearSpeed': 0/updatesPerSecond, 'angularSpeed': 0.6/updatesPerSecond}\nstopSpeed = {'linearSpeed': 0, 'angularSpeed': 0}\n\n# spawn walls and other obstacles\nBOXSIZE = WINDOWWIDTH - 100\nWALLWIDTH = 50\nobstacles = dispUtils.createWalls(windowSurface,BOXSIZE,WALLWIDTH)\ndispUtils.drawObstacles(windowSurface, [0,0,0], obstacles)\n\n# box\nboxX = int(random.uniform(200, 800))\nboxY = int(random.uniform(200, 800))\n\nblacklist = []\nwhile not dispUtils.noCollideSpawnCheck(boxX,boxY,blacklist,200):\n boxX = int(random.uniform(200, 800))\n boxY = int(random.uniform(200, 800))\n\nbox = {'position':[boxX,boxY], 'width':80, 'height': 120}\nif spawnBox:\n obstacles.append(dispUtils.centreRecttoPoly(box))\n\ndef redrawBox():\n boxX = int(random.uniform(200, 800))\n boxY = int(random.uniform(200, 800))\n blacklist = []\n blacklist.extend(robots)\n blacklist.extend(goals)\n while not dispUtils.noCollideSpawnCheck(boxX,boxY,blacklist,200):\n boxX = int(random.uniform(200, 800))\n boxY = int(random.uniform(200, 800))\n\n box['position'] = [boxX,boxY]\n obstacles[-1] = dispUtils.centreRecttoPoly(box)\n\n\n\n# create the AI\npath = os.path.dirname(os.path.abspath(__file__)) + '/training_results/' + training_name + '_ep'\nreward_file = os.path.dirname(os.path.abspath(__file__)) + '/training_results/' + training_name + '_reward'\nperformacneReward_file = os.path.dirname(os.path.abspath(__file__)) + '/training_results/' + training_name + '_Performance'\n\nif AImode == 0:\n #Fresh training\n resume_epoch = '0'\n resume_path = os.path.dirname(os.path.abspath(__file__)) + '/training_results/dqn_ep0'\n params_json = resume_path + '.json'\n\n dataUtils.create_csv(reward_file)\n if performaceCheck:\n dataUtils.create_csv(performacneReward_file)\n\n AIparams = dataUtils.loadAIParams(params_json)\n epochs, steps, updateTargetNetwork, explorationRate, epsilon_decay, minibatch_size, learnStart, learningRate, discountFactor, memorySize, network_inputs, network_outputs, network_structure, current_epoch = dataUtils.setAIParams(AIparams)\n network_inputs = network_inputs + (3 - 1)*4\n\nelif AImode == 1:\n #continue training\n resume_path = path + resume_epoch\n weights_path = resume_path + '.h5'\n params_json = resume_path + '.json'\n\n AIparams = dataUtils.loadAIParams(params_json)\n epochs, steps, updateTargetNetwork, explorationRate, epsilon_decay, minibatch_size, learnStart, learningRate, discountFactor, memorySize, network_inputs, network_outputs, network_structure, current_epoch = dataUtils.setAIParams(AIparams)\n\nelif AImode == 2:\n #fresh training but with old weights\n params_json = os.path.dirname(os.path.abspath(__file__)) + '/training_results/dqn_ep0.json'\n weights_path = os.path.dirname(os.path.abspath(__file__)) + '/training_results/' + loaded_training_name + '_ep' + resume_epoch + '.h5'\n\n dataUtils.create_csv(reward_file)\n if performaceCheck:\n dataUtils.create_csv(performacneReward_file)\n AIparams = dataUtils.loadAIParams(params_json)\n epochs, steps, updateTargetNetwork, explorationRate, epsilon_decay, minibatch_size, learnStart, learningRate, discountFactor, memorySize, network_inputs, network_outputs, network_structure, current_epoch = dataUtils.setAIParams(AIparams)\n network_inputs = network_inputs + (3 - 1)*4\n\nelse:\n #start running\n resume_path = path + resume_epoch\n weights_path = resume_path + '.h5'\n params_json = resume_path + '.json'\n\n AIparams = dataUtils.loadAIParams(params_json)\n epochs, steps, updateTargetNetwork, explorationRate, epsilon_decay, minibatch_size, learnStart, learningRate, discountFactor, memorySize, network_inputs, network_outputs, network_structure, current_epoch = dataUtils.setAIParams(AIparams)\n explorationRate = 0.01 #Since we dont want any more learning\n\nbrain = AI.DeepQ(network_inputs, network_outputs, memorySize, discountFactor, learningRate, learnStart)\nbrain.initNetworks(network_structure)\n\n\n# create the training variables\nif not AImode == 0:\n brain.loadWeights(weights_path)\n if not AImode == 2:\n epoch = int(current_epoch)\n else:\n epoch = 0\nelse:\n epoch = 0\n\nepisode_steps = 0\nstepCounter = 0\ncumulated_reward = 0\nhighest_reward = 0\ndone = False\nhighest_performance = 200\n\n# Reward Function variablesm\ngoal_Distance = dataUtils.listOfSize(numberOfAgents,10000)\ngoal_ang = dataUtils.listOfSize(numberOfAgents,180)\npreviousVisibility = dataUtils.listOfSize(numberOfAgents,False)\n\n# MultiAgent variables\nobservation = dataUtils.listOfSize(numberOfAgents,0)\nreward = dataUtils.listOfSize(numberOfAgents,0)\naction = dataUtils.listOfSize(numberOfAgents,0)\nlastAction = dataUtils.listOfSize(numberOfAgents,0)\npathdata = dataUtils.listOfSize(numberOfAgents,0)\ncurrentSubgoalIndex = dataUtils.listOfSize(numberOfAgents,-1)\ngoalsHit = dataUtils.listOfSize(numberOfAgents,0)\nlast20 = dataUtils.listOfSize(numberOfAgents,dataUtils.listOfSize(20,0))\n\n# Spawn All Agents and Goals\n# Agent colours\nagentColours = [[255,0,0],[0,255,0],[0,0,255],[255,255,0],[255,0,255]]\npathColours = [[255,128,0],[0,255,128],[128,0,255],[255,255,128],[255,128,255]]\n\nrobots = []\ngoals = []\nsubgoalIndex = []\nfor i in range(numberOfAgents):\n # create agent\n robots.append({'position':[100,100], 'size':25 , 'dir':0, 'linearSpeed': 50, 'angularSpeed': 0.1})\n dispUtils.noCollideSpawn(windowSurface,robots[-1],robots[:-1],200)\n\n # spawn goalpoint\n blacklist = []\n blacklist.extend(robots)\n blacklist.extend(goals)\n goals.append({'position':[100,100], 'size':100})\n dispUtils.noCollideSpawn(windowSurface,goals[-1],blacklist,200)\n\n # If the agent does not have a sight of a wide enough path to walk though to goal, then plan path\n if dispUtils.corridorOfSight(windowSurface,robots[-1],goals[-1],55,65)==False:\n pathdata[i] = planner.planPath(windowSurface,robots[i],goals[i])\n subgoalIndex.append(len(pathdata[i][1])-1)\n else:\n pathdata[i] = 0\n subgoalIndex.append(-1)\n\n# PerformanceCheck variables\nPerformanceCheckEpochCounter = 0\nperformanceReward = 0\ninPerformanceCheck = False\nprePerformanceCheckExplorationRate = 0\ncheckComplete = False\n\n# run the game loop\nwhile True:\n #Fill Screen with background colour\n windowSurface.fill(map_data[1])\n\n # check for the surfaceQUIT event\n for event in pygame.event.get():\n if event.type == QUIT:\n print(highest_reward)\n print(datetime.now() - startTime)\n dataUtils.plotReward(reward_file,'Culmuilated Reward Over Training')\n\n if performaceCheck:\n dataUtils.plotReward(performacneReward_file,'Culmuilated Reward Each Performance Check')\n\n dataUtils.showPlots(False)\n\n if AImode!=3 and stepCounter>10000:\n brain.saveModel(path+str(epoch)+'.h5')\n parameter_keys = ['epochs','steps','updateTargetNetwork','explorationRate','epsilon_decay','minibatch_size','learnStart','learningRate','discountFactor','memorySize','network_inputs','network_outputs','network_structure','current_epoch']\n parameter_values = [epochs, steps, updateTargetNetwork, explorationRate,epsilon_decay, minibatch_size, learnStart, learningRate, discountFactor, memorySize, network_inputs, network_outputs, network_structure, epoch]\n parameter_dictionary = dict(zip(parameter_keys, parameter_values))\n with open(path+str(epoch)+'.json', 'w') as outfile:\n json.dump(parameter_dictionary, outfile)\n\n pygame.quit()\n sys.exit()\n\n #Quit sim if number of epochs is reached in training mode\n if epoch == epochs and AImode != 3:\n print(highest_reward)\n print(datetime.now() - startTime)\n dataUtils.plotReward(reward_file,'Culmuilated Reward Over Training')\n\n if performaceCheck:\n dataUtils.plotReward(performacneReward_file,'Culmuilated Reward Each Performance Check')\n\n dataUtils.showPlots(False)\n #Save the weights at the end of the sim\n brain.saveModel(path+str(epoch)+'.h5')\n parameter_keys = ['epochs','steps','updateTargetNetwork','explorationRate','epsilon_decay','minibatch_size','learnStart','learningRate','discountFactor','memorySize','network_inputs','network_outputs','network_structure','current_epoch']\n parameter_values = [epochs, steps, updateTargetNetwork, explorationRate,epsilon_decay, minibatch_size, learnStart, learningRate, discountFactor, memorySize, network_inputs, network_outputs, network_structure, epoch]\n parameter_dictionary = dict(zip(parameter_keys, parameter_values))\n with open(path+str(epoch)+'.json', 'w') as outfile:\n json.dump(parameter_dictionary, outfile)\n pygame.quit()\n sys.exit()\n\n # Draw all the goals and obstacles\n for g in range(len(goals)):\n dispUtils.drawGoal(windowSurface,goals[g],agentColours[g%len(agentColours)])\n dispUtils.drawObstacles(windowSurface, [0,0,0], obstacles)\n\n dispUtils.drawObstacles(windowSurface, map_data[2], map_data[0])\n\n # Simulate Each Agent\n if sum(goalsHit) < (goalPerEpoch+1)*numberOfAgents:\n for i in range(numberOfAgents):\n if goalsHit[i]1:\n pygame.draw.lines(windowSurface, pathColours[i%len(pathColours)],False, pathdata[i][0])\n\n\n if currentSubgoalIndex[i] == subgoalIndex[i]:\n lastleg = True\n currentTarget = goals[i]\n else:\n lastleg = False\n currentTarget = pathdata[i][1][currentSubgoalIndex[i]+1]\n\n # dispUtils.corridorOfSight(windowSurface,robots[i],currentTarget,54,108)\n\n newObservation = ObsNRewards.getObsevation(windowSurface,laserScanData,robots[i],currentTarget,others,lastleg)\n # print(newObservation)\n reward[i], goal_ang[i], goal_Distance[i] = ObsNRewards.getReward(windowSurface, robots[i], currentTarget, others, newObservation, action[i], goal_ang[i], goal_Distance[i])\n cumulated_reward += reward[i]\n if cumulated_reward > highest_reward:\n highest_reward = cumulated_reward\n\n\n\n # Check if the previous action has lead to an ending state ie collison or goal\n if newObservation[-1] == 1 and lastleg:\n isFinal = True\n print(reward[i])\n\n # add this memory to the AI, this only happens after the AI has made a decision and observed the result\n if stepCounter > 0 and AImode != 3 and not inPerformanceCheck:\n brain.addMemory(numpy.array(observation[i]), action[i], reward[i], numpy.array(newObservation), isFinal)\n\n\n isFinal = False\n\n if i == 0:\n print(str(observation[i]) + 'action:' + str(action[i]) + 'reward:' +str(reward[i]))\n\n\n if episode_steps == steps-1:\n done = True\n\n\n # Begin learning from the collected memories once a sufficient number have been collected\n if stepCounter >= learnStart and stepCounter%minibatch_size == 0 and AImode != 3 and not inPerformanceCheck:\n #print('learning')\n if stepCounter <= updateTargetNetwork:\n brain.learnOnMiniBatch(minibatch_size, False)\n else :\n brain.learnOnMiniBatch(minibatch_size, True)\n\n #AI makes decision\n lastAction[i] = action[i]\n qValues = brain.getQValues(numpy.array(newObservation))\n action[i] = brain.selectAction(qValues, explorationRate)\n last20[i][episode_steps%20] = action[i]\n\n\n if episode_steps>20:\n last20Odd = last20[i][::2]\n if last20Odd[1:] == last20Odd[:-1] and (last20Odd[0]== 1 or last20Odd[0]==2 or last20Odd[0]==3) :\n action[i] = random.randint(0,2)\n last20[i][episode_steps%20]\n print('infinite loop detected')\n if not (inPerformanceCheck or AImode==3):\n explorationRate /= epsilon_decay\n explorationRate = min(1, explorationRate)\n print('Epoch: ' + str(epoch) + ', Exploration Rate: ' + str(explorationRate))\n\n # action[i] = 1\n # print(qValues.flatten())\n # if action[i] == 3:\n # # print(qValues.flatten())\n #\n # action[i] = numpy.argsort(qValues.flatten())[-2]\n # if action[i] == 3:\n # action[i] = random.randint(0,2)\n #\n #\n # if min(laserScanData[3:6])<1.25 and (action[i] == 0 or action[i] == 3):\n # # print(qValues.flatten())\n #\n # action[i] = numpy.argsort(qValues.flatten())[-2]\n # if action[i] == 0 or action[i] == 3:\n # action[i] = random.randint(1,2)\n\n # if min(laserScanData[3:6])<1.25 and (action[i] == 0):\n # # print(qValues.flatten())\n #\n # action[i] = numpy.argsort(qValues.flatten())[-2]\n # if action[i] == 0:\n # action[i] = random.randint(1,3)\n\n # Process action\n if action[i] == 0: # FORWARD\n robots[i]['linearSpeed'] = forawardSpeed['linearSpeed']\n robots[i]['angularSpeed'] = forawardSpeed['angularSpeed']\n elif action[i] == 1: # LEFT\n robots[i]['linearSpeed'] = leftSpeed['linearSpeed']\n robots[i]['angularSpeed'] = leftSpeed['angularSpeed']\n elif action[i] == 2: # RIGHT\n robots[i]['linearSpeed'] = rightSpeed['linearSpeed']\n robots[i]['angularSpeed'] = rightSpeed['angularSpeed']\n elif action[i] == 3: # STOP\n robots[i]['linearSpeed'] = stopSpeed['linearSpeed']\n robots[i]['angularSpeed'] = stopSpeed['angularSpeed']\n # Observations are now old\n observation[i] = newObservation\n\n if goalsHit[i]==goalPerEpoch:\n goalsHit[i]+=1\n\n else:\n dispUtils.drawAgent(windowSurface,agentColours[i%len(agentColours)],robots[i])\n robots[i]['linearSpeed'] = stopSpeed['linearSpeed']\n robots[i]['angularSpeed'] = stopSpeed['angularSpeed']\n else:\n done = True\n\n if done == True:\n if inPerformanceCheck or epoch%saveRate == 0:\n map_data = dispUtils.mapGeneration(windowSurface,map_type)\n windowSurface.fill(map_data[1])\n dispUtils.drawObstacles(windowSurface, [0,0,0], obstacles)\n dispUtils.drawObstacles(windowSurface, map_data[2], map_data[0])\n # If we're spawning boxs then reset that too\n if spawnBox:\n redrawBox()\n\n # This is for when the robots have collided or taken too many steps\n\n if numberOfAgents == 2:\n side = random.randint(0, 1)\n if side == 0:\n desiredPoints = [(WINDOWWIDTH/4, WINDOWHEIGHT/2),(WINDOWWIDTH*3/4, WINDOWHEIGHT/2)]\n else:\n desiredPoints = [(WINDOWWIDTH*3/4, WINDOWHEIGHT/2),(WINDOWWIDTH/4, WINDOWHEIGHT/2)]\n\n for i in range(numberOfAgents):\n # reset start points\n if numberOfAgents == 2:\n dispUtils.noCollideSpawnWithinRadius(windowSurface,robots[i],robots[:i],150,desiredPoints[i],100)\n else:\n dispUtils.noCollideSpawn(windowSurface,robots[i],robots[:i],200)\n\n\n\n\n # reset goals goalpoint\n blacklist = []\n blacklist.extend(robots)\n blacklist.extend(goals[:i])\n\n if numberOfAgents == 2:\n dispUtils.noCollideSpawnWithinRadius(windowSurface,goals[i],[],100,desiredPoints[(i+1)%2],100)\n else:\n dispUtils.noCollideSpawn(windowSurface,goals[i],blacklist,200)\n\n goalsHit[i] = 0\n\n if map_type!= '':\n los_flip = random.uniform(0, 2)\n failcount = 0\n if los_flip>=0.5:\n while dispUtils.corridorOfSight(windowSurface,robots[i],goals[i],54,65) and failcount<1000:\n dispUtils.noCollideSpawn(windowSurface,goals[i],blacklist,200)\n failcount +=1\n\n\n if dispUtils.corridorOfSight(windowSurface,robots[i],goals[i],54,65) ==False:\n pathdata[i] = planner.planPath(windowSurface,robots[i],goals[i])\n subgoalIndex[i] = len(pathdata[i][1])-1\n else:\n pathdata[i] = 0\n subgoalIndex[i] = -1\n currentSubgoalIndex[i] = -1\n # If we're training then save the reward from that epoch\n if AImode != 3 and not inPerformanceCheck:\n dataUtils.save_rewards(reward_file,epoch,cumulated_reward,stepCounter)\n\n # Save the model if we've hit saveRate number of epochs\n if (epoch)%saveRate==0 and epoch>=100 and AImode != 3 and not inPerformanceCheck:\n #save model weights and monitoring data every 100 epochs.\n brain.saveModel(path+str(epoch)+'.h5')\n parameter_keys = ['epochs','steps','updateTargetNetwork','explorationRate','epsilon_decay','minibatch_size','learnStart','learningRate','discountFactor','memorySize','network_inputs','network_outputs','network_structure','current_epoch']\n parameter_values = [epochs, steps, updateTargetNetwork, explorationRate,epsilon_decay, minibatch_size, learnStart, learningRate, discountFactor, memorySize, network_inputs, network_outputs, network_structure, epoch]\n parameter_dictionary = dict(zip(parameter_keys, parameter_values))\n with open(path+str(epoch)+'.json', 'w') as outfile:\n json.dump(parameter_dictionary, outfile)\n\n\n if inPerformanceCheck:\n if PerformanceCheckEpochCounter >= numberOfEpochsToTest-1:\n performanceResult = performanceReward/numberOfEpochsToTest\n print('Performance Check Complete. Reward Obtained: ' + str(performanceResult))\n inPerformanceCheck = False\n dataUtils.save_rewards(performacneReward_file,epoch,performanceResult,stepCounter)\n explorationRate = prePerformanceCheckExplorationRate\n epoch += 1\n checkComplete = True\n if highest_performance < performanceResult:\n highest_performance = performanceResult\n brain.saveModel(path+'max.h5')\n parameter_keys = ['epochs','steps','updateTargetNetwork','explorationRate','epsilon_decay','minibatch_size','learnStart','learningRate','discountFactor','memorySize','network_inputs','network_outputs','network_structure','current_epoch']\n parameter_values = [epochs, steps, updateTargetNetwork, explorationRate,epsilon_decay, minibatch_size, learnStart, learningRate, discountFactor, memorySize, network_inputs, network_outputs, network_structure, epoch]\n parameter_dictionary = dict(zip(parameter_keys, parameter_values))\n with open(path+'max.json', 'w') as outfile:\n json.dump(parameter_dictionary, outfile)\n\n PerformanceCheckEpochCounter += 1\n performanceReward += cumulated_reward\n\n if performaceCheck and not inPerformanceCheck and epoch%testingInterval == 0 and AImode != 3 and epoch>=startTesting:\n print('Starting Performance Check. Epoch: ' + str(epoch))\n inPerformanceCheck = True\n PerformanceCheckEpochCounter = 0\n performanceReward = 0\n prePerformanceCheckExplorationRate = explorationRate\n explorationRate = 0.05\n else:\n if not inPerformanceCheck:\n if checkComplete:\n checkComplete = False\n else:\n epoch += 1\n stepCounter += 1\n\n episode_steps = 0\n done = False\n cumulated_reward = 0\n\n #Decrease exploration\n if explorationRate > 0.05:\n explorationRate *= epsilon_decay\n explorationRate = max(0.05, explorationRate)\n if epoch%200==0:\n print('Epoch: ' + str(epoch) + ', Exploration Rate: ' + str(explorationRate))\n else:\n for i in range(numberOfAgents):\n # Reset that agents goal if they reached it\n if observation[i][-1]==1 and currentSubgoalIndex[i] == subgoalIndex[i] and goalsHit[i]=0.5:\n while dispUtils.corridorOfSight(windowSurface,robots[i],goals[i],54,65) and failcount<1000:\n dispUtils.noCollideSpawn(windowSurface,goals[i],blacklist,200)\n failcount +=1\n\n if dispUtils.corridorOfSight(windowSurface,robots[i],goals[i],54,65) ==False:\n pathdata[i] = planner.planPath(windowSurface,robots[i],goals[i])\n subgoalIndex[i] = len(pathdata[i][1])-1\n else:\n pathdata[i] = 0\n subgoalIndex[i] = -1\n currentSubgoalIndex[i] = -1\n elif observation[i][-1]==1 and currentSubgoalIndex[i] != subgoalIndex[i]:\n currentSubgoalIndex[i] += 1\n # Update agent's position\n dispUtils.updateAgent(robots[i])\n\n if not inPerformanceCheck:\n stepCounter += 1\n\n episode_steps += 1\n\n if stepCounter % updateTargetNetwork == 0 and AImode != 3 and not inPerformanceCheck:\n brain.updateTargetNetwork()\n print (\"Step \" + str(stepCounter) + \": updating target network\")\n\n pygame.display.update()\n mainClock.tick(updatesPerSecond * realTimeFactor)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":26231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"413286501","text":"\"\"\"\r\nRNN Vocal Generation Model\r\n\r\nTIMIT data feeders.\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport random\r\nimport time\r\nimport os\r\nimport glob\r\n\r\n__base = [\r\n ('Local', 'datasets/'), \r\n]\r\n\r\n__TIMIT_file = 'TIMIT/TIMIT_{}.npy'\r\n\r\n__train_mask = lambda s: s.format('train_mask')\r\n__train_up = lambda s: s.format('train_up')\r\n__train8k = lambda s: s.format('train_8k')\r\n__valid_mask = lambda s: s.format('valid_mask')\r\n__valid_up = lambda s: s.format('valid_up')\r\n__valid8k = lambda s: s.format('valid_8k')\r\n__test_mask = lambda s: s.format('test_mask')\r\n__test_up = lambda s: s.format('test_up')\r\n__test8k = lambda s: s.format('test_8k')\r\n\r\ndef find_dataset(filename):\r\n for (k, v) in __base:\r\n tmp_path = os.path.join(v, filename)\r\n if os.path.exists(tmp_path):\r\n return tmp_path\r\n raise Exception('{} NOT FOUND!'.format(filename))\r\n\r\n### Basic utils ###\r\ndef __round_to(x, y):\r\n \"\"\"round x up to the nearest y\"\"\"\r\n return int(np.ceil(x / float(y))) * y\r\n\r\ndef __normalize(data):\r\n \"\"\"To range [0., 1.]\"\"\"\r\n data -= data.min(axis=1)[:, None]\r\n data /= data.max(axis=1)[:, None]\r\n return data\r\n\r\ndef __linear_quantize(data, q_levels):\r\n \"\"\"\r\n floats in (0, 1) to ints in [0, q_levels-1]\r\n scales normalized across axis 1\r\n \"\"\"\r\n # Normalization is on mini-batch not whole file\r\n #eps = numpy.float64(1e-5)\r\n #data -= data.min(axis=1)[:, None]\r\n #data *= ((q_levels - eps) / data.max(axis=1)[:, None])\r\n #data += eps/2\r\n #data = data.astype('int32')\r\n\r\n eps = np.float64(1e-5)\r\n data *= (q_levels - eps)\r\n data += eps/2\r\n data = data.astype('int32')\r\n return data\r\n\r\ndef linear2mu(x, mu=255):\r\n \"\"\"\r\n From Joao\r\n x should be normalized between -1 and 1\r\n Converts an array according to mu-law and discretizes it\r\n\r\n Note:\r\n mu2linear(linear2mu(x)) != x\r\n Because we are compressing to 8 bits here.\r\n They will sound pretty much the same, though.\r\n\r\n :usage:\r\n >>> bitrate, samples = scipy.io.wavfile.read('orig.wav')\r\n >>> norm = __normalize(samples)[None, :] # It takes 2D as inp\r\n >>> mu_encoded = linear2mu(2.*norm-1.) # From [0, 1] to [-1, 1]\r\n >>> print mu_encoded.min(), mu_encoded.max(), mu_encoded.dtype\r\n 0, 255, dtype('int16')\r\n >>> mu_decoded = mu2linear(mu_encoded) # Back to linear\r\n >>> print mu_decoded.min(), mu_decoded.max(), mu_decoded.dtype\r\n -1, 0.9574371, dtype('float32')\r\n \"\"\"\r\n x_mu = np.sign(x) * np.log(1 + mu*np.abs(x))/np.log(1 + mu)\r\n return ((x_mu + 1)/2 * mu).astype('int16')\r\n\r\ndef mu2linear(x, mu=255):\r\n \"\"\"\r\n From Joao with modifications\r\n Converts an integer array from mu to linear\r\n\r\n For important notes and usage see: linear2mu\r\n \"\"\"\r\n mu = float(mu)\r\n x = x.astype('float32')\r\n y = 2. * (x - (mu+1.)/2.) / (mu+1.)\r\n return np.sign(y) * (1./mu) * ((1. + mu)**np.abs(y) - 1.)\r\n\r\ndef __mu_law_quantize(data):\r\n return linear2mu(data)\r\n\r\ndef __batch_quantize(data, q_levels, q_type):\r\n \"\"\"\r\n One of 'linear', 'a-law', 'mu-law' for q_type.\r\n \"\"\"\r\n data = data.astype('float64')\r\n #data = __normalize(data)\r\n if q_type == 'linear':\r\n return __linear_quantize(data, q_levels)\r\n if q_type == 'mu-law':\r\n # from [0, 1] to [-1, 1]\r\n #data = 2.*data-1.\r\n # Automatically quantized to 256 bins.\r\n return __mu_law_quantize(data)\r\n raise NotImplementedError\r\n\r\n__RAND_SEED = 123\r\ndef __fixed_shuffle(inp_list):\r\n if isinstance(inp_list, list):\r\n random.seed(__RAND_SEED)\r\n random.shuffle(inp_list)\r\n return\r\n if isinstance(inp_list, np.ndarray):\r\n np.random.seed(__RAND_SEED)\r\n np.random.shuffle(inp_list)\r\n return\r\n\r\n raise ValueError(\"inp_list is neither a list nor a numpy.ndarray but a \"+type(inp_list))\r\n\r\ndef __make_random_batches(inp_list, batch_size,shuffle=True):\r\n batches = []\r\n for i in xrange(len(inp_list) / batch_size+1):\r\n if i==len(inp_list) / batch_size:\r\n if len(inp_list)%batch_size==0:\r\n break\r\n else:\r\n batches.append(inp_list[i*batch_size:])\r\n else:\r\n batches.append(inp_list[i*batch_size:(i+1)*batch_size])\r\n\r\n if shuffle:\r\n __fixed_shuffle(batches)\r\n return batches\r\n\r\ndef __mask_sort(mask_matrix):\r\n ind=[]\r\n for i in xrange(len(mask_matrix)):\r\n ind.append(len(np.where(mask_matrix[i]==1)[0]))\r\n b=zip(ind,range(len(ind)))\r\n b.sort(key=lambda x:x[0],reverse=True)\r\n index=[x[1] for x in b]\r\n\r\n return index\r\n\r\n### TIMIT DATASET LOADER ###\r\ndef __TIMIT_feed_epoch(files,\r\n mask_files, \r\n shuffle,\r\n is_train,\r\n batch_size,\r\n seq_len,\r\n overlap,\r\n q_levels,\r\n q_zero,\r\n q_type,\r\n real_valued=False):\r\n \"\"\"\r\n Helper function to load blizzard dataset.\r\n Generator that yields training inputs (subbatch, reset). `subbatch` contains\r\n quantized audio data; `reset` is a boolean indicating the start of a new\r\n sequence (i.e. you should reset h0 whenever `reset` is True).\r\n\r\n Feeds subsequences which overlap by a specified amount, so that the model\r\n can always have target for every input in a given subsequence.\r\n\r\n Assumes all flac files have the same length.\r\n\r\n returns: (subbatch, reset)\r\n subbatch.shape: (BATCH_SIZE, SEQ_LEN + OVERLAP)\r\n reset: True or False\r\n \"\"\"\r\n if is_train:\r\n sort_index=__mask_sort(mask_files)\r\n batches_8k = __make_random_batches(files[0][sort_index], batch_size,shuffle)\r\n batches_up = __make_random_batches(files[1][sort_index], batch_size,shuffle)\r\n mask_batches=__make_random_batches(mask_files[sort_index],batch_size,shuffle)\r\n else:\r\n batches_8k = __make_random_batches(files[0], batch_size,shuffle)\r\n batches_up = __make_random_batches(files[1], batch_size,shuffle)\r\n mask_batches=__make_random_batches(mask_files,batch_size,shuffle)\r\n\r\n for index,bch_8k in enumerate(batches_8k):\r\n\r\n batch_num=len(bch_8k)\r\n bch_up=batches_up[index]\r\n mask=mask_batches[index]\r\n mask_sum=np.sum(mask,axis=0)\r\n mask_all0_index=np.where(mask_sum==0)[0]\r\n if len(mask_all0_index!=0):\r\n bch_up=bch_up[:,:-len(mask_all0_index)]\r\n bch_8k=bch_8k[:,:-len(mask_all0_index)]\r\n mask=mask[:,:-len(mask_all0_index)]\r\n\r\n batch_seq_len = len(bch_8k[0]) \r\n batch_seq_len = __round_to(batch_seq_len, seq_len)\r\n\r\n batch_8k = np.zeros(\r\n (batch_num, batch_seq_len),\r\n dtype='float64'\r\n )\r\n batch_up = np.zeros(\r\n (batch_num, batch_seq_len),\r\n dtype='float64'\r\n )\r\n\r\n mask=np.pad(mask,[[0,0],[0,batch_seq_len-mask.shape[1]]],'constant')\r\n for i, data in enumerate(bch_8k):\r\n batch_8k[i, :len(data)] = data\r\n for i, data in enumerate(bch_up):\r\n batch_up[i, :len(data)] = data\r\n\r\n batch_8k_real=np.concatenate([\r\n batch_8k,\r\n np.full((batch_num, overlap), 0, dtype='float32')\r\n ], axis=1)\r\n if not real_valued:\r\n batch_8k = __batch_quantize(batch_8k, q_levels, q_type)\r\n batch_up = __batch_quantize(batch_up, q_levels, q_type)\r\n\r\n batch_8k = np.concatenate([\r\n batch_8k,\r\n np.full((batch_num, overlap), q_zero, dtype='int32')\r\n ], axis=1)\r\n\r\n batch_up = np.concatenate([\r\n batch_up,\r\n np.full((batch_num, overlap), q_zero, dtype='int32')\r\n ], axis=1)\r\n\r\n mask = np.concatenate([\r\n mask,\r\n np.full((batch_num, overlap), 0, dtype='float32')\r\n ], axis=1)\r\n\r\n\r\n for i in xrange(batch_seq_len // seq_len):\r\n reset = np.int32(i==0)\r\n end_flag=np.int32(i==batch_seq_len // seq_len-1)\r\n subbatch_8k_real=batch_8k_real[:, i*seq_len : (i+1)*seq_len+overlap]\r\n subbatch_8k = batch_8k[:, i*seq_len : (i+1)*seq_len+overlap]\r\n subbatch_up = batch_up[:, i*seq_len : (i+1)*seq_len+overlap]\r\n submask = mask[:, i*seq_len : (i+1)*seq_len+overlap]\r\n yield (subbatch_8k, subbatch_up,reset, end_flag,submask,batch_num,subbatch_8k_real)\r\n\r\ndef TIMIT_train_feed_epoch(*args):\r\n \"\"\"\r\n :parameters:\r\n batch_size: int\r\n seq_len:\r\n overlap:\r\n q_levels:\r\n q_zero:\r\n q_type: One the following 'linear', 'a-law', or 'mu-law'\r\n\r\n THE NEW SEG IS:\r\n 20.48hrs 36*256\r\n 3*256\r\n 3*256\r\n\r\n :returns:\r\n A generator yielding (subbatch, reset, submask)\r\n \"\"\"\r\n # Just check if valid/test sets are also available. If not, raise.\r\n find_dataset(__valid_up(__TIMIT_file))\r\n find_dataset(__valid8k(__TIMIT_file))\r\n find_dataset(__valid_mask(__TIMIT_file))\r\n find_dataset(__test_up(__TIMIT_file))\r\n find_dataset(__test8k(__TIMIT_file))\r\n find_dataset(__test_mask(__TIMIT_file))\r\n # Load train set\r\n data_path_8k = find_dataset(__train8k(__TIMIT_file))\r\n data_path_up = find_dataset(__train_up(__TIMIT_file))\r\n data_mask_path=find_dataset(__train_mask(__TIMIT_file))\r\n files=[]\r\n files.append(np.load(data_path_8k))\r\n files.append(np.load(data_path_up))\r\n mask_files=np.load(data_mask_path)\r\n shuffle=True\r\n is_train=True\r\n generator = __TIMIT_feed_epoch(files, mask_files,shuffle,is_train,*args)\r\n return generator\r\n\r\ndef TIMIT_valid_feed_epoch(*args):\r\n \"\"\"\r\n See:\r\n TIMIT_train_feed_epoch\r\n \"\"\"\r\n data_path_8k = find_dataset(__valid8k(__TIMIT_file))\r\n data_path_up = find_dataset(__valid_up(__TIMIT_file))\r\n data_mask_path=find_dataset(__valid_mask(__TIMIT_file))\r\n files=[]\r\n files.append(np.load(data_path_8k))\r\n files.append(np.load(data_path_up))\r\n mask_files=np.load(data_mask_path)\r\n shuffle=True\r\n is_train=False\r\n generator = __TIMIT_feed_epoch(files, mask_files,shuffle,is_train,*args)\r\n return generator\r\n\r\ndef TIMIT_test_feed_epoch(*args):\r\n \"\"\"\r\n See:\r\n TIMIT_train_feed_epoch\r\n \"\"\"\r\n data_path_8k = find_dataset(__test8k(__TIMIT_file))\r\n data_path_up = find_dataset(__test_up(__TIMIT_file))\r\n data_mask_path=find_dataset(__test_mask(__TIMIT_file))\r\n files=[]\r\n files.append(np.load(data_path_8k))\r\n files.append(np.load(data_path_up))\r\n mask_files=np.load(data_mask_path)\r\n shuffle=False\r\n is_train=False\r\n generator = __TIMIT_feed_epoch(files, mask_files,shuffle,is_train,*args)\r\n return generator\r\n","sub_path":"RNN_Waveform_BWE/HRNN_HF/datasets/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":10832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"102293617","text":"from PyQt5 import QtWidgets, QtCore\nimport sip\nimport idaapi\nimport idc\n\nimport classy.util as util\nimport classy.database as database\nimport classy.database_entries as database_entries\nfrom classy.signature_dialog import SignatureDialog\nfrom classy.choose_struct_dialog import ChooseStructDialog\n\n\nclass ClassyGui(idaapi.PluginForm):\n\n def __init__(self, plugin):\n idaapi.PluginForm.__init__(self)\n self.plugin = plugin\n self.parent = None\n self.items_by_class = {}\n\n\n def show(self):\n idaapi.PluginForm.Show(self, 'Classy')\n\n\n def OnCreate(self, form):\n self.parent = self.FormToPyQtWidget(form)\n\n layout = QtWidgets.QVBoxLayout()\n\n # Setup left side\n left_widget = QtWidgets.QWidget()\n left_layout = QtWidgets.QVBoxLayout(left_widget)\n\n splitter = QtWidgets.QSplitter(QtCore.Qt.Horizontal)\n layout.addWidget(splitter)\n\n self.class_tree = QtWidgets.QTreeWidget()\n self.class_tree.header().hide()\n self.class_tree.setContextMenuPolicy(QtCore.Qt.CustomContextMenu)\n self.class_tree.customContextMenuRequested.connect(self.handle_class_tree_context_menu)\n self.class_tree.setSelectionMode(QtWidgets.QAbstractItemView.SingleSelection)\n self.class_tree.itemSelectionChanged.connect(self.handle_class_tree_selection_change)\n left_layout.addWidget(self.class_tree)\n\n button_layout = QtWidgets.QHBoxLayout()\n\n add_button = QtWidgets.QPushButton('Add')\n add_button.clicked.connect(self.add_class)\n button_layout.addWidget(add_button)\n\n remove_button = QtWidgets.QPushButton('Remove')\n remove_button.clicked.connect(self.remove_class)\n button_layout.addWidget(remove_button)\n\n left_layout.addLayout(button_layout)\n\n splitter.addWidget(left_widget)\n\n # Setup right side\n self.class_edit = ClassWidget(self)\n splitter.addWidget(self.class_edit)\n\n splitter.setSizes([100, 100000])\n\n self.parent.setLayout(layout)\n\n self.update_fields()\n\n\n def update_fields(self):\n self.reload_tree()\n self.class_edit.update_fields()\n\n\n def reload_tree(self):\n db = database.get()\n\n self.items_by_class = {}\n self.class_tree.clear()\n for c in db.root_classes:\n self.add_child_class_item(self.class_tree, c)\n\n\n def add_child_class_item(self, parent, c):\n if parent is None:\n parent = self.class_tree\n\n item = QtWidgets.QTreeWidgetItem(parent, [c.name])\n item.setData(0, QtCore.Qt.UserRole, c)\n self.items_by_class[c] = item\n for d in c.derived:\n self.add_child_class_item(item, d)\n return item\n\n\n def add_class(self):\n c = database_entries.Class.s_create()\n if c is None:\n return\n\n if c.base is not None:\n parent_item = self.items_by_class[c.base]\n else:\n parent_item = None\n\n item = self.add_child_class_item(parent_item, c)\n\n self.class_tree.clearSelection()\n self.class_tree.scrollToItem(item)\n item.setSelected(True)\n\n\n def remove_class(self):\n item = self.class_tree.selectedItems()[0] if len(self.class_tree.selectedItems()) else None\n if item is None:\n return\n\n c = item.data(0, QtCore.Qt.UserRole)\n if type(c) != database_entries.Class:\n return\n\n if not util.ask_yes_no('Do you really want to remove the class \"%s\"? All methods and new virtual methods will be unlinked' % c.name, False):\n return\n\n try:\n c.unlink()\n del self.items_by_class[c]\n sip.delete(item)\n idaapi.refresh_idaview_anyway()\n except ValueError as e:\n idaapi.warning(str(e))\n\n\n def update_class(self, c):\n try:\n item = self.items_by_class[c]\n except KeyError:\n return\n\n item.setText(0, c.name)\n\n\n def generate_class_header_to_file(self):\n item = self.class_tree.selectedItems()[0] if len(self.class_tree.selectedItems()) else None\n if item is None:\n return\n\n c = item.data(0, QtCore.Qt.UserRole)\n if type(c) != database_entries.Class:\n return\n\n path = QtWidgets.QFileDialog.getSaveFileName(None,\n 'Export class definition', c.name + '.h',\n 'C++ Header file (*.h);;All Files (*)')\n if not path[0]:\n return\n\n f = open(path[0], 'w')\n f.write(c.generate_cpp_definition())\n f.close()\n\n def generate_class_header_to_clipboard(self):\n item = self.class_tree.selectedItems()[0] if len(self.class_tree.selectedItems()) else None\n if item is None:\n return\n\n c = item.data(0, QtCore.Qt.UserRole)\n if type(c) != database_entries.Class:\n return\n\n QtWidgets.QApplication.clipboard().setText(c.generate_cpp_definition())\n\n\n def handle_class_tree_selection_change(self):\n item = self.class_tree.selectedItems()[0] if len(self.class_tree.selectedItems()) else None\n if item is None:\n self.class_edit.set_edit_class(None)\n else:\n c = item.data(0, QtCore.Qt.UserRole)\n if type(c) == database_entries.Class:\n self.class_edit.set_edit_class(c)\n else:\n self.class_edit.set_edit_class(None)\n\n\n def handle_class_tree_context_menu(self, point):\n item = self.class_tree.itemAt(point)\n\n menu = QtWidgets.QMenu()\n menu.addAction('Add', self.add_class)\n\n if item is not None:\n menu.addAction('Remove', self.remove_class)\n menu.addAction('Generate C++ Header (File)', self.generate_class_header_to_file)\n menu.addAction('Generate C++ Header (Clipboard)', self.generate_class_header_to_clipboard)\n\n menu.exec_(self.class_tree.mapToGlobal(point))\n\n\n\nclass ClassWidget(QtWidgets.QWidget):\n def __init__(self, parent_gui):\n QtWidgets.QWidget.__init__(self)\n\n self.parent_gui = parent_gui\n\n self.edit_class = None\n\n layout = QtWidgets.QGridLayout(self)\n\n self.name = QtWidgets.QLabel()\n layout.addWidget(self.name, 0, 0)\n\n self.set_name = QtWidgets.QPushButton('Set')\n self.set_name.setMaximumWidth(50)\n self.set_name.clicked.connect(self.handle_set_name)\n layout.addWidget(self.set_name, 0, 1)\n\n self.base_class = QtWidgets.QLabel()\n layout.addWidget(self.base_class, 1, 0, 1, 2)\n\n self.derived_classes = QtWidgets.QLabel()\n self.derived_classes.setWordWrap(True)\n layout.addWidget(self.derived_classes, 2, 0, 1, 2)\n\n self.struct = util.ClickableQLabel()\n self.struct.doubleClicked.connect(self.handle_struct_double_clicked)\n layout.addWidget(self.struct, 3, 0)\n\n self.set_struct = QtWidgets.QPushButton('Set')\n self.set_struct.setMaximumWidth(50)\n self.set_struct.clicked.connect(self.handle_set_struct)\n layout.addWidget(self.set_struct, 3, 1)\n\n self.vtable_range = QtWidgets.QLabel()\n layout.addWidget(self.vtable_range, 4, 0)\n\n self.set_vtable_range = QtWidgets.QPushButton('Set')\n self.set_vtable_range.setMaximumWidth(50)\n self.set_vtable_range.clicked.connect(self.handle_set_vtable_range)\n layout.addWidget(self.set_vtable_range, 4, 1)\n\n self.vtable = util.EnterPressQTableWidget()\n self.vtable.setSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)\n self.vtable.setColumnCount(4)\n self.vtable.setHorizontalHeaderLabels(['ID', 'Address', 'Function', 'Type'])\n vtable_header = self.vtable.horizontalHeader()\n vtable_header.setSectionResizeMode(2, QtWidgets.QHeaderView.Stretch)\n self.vtable.verticalHeader().hide()\n self.vtable.setEditTriggers(QtWidgets.QAbstractItemView.NoEditTriggers)\n self.vtable.cellDoubleClicked.connect(self.handle_vtable_interaction)\n self.vtable.cellEnterPressed.connect(self.handle_vtable_interaction)\n layout.addWidget(self.vtable, 5, 0, 1, 2)\n\n self.methods = util.EnterPressQTableWidget()\n self.methods.setSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)\n self.methods.setColumnCount(2)\n self.methods.setHorizontalHeaderLabels(['Address', 'Function'])\n methods_header = self.methods.horizontalHeader()\n methods_header.setSectionResizeMode(1, QtWidgets.QHeaderView.Stretch)\n self.methods.verticalHeader().hide()\n self.methods.setEditTriggers(QtWidgets.QAbstractItemView.NoEditTriggers)\n # self.methods.setSortingEnabled(True) Todo\n self.methods.cellDoubleClicked.connect(self.handle_methods_interaction)\n self.methods.cellEnterPressed.connect(self.handle_methods_interaction)\n layout.addWidget(self.methods, 6, 0, 1, 2)\n\n method_btn_layout = QtWidgets.QHBoxLayout()\n\n self.add_method_btn = QtWidgets.QPushButton('Add')\n self.add_method_btn.setMaximumWidth(50)\n self.add_method_btn.clicked.connect(self.handle_add_method)\n method_btn_layout.addWidget(self.add_method_btn)\n\n self.remove_method_btn = QtWidgets.QPushButton('Remove')\n self.remove_method_btn.setMaximumWidth(50)\n self.remove_method_btn.clicked.connect(self.handle_remove_method)\n method_btn_layout.addWidget(self.remove_method_btn)\n\n method_btn_layout.addItem(QtWidgets.QSpacerItem(0, 0, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum))\n\n layout.addLayout(method_btn_layout, 7, 0, 1, 2)\n\n self.update_fields()\n\n\n def set_edit_class(self, edit_class):\n self.edit_class = edit_class\n self.update_fields()\n\n\n def update_fields(self):\n if self.edit_class is None:\n self.setDisabled(True)\n self.name.setText('Name: -')\n self.base_class.setText('Base class: -')\n self.derived_classes.setText('Derived classes: -')\n self.struct.setText('Struct: -')\n self.vtable_range.setText('VTable: -')\n self.vtable.setRowCount(0)\n self.methods.setRowCount(0)\n\n else:\n self.setEnabled(True)\n self.name.setText('Name: %s' % self.edit_class.name)\n self.base_class.setText('Base class: %s' % (self.edit_class.base.name if self.edit_class.base is not None else 'None'))\n\n derived_classes_txts = []\n for dc in self.edit_class.derived:\n derived_classes_txts.append(dc.name)\n derived_classes_txt = ', '.join(derived_classes_txts)\n if not derived_classes_txt:\n derived_classes_txt = 'None'\n self.derived_classes.setText('Derived classes: %s' % derived_classes_txt)\n\n if self.edit_class.struct_id == idc.BADADDR:\n struct_txt = 'Not set'\n else:\n struct_txt = '%s (%d)' % (idc.get_struc_name(self.edit_class.struct_id), idc.get_struc_idx(self.edit_class.struct_id))\n self.struct.setText('Struct: %s' % struct_txt)\n\n if self.edit_class.vtable_start is None or self.edit_class.vtable_end is None:\n vtable_range_txt = 'Not set'\n else:\n vtable_range_txt = '0x%X - 0x%X' % (self.edit_class.vtable_start, self.edit_class.vtable_end)\n self.vtable_range.setText('VTable: %s' % vtable_range_txt)\n\n self.vtable.setRowCount(len(self.edit_class.vmethods))\n for idx, vm in enumerate(self.edit_class.vmethods):\n self.vtable.setItem(idx, 0, QtWidgets.QTableWidgetItem(str(idx)))\n self.vtable.setItem(idx, 1, QtWidgets.QTableWidgetItem((\"0x%X\" % vm.ea) if vm.ea != idc.BADADDR else '-'))\n self.vtable.setItem(idx, 2, QtWidgets.QTableWidgetItem(vm.get_signature()))\n self.vtable.setItem(idx, 3, QtWidgets.QTableWidgetItem(vm.type_name()))\n\n # This way of doing won't work when allowing sorting\n self.methods.setRowCount(len(self.edit_class.methods))\n for idx, m in enumerate(self.edit_class.methods):\n address_item = QtWidgets.QTableWidgetItem(m.ea)\n address_item.setData(QtCore.Qt.DisplayRole, \"0x%X\" % m.ea)\n address_item.setData(QtCore.Qt.UserRole, m)\n self.methods.setItem(idx, 0, address_item)\n self.methods.setItem(idx, 1, QtWidgets.QTableWidgetItem(m.get_signature()))\n\n\n def handle_set_struct(self):\n if self.edit_class is None:\n return\n\n default_struct_name = idc.get_struc_name(self.edit_class.struct_id) \\\n if self.edit_class.struct_id != idc.BADADDR else \\\n self.edit_class.name\n\n dlg = ChooseStructDialog(default_struct_name, has_none_btn=True)\n if dlg.exec_() != QtWidgets.QDialog.Accepted:\n return\n\n if dlg.struct_id == self.edit_class.struct_id:\n return\n\n db = database.get()\n if dlg.struct_id in db.classes_by_struct_id:\n idaapi.warning('The struct \"%s\" is already linked to the class \"%s\"' %\n (idc.get_struc_name(dlg.struct_id), b.classes_by_struct_id[dlg.struct_id]))\n return\n\n delete_orphaned = False\n if self.edit_class.struct_id != idc.BADADDR:\n delete_orphaned = util.ask_yes_no('Do you want to delete the orphaned class', False)\n\n self.edit_class.set_struct_id(dlg.struct_id, delete_orphaned)\n self.update_fields()\n\n\n def handle_struct_double_clicked(self):\n if self.edit_class is None:\n return\n\n if self.edit_class.struct_id == idc.BADADDR:\n return\n\n idaapi.open_structs_window(self.edit_class.struct_id)\n\n\n def handle_set_name(self):\n if self.edit_class is None:\n return\n\n new_name = idaapi.ask_str(self.edit_class.name, idaapi.HIST_IDENT,'Enter a class name')\n if new_name is None or new_name == self.edit_class.name:\n return\n\n if new_name in database.get().classes_by_name:\n idaapi.warning('That name is already used.')\n return\n\n if not database_entries.Class.s_name_is_valid(new_name):\n idaapi.warning('The class name \"%s\" is invalid.' % new_name)\n return\n\n self.edit_class.rename(new_name)\n self.update_fields()\n self.parent_gui.update_class(self.edit_class)\n\n\n def handle_set_vtable_range(self):\n if self.edit_class is None:\n return\n\n p0 = idaapi.twinpos_t()\n p1 = idaapi.twinpos_t()\n view = idaapi.get_current_viewer()\n\n success = idaapi.read_selection(view, p0, p1)\n\n if not success:\n idaapi.warning('Please, select region in ida dissasembler')\n\n ea0 = p0.place(view).ea\n ea1 = p1.place(view).ea\n\n # Check selection\n if ea0 == idc.BADADDR or ea1 == idc.BADADDR:\n return\n\n if ea0 > ea1:\n return\n\n if ea0 != idc.get_screen_ea() and ea1 != idc.get_screen_ea():\n return\n\n # Warning for large ranges\n if (ea1 - ea0) > 0x1000:\n if not util.ask_yes_no('Warning: The VTable range is longer than 0x1000 bytes. Continue?', False):\n return\n\n try:\n self.edit_class.set_vtable_range(ea0, ea1)\n self.update_fields()\n except ValueError as e:\n idaapi.warning(str(e))\n\n\n def handle_vtable_interaction(self, row, column):\n if self.edit_class is None:\n return\n\n vm = self.edit_class.vmethods[row]\n\n if column == 0: # Go to vtable offset\n idc.jumpto(self.edit_class.vtable_start + row*4)\n elif column == 1: # Go to address\n idc.jumpto(vm.ea)\n elif column == 2: # Edit signature\n dlg = SignatureDialog(vm.return_type, vm.owner.name, vm.name, vm.args, vm.is_const, vm.ctor_type, vm.dtor_type, fixed_owner_type=True)\n if dlg.exec_() != QtWidgets.QDialog.Accepted:\n return\n vm.set_signature(dlg.name, dlg.args, dlg.return_type, dlg.is_const, dlg.ctor_type, dlg.dtor_type)\n self.vtable.setItem(row, 2, QtWidgets.QTableWidgetItem(vm.get_signature()))\n idaapi.refresh_idaview_anyway()\n\n\n def handle_add_method(self):\n db = database.get()\n\n if self.edit_class is None:\n return\n\n sel_ea = idc.get_screen_ea()\n\n if sel_ea == idc.BADADDR:\n return\n\n existing_method = None\n if sel_ea in db.known_methods:\n existing_method = db.known_methods[sel_ea]\n if type(existing_method) != database_entries.Method:\n idaapi.warning(\"Cannot unlink function that is in a VTable\")\n return\n\n name = idc.get_name(sel_ea, 0)\n if name.startswith('_Z'): # Ignore already mangled names\n name = ''\n if not name:\n name = 'sub_%X' % sel_ea\n\n dlg = SignatureDialog(name=name, owner_type=self.edit_class.name, fixed_owner_type=True)\n if dlg.exec_() != QtWidgets.QDialog.Accepted:\n return\n\n if existing_method is not None:\n existing_method.unlink()\n\n method = database_entries.Method(sel_ea, self.edit_class, dlg.name)\n method.set_signature(dlg.name, dlg.args, dlg.return_type, dlg.is_const, dlg.ctor_type, dlg.dtor_type)\n self.edit_class.methods.append(method)\n method.refresh()\n\n self.update_fields()\n\n\n def handle_remove_method(self):\n db = database.get()\n\n if self.edit_class is None:\n return\n\n row_item = self.methods.item(self.methods.currentRow(), 0)\n if row_item is None:\n return\n\n m = row_item.data(QtCore.Qt.UserRole)\n if type(m) != database_entries.Method or m not in self.edit_class.methods:\n return\n\n m.unlink()\n\n self.update_fields()\n\n\n\n def handle_methods_interaction(self, row, column):\n if self.edit_class is None:\n return\n\n m = self.methods.item(row, 0).data(QtCore.Qt.UserRole)\n if type(m) != database_entries.Method or m not in self.edit_class.methods:\n return\n\n elif column == 0: # Go to address\n idc.jumpto(m.ea)\n elif column == 1: # Edit signature\n dlg = SignatureDialog(m.return_type, m.owner.name, m.name, m.args, m.is_const, m.ctor_type, m.dtor_type, fixed_owner_type=True)\n if dlg.exec_() != QtWidgets.QDialog.Accepted:\n return\n m.set_signature(dlg.name, dlg.args, dlg.return_type, dlg.is_const, dlg.ctor_type, dlg.dtor_type)\n self.methods.setItem(row, 1, QtWidgets.QTableWidgetItem(m.get_signature()))\n idaapi.refresh_idaview_anyway()\n\n","sub_path":"classy/gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":19136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"275024355","text":"import os\nfrom fltk import Fl\nfrom skate_cma.skate_env2 import SkateDartEnv\nfrom PyCommon.modules.GUI import hpSimpleViewer as hsv\nfrom PyCommon.modules.Renderer import ysRenderer as yr\nimport numpy as np\nimport pickle\nimport math\n\nfrom scipy.spatial.transform import Rotation\n\nimport pydart2 as pydart\nfrom PyCommon.modules.Math import mmMath as mm\n\nfrom SkateUtils.DartMotionEdit import skelqs2bvh\n\ndef axis2Euler(vec):\n r = Rotation.from_rotvec(vec).as_dcm()\n r_after = np.dot(np.dot(mm.rotY(-math.pi/2.), r), mm.rotY(-math.pi/2.).T)\n return Rotation.from_dcm(r_after).as_euler('ZXY', True)\n # return Rotation.from_rotvec(vec).as_euler('ZXY', True)\n\ndef main():\n MOTION_ONLY = False\n np.set_printoptions(precision=5)\n\n pydart.init()\n\n # env_name = 'hmr_skating_basic4'\n # env_name = 'jump0507_2'\n env_name = 'hmr_skating_crossover_back3'\n max_time = 2.4\n\n with open(env_name + '.skkey', 'rb') as skkey_file:\n skkey_states = pickle.load(skkey_file)\n\n # for bvh file\n bvh_qs = []\n bvh_file_name = 'crossover_back.bvh'\n\n angles = []\n count = 0\n state = skkey_states[0]\n state_duration = []\n while count < int(max_time * 10.):\n state_count = 0\n for _ in range(int(state.dt*10.)):\n angles.append(state.angles[6:])\n state_count += 1\n count += 1\n if count == int(max_time * 10.):\n break\n state_duration.append(state_count)\n state = state.get_next()\n angles.append(angles[-1])\n\n env = SkateDartEnv(env_name)\n\n q = [skkey_states[0].angles.copy()]\n dq = [np.zeros_like(q[0])]\n # dq[0][3] = 1.\n\n x0t = np.zeros_like(q[0][6:])\n frame_offset = [0]\n cumulative_frame_duration = [sum(state_duration[:i]*3) for i in range(len(state_duration)+1)]\n\n x = [x0t]\n # file_path = 'hmr_skating_crossover_back_model_201906051706/xbest.skcma'\n file_path = 'hmr_skating_crossover_back3_model_201906071952/xbest.skcma'\n with open(file_path, 'r') as f:\n lines = f.read().splitlines()\n state_list_in_file = list(map(int, [line.split()[0] for line in lines]))\n for i in range(len(state_duration)):\n if i in state_list_in_file:\n state_index_in_file = state_list_in_file.index(i)\n x_state = np.asarray(list(map(float, lines[state_index_in_file].split()[1:])))\n x.extend(np.split(x_state, state_duration[i]))\n else:\n x.extend([np.zeros_like(x0t) for _ in range(state_duration[i])])\n\n # viewer settings\n rd_contact_positions = [None]\n rd_contact_forces = [None]\n rd_COM = [None]\n dart_world = env.world\n viewer_w, viewer_h = 1280, 720\n viewer = hsv.hpSimpleViewer(rect=(0, 0, viewer_w + 300, 1 + viewer_h + 55), viewForceWnd=False)\n viewer.doc.addRenderer('MotionModel', yr.DartRenderer(env.ref_world, (194,207,245), yr.POLYGON_FILL))\n if not MOTION_ONLY:\n viewer.doc.addRenderer('controlModel', yr.DartRenderer(dart_world, (255,255,255), yr.POLYGON_FILL))\n viewer.doc.addRenderer('contact', yr.VectorsRenderer(rd_contact_forces, rd_contact_positions, (255,0,0)))\n viewer.doc.addRenderer('COM projection', yr.PointsRenderer(rd_COM))\n\n def simulateCallback(frame):\n if 0 in [ii - frame for ii in cumulative_frame_duration]:\n index = [ii - frame for ii in cumulative_frame_duration].index(0)\n duration = state_duration[index]\n env.update_ref_states(\n x[sum(state_duration[:index]):sum(state_duration[:index+1])+1],\n angles[sum(state_duration[:index]):sum(state_duration[:index+1])+1],\n q[0], dq[0], duration)\n frame_offset[0] = cumulative_frame_duration[index]\n\n env.step((frame-frame_offset[0])/3)\n q[0] = np.asarray(env.skel.q)\n dq[0] = np.asarray(env.skel.dq)\n\n bvh_qs.append(env.skel.q)\n\n # contact rendering\n contacts = env.world.collision_result.contacts\n del rd_contact_forces[:]\n del rd_contact_positions[:]\n for contact in contacts:\n if contact.skel_id1 == 0:\n rd_contact_forces.append(-contact.f/1000.)\n else:\n rd_contact_forces.append(contact.f/1000.)\n rd_contact_positions.append(contact.p)\n\n del rd_COM[:]\n com = env.skel.com()\n com[1] = 0.\n rd_COM.append(com)\n # print(env.skel.com())\n\n viewer.setSimulateCallback(simulateCallback)\n viewer.setMaxFrame(cumulative_frame_duration[-1]-1)\n viewer.startTimer(1./30.)\n viewer.show()\n\n Fl.run()\n\n # skelqs2bvh(bvh_file_name, env.skel, bvh_qs)\n\nif __name__ == '__main__':\n main()\n","sub_path":"skate_cma/render_crossover_new_backward.py","file_name":"render_crossover_new_backward.py","file_ext":"py","file_size_in_byte":4724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"631549579","text":"from sklearn import datasets \n\n#importing k neigbhor classifier \nfrom sklearn.neighbors import KNeighborsClassifier\n\n\n#loading data\niris = datasets.load_iris()\n\n# print(iris.DESCR) \nfeatures = iris.data #data me features hote hain\nlabels = iris.target #target me labels hote\n\nprint(features[0], labels[0])\n# [5.1 3.5 1.4 0.2] 0 is me 3 classes hoti 0 1 aur 2 -0 Iris-Setosa -1 Iris-Versicolour -2 Iris-Virginica\n \nclf = KNeighborsClassifier() #we create classifier here\n#every classifier has a fit function and predict function\n \nclf.fit(features,labels) #it fit himself in old data\n\npreds = clf.predict([[22,1,1,1]]) # is me hum 2d array me data dete aur wo bata deta\nprint(preds)\n\n","sub_path":"sklearn/k_nearest_classification.py","file_name":"k_nearest_classification.py","file_ext":"py","file_size_in_byte":700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"426683789","text":"from flask import *\nimport json\nimport numpy as np\nimport hashlib \nimport tensorflow as tf\nfrom tensorflow import keras \nimport numpy as np \nimport cv2\nimport matplotlib.pyplot as plot\nfrom flask_mongoengine import MongoEngine\nfrom math import *\nimport random\nimport string\nfrom datetime import datetime\nfrom bson.binary import Binary\nimport pickle\nfrom PIL import Image\nimport io\n\nmongodb_pass = 'gobears'\ndb_name = 'Main'\n\napp = Flask(__name__)\ndb_name = \"Main\"\nDB_URI = \"mongodb+srv://s3kim2018:{}@cluster0.nw98u.mongodb.net/{}?retryWrites=true&w=majority\".format(mongodb_pass, db_name)\napp.config[\"MONGODB_HOST\"] = DB_URI\ndb = MongoEngine()\ndb.init_app(app)\n\n\nclass User(db.Document):\n apikey = db.StringField()\n def to_json(self): \n return {\n \"apikey\": self.apikey\n } \n\nclass Model(db.Document):\n modelid = db.StringField()\n apikey = db.StringField()\n modelname = db.StringField()\n type = db.StringField()\n description = db.StringField()\n date = db.DateTimeField()\n model = db.StringField()\n def to_json(self):\n return {\n \"modelid\": self.modelid,\n \"apikey\": self.apikey,\n \"modelname\": self.modelname,\n \"type\": self.type,\n \"description\": self.description,\n \"date\": self.date,\n \"model\": self.model\n }\n\nclass Dataset(db.Document):\n datasetid = db.StringField()\n modelid = db.StringField()\n apikey = db.StringField()\n datasetname = db.StringField()\n datatype = db.StringField()\n size = db.StringField()\n imgw = db.StringField()\n imgh = db.StringField()\n def to_json(self):\n return {\n \"datasetid\": self.datasetid,\n \"modelid\": self.modelid,\n \"apikey\": self.apikey, \n \"datasetname\": self.datasetname,\n \"datatype\": self.datatype,\n \"size\": self.size,\n \"imgw\": self.imgw,\n \"imgh\": self.imgh\n }\n\nclass Data(db.Document): \n apikey = db.StringField()\n datasetid = db.StringField()\n node = db.StringField()\n binarynode = db.FileField()\n classification = db.StringField()\n\n\n\n\n# class testset(db.Document):\n\n@app.route('/register', methods = ['GET'])\ndef register(): #Returns an length 6 api key \n key = ''.join(random.choices(string.ascii_uppercase + string.digits, k=6))\n hashkey = str(hashlib.sha1(key.encode('utf-8')).hexdigest())\n newuser = User(apikey = hashkey)\n newuser.save()\n return jsonify(key)\n\n@app.route('/createmodel', methods = ['PUT'])\ndef createmodel():\n apikey = request.headers.get('apikey')\n compkey = str(hashlib.sha1(apikey.encode('utf-8')).hexdigest())\n name = str(request.args.get('name'))\n modeltype = str(request.args.get('model'))\n description = str(request.args.get('desc'))\n id = str(request.args.get('id'))\n if Model.objects(modelid = id) or Model.objects(modelname = name):\n return make_response(\"Model ID or Model Name already exists\", 409)\n if User.objects(apikey = compkey):\n network = keras.Sequential().to_json()\n now = datetime.now()\n mod = Model(modelid = id, apikey = compkey, modelname = name, type = modeltype, description = description, date = now, model = network)\n mod.save()\n return make_response(\"success\", 201)\n else: \n return make_response(\"Invalid API-KEY\", 401)\n\n@app.route('/getmodel', methods = [\"GET\"])\ndef getmodel(): \n apikey = request.headers.get('apikey')\n id = str(request.args.get('id'))\n compkey = str(hashlib.sha1(apikey.encode('utf-8')).hexdigest())\n\n if User.objects(apikey = compkey):\n if Model.objects(modelid = id, apikey = compkey):\n val = Model.objects(modelid = id, apikey = compkey).first()\n lst = {\"ModelID\":val.modelid, \"ModelName\":val.modelname, \"ModelType\":val.type, \"ModelDesc\":val.description, \"ModelDate\":val.date}\n return make_response(jsonify(lst), 200)\n else:\n return make_response(\"Cannot find model with name: \" + str(name), 400)\n else:\n return make_response(\"Invalid API-KEY\", 401)\n\n@app.route('/getallmodels', methods = [\"GET\"])\ndef getall():\n apikey = request.headers.get('apikey')\n compkey = str(hashlib.sha1(apikey.encode('utf-8')).hexdigest())\n if User.objects(apikey = compkey):\n retset = []\n if Model.objects(apikey = compkey):\n val = Model.objects(apikey = compkey)\n for v in val.all():\n lst = {\"ModelID\":v.modelid, \"ModelName\":v.modelname, \"ModelType\":v.type, \"ModelDesc\":v.description, \"ModelDate\":v.date}\n retset.append(lst)\n return make_response(jsonify(retset), 200)\n else:\n return make_response(jsonify([]), 400)\n else: \n return make_response(\"Invalid API-KEY\", 401)\n\n@app.route('/editmodel', methods = [\"POST\"])\ndef editmodel(): \n apikey = request.headers.get('apikey')\n compkey = str(hashlib.sha1(apikey.encode('utf-8')).hexdigest())\n name = str(request.args.get('name'))\n description = str(request.args.get('desc'))\n id = str(request.args.get('id'))\n if User.objects(apikey = compkey):\n if Model.objects(apikey = compkey, modelid = id):\n val = Model.objects(modelid = id, apikey = compkey).first()\n val.modelname = name\n val.description = description\n val.save()\n return make_response(\"Success\", 202)\n else:\n return make_response(\"Cannot find model associated with model id: \" + str(id), 400)\n else: \n return make_response(\"Invalid API-KEY\", 401)\n\n@app.route('/appenddenselayer', methods = [\"POST\"])\ndef appenddenselayer():\n apikey = request.headers.get('apikey') \n return make_response(\"Success\", 202)\n\n\n@app.route('/deletemodel', methods = [\"DELETE\"])\ndef deletemodel(): \n apikey = request.headers.get('apikey')\n id = str(request.args.get('id'))\n compkey = str(hashlib.sha1(apikey.encode('utf-8')).hexdigest())\n if User.objects(apikey = compkey):\n if Model.objects(apikey = compkey, modelid = id):\n val = Model.objects(modelid = id, apikey = compkey).first()\n val.delete()\n return make_response(\"Success\", 202)\n else:\n return make_response(\"Cannot find model associated with model id: \" + str(id), 400)\n else: \n return make_response(\"Invalid API-KEY\", 401)\n\n\n@app.route('/createdataset', methods = [\"PUT\"])\ndef createdataset():\n apikey = request.headers.get('apikey')\n compkey = str(hashlib.sha1(apikey.encode('utf-8')).hexdigest())\n if User.objects(apikey = compkey):\n datasetid = str(request.args.get('datasetid'))\n modelid = str(request.args.get('modelid'))\n datasetname = str(request.args.get('datasetname'))\n datatype = str(request.args.get('datatype'))\n if datatype == 'img':\n width = str(request.args.get('imgw'))\n height = str(request.args.get('imgh'))\n if not Model.objects(modelid = modelid, apikey = compkey):\n return make_response(\"Model ID associated with APIkey not found\", 404)\n if Dataset.objects(datasetname = datasetname) or Dataset.objects(datasetid = datasetid):\n return make_response(\"Dataset ID or Dataset Name already exists\", 409)\n if datatype != 'img':\n val = Dataset(datasetid = datasetid, modelid = modelid, apikey = compkey, datasetname = datasetname, datatype = datatype, size = '0')\n val.save()\n return make_response(\"Success\", 201)\n else:\n val = Dataset(datasetid = datasetid, modelid = modelid, apikey = compkey, datasetname = datasetname, datatype = datatype, size = '0', imgw = width, imgh = height)\n val.save()\n return make_response(\"Success\", 201)\n else:\n return make_response(\"Invalid API-KEY\", 401)\n\n@app.route('/adddata', methods = [\"PUT\"])\ndef adddata(): \n apikey = request.headers.get('apikey')\n compkey = str(hashlib.sha1(apikey.encode('utf-8')).hexdigest())\n if User.objects(apikey = compkey):\n datasetid = str(request.args.get('datasetid'))\n classif = str(request.args.get('classification'))\n if (Dataset.objects(apikey = compkey, datasetid = datasetid)):\n val = Dataset.objects(apikey = compkey, datasetid = datasetid).first() \n datatype = val.datatype\n if datatype == \"int\" or datatype == \"str\":\n data = str(request.args.get('data'))\n if data == 'None' or classif == 'None':\n return make_response(\"Data or Classification not found\", 404)\n val = Data(apikey = compkey, datasetid = datasetid, node = data, classification = classif)\n return make_response(\"Success\", 201)\n else:\n binary = request.get_data()\n print(classif)\n if len(binary) < 3 or classif == 'None':\n return make_response(\"Image or Classification not found\", 404)\n else:\n val = Data(apikey = compkey, datasetid = datasetid, binarynode = binary, classification = classif)\n val.save()\n # img = Image.open(io.BytesIO(binary))\n # img_np = np.array(img)\n return make_response(\"Success\", 201)\n else:\n return make_response(\"Dataset associated with the datasetid not found\", 404)\n else:\n return make_response(\"Invalid API-KEY\", 401)\n\n\n\n \n\n\n\n\nif __name__ == '__main__':\n app.run(debug = True)","sub_path":"API/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":9595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"84808357","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Dec 18 14:10:46 2019\n\n@author: Matthaeus Hilpold\n\"\"\"\n\nimport numpy as np\nfrom mne.filter import filter_data\n\nclass Preproc :\n \n def __init__(self, bands=None, classes=2, fs=100, windowlength=3.5):\n \"\"\"\n :param bands : dict. one or multiple frequency bands\n :param classes : int. number of classes that will be evaluated. Default 2 for binary classification. Multiclass will be evaluated as one-versus-rest.\n :param fs : int. sampling frequency\n :param windowlength : float. length of a window in seconds. \n \n :attribute epoched_data_ : numpy array of epoched data. Shape: bands * epochs *channels * samples\n \"\"\"\n\n self.classes = classes\n self.fs = fs\n self.windowlength = windowlength\n \n if bands == None :\n self.bands = {'sub-band' : (7, 30)}\n elif bands == 'all' :\n self.bands = {'delta': (1, 4), 'theta': (4, 8), 'alpha': (8, 12), 'beta': (12, 30), 'gamma': (30, 45)}\n else:\n self.bands = bands\n \n def center_data(self, raw_data) :\n \n #center data. remove mean from each channel.\n raw_data = np.array([(raw_data.T[channel] - np.mean(raw_data.T[channel])) for channel in range(raw_data.shape[0])])\n print(\"Centering complete. Centered data shape: \" + str(raw_data.shape))\n\n def bpfilter_data(self, raw_data) :\n \n if len(self.bands.items()) > 1 :\n #band-pass filter raw data\n raw_data_filtered = np.zeros((len(self.bands.items()), raw_data.shape[0], raw_data.shape[1]))\n \n for channel in range(raw_data.shape[0]) :\n i = 0\n for name, band in self.bands.items() :\n raw_data_filtered[i, channel, :] = filter_data(raw_data[channel], self.fs, band[0], band[1], copy=True,verbose=0)\n i += 1\n raw_data = raw_data_filtered\n print(\"Bandpass filtering complete. Bandpass filtered data shape: \" + str(raw_data.shape))\n \n else :\n for key, value in self.bands.items() :\n raw_data = np.array([filter_data(raw_data[channel], self.fs, value[0], value[1], copy=True, verbose=0) for channel in range(raw_data.shape[0])])\n print(\"Bandpass filtering complete. Bandpass filtered data shape: \" + str(raw_data.shape))\n \n def epoch(self, raw_data, label_pos) :\n \n #extract epochs \n samples_per_epoch = int(self.fs*self.windowlength) #number of samples in a window\n \n if len(self.bands) > 1 :\n \n self.epoched_data_ = np.zeros((len(self.bands.items()), len(label_pos), raw_data.shape[0], samples_per_epoch)) #filter-bank time domain signal with epochs\n \n for epoch in range(len(label_pos)) :\n self.epoched_data_[:, epoch, :, :] = raw_data[:,:,label_pos[epoch]:(label_pos[epoch]+samples_per_epoch)]\n print(\"Epoching complete. Epoched data shape: \" + str(self.epoched_data_.shape))\n \n else :\n self.epoched_data_ = np.zeros((len(label_pos), raw_data.shape[0], samples_per_epoch)) #time domain signal with epochs for a single band.\n \n for epoch in range(len(label_pos)) :\n self.epoched_data_[epoch, :, :] = raw_data[:,label_pos[epoch]:(label_pos[epoch]+samples_per_epoch)]\n print(\"Epoching complete. Epoched data shape: \" + str(self.epoched_data_.shape))\n \n def preproc(self, raw_data, label_pos) :\n \n raw_data = raw_data.astype(float) #filter_data requires float64 unfortunately, therefore int16 needs to be cast to f64\n \n #center data. remove mean from each channel.\n self.center_data(raw_data) # maybe sklearn.preprocessing.scale better? \n \n #band-pass filter raw data\n self.bpfilter_data(raw_data)\n \n #extract epochs \n self.epoch(raw_data, label_pos)\n \n print(\"Pre-processing complete. Data shape: \" + str(self.epoched_data_.shape))\n \n return self.epoched_data_\n\n","sub_path":"fbcsp/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":4206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"132214912","text":"import svgwrite\n\nDEFAULTS = {\"stroke_width\" : \"3\", \"stroke\" : \"black\",\n \"fill\" : \"white\", \"fill_opacity\" : \"1\",\n \"font_size\" : \"36pt\"}\n\nclass SVGEngine():\n def __init__(self, width, height, filename = None):\n self.size = (width,height)\n self.filename = filename\n self.svg_doc = svgwrite.Drawing(filename,self.size)\n\n def __str__(self):\n return self.svg_doc.tostring()\n\n def save(self, filename):\n self.svg_doc.save()\n\n def getSVG(self):\n return self.svg_doc\n\n #SHAPE CONSTRUCTION\n def _add_defaults(self, **kwargs):\n #adds design defaults to kwargs of draw methods when not specified\n for kwarg, default in DEFAULTS.items():\n if kwarg not in kwargs:\n kwargs[kwarg] = default\n return kwargs\n\n def draw_circle(self, center, radius = 50, **kwargs):\n kwargs = self._add_defaults(**kwargs)\n circle = self.svg_doc.circle(center, r = radius, **kwargs)\n self.svg_doc.add(circle)\n\n def draw_arc(self, start, finish, radius = 50, **kwargs):\n kwargs = self._add_defaults(**kwargs)\n arc = self.svg_doc.path(**kwargs)\n arc.push(\"M%f %f\" % start)\n arc.push(\"A%f %f 0 0 0 %f %f\" % (radius, radius, *finish))\n self.svg_doc.add(arc)\n\n def draw_wedge(self, apex, start, finish, radius = 50, **kwargs):\n kwargs = self._add_defaults(**kwargs)\n wedge = self.svg_doc.path(**kwargs)\n wedge.push(\"M%f %f\" % apex)\n wedge.push(\"L%f %f\" % start)\n wedge.push(\"A%f %f 0 0 0 %f %f\" % (radius, radius, *finish))\n wedge.push(\"L%f %f\" % apex)\n self.svg_doc.add(wedge)\n\n def draw_line(self, start, end, **kwargs):\n kwargs = self._add_defaults(**kwargs)\n line = self.svg_doc.line(start, end, **kwargs)\n self.svg_doc.add(line)\n\n def draw_rect(self, left_upper_corner, width=50, height=50, **kwargs):\n kwargs = self._add_defaults(**kwargs)\n rect = self.svg_doc.rect(insert=left_upper_corner, size=(width,height), **kwargs)\n self.svg_doc.add(rect)\n\n def draw_polygon(self, points, **kwargs):\n kwargs = self._add_defaults(**kwargs)\n polygon = self.svg_doc.path(**kwargs)\n polygon.push(\"M%f %f\" % tuple(points[0]))\n for i, p in enumerate(points):\n polygon.push(\"L%f %f\" % tuple(p))\n polygon.push(\"L%f %f\" % tuple(points[0]))\n self.svg_doc.add(polygon)\n\n def draw_rect_center(self, center, width=50, height=50, **kwargs):\n left_upper_x = center[0] - width/2\n left_upper_y = center[1] - height/2\n corner = (left_upper_x, left_upper_y)\n rect = self.draw_rect(self, corner, width, height, **kwargs)\n self.svg_doc.add(rect)\n\n def draw_rounded_rect(self, left_upper_corner, width=50, height=50, rx=5, ry=5, **kwargs):\n kwargs = self._add_defaults(**kwargs)\n rect = self.svg_doc.rect(insert=left_upper_corner,\n size=(width,height),\n rx=rx,\n ry=ry,\n **kwargs)\n self.svg_doc.add(rect)\n\n def draw_ellipse(self, center, rx=75, ry=50, **kwargs):\n kwargs = self._add_defaults(**kwargs)\n ellipse = self.svg_doc.ellipse(center, r=(rx,ry), **kwargs)\n self.svg_doc.add(ellipse)\n\n def draw_text_default(self, text, left_lower_corner, **kwargs):\n \"\"\"text defined by upper left corner point\"\"\"\n\n kwargs = self._add_defaults(**kwargs)\n text = self.svg_doc.text(text, left_lower_corner, **kwargs)\n self.svg_doc.add(text)\n\n def draw_text_center(self, text, center, **kwargs):\n \"\"\"text defined by center point\"\"\"\n\n kwargs = self._add_defaults(**kwargs)\n text = self.svg_doc.text(text, center, text_anchor=\"middle\", dominant_baseline=\"central\", **kwargs)\n #note: some image viewers don't recognize the dominant_baseline attribute dominant_baseline=\"central\",\n self.svg_doc.add(text)\n\n def draw_arrow(self, start, end, **kwargs):\n \"\"\" See http://vanseodesign.com/web-design/svg-markers/ for more info on drawing arrowheads\"\"\"\n\n kwargs = self._add_defaults(**kwargs)\n arrow_marker = self.svg_doc.marker(id=\"arrow\", insert=(1,3), size=(9,6),\n orient=\"auto\", markerUnits=\"strokeWidth\", viewBox=\"0 0 10 10\")\n arrow_marker.add(self.svg_doc.path(d=\"M0,0 L0,6 L9,3 z\", fill=kwargs[\"stroke\"]))\n self.svg_doc.add(arrow_marker)\n arrow = self.svg_doc.line(start, end, marker_end=arrow_marker.get_funciri(), **kwargs)\n self.svg_doc.add(arrow)\n\n def draw_pointer(self, start, end, **kwargs):\n #TODO: implement pointer drawing\n self.draw_arrow(start, end, **kwargs)\n#TODO: pointer start dots, regular polygons, text along a line\n\nif __name__ == '__main__':\n textstyle = {'fill': 'black', 'stroke_width': 0, 'font_size': '24pt'}\n s = SVGEngine(300, 300)\n s.draw_line((10,10), (200,250), **{'class':'theline'})\n s.draw_circle((160, 118), 70)\n s.draw_text_center(\"It's text!\", (160,125), **textstyle)\n print(s)\n","sub_path":"archive/svghelper.py","file_name":"svghelper.py","file_ext":"py","file_size_in_byte":5188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"204150794","text":"def solve(graph_grid):\n n = len(graph_grid)\n if n < 2: return []\n # sums is the key of this algorithm\n sums = [0] * n\n for i in xrange(n):\n for j in xrange(n):\n if i != j:\n sums[i] -= graph_grid[i][j]\n sums[j] += graph_grid[i][j]\n sums = [(value,i) for i, value in enumerate(sums) if value != 0]\n if len(sums) == 0: return []\n sums.sort()\n # sums.p()\n first_v, first_i = sums.pop(0)\n last_v, last_i = sums.pop()\n results = []\n while len(sums) > 0 or last_v != 0:\n # (first_v, first_i, last_v, last_i).p()\n cur_sum = first_v + last_v\n if cur_sum > 0:\n # (first_i, last_i, -first_v).p()\n results += (first_i, last_i, -first_v),\n first_v, first_i = sums.pop(0)\n last_v = cur_sum\n elif cur_sum < 0:\n # (first_i, last_i, last_v).p()\n results += (first_i, last_i, last_v),\n last_v, last_i = sums.pop()\n first_v = cur_sum\n else:\n # (first_i, last_i, last_v).p()\n results += (first_i, last_i, last_v),\n if len(sums) == 0: break\n first_v, first_i = sums.pop(0)\n last_v, last_i = sums.pop()\n return results\n\nif __name__ == '__main__':\n from minitest import *\n\n with test(solve):\n graph_grid = [\n [0, 1000, 2000],\n [0, 0, 5000],\n [0, 0, 0]\n ]\n solve(graph_grid).must_equal([(1, 2, 4000), (0, 2, 3000)])\n graph_grid = [\n [0, 100, 200, 0, 0, 0],\n [0, 0, 500, 50, 0, 0],\n [0, 0, 0, 0, 650, 0],\n [0, 50, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 150],\n [0, 0, 0, 0, 0, 0],\n ]\n solve(graph_grid).must_equal(\n [(1, 4, 400), (0, 4, 100), (0, 5, 150), (0, 2, 50)])\n\n\n\n","sub_path":"python/leetcode/algorithm/greedy/geeksforgeeks/top_16_Minimize_Cash_Flow.py","file_name":"top_16_Minimize_Cash_Flow.py","file_ext":"py","file_size_in_byte":1867,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"285241082","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jun 21 22:57:29 2016\n\n@author: Michael\n\"\"\"\n\nfrom sklearn.preprocessing import OneHotEncoder\nimport numpy as np\n\ndef cost(all_thetas, weights, X, y, lamb):\n thetas = unpack_thetas(all_thetas, weights)\n \n # add column of 1's\n X = X/255\n a1 = np.insert(X, 0, 1, 1)\n \n # create a binary index matrix of y data and initialize activation layers\n encoder = OneHotEncoder(sparse=False)\n y_matrix = encoder.fit_transform(y.T)\n act_layers = activation_layers(a1, thetas)\n \n # cost function created in seperate parts\n first = np.multiply(-y_matrix, np.log(act_layers[-1]))\n second = np.multiply(1 - y_matrix, np.log(1 - act_layers[-1]))\n \n # regularization\n reg_1 = lamb/(2 * len(X))\n reg_2 = 0\n for i in range(len(thetas)):\n reg_2 += np.power(thetas[i][...,1:], 2).sum()\n \n J = 1/len(X) * (first - second).sum() + (reg_1 * reg_2)\n print('Current Cost')\n print(J)\n print('*' * 20)\n return J\n\n\ndef gradient(all_thetas, weights, X, y, lamb):\n thetas = unpack_thetas(all_thetas, weights)\n # add column of 1's\n X = X/255\n a1 = np.insert(X, 0, 1, 1)\n \n # create a binary index matrix of y data and activation layers\n encoder = OneHotEncoder(sparse=False)\n y_matrix = encoder.fit_transform(y.T) \n act_layers = activation_layers(a1, thetas)\n \n \n #slice out first column in all thetas except theta1 \n theta_delta = []\n for i in range(len(thetas)):\n theta_delta.append(thetas[i][:, 1:])\n \n d = []\n Delta = []\n theta_grad = []\n for i in range(len(thetas)):\n if i == 0:\n d.insert(0, act_layers[-(i+1)] - y_matrix) #Work backwards through act_layers\n \n else:\n d.insert(0, np.multiply(d[0] * theta_delta[-i],np.multiply(\n act_layers[-(i+1)][:, 1:], (1-act_layers[-(i+1)][:, 1:]))))\n\n # Create Deltas\n Delta.insert(0, d[0].T * act_layers[-(i+2)])\n \n #Create Theta_grad\n theta_grad.insert(0, Delta[0] / len(y_matrix))\n \n # place a column of 0's in the first column of all thetas\n for i in range(len(thetas)):\n theta_delta[i] = lamb/len(y_matrix) * theta_delta[i]\n theta_grad[i] += np.insert(theta_delta[i], 0, 0, 1) \n gradient_theta = pack_thetas(theta_grad)\n print(act_layers[-1])\n return gradient_theta\n\n\ndef forward_propagate(all_thetas, weights, X, y):\n thetas = unpack_thetas(all_thetas, weights)\n # add column of 1's\n X = X/255\n a1 = np.insert(X, 0, 1, 1)\n act_layers = activation_layers(a1, thetas)\n \n predict = np.argmax(act_layers[-1], axis=1)\n print(predict[:10])\n print(y[:10].T)\n correct = [1 if a==b else 0 for (a,b) in zip(predict, y.T)]\n accuracy = (sum(map(int, correct))/ float(len(correct)))\n return 'accuracy = {0}%'.format(accuracy * 100)\n \n# np.savetxt('digit_sigmoid.csv', \n# np.c_[range(1, len(predict)+1), predict], \n# delimiter=',', \n# header = 'ImageId,Label', \n# comments = '', \n# fmt='%d') \n\n \ndef sigmoid(z):\n return 1/(1 + np.exp(-z))\n \n \ndef activation_layers(a1, thetas):\n act_layers = []\n act_layers.append(a1)\n for i in range(len(thetas)):\n act_layers.append(sigmoid(act_layers[i] * thetas[i].T))\n if i != (len(thetas) - 1):\n act_layers[i+1] = np.insert(act_layers[i + 1], 0, 1, 1)\n return act_layers\n \n \ndef pack_thetas(thetas):\n new_thetas = np.matrix(np.ravel(thetas[0])).T\n \n for i in range(1, len(thetas)):\n new_thetas = np.concatenate((new_thetas, np.matrix(np.ravel(thetas[i])).T), axis=0)\n return new_thetas\n \n \ndef unpack_thetas(all_new_thetas, weights):\n theta_temp = []\n temp = 0\n wght_totals = [l * m for l,m in weights]\n for i in range(len(weights)):\n theta_temp.append(np.reshape(all_new_thetas[temp:temp + wght_totals[i]], weights[i]))\n temp += wght_totals[i]\n return theta_temp\n","sub_path":"functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":4058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"298875023","text":"#!/usr/bin/python3\n#coding=utf-8\n\nimport json\nimport sys\nsys.path.append('../../')\n# from Config.Setting import *\n# from Core.WMS.Base_ERP import Base_ERP\nfrom Core.WMS.Base_ERP import Base_ERP\nfrom Utils.Logger import logger\n\nclass ERP_Purchase(Base_ERP):\n\t'''\n\t国内履约端-采购环节的基础类，仅包含：\n\t1. 采购下单菜单- 业务流程\n\t'''\n\n\tdef __init__(self):\n\t\tsuper(ERP_Purchase, self).__init__()\n\t\tself.dateStarted = ERP_Purchase.CONFIG.get('WMS_DateStarted')['PlaceOrder']\n\t\tself.payTime = self.map_StartDate_By_Module('PlaceOrder')\n\n\tdef get_GenTrackNo(self, purchaseOrderId):\n\t\t# 针对美国台湾的订单，优先生成4位长度的运单号。\n\t\t# 针对美国台湾订单，需要先生成运单号，4位长度的字母组合。\n\t\tdata = {'PurchaseOrderIds': [str(purchaseOrderId)]}\n\t\tURL = self.domain + '/api/eltreum.Purchase/GenTrackNo'\n\t\tstatus, content = self.Ezbuy_Post(URL, datas=data)\t\n\t\tif status and content:\n\t\t\tlogger.debug('采购单 {0} 生成的运单号是 {1} '.format(purchaseOrderId, content['TrackNo']))\n\n\tdef get_PurchaseOrderGet(self, orderNumber=None, skuId=None):\n\t\t# 基于订单号 获取采购订单的信息\n\t\t# 通过RegionID + OrderNumber 获取采购单信息。\n\t\tdata = {\n\t\t\t'BanKeyword': 'BankeywordAll',\n\t\t\t# 'BuyerId':'0',\n\t\t\t# 'DepartmentId': '0',\n\t\t\t# 'DomainId': '0',\n\t\t\t'Issue': 'IssueAll',\n\t\t\t# 'LabelId':'0',\n\t\t\t'OrderByVenderSku': False,\n\t\t\t'RegionId': self.regionId,\n\t\t\t'SearchAllBuyer': True,\n\t\t\t'Remark': 'RemarkAll',\n\t\t\t'UserNickName':self.customer,\n\t\t\t'Size':'30'\n\t\t\t}\n\n\t\tif orderNumber:\n\t\t\torderFilter = {'OldOrderNumber':orderNumber}\n\t\t\tdata.update(orderFilter)\n\n\t\telif skuId:\n\t\t\torderId, orderItemId = skuId.split('-')\n\t\t\tnewOrderNumberSKU = orderId + '00' + orderItemId\n\t\t\torderFilter = {'OrderNumberSKU':newOrderNumberSKU}\n\t\t\tdata.update(orderFilter)\n\n\t\telse:\n\t\t\tnameFilter = {'UserNickName': self.customer}\t\n\t\t\tdata.update(nameFilter)\n\n\t\t# print(json.dumps(data))\n\t\tURL = self.domain + '/api/eltreum.Purchase/PurchaseOrderGet'\n\t\tstatus, content = self.Ezbuy_Post(URL, datas=data)\t\t\n\t\tif status:\n\t\t\torderList = content['Orders']\n\t\t\t# print(orderList)\n\t\t\tnewOrderList = []\n\t\t\tif orderList:\n\t\t\t\tfor item in orderList:\n\t\t\t\t\tif int(item['PayTime']) >= int(self.payTime):\n\t\t\t\t\t\tnewOrderList.append(item)\n\t\t\t\treturn newOrderList\n\t\t\telse:\n\t\t\t\tlogger.warning('没找到匹配的订单信息')\n\n\t# 采购下单\n\tdef get_PlaceOrder(self, purchaseOrderId, totalQty):\n\t\t# 数据请求：Nickname, OrderIdList, totalQty \n\t\t# 返回： 采购下单成功，没有记录返回\n\t\tdata = {\n\t\t\t'IsBatch': True,\n\t\t\t'PurchaseOrderIds':[str(purchaseOrderId)],\n\t\t\t'Conditions': [{\n\t\t\t\t'PoNumber': 'Python Scripts',\n\t\t\t\t'PlaceAccount': self.customer,\n\t\t\t\t'InternalUnitPrice': 0,\n\t\t\t\t'InternalShipmentFee': 0,\n\t\t\t\t'Qty': totalQty,\n\t\t\t\t'IsOutSite': True\n\t\t\t\t}]\n\t\t\t}\n\t\tURL = self.domain + '/api/eltreum.Purchase/PlaceOrder'\n\t\tstatus, content = self.Ezbuy_Post(URL, datas=data)\n\t\tif status:\n\t\t\t# logger.debug('采购单{0} 下单成功'.format(purchaseOrderId))\n\t\t\treturn True\n\t\telse:\n\t\t\t# logger.warning('采购单{0} 下单失败'.format(purchaseOrderId))\n\t\t\treturn False\n\n\t# 采购下单 - 修改运输方式\n\tdef get_ChangeShipmentType(self, purchaseOrderId, fromShipmentTypeId, toShipmentTypdId):\n\t\tif purchaseOrderId:\n\t\t\tdata = {\n\t\t\t\t'PurchaseOrderIds':[str(purchaseOrderId)],\n\t\t\t\t'ShipmentTypeId': toShipmentTypdId,\n\t\t\t\t'Remark': str(fromShipmentTypeId) + '>>>>>>' + str(toShipmentTypdId)\n\t\t\t\t}\n\t\t\tURL = self.domain + '/api/eltreum.Purchase/ChangeShipmentType'\n\t\t\tstatus, content = self.Ezbuy_Post(URL, datas = data)\n\t\t\tlogger.debug('修改运输方式结果:{}'.format(content))\n\t\t\tif status:\n\t\t\t\treturn True\n\t\t\telse:\n\t\t\t\treturn False\n\t\telse:\n\t\t\tlogger.warning('请传入有效的采购单ID')\n\t\t\treturn False\n\n\t# 采购下单的业务操作\n\tdef oper_PlaceOrder(self, orderNumber=None, skuId=None, purchaseOrderInfo=None):\n\t\t# 如果传的是OrderNumber\n\t\tif orderNumber:\n\t\t\tpurchaseOrderList = self.get_PurchaseOrderGet(orderNumber=orderNumber)\n\t\t\t\n\t\telif skuId:\n\t\t\tpurchaseOrderList = self.get_PurchaseOrderGet(skuId=skuId)\n\n\t\telse:\n\t\t\tpurchaseOrderList = purchaseOrderInfo\t# 基于会员名查询到的采购单列表\n\n\t\tif purchaseOrderList:\n\t\t\tprint('一共有 ',len(purchaseOrderList), ' 个订单需要内采。')\n\t\t\tfor purchaseOrder in purchaseOrderList:\n\t\t\t\t# print(purchaseOrder)\n\t\t\t\tpurchaseOrderId = purchaseOrder['OrderId']\n\t\t\t\twarehouseId = purchaseOrder['WarehouseId']\n\t\t\t\tactualQty = purchaseOrder['ActualQty']\n\t\t\t\tskuNumber = purchaseOrder['OrderNumberSKU']\n\t\t\t\torderNumber = purchaseOrder['OldOrderNumber']\n\t\t\t\tpayTime = int(purchaseOrder['PayTime'])\n\t\t\t\t# 美国台湾的订单需要自动生成运单号\n\t\t\t\tif int(warehouseId) in (3,4):\n\t\t\t\t\ttrack_result = self.get_GenTrackNo(purchaseOrderId)\n\t\t\t\t\tlogger.debug('美国台湾订单的运单号:{}'.format(track_result))\n\t\t\t\t\t# print('生成运单号结果:',track_result)\n\n\t\t\t\tresult = self.get_PlaceOrder(purchaseOrderId, actualQty)\n\t\t\t\tlogger.warning('老订单:{};新订单:{},下单结果: {}'.format(orderNumber,skuNumber,result))\n\n\t# >>>>>>>>>>>>>>>>>>>>>新增当前页面的其他的操作<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n\t# 退单 - 单个SKU\n\tdef get_CancelOrder(self, abnormalReasonInfo,oldOrderId, purchaseOrderId, domesticShippingFee=0, handlingFee=0):\n\t\t# 基于新的框架下的退单接口功能\n\t\tcancelReason = abnormalReasonInfo['reason']\n\t\tcancelRemark = 'Cancel as Request for sku ' + skuNo + ' .'\n\t\toperateTypeId = abnormalReasonInfo['typeId']\n\t\ttransactionMappingReason = abnormalReasonInfo['transactionMappingReason']\n\t\tURL = self.domain + '/api/eltreum.Purchase/CancelOrder'\n\t\tdata = {\n\t\t\t'CancelReason':cancelReason,\n\t\t\t'CancelRemark':cancelRemark,\n\t\t\t'domesticShippingFee': str(domesticShippingFee),\n\t\t\t'handlingFee': str(handlingFee),\n\t\t\t'ManualFee':0,\n\t\t\t'operateTypeId':str(operateTypeId),\n\t\t\t'OldOrderId': str(oldOrderId),\n\t\t\t'PurchaseOrderId':str(purchaseOrderId),\n\t\t\t'transactionMappingReason': transactionMappingReason\n\t\t\t}\n\t\tstatus, content = self.Ezbuy_Post(URL, datas=data)\n\t\tlogger.debug('接口 {} 调用结果:{}'.format(URL, content))\n\t\tif status:\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\n\t# 采购下单 - 单个退单\n\tdef oper_CancelOrder(self, skuNo):\n\t\tskuId = skuNo.replace('-','00')\n\t\tpurchaseOrdersInfo = self.get_PurchaseOrderGet(skuId=skuId)\n\t\tif purchaseOrdersInfo:\n\t\t\t# 默认只有一条采购单记录\n\t\t\tpurchaseOrderInfo = purchaseOrdersInfo[0]\n\t\t\toldOrderId = purchaseOrderInfo['OldOrderId']\n\t\t\toldOrderNumber = purchaseOrderInfo['OldOrderNumber']\n\t\t\tpurchaseOrderId = purchaseOrderInfo['OrderId']\n\t\t\tentry = 'EntryPurchase'\n\t\t\tisForbidden = self.get_GetSkuPreCancelInfo_By_Entry(oldOrderNumber, skuNo, entry)\n\t\t\t# 默认返回False\n\t\t\tif not isForbidden:\n\t\t\t\tabnormalReasonInfo = self.get_GetAbnormalOperateReasonByTypeIdPartyId()\n\t\t\t\tisReady = self.get_RpcGetCancelOrder(oldOrderNumber, oldOrderId)\n\t\t\t\t# print(isReady)\n\t\t\t\tplatform = 'PlatformPurchase'\n\t\t\t\tdomesticShippingFee=0\n\t\t\t\thandlingFee=0\n\t\t\t\tif isReady:\n\t\t\t\t\tstatus = self.get_CancelOrder(abnormalReasonInfo, oldOrderId, purchaseOrderId, domesticShippingFee,handlingFee)\n\t\t\t\t\tif status:\n\t\t\t\t\t\tlogger.debug('sku {} was cancelled successfully from Eltreum.'.format(skuNo))\n\t\t\t\t\telse:\n\t\t\t\t\t\tlogger.warning('sku {} was cancelled failed from Eltreum.'.format(skuNo))\n\t\t\t\telse:\n\t\t\t\t\tprint('当前订单 {} 不允许操作退单.'.format(skuNo))\n\t\t\telse:\n\t\t\t\tprint('当前订单 {} 不允许操作退单.'.format(skuNo))\n\ndef Exec_PlaceOrder(orderNumber=None, skuId=None):\n\tprint('>>>>> 采购下单 <<<<<<')\n\tdemo = ERP_Purchase()\n\tprint('采购下单 - 下单时间起始: {}'.format(demo.dateStarted))\n\t# print('采购时间:',demo.payTime)\n\n\tif orderNumber:\n\t\tdemo.oper_PlaceOrder(orderNumber=orderNumber)\n\telif skuId:\n\t\tdemo.oper_PlaceOrder(skuId=skuId)\n\telse:\n\t\tpurchaseOrderList = demo.get_PurchaseOrderGet()\n\t\tdemo.oper_PlaceOrder(purchaseOrderInfo=purchaseOrderList)\n\t\t\t\t\nif __name__ =='__main__':\n\tskuNo = '26157470561101056-6'\n\tExec_PlaceOrder(skuId = skuNo)\n\t# Exec_PlaceOrder()\n\n","sub_path":"Core/WMS/ERP_Purchase_PlaceOrder.py","file_name":"ERP_Purchase_PlaceOrder.py","file_ext":"py","file_size_in_byte":8050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"415414807","text":"# \n# Programming Assignment 2, CS640\n#\n# A Gomoku (Gobang) Game\n#\n# Adapted from CS111\n# By Yiwen Gu\n#\n# Playing the game \n# \n\nfrom pa2_gomoku import Board, Player\nfrom pa2 import *\n\n\ndef process_move(player, board):\n \"\"\" Process the next move by the specified player using the\n specified board.\n inputs: player is an instance of the Player class or one of its\n subclasses.\n board is a Board object.\n \"\"\"\n print(str(player) + \"'s turn\")\n \n move = player.next_move(board)\n \n board.add_checker(player.checker, move[0], move[1])\n print()\n print(board)\n\n if board.is_win_for(player.checker, move[0], move[1]):\n print(player, 'wins in', player.num_moves, 'moves.')\n print('Congratulations!')\n return True\n elif board.is_full():\n print(\"It's a tie!\")\n return True\n else:\n return False\n \ndef gomoku(p1, p2):\n \"\"\" Plays the Gomoku between the two specified players,\n and returns the Board object as it looks at the end of the game.\n inputs: p1 and p2 are objects representing players \n One player should use 'X' checkers and the other should\n use 'O' checkers.\n \"\"\"\n # Make sure one player is 'X' and one player is 'O'.\n if p1.checker not in 'XO' or p2.checker not in 'XO' \\\n or p1.checker == p2.checker:\n print('need one X player and one O player.')\n return None\n\n print('Welcome to Gomoku!')\n print()\n b = Board(10,10)\n print(b)\n p1.num_moves = 0\n p2.num_moves = 0\n \n while True:\n if process_move(p1, b) == True:\n return b\n\n if process_move(p2, b) == True:\n return b\n\n","sub_path":"pa2_process.py","file_name":"pa2_process.py","file_ext":"py","file_size_in_byte":1712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"95902317","text":"\"\"\"\nInput 5, 20\nOutput\n[[7], [6, 3], [6, 0, 15], [4, 20, 1, 8], [6, 4, 4, 1, 0]]\n 7\n 6 3\n 6 0 15\n 4 20 1 8\n 6 4 4 1 0\nmin path = 12\n\"\"\"\nfrom typing import List, Optional\nimport random\n\n\ndef generate_triangle_list(depth: int, max_num: int) -> List[List[int]]:\n return [[random.randint(0, max_num) for _ in range(i)] for i in range(1, depth+1)]\n\n\ndef print_triangle(data: List[List[int]]) -> None:\n max_digit = len(str(max([max(l) for l in data])))\n width = max_digit + (max_digit % 2) + 2\n for index, line in enumerate(data):\n numbers = ''.join([str(i).center(width, ' ') for i in line])\n print(' ' * int(width/2) * (len(data) - index), numbers)\n\n\ndef sum_min_path(triangle: List[List[int]]) -> Optional[int]:\n tree_sum = triangle[:]\n j, len_triangle = 1, len(triangle)\n if not len_triangle:\n return\n\n while j < len_triangle:\n line = triangle[j]\n line_path_sum = []\n for i, value in enumerate(line):\n if i == 0:\n sum_value = line[i] + tree_sum[j-1][0]\n elif i == len(line) - 1:\n sum_value = line[i] + tree_sum[j-1][i-1]\n else:\n min_path = min(tree_sum[j-1][i-1], tree_sum[j-1][i])\n sum_value = line[i] + min_path\n line_path_sum.append(sum_value)\n tree_sum[j] = line_path_sum\n j += 1\n return min(tree_sum[-1])\n\n\nif __name__ == '__main__':\n data = generate_triangle_list(5, 9)\n print(data)\n print_triangle(data)\n print('min path =', sum_min_path(data))\n\n","sub_path":"algo_sample/63_quiz_triangle_min_path/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"560273677","text":"import numpy as np\n\nN_DANCER = 8\nN_PART = 5\nSHAPE = [88, 48, 60, 36, 36]\nMAX_FRAME_N = 2100\n\nBPM_1 = 120.000\nBPM_2 = 150.000\nBPM_3 = 128.000\nBPM_4 = 180.000\n\nSEC_BEAT_1 = 60. / BPM_1\nSEC_BEAT_2 = 60. / BPM_2\nSEC_BEAT_3 = 60. / BPM_3\nSEC_BEAT_4 = 60. / BPM_4\n\nchr2Num = {\n 'A' : 0, \n 'B' : 1, \n 'C' : 2,\n 'D' : 3,\n\t'E' : 4\n}\n\ndef bbf2sec(bbf):\n tokens = bbf.split('-')\n bar = int(tokens[0]) - 1\n beat = int(tokens[1]) - 1\n frac = 0\n sec = 0\n if len(tokens) >= 3:\n a, b = tokens[2].split('/')\n frac = float(a) / float(b)\n if bar <= 41 :\n sec = ( bar * 4 + beat + frac ) * SEC_BEAT_1\n elif bar <= 64 :\n sec = 82.00 + ((bar-41)*4+beat+frac) * SEC_BEAT_2\n elif bar <= 86:\n sec = 118.80 + ((bar-64)*4+beat+frac) * SEC_BEAT_3\n else :\n sec = 160.05 + ((bar-86)*4+beat+frac) * SEC_BEAT_4\n \n return sec\n\ndef translate_ws(fname):\n lst = [x.strip() for x in open(fname, encoding='utf-8')]\n res = []\n for i in range(N_DANCER):\n v = []\n for j in range(N_PART):\n v.append([])\n res.append(v)\n\n for line in lst:\n if line.strip() == '' or line[0] == '#':\n continue\n tokens = line.split()\n start = (int)(bbf2sec(tokens[0])*10)\n parts = parse_parts(tokens[1])\n anim = (tokens[2])\n\n for i, j in parts:\n res[i][j].append((start, anim))\n\n return res\n\ndef parse_single_part(s):\n\tres = []\n\tdancers = []\n\tfor x in range(len(s)):\n\t\tif ord(s[x]) <= ord('9'):\n\t\t\tdancers.append(ord(s[x])-ord('0'))\n\t\telse:\n\t\t\tfor y in dancers:\n\t\t\t\tres.append( (y,chr2Num[s[x]]) )\n\t# print (res)\n\treturn res\n\ndef parse_parts(s):\n res = []\n parts = s.split('+')\n for p in parts:\n res += parse_single_part(p)\n return list(set(res))\n\nif __name__ == '__main__':\n\timport json\n\timport time\n\timport os\n\n\tres = translate_ws('test.ws')\n\t# res.shape = (N_DANCER, N_PART, n_animation)\n\tframes = []\n\tfor i in range(len(res)):\n\t\tframes.append([])\n\t\tfor j in range(len(res[0])):\n\t\t\tframes[i].append(np.zeros((MAX_FRAME_N,SHAPE[j],3),dtype=np.int))\n\t\t\tfor tup in res[i][j]:\n\t\t\t\tanim = np.load( os.path.join('test_npy', tup[1]+'.npy') )\n\t\t\t\tstart = tup[0]\n\t\t\t\tend = start + anim.shape[0]\n\t\t\t\tframes[i][j][start:end] = anim\n\t\t\tframes[i][j] = frames[i][j].tolist()\n\twith open('data_ws.json', 'w') as outfile:\n\t\tjson.dump(frames, outfile)","sub_path":"control_7688/led_gen/gen_json.py","file_name":"gen_json.py","file_ext":"py","file_size_in_byte":2392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"293994481","text":"#y=sin(2pi*x) \r\n# 共轭梯度\r\nimport numpy as np\r\nimport math\r\nimport matplotlib.pyplot as plt\r\nimport random\r\n\r\n#共轭梯度本质是求解 W^T*X^T*X*W-2*W^TX^T*T 的最小值\r\n#根据推导过程，最小值已经确定，只要找到共轭向量带入即可\r\n\r\n#数据个数，最好偶数\r\nnumber=50\r\nstep=1/number\r\nhalfnumber=25 #number的一半\r\n\r\n#控制次数\r\ndegree=10\r\nLAMBDA=0.00005\r\nGAMMA=0.001 #梯度下降的系数\r\ne=0.000001 #停止梯度下降的边界\r\n\r\ntestdata=list()\r\ncurvedata=list()\r\n\r\nclass Dot:\r\n def __init__(self,_x,_y):\r\n self.x=_x\r\n self.y=_y\r\n\r\n#生成点数据\r\nfor i in range(number):\r\n dot=Dot(i*step,math.sin(2*math.pi*i*step)+(random.random()-0.5)/20)\r\n if i%2==0:\r\n testdata.append(dot)\r\n else:\r\n curvedata.append(dot)\r\n\r\n#print(curvedata[1].x)\r\nX=np.zeros(shape=(halfnumber,degree+1))\r\nT=np.zeros(shape=(halfnumber,1))\r\nfor i in range(halfnumber):\r\n T[i]=curvedata[i].y\r\n for j in range(degree+1): #第一列是常数\r\n X[i,j]=math.pow(curvedata[i].x,j)\r\n#print(T)\r\n\r\nW=np.zeros(shape=(degree+1,1)) #W是最终结果\r\n\r\nNORMAL=LAMBDA*np.ones(shape=[degree+1,degree+1]) #正则项\r\nR=(X.T).dot(T)-((X.T).dot(X)+NORMAL).dot(W)\r\nD=R\r\n\r\ndef ifbreak(R):\r\n ifbreak=True\r\n for i in range(degree+1):\r\n if R[i,0]>e:\r\n ifbreak=False\r\n return ifbreak\r\n\r\nwhile(True):\r\n A=(R.T).dot(R).dot(np.linalg.inv((D.T).dot((X.T).dot(X)+NORMAL).dot(D)))\r\n W=W+A*D\r\n R_2=R-A*((X.T).dot(X)+NORMAL).dot(D)\r\n if(ifbreak(R_2)): \r\n break\r\n B=(R_2.T).dot(R_2).dot(np.linalg.inv((R.T).dot(R)))\r\n D=R_2+B*D\r\n R=R_2\r\n\r\nprint(\"系数矩阵为：\",W)\r\n\r\nx=np.zeros(shape=(halfnumber,1))\r\nfor i in range(1,halfnumber):\r\n x[i]=i*2*step+step\r\n\r\naccuracy=0 #测试生成曲线的准确性\r\nfor dot in testdata:\r\n temp=np.zeros(shape=(1,degree+1))\r\n for j in range(degree+1):\r\n temp[0,j]=math.pow(dot.x,j)\r\n accuracy+=math.pow((dot.y-temp.dot(W))[0,0],2)\r\nprint(accuracy)\r\n\r\nplt.plot(x,X.dot(W),'r') #拟合曲线\r\nplt.plot(x,T,'g.') #自己生成的数据\r\nplt.title(\"degree=\"+str(degree))\r\nplt.show()","sub_path":"ML/Lab1/ML1-4.py","file_name":"ML1-4.py","file_ext":"py","file_size_in_byte":2141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"26347175","text":"import telebot\nfrom peewee import SqliteDatabase, fn\n\nimport conf\nfrom banecbot.model import Anecdote, db\nfrom banecbot.telegram_bot.chat_state import ChatState\n\n\nif conf.PROXY:\n telebot.apihelper.proxy = {'https': conf.PROXY}\n\nbot = telebot.TeleBot(conf.TG_ACCESS_TOKEN)\nchat_states = {}\n\n\n@bot.message_handler(commands=['start', 'help'])\ndef send_welcome(message):\n bot.send_message(message.chat.id, conf.WELCOME_MESSAGE,\n parse_mode='Markdown',\n reply_markup=telebot.types.ReplyKeyboardRemove())\n\n\ndef send_anecdotes(chat_id):\n global chat_states\n anecs = list(Anecdote.select().order_by(fn.Random()).limit(2))\n try:\n chat_states[chat_id].last_anecs = anecs\n except KeyError:\n bot.send_message(chat_id, 'Что-то пошло не так. Нажмите /play еще раз',\n reply_markup=telebot.types.ReplyKeyboardRemove())\n\n for i, anec in enumerate(anecs):\n reply_text = '\\n\\n'.join([f'АНЕКДОТ {i + 1}', anec.text])\n bot.send_message(chat_id, reply_text, parse_mode='HTML')\n\n keyboard = telebot.types.ReplyKeyboardMarkup(row_width=2,\n resize_keyboard=True,\n one_time_keyboard=True)\n button_first = telebot.types.KeyboardButton(chr(0x261D) + 'Первый')\n button_second = telebot.types.KeyboardButton(chr(0x270C) + 'Второй')\n not_anec_msg = chr(0x1F612) + 'Что-то из этого вообще не анек'\n button_not_anec = telebot.types.KeyboardButton(not_anec_msg)\n keyboard.add(button_first, button_second, button_not_anec)\n bot.send_message(chat_id,\n 'Какой из этих анеков набрал больше лайков?',\n reply_markup=keyboard)\n\n\n@bot.message_handler(commands=['play'])\ndef start_play(message):\n global chat_states\n chat_states[message.chat.id] = ChatState(message.chat.id)\n send_anecdotes(message.chat.id)\n\n\n@bot.message_handler(commands=['stop'])\ndef show_stats(message):\n chat_id = message.chat.id\n if chat_id not in chat_states.keys():\n bot.send_message(chat_id, 'Сначала надо начать игру, нажав /play',\n reply_markup=telebot.types.ReplyKeyboardRemove())\n return\n\n if chat_states[chat_id].won % 10 in range(2, 5):\n word_form = 'раза'\n else:\n word_form = 'раз'\n msg = (f'Ты угадал(а) {chat_states[chat_id].won} {word_form} '\n f'и ошибся(лась) {chat_states[chat_id].lost}.')\n bot.send_message(chat_id, msg,\n reply_markup=telebot.types.ReplyKeyboardRemove())\n bot.send_message(chat_id, 'Чтобы сыграть еще раз, нажми /play',\n reply_markup=telebot.types.ReplyKeyboardRemove())\n\n\ndef draw_keyboard(chat_id, response_text):\n keyboard = telebot.types.ReplyKeyboardMarkup(row_width=2,\n resize_keyboard=True,\n one_time_keyboard=True)\n button_continue = telebot.types.KeyboardButton(chr(0x1F60E) +\n 'Несите еще!')\n button_stop = telebot.types.KeyboardButton(chr(0x1F645) + 'Хватит')\n keyboard.add(button_continue, button_stop)\n bot.send_message(chat_id, response_text, reply_markup=keyboard)\n\n\n@bot.message_handler(func=lambda x: 'первый' in x.text.lower())\ndef first_is_chosen(message):\n global chat_states\n try:\n first_likes = chat_states[message.chat.id].last_anecs[0].likes\n second_likes = chat_states[message.chat.id].last_anecs[1].likes\n except KeyError:\n bot.reply_to(message, 'Что-то пошло не так. Нажмите /play еще раз',\n reply_markup=telebot.types.ReplyKeyboardRemove())\n return\n if first_likes >= second_likes:\n chat_states[message.chat.id].won += 1\n response_text = (f'Верно! Первый анек набрал {first_likes} лайков, '\n f'а второй – {second_likes}')\n else:\n chat_states[message.chat.id].lost += 1\n response_text = (f'Не-а. Первый анек набрал {first_likes} лайков, '\n f'а второй – {second_likes}')\n \n draw_keyboard(message.chat.id, response_text)\n\n\n@bot.message_handler(func=lambda x: 'второй' in x.text.lower())\ndef second_is_chosen(message):\n global chat_states\n try:\n first_likes = chat_states[message.chat.id].last_anecs[0].likes\n second_likes = chat_states[message.chat.id].last_anecs[1].likes\n except KeyError:\n bot.reply_to(message, 'Что-то пошло не так. Нажмите /play еще раз',\n reply_markup=telebot.types.ReplyKeyboardRemove())\n return None\n if second_likes >= first_likes:\n chat_states[message.chat.id].won += 1\n response_text = (f'Верно! Второй анек набрал {second_likes} лайков, '\n f'а первый – {first_likes}')\n else:\n chat_states[message.chat.id].lost += 1\n response_text = (f'Не-а. Второй анек набрал {second_likes} лайков, '\n f'а первый – {first_likes}')\n\n draw_keyboard(message.chat.id, response_text)\n\n\n@bot.message_handler(func=lambda x: 'не анек' in x.text.lower())\ndef not_anec(message):\n bot.send_message(message.chat.id, 'Ок, вот другие посты!',\n reply_markup=telebot.types.ReplyKeyboardRemove())\n send_anecdotes(message.chat.id)\n\n\n@bot.message_handler(func=lambda x: 'еще' in x.text.lower().replace('ё', 'е'))\ndef more_anecs(message):\n send_anecdotes(message.chat.id)\n\n\n@bot.message_handler(func=lambda x: 'хватит' in x.text.lower())\ndef enough(message):\n show_stats(message)\n\n\ndef main():\n db.connect()\n try:\n bot.polling()\n finally:\n db.close()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"banecbot/telegram_bot/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":6222,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"172683026","text":"import tkinter as tk\nimport math\nimport random\nimport fileinput\n\nimport matplotlib\nmatplotlib.use(\"TkAgg\")\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\nfrom matplotlib.figure import Figure\n\nmatplotlib.use(\"TkAgg\")\nfenetre = tk.Tk()\n\neye_frame = tk.Frame(fenetre, borderwidth=2, relief=tk.GROOVE)\neye_frame.grid(row=1, column=1)\n\neye_info_frame = tk.Frame(fenetre, borderwidth=2, relief=tk.GROOVE)\neye_info_frame.grid(row=2, column=1)\n\ncanvas = tk.Canvas(eye_frame, width=400, height=400, background='white')\n\nindividuals = []\n\nfor line in fileinput.input():\n line = line.split()\n individuals.append(line)\n\ncurrent_index = 0\n\n\ndef refresh():\n global current_index\n individual = individuals[current_index]\n\n omega = float(individual[0])*200\n rhoc = float(individual[1])*200\n i = float(individual[2])*200\n phi1 = float(individual[3])\n n0 = float(individual[4])\n fitness = float(individual[5])\n\n #draw eye\n canvas.delete(\"all\")\n\n\n cx, cy = 150, 150\n\n if rhoc == omega/2:\n xy = cx-rhoc, cy-rhoc, cx+rhoc, cy+rhoc\n canvas.create_arc(xy, start=180-phi1*180/math.pi, extent=180+2*phi1*180/math.pi, style=tk.ARC)\n Dx, Dy = cx + math.cos(phi1)*rhoc, cy - math.sin(phi1)*rhoc\n Fx, Fy = Dx-i, Dy\n canvas.create_line(Dx, Dy, Dx-i, Dy)\n Cx, Cy = cx + math.cos(math.pi-phi1)*rhoc, cy - math.sin(math.pi-phi1)*rhoc\n canvas.create_line(Cx, Cy, Cx+i, Dy)\n Ex, Ey = Cx+i, Cy\n\n # draw lense\n canvas.create_line(Fx, Fy, Ex, Ey, width=int((n0-1.35)/0.2*10), fill='blue')\n\n elif rhoc > omega/2:\n alpha = math.acos(omega/(2*rhoc))\n H = math.sqrt(rhoc*rhoc - omega*omega/4)\n xy = cx-rhoc, cy-H-rhoc, cx+rhoc, cy-H+rhoc\n canvas.create_arc(xy, start=180+alpha*180/math.pi, extent=180-2*alpha*180/math.pi, style=tk.ARC)\n Bx, By = cx + math.cos(alpha)*rhoc, cy\n Fx, Fy = Bx-i, By\n canvas.create_line(Bx, By, Fx, Fy)\n Ax, Ay = cx - math.cos(alpha)*rhoc, cy\n Ex, Ey = Ax+i, Ay\n canvas.create_line(Ax, Ay, Ex, Ey)\n\n # draw lense\n canvas.create_line(Fx, Fy, Ex, Ey, width=int((n0-1.35)/0.2*10), fill='blue')\n\n canvas.pack()\n\n # update eye info\n for child in eye_info_frame.winfo_children():\n child.destroy()\n text = tk.Text(eye_info_frame)\n text.insert(tk.INSERT, 'Fitness : {}\\n'.format(fitness))\n text.insert(tk.INSERT, 'Rayon de courbure : {}\\n'.format(rhoc))\n text.insert(tk.INSERT, 'Taille iris : {}\\n'.format(i))\n text.insert(tk.INSERT, 'Angle phi1 : {}\\n'.format(phi1))\n text.insert(tk.INSERT, 'Indice de réfraction : {}\\n'.format(n0))\n text.pack()\n\n current_index += 1\n\n if current_index < len(individuals):\n fenetre.after(10, refresh) # reschedule event in 2 seconds\n\n\nfenetre.after(200, refresh)\nfenetre.mainloop()\n","sub_path":"visualization.py","file_name":"visualization.py","file_ext":"py","file_size_in_byte":2862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"426485445","text":"# -*- coding: utf-8 -*-\nfrom os import path\n\nfrom setuptools import find_packages, setup\n\n# read the contents of the README file\nthis_directory = path.abspath(path.dirname(__file__))\nwith open(path.join(this_directory, \"README.md\"), encoding=\"utf-8\") as f:\n long_description = f.read()\n\nsetup(\n name=\"django-urlconf-export\",\n version=\"1.1.1\",\n description=\"Make URLs for your website from anywhere.\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n author=\"Lyst Ltd.\",\n author_email=\"devs@lyst.com\",\n package_dir={\"\": \"src\"},\n packages=find_packages(\"src\", include=[\"django_urlconf_export\", \"django_urlconf_export.*\"]),\n zip_safe=False,\n install_requires=[\"django\", \"requests\"],\n python_requires=\">=3.6\",\n url=\"https://github.com/lyst/django-urlconf-export\",\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"75763138","text":"from result import Result\n\n\nclass Solver:\n def __init__(self):\n self.visited = set()\n self.solution_found = False\n\n def run(self):\n while not self.solution_found and self.fringe.has_nodes():\n self.check_next_node()\n if not self.solution_found:\n return None\n return Result().compute(self.visited, self.fringe)\n\n def check_next_node(self):\n node = self.fringe.get_next_node()\n self.add_to_visited(node)\n\n if node.board == [0, 1, 2, 3, 4, 5, 6, 7, 8]:\n self.solution_found = True\n return\n\n next_nodes = []\n for next_node in node.next_states:\n if self.should_add_to_fringe(next_node):\n next_nodes.append(next_node)\n\n if len(next_nodes) > 0:\n self.fringe.add_all(next_nodes)\n\n def add_to_visited(self, current_node):\n self.visited.add(current_node)\n\n def should_add_to_fringe(self, next_node):\n return next_node not in self.visited and next_node not in self.fringe\n","sub_path":"solver.py","file_name":"solver.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"144027032","text":"#!/usr/bin/python\n\n\n# Clint Edwards\n# August 24th 2012\n# Takes user input and not only creates entries in the Asset tracking database\n# but displays the information that the user needs. \n\nimport MySQLdb\nimport datetime\n\n\n\n######Information\n\n#Collect information about Asset\nprint(\"\\n\")\n#filename = raw_input(\"Please enter the file you want to write too: \")\nfirstn = raw_input(\"Please enter your first name: \")\n#middlen = raw_input(\"Please enter your middle name: \")\nlastn = raw_input(\"Please enter your last name: \")\ncomputer = raw_input(\"Enter your computer model: \")\nasset = input(\"Enter your asset tag number: \")\nmac = input(\"Enter the computer mac address without dashes: \")\nserial = raw_input(\"Enter the computer serial number: \")\nprint(\"\\n\")\n\n######Formatting\n\n#Create Username\nfirsts = firstn[:3]\nlasts = lastn[:3].lower()\nusername = firsts + lasts\n\n#Create Full name\nissuedto = firstn + \" \" + lastn\n\n#Create Computer name\ncompname= username + \"-\" + computer \n\n######Getting current Date and Time\n\nnow = datetime.datetime.now()\ncurdate = now.strftime(\"%Y-%m-%d\") \n\nprint (\"\\n\")\nprint (\"Your computer's name is: \" + compname)\nprint (\"Your username is: \" + username)\n\n######Database connection and data entry\n\n\ndb = MySQLdb.connect(\"localhost\",\"adviseadmin\",\"Advise123\",\"itadmin\")\n\ncursor = db.cursor()\n\n#database = \"\"\" \"\"\"\nsql = \"INSERT INTO comp_info VALUES ('%d','%s','%s','%s','%s','%s','%s')\" % \\\n\t\t\t(asset,issuedto,compname,mac,computer,serial,curdate)\n\n\ntry:\n\t#cursor.execute(database)\n\tcursor.execute(sql)\n\tdb.commit()\nexcept:\n\tdb.rollback()\n\n\ndb.close()\n\n","sub_path":"RandomScripts/UAN.py","file_name":"UAN.py","file_ext":"py","file_size_in_byte":1570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"553000221","text":"\r\nimport tkinter as tk\r\nfrom tkinter import ttk\r\nfrom PIL import ImageTk, Image\r\nimport PIL\r\nimport requests\r\nfrom io import BytesIO\r\nfrom tkinter import *\r\nfrom tkinter.tix import *\r\nfrom tkinter import filedialog\r\nfrom tkinter import messagebox\r\nfrom pytube import *\r\nimport pytube\r\nimport threading as th\r\nfrom tkinter.filedialog import *\r\n\r\nmain = tk.Tk()\r\n\r\nfolder_path = StringVar() # folder path\r\nvarsw = [] # Resolution list\r\nAudio_Video = IntVar() # Audio or video check box\r\n# to keep all IntVars for all filenames\r\nintvar_dict = {}\r\n# to keep all Checkbuttons for all filenames\r\ncheckbutton_list = []\r\n# FileSize of the Video\r\nfile_size = 0\r\n\r\n# main window size\r\nsizex = 1000\r\nsizey = 650\r\nposx = 25\r\nposy = 10\r\nmain.geometry(\"%dx%d\" % (sizex, sizey))\r\nmain.resizable(0, 0)\r\nmain.title(\"YouTube Downloader\")\r\n#youtube_icon=PhotoImage(file=\"youtube.ico\")\r\nmain.iconbitmap('.asset\\\\ytd.ico')\r\n\r\n\r\n#Menu Bar\r\n\r\nmenu_bar=Menu(main)\r\nmain.config(menu=menu_bar)\r\n\r\n\r\n# Exit GUI cleanly\r\ndef _quit():\r\n main.quit()\r\n main.destroy()\r\n exit()\r\n\r\n#Instructions Message box\r\ndef _msgBox():\r\n messagebox.showwarning('Instructions','1.Copy URL from the YouTube and paste it in the Enter URL entry box\\n\\n'\r\n '2.Click on Browse button to select the path for saving your downloaded video\\n\\n'\r\n '3.As per your requirement select Audio or Video\\n\\n'\r\n '4.Click on the Download button')\r\n\r\n# Add menu items\r\nfile_menu = Menu(menu_bar, tearoff=0)\r\n#file_menu.add_command(label=\"Instructions\",command=_msgBox)\r\n#file_menu.add_separator()\r\n#file_menu.add_command(label=\"Exit\",command=_quit)\r\n#menu_bar.add_cascade(label=\"Help\", menu=file_menu)\r\nmenu_bar.add_cascade(label=\"Instructions\",command=_msgBox)\r\nmenu_bar.add_cascade(label=\"Exit\",command=_quit)\r\n\r\n\r\n\r\n# ===================================================================\r\n# This is Tooltip i.e if we hover our cursor on that window then this will show us text\r\nclass ToolTip(object):\r\n def __init__(self, widget):\r\n self.widget = widget\r\n self.tip_window = None\r\n\r\n def show_tip(self, tip_text):\r\n \"Display text in a tooltip window\"\r\n if self.tip_window or not tip_text:\r\n return\r\n x, y, _cx, cy = self.widget.bbox(\"insert\") # get size of widget\r\n x = x + self.widget.winfo_rootx() + 25 # calculate to display tooltip\r\n y = y + cy + self.widget.winfo_rooty() + 10 # below and to the right\r\n self.tip_window = tw = tk.Toplevel(self.widget) # create new tooltip window\r\n tw.wm_overrideredirect(True) # remove all Window Manager (wm) decorations\r\n # tw.wm_overrideredirect(False) # uncomment to see the effect\r\n tw.wm_geometry(\"+%d+%d\" % (x, y)) # create window size\r\n\r\n label = tk.Label(tw, text=tip_text, justify=tk.LEFT,\r\n background=\"#e6e6e6\", relief=tk.SOLID, borderwidth=1,\r\n font=(\"tahoma\", \"8\", \"normal\"))\r\n label.pack(ipadx=1)\r\n\r\n def hide_tip(self):\r\n tw = self.tip_window\r\n self.tip_window = None\r\n if tw:\r\n tw.destroy()\r\n\r\n\r\ndef create_ToolTip(widget, text):\r\n toolTip = ToolTip(widget) # create instance of class\r\n\r\n def enter(event):\r\n toolTip.show_tip(text)\r\n\r\n def leave(event):\r\n toolTip.hide_tip()\r\n\r\n widget.bind('', enter) # bind mouse events\r\n widget.bind('', leave)\r\n\r\n\r\n# ====================================================================================\r\n\r\n\r\n# =====================================================================================\r\n# This the Context Menu i.e if we right click on the entry box then it must show us cut,copy,paste\r\ndef rClicker(e):\r\n ''' right click context menu for all Tk Entry and Text widgets\r\n '''\r\n\r\n try:\r\n def rClick_Copy(e, apnd=0):\r\n e.widget.event_generate('')\r\n\r\n def rClick_Cut(e):\r\n e.widget.event_generate('')\r\n\r\n def rClick_Paste(e):\r\n e.widget.event_generate('')\r\n\r\n e.widget.focus()\r\n\r\n nclst = [\r\n (' Cut', lambda e=e: rClick_Cut(e)),\r\n (' Copy', lambda e=e: rClick_Copy(e)),\r\n (' Paste', lambda e=e: rClick_Paste(e)),\r\n ]\r\n\r\n rmenu = Menu(None, tearoff=0, takefocus=0)\r\n\r\n for (txt, cmd) in nclst:\r\n rmenu.add_command(label=txt, command=cmd)\r\n\r\n rmenu.tk_popup(e.x_root + 40, e.y_root + 10, entry=\"0\")\r\n\r\n except TclError:\r\n print(' - rClick menu, something wrong')\r\n pass\r\n\r\n return \"break\"\r\n\r\n\r\ndef rClickbinder(r):\r\n try:\r\n for b in ['Text', 'Entry', 'Listbox', 'Label']: #\r\n r.bind_class(b, sequence='',\r\n func=rClicker, add='')\r\n except TclError:\r\n print(' - rClickbinder, something wrong')\r\n pass\r\n\r\n\r\n# ===============================================================================================\r\n\r\n\r\ndef resolution_list():\r\n global Audio_Video\r\n global url_str\r\n\r\n try:\r\n\r\n ob = YouTube(url_str.get())\r\n text = []\r\n itag_list = []\r\n\r\n if (Audio_Video.get() == 1):\r\n\r\n stri = ob.streams.filter(type=\"video\")\r\n for i in stri:\r\n if i.resolution is None:\r\n continue\r\n k = f'Res:{i.resolution}, FPS:{i.fps}, Video codec:{i.video_codec}, Audio codec:{i.audio_codec},' \\\r\n f' File type:{i.mime_type.split(\"/\")[1]}, Size:{str(round(i.filesize / (1024 * 1024)))} MB'\r\n\r\n text.append(k)\r\n itag_list.append(i.itag)\r\n\r\n elif (Audio_Video.get() == 2):\r\n stri = ob.streams.filter(type=\"audio\")\r\n itag_list = []\r\n text = []\r\n for i in stri:\r\n k = f'Codec: {i.audio_codec}, ABR: {i.abr}, File type: {i.mime_type.split(\"/\")[1]}, ' \\\r\n f'Size: {str(round(i.filesize / (1024 * 1024)))} MB'\r\n\r\n text.append(k)\r\n itag_list.append(i.itag)\r\n return [text, itag_list]\r\n except Exception as e:\r\n messagebox.showinfo(\"Media error\",\r\n f'Reasons for this error:\\n'\r\n f' 1.The Input URL is incorrect.\\n 2.The Video is Age Restricted\\n '\r\n f'3.The video is restricted for using in any 3rd party application.')\r\n confirm['text'] = \"Confirm\"\r\n confirm.config(state=NORMAL)\r\n\r\n\r\ndef confirm_Button():\r\n global intvar_dict\r\n intvar_dict.clear() # Variable dictionary for resolution list\r\n text_check, itag_check = resolution_list()\r\n\r\n for cb in checkbutton_list:\r\n cb.destroy()\r\n checkbutton_list.clear()\r\n\r\n content = tk.Frame(hel_frame, relief='groove', bd=2)\r\n canvas = tk.Canvas(content, borderwidth=0, height=395, width=578)\r\n myscrollbar = Scrollbar(content, orient=\"vertical\", command=canvas.yview)\r\n frame = ttk.Frame(canvas)\r\n canvas.configure(yscrollcommand=myscrollbar.set)\r\n content.grid_configure(row=5, column=0, rowspan=1, sticky='NSEW')\r\n myscrollbar.pack(side=\"right\", fill=\"y\")\r\n canvas.pack(side='left')\r\n canvas.create_window((0, 0), window=frame, anchor=\"nw\", tags=\"frame\")\r\n confirm['text'] = \"Fetching...\"\r\n confirm.config(state=DISABLED)\r\n frame.bind(\"\", lambda e: canvas.configure(scrollregion=canvas.bbox(\"all\")))\r\n confirm['text'] = \"Confirm\"\r\n confirm.config(state=NORMAL)\r\n\r\n # Add scroll (Incomplete)\r\n i = 0\r\n for resname in text_check:\r\n intvar_dict[resname] = tk.IntVar()\r\n # create Checkbutton for filename and keep on list\r\n cb = tk.Checkbutton(frame, text=resname, variable=intvar_dict[resname], onvalue=itag_check[i], offvalue=0,\r\n command=lambda: test_v())\r\n i += 1\r\n cb.grid(row=i, column=0, sticky=\"\")\r\n checkbutton_list.append(cb)\r\n\r\n\r\ndef check_video():\r\n global Audio_Video\r\n if len(path.get()) == 0:\r\n messagebox.showinfo(\"URL Info\", \"Enter the URL First\")\r\n confirm['text'] = \"Confirm\"\r\n confirm.config(state=NORMAL)\r\n\r\n elif (Audio_Video.get() == 0):\r\n messagebox.showinfo(\"Box Selection\", \"Check the Audio or Video Box\")\r\n elif (Audio_Video.get() != 0) and len(path.get()) > 0:\r\n confirm['text'] = \"Fetching...\"\r\n confirm.config(state=DISABLED)\r\n confirm_Button()\r\n #th.Thread(target=confirm_Button).start()\r\n\r\n\r\ndef test_v():\r\n \"\"\"url = YouTube(str(path.get()))\r\n url_link = str(url.thumbnail_url)\r\n res = requests.get(url_link)\r\n img_d = res.content\r\n img = ImageTk.PhotoImage(PIL.Image.open(BytesIO(img_d)))\r\n to = Label(btn_Frame, image=img,height=100,width=80)\r\n to.place(x=650, y=200,anchor = CENTER)\r\n\"\"\"\r\n global intvar_dict\r\n c = 0\r\n ret = None\r\n val = list(intvar_dict.values())\r\n val1 = [i.get() for i in val]\r\n\r\n ind = 0\r\n for key, value in intvar_dict.items():\r\n if value.get() > 0:\r\n ret = value.get()\r\n ind = val1.index(ret)\r\n c = 1\r\n if (c == 0):\r\n for i in range(len(intvar_dict)):\r\n checkbutton_list[i].config(state='normal')\r\n elif (c == 1):\r\n for i in range(len(intvar_dict)):\r\n if (i == ind):\r\n continue\r\n checkbutton_list[i].config(state='disabled')\r\n return ret\r\n\r\n\r\ndef browse_button():\r\n global folder_path\r\n global filename\r\n filename = filedialog.askdirectory(title='Choose a file')\r\n folder_path.set(filename)\r\n\r\n\r\ndef progressDownlaod(stream=None, chunk=None, bytes_remaining=None):\r\n percentage = ((file_size - bytes_remaining) / (file_size)) * 100\r\n main.update_idletasks()\r\n progress_bar['value'] = percentage\r\n\r\n\r\ndef Download():\r\n ita_ret = test_v()\r\n if len(path.get())==0:\r\n messagebox.showinfo(\"URL Info\", \"Enter the URL\")\r\n elif len(folder_path.get()) == 0:\r\n messagebox.showinfo(\"Empty Path\", \"Select Path\")\r\n elif ita_ret is None:\r\n messagebox.showinfo(\"Media\",\"Select the media\")\r\n global file_size\r\n\r\n my_video = YouTube(path.get())\r\n stre = my_video.streams.get_by_itag(ita_ret)\r\n my_video.register_on_progress_callback(progressDownlaod)\r\n file_size = stre.filesize\r\n if len(filename)!=0:\r\n download_button['text']=\"Downloading...\"\r\n download_button.config(state=DISABLED)\r\n stre.download(filename)\r\n download_button['text'] = \"Download\"\r\n download_button.config(state=NORMAL)\r\n messagebox.showinfo('Complete', \"Video is Downloaded check your file location\")\r\n \r\n\r\nback=PhotoImage(file=\".asset\\\\fixed2.png\")\r\n\r\nbtn_Frame = Frame(main)\r\nbtn_Frame.place(relheight=1, relwidth=1)\r\n\r\nback_ground=Label(btn_Frame,image=back)\r\nback_ground.place(relheight=1, relwidth=1)\r\n\r\n#header_label = tk.Label(btn_Frame, height=1, width=80)\r\n#header_label.grid(row=1, column=1, padx=10, pady=10, columnspan=5)\r\n\r\n# Enter URL Label\r\n\r\nurl_img=PhotoImage(file=\".asset\\\\url.png\")\r\nlbl_url = tk.Label(btn_Frame, image=url_img, width=15, height=1, fg=\"red\", bg=\"#ccf2ff\",\r\n font=('League Spartan', 10, ' bold '))\r\nlbl_url.grid(row=2, column=0, padx=30, pady=20,ipadx=33,ipady=15)\r\n\r\n# Enter path label\r\nfile_img=PhotoImage(file=\".asset\\\\path.png\")\r\nlbl_path = tk.Label(btn_Frame,image=file_img, text=\"Enter path\", width=13, height=1, fg=\"red\", bg=\"#ccf2ff\",\r\n font=('League Spartan', 10, ' bold '))\r\nlbl_path.grid(row=3, column=0, padx=30,ipadx=36,ipady=15)\r\n\r\n# Url Entry\r\nurl_str = StringVar()\r\npath = ttk.Entry(btn_Frame, width=60, textvariable=url_str, font=('DM Sans', 10))\r\npath.grid(row=2, column=1, ipady=2, padx=0, pady=5, sticky=\"W\", columnspan=2)\r\n# Context Menu(i.e, cut,copy,paste on right click)\r\npath.bind('', rClicker, add='')\r\n\r\n# Add a Tooltip\r\ncreate_ToolTip(path, 'Press CTRL+V to Paste')\r\n\r\n# path Entry\r\nfile = ttk.Entry(btn_Frame, width=60, textvariable=folder_path, font=('DM Sans', 10))\r\nfile.grid(row=3, column=1, ipady=2, padx=0, pady=5, columnspan=2)\r\n# Context Menu(i.e, cut,copy,paste on right click)\r\nfile.bind('', rClicker, add='')\r\n\r\n# video checkbox\r\nvideo_icon=PhotoImage(file=\".asset\\\\video1.png\")\r\nvideo_check = tk.Checkbutton(btn_Frame,image=video_icon, text=\"video\", onvalue=1, variable=Audio_Video,bg=\"#f85646\",relief=\"solid\")\r\nvideo_check.grid(row=4, column=1, padx=0)\r\n\r\n# audio checkbox\r\naudio_icon=PhotoImage(file=\".asset\\\\audio1.png\")\r\naudio_check = tk.Checkbutton(btn_Frame,image=audio_icon, text=\"Audio\", onvalue=2, variable=Audio_Video,bg=\"#f85646\", relief=\"solid\")\r\naudio_check.grid(row=4, column=2, padx=0)\r\n\r\n# confirm Button\r\n\r\nconfirm_img=PhotoImage(file=\".asset\\\\checkvideo.png\")\r\nconfirm = tk.Button(btn_Frame, image=confirm_img,text=\"Check video\",width=85,height=29,bd=0, command=lambda: check_video())\r\nconfirm.grid(row=2, column=3, padx=10, pady=5)\r\n\r\n# browse Button\r\nimf=PhotoImage(file=\".asset\\\\Browse2.png\")\r\nbrowse = Button(btn_Frame, image=imf,text=\"Browse\", width=85,height=29, command=lambda: browse_button(),bd=0)\r\nbrowse.grid(row=3, column=3, padx=6, pady=5)\r\n\r\nhel_frame = ttk.Frame(btn_Frame, relief='groove', height=400, width=600)\r\nhel_frame.grid(row=5, column=0, padx=20, pady=20, rowspan=1, columnspan=4)\r\n\r\n# Download Progress\r\ndownload_prog=PhotoImage(file=\".asset\\\\downloadprogr1.png\")\r\ndownload_Frame = tk.Label(btn_Frame,image=download_prog, height=80, width=290,bg=\"#f85646\").place(x=665, y=255)\r\n#tk.Label(btn_Frame,image=download_prog,text=\"Download Progress :\", font=('verdana', 15)).place(x=630, y=270)\r\nprogress_bar = ttk.Progressbar(btn_Frame, orient='horizontal', length=230, mode='determinate')\r\nprogress_bar.place(x=698, y=300, relheight=0.04)\r\n\r\nprogress_bar['value'] = 0\r\nprogress_bar['maximum'] = 100\r\n\r\n\r\n# Download Button\r\n#s = ttk.Style()\r\n#s.configure('my.TButton', font=('verdana', 20))\r\nty=PhotoImage(file=\".asset\\\\downloadicon1.png\")\r\n\r\ndownload_button = tk.Button(main,text=\"Download\",image=ty,bg=\"#f85646\",height=73, width=207, command=lambda: th.Thread(target=Download).start(),bd=0)\r\ndownload_button.place(x=680, y=380)\r\n\r\nmain.mainloop()\r\n\r\n\r\n","sub_path":"YouTube.py","file_name":"YouTube.py","file_ext":"py","file_size_in_byte":14333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"168312352","text":"# from lns.common.visualization import visualize\nfrom lns.common.preprocess import Preprocessor\nfrom lns.yolo.train import YoloTrainer\nfrom lns.yolo.settings import YoloSettings\n\ndataset = Preprocessor.preprocess(\"nuscenes\")\npedestrian_id = dataset.classes.index(\"pedestrian\")\n\ndead_keys = []\nfor img, labels in dataset.annotations.items():\n labels = list(filter(lambda label: label.class_index == pedestrian_id, labels))\n if not labels:\n dead_keys.append(img)\n else:\n dataset.annotations[img] = labels\nfor dead_key in dead_keys:\n del dataset.annotations[dead_key]\n dataset.images.remove(dead_key)\n\ndataset = dataset.merge_classes({\"ped\": dataset.classes})\n\ndataset = dataset + Preprocessor.preprocess(\"SCALE\")\ndataset = dataset.merge_classes({\"pedestrian\": [\"ped\", \"Pedestrian\"]})\nprint(len(dataset))\nprint(dataset.classes)\n\n\n# scale = Preprocessor.preprocess(\"ScaleLights\")\n# scale_utias = Preprocessor.preprocess(\"ScaleLights_New_Utias\")\n# scale_yt = Preprocessor.preprocess(\"ScaleLights_New_Youtube\")\n# dataset = scale + scale_utias + scale_yt\n# bosch = Preprocessor.preprocess(\"Bosch\")\n# lisa = Preprocessor.preprocess(\"LISA\")\n# dataset = dataset + bosch + lisa\n\n# print(scale.classes)\n# visualize(scale)\n\n# dataset = dataset.merge_classes({\n# \"green\": [\"goLeft\", \"Green\", \"GreenLeft\", \"GreenStraightRight\", \"go\", \"GreenStraightLeft\", \"GreenRight\", \"GreenStraight\", \"3-green\", \"4-green\", \"5-green\"],\n# \"yellow\": [\"warning\", \"Yellow\", \"warningLeft\", \"3-yellow\", \"4-yellow\", \"5-yellow\"],\n# \"red\": [\"stop\", \"stopLeft\", \"RedStraightLeft\", \"Red\", \"RedLeft\", \"RedStraight\", \"RedRight\", \"3-red\", \"4-red\", \"5-red\", \"4-red-green\", \"5-red-green\", \"5-red-yellow\"],\n# \"off\": [\"OFF\", \"off\", \"3-off\", \"3-other\", \"4-off\", \"4-other\", \"5-off\", \"5-other\"]\n# })\n# dataset = dataset.merge_classes({\n# \"green\": [\"goLeft\", \"Green\", \"GreenLeft\", \"GreenStraightRight\", \"go\", \"GreenStraightLeft\", \"GreenRight\", \"GreenStraight\", \"3-green\", \"4-green\", \"5-green\"],\n# \"yellow\": [\"warning\", \"Yellow\", \"warningLeft\", \"3-yellow\", \"4-yellow\", \"5-yellow\"],\n# \"red\": [\"stop\", \"stopLeft\", \"RedStraightLeft\", \"Red\", \"RedLeft\", \"RedStraight\", \"RedRight\", \"3-red\", \"4-red\", \"5-red\"],\n# \"off\": [\"OFF\", \"off\", \"3-off\", \"3-other\", \"4-off\", \"4-other\", \"5-off\", \"5-other\"]\n# })\n# dataset = dataset.merge_classes({\"light\": [\"green\", \"yellow\", \"red\", \"off\"]})\n\n# print(dataset.classes)\n# visualize(dataset)\n\nfor img, labels in dataset.annotations.items():\n labels = list(filter(lambda label: label.bounds.width > 0 and label.bounds.height > 0, labels))\n dataset.annotations[img] = labels\n\nsettings = YoloSettings(\n img_size=(800, 640),\n batch_size=2,\n\n num_threads=2,\n prefetech_buffer=256,\n\n val_split=0.10,\n val_evaluation_epoch=2,\n\n warm_up_epoch=3,\n save_epoch=2,\n\n nms_threshold=0.10,\n score_threshold=0.10,\n eval_threshold=0.10,\n\n # restore_exclude=None,\n # restore_include=[],\n # update_part=None,\n)\n\ntrainer = YoloTrainer(\"ped_tiffany_test\", dataset)\ntrainer.train(settings)\n\n\n#model = trainer.generate_model()\n\n#visualize(trainer.dataset, model, show_truth=False)\n\n# for image in trainer.dataset.images:\n# print(image)\n# for prediction in model.predict_path(image):\n# b = prediction.bounds\n# print(b.left, b.top, b.width, b.height, prediction.class_index, prediction.score)\n","sub_path":"ped_test.py","file_name":"ped_test.py","file_ext":"py","file_size_in_byte":3375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"65503331","text":"# -*- coding: utf-8 -*-\n###\n# (C) Copyright (2012-2016) Hewlett Packard Enterprise Development LP\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n###\n\nimport unittest\n\nimport mock\n\nfrom hpOneView.connection import connection\nfrom hpOneView.exceptions import HPOneViewException\nfrom hpOneView.resources.settings.firmware_bundles import FirmwareBundles\nfrom hpOneView.resources.task_monitor import TaskMonitor\n\n\nclass FirmwareBundlesTest(unittest.TestCase):\n def setUp(self):\n self.host = '127.0.0.1'\n self.connection = connection(self.host)\n self._firmware_bundles = FirmwareBundles(self.connection)\n\n @mock.patch.object(TaskMonitor, 'wait_for_task')\n @mock.patch.object(connection, 'post_multipart')\n def test_upload(self, mock_upload, mock_wait_task):\n firmware_path = \"test/SPPgen9snap6.2015_0405.81.iso\"\n response = mock.MagicMock(status=200)\n\n body = {\n \"category\": \"tasks\",\n \"type\": \"TaskResourceV2\",\n \"associatedResource\": {\n \"resourceUri\": \"/rest/associatedresourceuri\"\n }}\n\n mock_upload.return_value = response, body\n mock_wait_task.return_value = {}\n\n self._firmware_bundles.upload(firmware_path)\n mock_upload.assert_called_once_with('/rest/firmware-bundles', None, firmware_path,\n 'SPPgen9snap6.2015_0405.81.iso')\n\n mock_wait_task.assert_called_once_with(body, -1)\n\n @mock.patch.object(TaskMonitor, 'wait_for_task')\n @mock.patch.object(connection, 'post_multipart')\n def test_upload_should_raise_exception(self, mock_upload, mock_wait_task):\n firmware_path = \"test/SPPgen9snap6.2015_0405.81.iso\"\n\n response = mock.MagicMock(status=400)\n\n body = {\"message\": \"The file you are attempting to upload is empty.\"}\n\n mock_upload.return_value = response, body\n\n try:\n self._firmware_bundles.upload(firmware_path)\n except HPOneViewException as e:\n self.assertEqual(e.msg, \"The file you are attempting to upload is empty.\")\n mock_wait_task.assert_not_called()\n else:\n self.fail(\"Expected exception was not raised\")\n","sub_path":"tests/unit/resources/settings/test_firmware_bundles.py","file_name":"test_firmware_bundles.py","file_ext":"py","file_size_in_byte":3217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"297498452","text":"import torch\nimport torch.nn as nn\nimport tqdm\nimport numpy as np\nfrom model.LSTMEncoder import EncoderRNN\nfrom model.LSTMDecoder import DecoderRNN\nfrom dataloader.dataloader import LoaderDemo\nfrom model.LinearModel import LinearModel\nfrom crit.SimilarityLoss import SimilarityLoss\nfrom model.languageModel import LanguageModelLoss\nimport pickle\nimport time\nimport sys\nimport argparse\nimport os\nfrom torch.utils.data import DataLoader\n\ndevice = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n\n\ndef getLengths(caps):\n\tbatchSize = len(caps)\n\tlengths = torch.zeros(batchSize, dtype=torch.int32)\n\tfor i in range(batchSize):\n\t\tcap = caps[i]\n\t\tnonz = (cap == 0).nonzero()\n\t\tlengths[i] = nonz[0][0] if len(nonz) > 0 else len(cap)\n\treturn lengths\n\n\ndef reloadModel(model_path, linNet, lstmEnc):\n\tpt = torch.load(model_path)\n\n\tdef subload(model, pt_dict):\n\t\tmodel_dict = model.state_dict()\n\t\tpretrained_dict = {}\n\t\tfor k, v in pt_dict.items():\n\t\t\tif (k in model_dict):\n\t\t\t\tpretrained_dict[k] = v if ('linear.weight' not in k) else v.transpose(1,0)\n\t\t# 2. overwrite entries in the existing state dict\n\t\tmodel_dict.update(pretrained_dict)\n\t\t# 3. load the new state dict\n\t\tmodel.load_state_dict(model_dict)\n\t\treturn model\n\n\tlinNet = subload(linNet, pt['linNet'])\n\tlstmEnc = subload(lstmEnc, pt['lstmEnc'])\n\tpt = None\n\tfor p in linNet.conv2.parameters():\n\t\tp.requires_grad = False\n\n\treturn linNet, lstmEnc\n\n\n\n\ndef train(loader, lstmDec, linNet, lstmEnc, LM, crit, optimizer, savepath):\n\tos.makedirs(savepath, exist_ok=True)\n\t# if torch.cuda.is_available():\n\tlstmDec = lstmDec.to(device)\n\tlinNet = linNet.to(device) # nn.DataParallel(linNet,device_ids=[0, 1]).to(device)\n\tlstmEnc = lstmEnc.to(device) # nn.DataParallel(lstmEnc,device_ids=[0, 1]).to(device)\n\tLM = LM.to(device)\n\tcrit = crit.to(device)\n\n\tepoch = 0\n\tlogger = open(os.path.join(savepath, 'loss_history'), 'w')\n\n\tdef saveStateDict(linNet, lstmEnc):\n\t\tmodels = {}\n\t\tmodels['linNet'] = linNet.state_dict()\n\t\tmodels['lstmEnc'] = lstmEnc.state_dict()\n\t\ttorch.save(models, os.path.join(savepath, 'lstmEnc.pt'))\n\n\tdef linOut2DecIn(global_hidden, box_feat):\t# box_feat [8, 4, 4096, 3, 3]\n\t\tglobal_hidden = global_hidden.unsqueeze(0)\n\t\tencoder_hidden = (global_hidden,torch.zeros_like(global_hidden).to(device))\n\t\tB,M,D,H,W = box_feat.size()\n\t\tencoder_outputs = box_feat.permute(0,1,3,4,2).contiguous().view(B,-1,D)\n\t\treturn encoder_hidden, encoder_outputs\n\n\tdef lstr(ts,pres=3):\n\t\treturn str(np.round(ts.data.cpu().numpy(), 3))\n\n\twhile True:\n\t\tld = iter(loader)\n\t\tnumiters = len(ld)\n\t\tqdar = tqdm.tqdm(range(numiters), total=numiters, ascii=True)\n\t\tloss_itr_list = []\n\n\t\tfor i in qdar:\n\n\t\t\t# step 1: load data\n\t\t\tbatchdata = next(ld)\n\t\t\tbox_feats, box_global_feats = makeInp(*batchdata) # box_feats: (numImage,numBoxes,512,7,7) box_global_feats: list, numImage [(512,34,56)]\n\t\t\t\n\t\t\t# step 2: data transform by linNet\n\t\t\tbox_feat, global_hidden = linNet(box_feats, box_global_feats)\n\t\t\t\n\t\t\t# step 3: decode to captions by lstmDec\n\t\t\tencoder_hidden, encoder_outputs = linOut2DecIn(global_hidden,box_feat)\n\t\t\tdecoder_outputs, decoder_hidden, ret_dict = lstmDec(encoder_hidden=encoder_hidden, encoder_outputs=encoder_outputs) # box_feat [8, 4, 4096, 3, 3]\n\t\t\t\n\t\t\t# step 4: calculate loss\n\t\t\t\t# Loss 1: Similarity loss\n\t\t\tlengths = torch.LongTensor(ret_dict['length']).to(device)\n\t\t\tdecoder_outputs = torch.stack([decoder_outputs[i] for i in range(len(decoder_outputs))], 1) # decoder_outputs [8, 15, 10878]\n\t\t\tencoder_outputs = lstmEnc(decoder_outputs, use_prob_vector=True, input_lengths=lengths)\n\t\t\tloss1, loss_reg = crit(box_feat, encoder_outputs, lengths) #box_feat [8, 5, 4096, 3, 3], encoder_outputs [8, 15, 4096]\n\t\t\t\t# Loss 2: LM loss\n\t\t\tloss2 = LM(decoder_outputs, lengths)\n\n\n\t\t\tloss = loss1+loss_reg+loss2\n\n\n\t\t\tloss_itr_list.append(lstr(loss))\n\n\t\t\tlstmEnc.zero_grad()\n\t\t\tLM.zero_grad()\n\t\t\toptimizer.zero_grad()\n\n\n\t\t\tloss.backward()\n\t\t\toptimizer.step()\n\n\t\t\tqdar.set_postfix(simiLoss=lstr(loss1),regLoss=lstr(loss_reg),lmLoss=lstr(loss2))\n\t\t\tif i > 0 and i % 1000 == 0:\n\t\t\t\tsaveStateDict(linNet, lstmEnc)\n\n\t\tloss_epoch_mean = np.mean(loss_itr_list)\n\t\tprint('epoch ' + str(epoch) + ' mean loss:' + str(np.round(loss_epoch_mean, 5)))\n\t\t# loss_epoch_list.append(loss_epoch_mean)\n\t\tlogger.write(str(np.round(loss_epoch_mean, 5)) + '\\n')\n\t\tlogger.flush()\n\t\tsaveStateDict(linNet, lstmEnc)\n\t\tepoch += 1\n\n\ndef inference(image_path,loader,linNet,lstmDec,symbolDec,save_path,sample_mode=['top',3]):\n\n\t# def draw(image,box_coords,decoder_outputs):\n\t# \t...\n\n\tdef linOut2DecIn(global_hidden, box_feat):\t# box_feat [8, 4, 4096, 3, 3]\n\t\tglobal_hidden = global_hidden.unsqueeze(0)\n\t\tencoder_hidden = (global_hidden,torch.zeros_like(global_hidden).to(device))\n\t\tB,M,D,H,W = box_feat.size()\n\t\tencoder_outputs = box_feat.permute(0,1,3,4,2).contiguous().view(B,-1,D)\n\t\treturn encoder_hidden, encoder_outputs\n\n\timage, box_scores, box_coords, box_feats, global_feat = loader.loadImage(image_path)\n\tbox_scores, box_coords, box_feats = loader.sampleBoxes(box_scores, box_coords, box_feats)\n\tbox_feats, global_feat = loader.makeInp(box_feats, global_feat) # box_feats: (numImage,numBoxes,512,7,7) box_global_feats: list, numImage [(512,34,56)]\t\t\n\t# step 2: data transform by linNet\n\tbox_feats, global_hidden = linNet(box_feats, global_feat)\n\t# step 3: decode to captions by lstmDec\n\tencoder_hidden, encoder_outputs = linOut2DecIn(global_hidden,box_feats)\n\tdecoder_outputs, decoder_hidden, ret_dict = lstmDec(encoder_hidden=encoder_hidden, encoder_outputs=encoder_outputs) # box_feat [8, 4, 4096, 3, 3]\n\n\t# todo: decode index to symbols\n\tword_seq = symbolDec.decode(ret_dict['sequence'])\n\tprint(word_seq)\n\t# todo: draw(image,box_coords,decoder_outputs)\n\n\n\n\ndef parseArgs():\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument('-e', '--evaluate_mode',\n\t\t\t\t\t\taction='store_true',\n\t\t\t\t\t\thelp='check similarity matrix.')\n\tparser.add_argument('-p', '--model_path',\n\t\t\t\t\t\tdefault='./lstmEnc.pt')\n\tparser.add_argument('-s', '--save_path',\n\t\t\t\t\t\tdefault='./save/default/')\n\tparser.add_argument('-b', '--batch_imgs',\n\t\t\t\t\t\tdefault=4, type=int)\n\targs = parser.parse_args()\n\treturn args\n\nclass SymbolDecoder(object):\n\t\"\"\"docstring for SymbolDecoder\"\"\"\n\tdef __init__(self,word_dict):\n\t\tsuper(SymbolDecoder, self).__init__()\n\t\tself.word_dict = word_dict\n\t\tself.ind2word = self.makeInd2Word()\n\t\n\tdef makeInd2Word(self):\n\t\tind2word = {}\n\t\tfor k,v in self.word_dict.items():\n\t\t\tind2word[v]=k\n\t\treturn ind2word\n\t\n\tdef decode(self,ind_seq):\n\t\tret = []\n\t\tif isinstance(ind_seq,list):\n\t\t\tfor seq in ind_seq:\n\t\t\t\tret.append(self.decode(seq))\n\t\t\treturn ret\n\t\telse:\n\t\t\treturn self.ind2word[int(ind_seq)]\n\nif __name__ == '__main__':\n\n\targs = parseArgs()\n\n\t# load vocab data\n\twith open('./data/VocabData.pkl', 'rb') as f:\n\t\tVocabData = pickle.load(f)\n\n\t# load linear model, transform feature tensor to semantic space\n\tlinNet = LinearModel(hiddenSize=4096)\n\n\tsos_id = VocabData['word_dict']['']\n\teos_id = VocabData['word_dict']['']\n\n\tlstmDec = DecoderRNN(vocab_size=len(VocabData['word_dict']),max_len=15,sos_id=sos_id, eos_id=eos_id , embedding_size=300,hidden_size=4096,\n\t\t\t\t\t\t embedding_parameter=VocabData['word_embs'], update_embedding=False ,use_attention=True)\n\n\t# todo: reload lstmEnc\n\tlinNet, lstmDec = reloadModel(args.model_path, linNet, lstmDec)\n\n\tloader = LoaderDemo()\n\t# loader = DataLoader(dataset, batch_size=args.batch_imgs, shuffle=False, num_workers=2,\n\t\t\t\t\t\t# collate_fn=dataset.collate_fn)\n\n\tsymbolDec = SymbolDecoder(VocabData['word_dict'])\n\n\t# enter interactive session, require user enter image path, then 'inference' function load the image, output \n\t# while True:\n\t# \t# get image_path interactively\n\t# \t...\n\t\t# do inference, show image, then loop back.\n\timage_path = './densecap/data_pipeline/29.jpg'\n\tlstmDec = lstmDec.to(device)\n\tlinNet = linNet.to(device) # nn.DataParallel(linNet,device_ids=[0, 1]).to(device)\n\tinference(image_path,loader,linNet,lstmDec,symbolDec,args.save_path,sample_mode=['top',3])\n\n\n\n\n\n\n\n\n\n","sub_path":"demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":7973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"164867998","text":"\"\"\"\nCreated by Matt Carter, 2015.\n\"\"\"\nimport os\nimport smtplib\nimport markdown\nfrom credentials import gmail\nfrom flask import Flask, Markup\nfrom flask import render_template, make_response, request, redirect, url_for\nfrom flask.ext.assets import Environment, Bundle\n\napp = Flask(__name__)\n\nAPP_ROOT = os.path.dirname(os.path.abspath(__file__))\nAPP_CONTENT = os.path.join(APP_ROOT, 'content')\n\n# Create CSS and JavaScript Bundle objects.\nassets = Environment(app)\njs = Bundle(os.path.join('bower_components', 'jquery', 'dist',\n 'jquery.min.js'),\n os.path.join('bower_components', 'bootstrap', 'dist',\n 'js', 'bootstrap.min.js'),\n os.path.join('rawjs', 'site.js'),\n filters='jsmin', output='js/packed.js')\nassets.register('js_all', js)\ncss = Bundle(os.path.join('bower_components', 'font-awesome', 'css',\n 'font-awesome.min.css'),\n os.path.join('bower_components', 'bootstrap-social',\n 'bootstrap-social.css'),\n os.path.join('bower_components', 'bootstrap', 'dist',\n 'css', 'bootstrap.min.css'),\n os.path.join('rawcss', 'style.css'),\n filters='cssmin', output='css/packed.css')\nassets.register('css_all', css)\n\n\ndef sortedPostList(cutoff=10):\n \"\"\"\n Return a list of blog post files, sorted by creation date.\n\n Arguments:\n cutoff (int):\n The number of files to return out of the total list. Default: 10.\n \"\"\"\n files = sorted(os.listdir(APP_CONTENT), key=lambda f:\n os.path.getctime(os.path.join(APP_CONTENT, f)))\n recent_files = sorted(files, reverse=True)[:10]\n return recent_files\n\n\n@app.route('/search', methods=['POST'])\ndef searchForm():\n \"\"\"\n Convert the search string into a list of search terms. Return the list of\n blog post files that contain any of the search terms.\n \"\"\"\n search = request.form['search']\n search_terms = search.strip().split()\n all_files = sortedPostList(1000)\n content = 'Search: \"%s\"\\n------\\n\\n' % search\n matching_pages = []\n for f in all_files:\n file_name = os.path.join(APP_CONTENT, f)\n with open(file_name, 'rU') as post:\n lines = post.read().lower()\n for search_term in search_terms:\n if search_term.lower() in lines:\n # Search term found in file.\n matching_pages.append(f)\n matching_pages = list(set(matching_pages))\n if matching_pages:\n # At least one page contains a search term.\n for page in matching_pages:\n base = page.split('.')[0]\n file_name = os.path.join(APP_CONTENT, page)\n with open(file_name, 'rU') as md_contents:\n try:\n title = md_contents.readlines()[0]\n except IndexError:\n # File is empty, use the file name as the title\n title = base\n content += \"* [%s](/%s)\\n\" % (title, base)\n else:\n # No pages match search terms.\n content += \"No pages found.\"\n content = Markup(markdown.markdown(content))\n return render_template('page.html', content=content)\n\n\n@app.route('/blog')\ndef blogPage():\n \"\"\"\n Render the template for the main blog page. Gather a list of the 10 most\n recent posts.\n \"\"\"\n content = \"Main Page\\n---------\\n\\n#### 10 Most Recent Posts\\n\\n\"\n recent_files = sortedPostList()\n for f in recent_files:\n base = f.split('.')[0]\n file_name = os.path.join(APP_CONTENT, f)\n with open(file_name, 'rU') as md_contents:\n try:\n title = md_contents.readlines()[0]\n except IndexError:\n # File is empty, use the file name as the title\n title = base\n content += \"* [%s](/%s)\\n\" % (title, base)\n content = Markup(markdown.markdown(content))\n return render_template('blog.html', content=content)\n\n\n@app.route('/archive')\ndef archivePage():\n \"\"\"\n Render the template for the blog archive page. Gather lists of all posts\n falling into any of the designated tag types.\n \"\"\"\n pass\n\n\n@app.route('/')\ndef contentPage(page=None):\n \"\"\"\n Render the template for a blog page. Read the proper Markdown file from the\n file system based on the file name specified in the route.\n \"\"\"\n try:\n content_path = os.path.join(APP_CONTENT, page + '.md')\n content = open(content_path, 'r').read()\n content = Markup(markdown.markdown(content))\n return render_template('page.html', content=content)\n except IOError:\n # File not found on file system. URL invalid.\n content = 'Uh oh. Page not found.\\n'\n content += '================'\n content = Markup(markdown.markdown(content))\n return render_template('page.html', content=content)\n\n\n@app.route('/email', methods=['POST'])\ndef sendEmail():\n \"\"\"\n Parse the HTML form information from the Contact modal and send me an\n email.\n \"\"\"\n first_name = request.form['form-first-name']\n last_name = request.form['form-last-name']\n sender = request.form['form-email']\n form_message = request.form['form-message']\n recipient = 'matt@m.attcarter.com'\n GMAIL_USERNAME = gmail.email\n GMAIL_PASSWORD = gmail.password\n message_text = \"%s %s, %s\\n\\n%s\" % (first_name,\n last_name,\n sender,\n form_message)\n subject = '[WEBSITE] ' + request.form['form-subject']\n\n session = smtplib.SMTP('smtp.gmail.com', 587)\n session.ehlo()\n session.starttls()\n session.login(GMAIL_USERNAME, GMAIL_PASSWORD)\n headers = \"\\r\\n\".join([\"from: \" + GMAIL_USERNAME,\n \"subject: \" + subject,\n \"to: \" + recipient,\n \"mime-version: 1.0\",\n \"content-type: text/html\"])\n\n # body_of_email can be plaintext or html!\n content = headers + \"\\r\\n\\r\\n\" + message_text\n session.sendmail(GMAIL_USERNAME, recipient, content)\n return redirect(url_for('index'))\n\n\n@app.route('/resume')\ndef downloadResume():\n \"\"\"\n Return a Response() object containing a PDF of my resume.\n \"\"\"\n path = os.path.join(APP_ROOT, 'static', 'downloads', 'Resume.pdf')\n with open(path, 'rb') as f:\n resume = f.read()\n response = make_response(resume)\n response.headers['Content-Type'] = 'application/pdf'\n response.headers['Content-Disposition'] = \\\n 'inline; filename=MatthewCarterResume.pdf'\n return response\n\n\n@app.route('/')\ndef index():\n \"\"\"\n Render the template for the website's home page.\n \"\"\"\n return render_template('index.html')\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":6892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"284069845","text":"# ex14_3.py\nimport os\ntotalsizes = 0\ntotalfiles = 0\n\nmydir = input(\"請輸入目錄 : \")\nif os.path.exists(mydir):\n for file in os.listdir(mydir):\n size = os.path.getsize(os.path.join(mydir,file))\n print(file, \":\", size)\n totalsizes += size\n totalfiles += 1\n print(\"全部檔案數量是 = \", totalfiles) \n print(\"全部檔案大小是 = \", totalsizes)\nelse:\n print(mydir,\"目錄不存在\")\n \n \n\n\n\n\n\n \n","sub_path":"04_The_Path_of_Python/T-resource_Python_201904/ex/ex14_3.py","file_name":"ex14_3.py","file_ext":"py","file_size_in_byte":454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"151963736","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Feb 13 10:47:13 2018\r\n\r\n@author: r.dewinter\r\n\"\"\"\r\nimport numpy as np\r\n#from mpl_toolkits.mplot3d import Axes3D\r\nimport matplotlib.pyplot as plt\r\n#from matplotlib import cm\r\n#from matplotlib.ticker import LinearLocator, FormatStrFormatter\r\n\r\nfrom paretofrontFeasible import paretofrontFeasible\r\n\r\nimport pandas as pd\r\ndf = pd.read_csv(\"moga3200Orig.csv\")\r\n\r\ndata = np.genfromtxt(\"moga_5100.csv\", delimiter=',')\r\nparameters = data[0:,1:7]\r\nconstraints = data[0:,7:-3]\r\nobjectives = data[0:,-3:-1]\r\nconstraints[:,:4] = constraints[:,:4]*-1+1\r\nconstraints[:,4:] = constraints[:,4:]*-1\r\n\r\n\r\n#data = np.genfromtxt(\"withinResults.csv\",delimiter=',')\r\n#parameters = data[1:,:6]\r\n#constraints = data[1:,6:-2]\r\n#objectives = data[1:,-2:]\r\n\r\n\r\nconstraints = np.genfromtxt('results/compute_ship/run4_new/con_run4.csv', delimiter=',')\r\nparameters = np.genfromtxt('results/compute_ship/run4_new/par_run4.csv', delimiter=',')\r\nobjectives = np.genfromtxt('results/compute_ship/run4_new/obj_run4.csv', delimiter=',')\r\nvisualizeResults(objectives, parameters, constraints)\r\n\r\n#data = np.genfromtxt('moga2',delimiter='\\t')\r\n\r\n#objectives = data[:,-3:-1]\r\n#parameters = data[:,1:7]\r\n#feasible = data[:,0]+1\r\n#feasible = np.nan_to_num(feasible)\r\n#feasible = feasible<1\r\n#\r\n#constraints = data[:,0]+1\r\n#constraints = np.asmatrix(constraints)\r\n#constraints = constraints.T\r\n#constraints = np.nan_to_num(constraints)\r\n#import seaborn as sns\r\ndef visualizeResults(objectives, parameters, constraints):\r\n paretoOptimal = paretofrontFeasible(objectives,constraints)\r\n n_constraints = constraints.shape[1]\r\n feasible = np.sum(constraints <= 0, axis = 1) == n_constraints\r\n \r\n# sns.set_style('darkgrid')\r\n plt.plot(objectives[:,0], objectives[:,1], '.', c='r')\r\n plt.plot(objectives[feasible][:,0], objectives[feasible][:,1], 'o',c='b')\r\n \r\n paretoOptimalObjOrder = np.argsort(objectives[paretoOptimal][:,0], axis=0)\r\n paretoOptimalObj = objectives[paretoOptimal][paretoOptimalObjOrder]\r\n plt.plot(paretoOptimalObj[:,0], paretoOptimalObj[:,1], '^',c='g')\r\n plt.plot(objectives[0,0], objectives[0,1], 'D', c='gold')\r\n plt.plot(paretoOptimalObj[:,0], paretoOptimalObj[:,1], c='g')\r\n plt.title('paretofront')\r\n plt.show()\r\n \r\n objectives = objectives[feasible]\r\n parameters = parameters[feasible]\r\n constraints = constraints[feasible]\r\n \r\n feasible = np.sum(constraints <= 0, axis = 1) == n_constraints\r\n paretoOptimal = paretofrontFeasible(objectives,constraints)\r\n\r\n plt.plot(objectives[:,0], objectives[:,1], '.', c='r')\r\n plt.plot(objectives[feasible][:,0], objectives[feasible][:,1], 'o',c='b')\r\n \r\n paretoOptimalObjOrder = np.argsort(objectives[paretoOptimal][:,0], axis=0)\r\n paretoOptimalObj = objectives[paretoOptimal][paretoOptimalObjOrder]\r\n plt.plot(paretoOptimalObj[:,0], paretoOptimalObj[:,1], '^',c='g')\r\n plt.plot(objectives[0,0], objectives[0,1], 'D', c='gold')\r\n plt.plot(paretoOptimalObj[:,0], paretoOptimalObj[:,1], c='g')\r\n plt.title('paretofront')\r\n plt.show()\r\n \r\n plt.plot(paretoOptimalObj[:,0], paretoOptimalObj[:,1], '^',c='g')\r\n plt.show()\r\n \r\n# for p in range(parameters.shape[1]):\r\n# x = objectives[:,0]\r\n# y = objectives[:,1]\r\n# z = parameters[:,p]\r\n# \r\n# fig = plt.figure()\r\n# ax = fig.gca(projection='3d')\r\n# \r\n# ax.plot_trisurf(x, y, z, linewidth=0.2, antialiased=True)\r\n# \r\n# for c in range(constraints.shape[1]):\r\n# x = objectives[:,0]\r\n# y = objectives[:,1]\r\n# z = constraints[:,c]\r\n# \r\n# fig = plt.figure()\r\n# ax = fig.gca(projection='3d')\r\n# \r\n# ax.plot_trisurf(x, y, z, linewidth=0.2, antialiased=True)\r\n","sub_path":"help scripts/createVisualizations.py","file_name":"createVisualizations.py","file_ext":"py","file_size_in_byte":3788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"79607210","text":"import json\r\nfrom util.Logginger import init_logger\r\nimport config.args as args\r\n\r\nlogger = init_logger(\"model_net\", logging_path=args.log_path)\r\n\r\n\r\nclass InputExample(object):\r\n\r\n def __init__(self, guid, text_a, text_b=None, label=None):\r\n \"\"\"创建一个输入实例\r\n Args:\r\n guid: 每个example拥有唯一的id\r\n text_a: 第一个句子的原始文本，一般对于文本分类来说，只需要text_a\r\n text_b: 第二个句子的原始文本，在句子对的任务中才有，分类问题中为None\r\n label: example对应的标签，对于训练集和验证集应非None，测试集为None\r\n \"\"\"\r\n self.guid = guid\r\n self.text_a = text_a\r\n self.text_b = text_b\r\n self.label = label\r\n\r\n\r\nclass InputFeature(object):\r\n\r\n def __init__(self, input_ids, input_mask, segment_ids, label_id, output_mask):\r\n self.input_ids = input_ids\r\n self.input_mask = input_mask\r\n self.segment_ids = segment_ids\r\n self.label_id = label_id\r\n self.output_mask = output_mask\r\n\r\n\r\nclass DataProcessor(object):\r\n \"\"\"数据预处理的基类，自定义的MyPro继承该类\"\"\"\r\n\r\n def get_train_examples(self, data_dir):\r\n \"\"\"读取训练集 Gets a collection of `InputExample`s for the train set.\"\"\"\r\n raise NotImplementedError()\r\n\r\n def get_dev_examples(self, data_dir):\r\n \"\"\"读取验证集 Gets a collection of `InputExample`s for the dev set.\"\"\"\r\n raise NotImplementedError()\r\n\r\n def get_labels(self):\r\n \"\"\"读取标签 Gets the list of labels for this data set.\"\"\"\r\n raise NotImplementedError()\r\n\r\n @classmethod\r\n def _read_json(cls, input_file):\r\n with open(input_file, \"r\", encoding='utf-8') as fr:\r\n lines = []\r\n for line in fr:\r\n _line = line.strip('\\n')\r\n lines.append(_line)\r\n return lines\r\n\r\n\r\nclass MyPro(DataProcessor):\r\n \"\"\"将数据构造成example格式\"\"\"\r\n\r\n def _create_example(self, lines, set_type):\r\n examples = []\r\n for i, line in enumerate(lines):\r\n guid = \"%s-%d\" % (set_type, i)\r\n line = json.loads(line)\r\n text_a = line[\"source\"]\r\n label = line[\"target\"]\r\n try:\r\n assert len(label.split()) == len(text_a.split())\r\n except:\r\n logger.info(f' Error data \\n')\r\n print(f'{label.split()}, {len(label.split())}')\r\n print(f'{text_a.split()}, {len(text_a.split())}')\r\n continue\r\n example = InputExample(guid=guid, text_a=text_a, label=label)\r\n examples.append(example)\r\n return examples\r\n\r\n def get_train_examples(self, path):\r\n lines = self._read_json(path)\r\n examples = self._create_example(lines, \"train\")\r\n return examples\r\n\r\n def get_dev_examples(self, path):\r\n lines = self._read_json(path)\r\n examples = self._create_example(lines, \"dev\")\r\n return examples\r\n\r\n def get_labels(self):\r\n return args.labels\r\n\r\n\r\ndef convert_examples_to_features(examples, label_list, max_seq_length, tokenizer):\r\n # 标签转换为数字\r\n label_map = {label: i for i, label in enumerate(label_list)}\r\n features = []\r\n for ex_index, example in enumerate(examples):\r\n tokens_a = tokenizer.tokenize(example.text_a)\r\n labels = example.label.split()\r\n\r\n if len(tokens_a) == 0 or len(labels) == 0:\r\n continue\r\n\r\n if len(tokens_a) > max_seq_length - 2:\r\n tokens_a = tokens_a[:(max_seq_length - 2)]\r\n labels = labels[:(max_seq_length - 2)]\r\n # ----------------处理source--------------\r\n ## 句子首尾加入标示符\r\n tokens = [\"[CLS]\"] + tokens_a + [\"[SEP]\"]\r\n segment_ids = [0] * len(tokens)\r\n ## 词转换成数字\r\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\r\n\r\n input_mask = [1] * len(input_ids)\r\n\r\n padding = [0] * (max_seq_length - len(input_ids))\r\n\r\n input_ids += padding\r\n input_mask += padding\r\n segment_ids += padding\r\n\r\n assert len(input_ids) == max_seq_length\r\n assert len(input_mask) == max_seq_length\r\n assert len(segment_ids) == max_seq_length\r\n\r\n # ---------------处理target----------------\r\n ## Notes: label_id中不包括[CLS]和[SEP]\r\n label_id = [label_map.get(l, len(label_map) - 1) for l in labels]\r\n label_padding = [-1] * (max_seq_length - len(label_id))\r\n label_id += label_padding\r\n\r\n # 不考虑cls和sep\r\n output_mask = [0] + len(tokens_a) * [1] + [0]\r\n output_mask += padding\r\n\r\n if ex_index < 1:\r\n logger.info(\"-----------------Example-----------------\")\r\n logger.info(\"guid: %s\" % (example.guid))\r\n logger.info(\"tokens: %s\" % \" \".join([str(x) for x in tokens]))\r\n logger.info(\"input_ids: %s\" % \" \".join([str(x) for x in input_ids]))\r\n logger.info(\"input_mask: %s\" % \" \".join([str(x) for x in input_mask]))\r\n logger.info(\"label: %s \" % \" \".join([str(x) for x in label_id]))\r\n logger.info(\"output_mask: %s \" % \" \".join([str(x) for x in output_mask]))\r\n # ----------------------------------------------------\r\n\r\n feature = InputFeature(input_ids=input_ids,\r\n input_mask=input_mask,\r\n segment_ids=segment_ids,\r\n label_id=label_id,\r\n output_mask=output_mask)\r\n features.append(feature)\r\n\r\n return features\r\n","sub_path":"bert_nlp/preprocessing/data_processor.py","file_name":"data_processor.py","file_ext":"py","file_size_in_byte":5700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"453588549","text":"__author__ = 'Steffen'\r\n\r\nfrom common.gcd import *\r\nfrom common.primes import *\r\n\r\n\r\ndef M(n):\r\n for a in range(n, 0, -1):\r\n if (a * a) % n == a:\r\n return a\r\n else:\r\n return 0\r\n\r\n\r\ndef pe407(N):\r\n sum = 0\r\n for i in range(1, N + 1):\r\n mi = M(i)\r\n sum += mi\r\n # print((i,mi))\r\n return sum\r\n\r\n\r\ndef product(list):\r\n p = 1\r\n for i in list:\r\n p *= i\r\n return p\r\n\r\n\r\ndef fun(n, factors, bin):\r\n # print(bin)\r\n a = [factors[i] for i in range(len(bin)) if bin[i] is 0]\r\n b = [factors[i] for i in range(len(bin)) if bin[i] is 1]\r\n x = product(a)\r\n y = product(b)\r\n # print((x,y))\r\n (d, r, s) = egcd(x, y)\r\n # print((d,r,s))\r\n a1 = (x * r) % n\r\n a2 = (y * s) % n\r\n # print((a1,a2))\r\n return max(a1, a2)\r\n\r\n\r\ndef backtrackBinary(i, n):\r\n global binary, factors\r\n if i > len(binary):\r\n return 0\r\n elif i == len(binary):\r\n return fun(n, factors, binary)\r\n else:\r\n binary[i] = 0\r\n x = backtrackBinary(i + 1, n)\r\n binary[i] = 1\r\n y = backtrackBinary(i + 1, n)\r\n return max(x, y)\r\n\r\n\r\ndef M2(n):\r\n global binary, factors, longFactors\r\n factors = [x for x in longFactors if x > 1]\r\n binary = [0 for i in range(len(factors))]\r\n # print(str(n) + ' ' + str(factors))\r\n return backtrackBinary(1, n)\r\n\r\n\r\nprimes = []\r\nbinary = []\r\nfactors = []\r\nlongFactors = []\r\n\r\n\r\ndef g(n, i, j, N):\r\n global primes, longFactors\r\n # print((j,i))\r\n m = M2(n)\r\n # print(str(n) + ' ' + str(coef) + ' ' + str(factors) + ' ' + str(m))\r\n # print(str(n) + ' ' + str(longFactors) + ' ' + str(m))\r\n\r\n if n * primes[i] <= N:\r\n fj = longFactors[j]\r\n longFactors[j] *= primes[i]\r\n m += g(n * primes[i], i, j, N)\r\n longFactors[j] = fj\r\n\r\n ii = i + 1\r\n while ii < len(primes) and n * primes[ii] <= N:\r\n longFactors[j + 1] = primes[ii]\r\n m += g(n * primes[ii], ii, j + 1, N)\r\n ii += 1\r\n longFactors[j + 1] = 1\r\n return m\r\n\r\n\r\ndef pe407b(N):\r\n global primes, longFactors\r\n (primes, comp) = prime_list(N + 1)\r\n longFactors = [1 for i in range(9)]\r\n res = 0\r\n for i in range(len(primes)):\r\n longFactors[0] = primes[i]\r\n res += g(primes[i], i, 0, N)\r\n return res\r\n\r\n\r\nN = 100000\r\nprint(pe407b(N))\r\n# print(pe407(N))\r\n","sub_path":"pe407/pe407.py","file_name":"pe407.py","file_ext":"py","file_size_in_byte":2364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"455874513","text":"import matplotlib.pylab as plt\nimport numpy as np\n\n\nclass PieCharts:\n\n def __init__(self, labels=None, sizes=None, explode=None):\n self.labels = labels\n self.sizes = sizes\n self.explode = explode\n\n def basic_pie_chart(self, title='', units=''):\n fig, ax = plt.subplots()\n\n wedges, texts, autotexts = ax.pie(self.sizes, explode=self.explode, labels=self.labels, autopct='%1.1f%%', startangle=90, textprops=dict(color='w'))\n ax.legend(wedges, self.labels, title='Unit: {}'.format(units), loc='upper right')\n plt.setp(autotexts, size=8, weight='bold')\n ax.set_title(title, size=12)\n ax.axis('equal')\n\n def donut_chart(self):\n fig, ax = plt.subplots()\n wedges, texts = ax.pie(self.sizes, explode=self.explode, wedgeprops=dict(width=0.5), startangle=90)\n bbox_props = dict(boxstyle=\"square,pad=0.3\", fc=\"w\", ec=\"k\", lw=0.72)\n kw = dict(arrowprops=dict(arrowstyle=\"-\"),\n bbox=bbox_props, zorder=0, va=\"center\")\n\n for i, p in enumerate(wedges):\n ang = (p.theta2 - p.theta1) / 2. + p.theta1\n y = np.sin(np.deg2rad(ang))\n x = np.cos(np.deg2rad(ang))\n horizontal_alignment = {-1: \"right\", 1: \"left\"}[int(np.sign(x))]\n connection_style = \"angle,angleA=0,angleB={}\".format(ang)\n kw[\"arrowprops\"].update({\"connectionstyle\": connection_style})\n ax.annotate(self.labels[i], xy=(x, y), xytext=(1.2 * np.sign(x), 1.4 * y),\n horizontalalignment=horizontal_alignment, **kw)\n","sub_path":"piecharts.py","file_name":"piecharts.py","file_ext":"py","file_size_in_byte":1580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"344481924","text":"\"\"\"A mechanism to load header files in separate processes using the `multiprocessing` module \"\"\"\nimport logging\nimport os\nfrom multiprocessing import Pool\nfrom multiprocessing.pool import AsyncResult\nfrom typing import Sequence, List, Optional, Tuple\n\nfrom PyQt5 import QtCore\nfrom sastool.io.credo_cct import Header\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.DEBUG)\n\n\ndef loadHeader(fsn: int, dirs: Sequence[str], headerfileformat: str) -> Tuple[int, Optional[Header]]:\n for d in dirs:\n try:\n return (fsn, Header.new_from_file(os.path.join(d, headerfileformat.format(fsn))))\n except FileNotFoundError:\n continue\n return (fsn, None)\n\n\nclass HeaderLoader(QtCore.QObject):\n TIMERINTERVAL: int = 100 # milliseconds\n pool: Optional[Pool]\n outstanding: List[AsyncResult]\n fsns: Sequence[int]\n dirs: Sequence[str]\n headerfileformat: str\n results: List[Tuple[int, Optional[Header]]]\n finished = QtCore.pyqtSignal() # loading finished.\n progress = QtCore.pyqtSignal(int, int) # total count, ready count\n timerid: int = 0\n\n def __init__(self, fsns: Sequence[int], dirs: Sequence[str], headerfileformat: str):\n super().__init__()\n self.pool = None # do not create a pool yet\n self.outstanding = []\n self.fsns = fsns\n self.dirs = dirs\n self.headerfileformat = headerfileformat\n self.results = []\n\n @property\n def idle(self) -> bool:\n logger.debug('Outstanding jobs in header loader: {}'.format(len(self.outstanding)))\n return not self.outstanding\n\n def submit(self):\n logger.debug('Submitting header loading jobs for {} fsns.'.format(len(self.fsns)))\n self.pool = Pool()\n self.results = []\n self.outstanding = [self.pool.apply_async(loadHeader, [f, self.dirs, self.headerfileformat]) for f in self.fsns]\n self.timerid = self.startTimer(self.TIMERINTERVAL)\n self.progress.emit(len(self.fsns), 0)\n\n def timerEvent(self, event: QtCore.QTimerEvent) -> None:\n ready = [o for o in self.outstanding if o.ready()] # select those which are ready\n if (not ready) and (self.outstanding):\n # do not block the event loop\n event.accept()\n return\n self.results.extend([r.get() for r in ready])\n self.outstanding = [o for o in self.outstanding if o not in ready]\n self.progress.emit(len(self.fsns), len(self.results))\n if not self.outstanding:\n # we have finished\n self.pool.close()\n self.pool.join()\n self.pool = None\n self.killTimer(self.timerid)\n self.finished.emit()\n event.accept()\n\n def stop(self):\n self.pool.terminate()\n self.pool.join()\n self.pool = None\n self.outstanding = []\n\n def isRunning(self) -> bool:\n return bool(self.outstanding) or (self.pool is not None)\n\n def headers(self) -> List[Header]:\n return sorted([header for fsn, header in self.results if header is not None], key=lambda h: h.fsn)\n\n def setFSNs(self, fsns:Sequence[int]):\n if self.isRunning():\n raise ValueError('Cannot set FSNs while running')\n self.fsns = list(fsns)\n self.results = []\n self.outstanding = []\n\n def setPath(self, path:List[str]):\n self.dirs = list(path)\n","sub_path":"cct/processinggui/project/headerloader.py","file_name":"headerloader.py","file_ext":"py","file_size_in_byte":3394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"253154193","text":"\n# 1. https://www.jiuzhang.com/solution/group-anagrams/#tag-highlight-lang-python\nclass Solution(object):\n def groupAnagrams(self, strs):\n \"\"\"\n :type strs: List[str]\n :rtype: List[List[str]]\n \"\"\"\n dict = {}\n for item in sorted(strs):\n \tprint('\\nitem = ', item)\n \tprint('sorted(item) = ', sorted(item))\n \tsortedItem = ''.join(sorted(item))\n \tprint('sortedItem = ', sortedItem)\n\n \tdict[sortedItem] = dict.get(sortedItem, []) + [item]\n \tprint('dict[sortedItem] = ', dict[sortedItem])\n\n \tprint('dict.values = ', dict.values)\n \tprint('dict = ', dict)\n return dict.values\n\nstrs = [\"eat\", \"tea\", \"tan\", \"ate\", \"nat\", \"bat\"]\n\nSolution().groupAnagrams(strs)","sub_path":"49_group_anagrams.py","file_name":"49_group_anagrams.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"22953708","text":"import time\nimport os\nimport pandas as pd\nimport sqlite3\nimport copy\nimport logging\nimport tempfile\n\nlogger = logging.getLogger(__name__)\n\nfrom collections import UserDict, namedtuple\n\nfrom . import serialization\nfrom .utils import open_or_reuse\n\n\nclass History:\n \"\"\"\n Store and retrieve values from a sqlite database.\n \"\"\"\n\n def __init__(self, name=None, db_path=None, backup=False):\n self._db = None\n\n if db_path is None:\n if not name:\n name = time.time()\n _, db_path = tempfile.mkstemp(suffix='{}.sqlite'.format(name))\n\n if backup and os.path.exists(db_path):\n newname = db_path + '.backup{}.sqlite'.format(time.time())\n os.rename(db_path, newname)\n\n self.db_path = db_path\n\n self.db = db_path\n\n with self.db:\n logger.debug('Creating database {}'.format(self.db_path))\n self.db.execute('''CREATE TABLE IF NOT EXISTS history (agent_id text, t_step int, key text, value text text)''')\n self.db.execute('''CREATE TABLE IF NOT EXISTS value_types (key text, value_type text)''')\n self.db.execute('''CREATE UNIQUE INDEX IF NOT EXISTS idx_history ON history (agent_id, t_step, key);''')\n self._dtypes = {}\n self._tups = []\n\n @property\n def db(self):\n try:\n self._db.cursor()\n except (sqlite3.ProgrammingError, AttributeError):\n self.db = None # Reset the database\n return self._db\n\n @db.setter\n def db(self, db_path=None):\n self._close()\n db_path = db_path or self.db_path\n if isinstance(db_path, str):\n logger.debug('Connecting to database {}'.format(db_path))\n self._db = sqlite3.connect(db_path)\n else:\n self._db = db_path\n\n def _close(self):\n if self._db is None:\n return\n self.flush_cache()\n self._db.close()\n self._db = None\n\n @property\n def dtypes(self):\n self.read_types()\n return {k:v[0] for k, v in self._dtypes.items()}\n\n def save_tuples(self, tuples):\n '''\n Save a series of tuples, converting them to records if necessary\n '''\n self.save_records(Record(*tup) for tup in tuples)\n\n def save_records(self, records):\n '''\n Save a collection of records\n '''\n for record in records:\n if not isinstance(record, Record):\n record = Record(*record)\n self.save_record(*record)\n\n def save_record(self, agent_id, t_step, key, value):\n '''\n Save a collection of records to the database.\n Database writes are cached.\n '''\n value = self.convert(key, value)\n self._tups.append(Record(agent_id=agent_id,\n t_step=t_step,\n key=key,\n value=value))\n if len(self._tups) > 100:\n self.flush_cache()\n\n def convert(self, key, value):\n \"\"\"Get the serialized value for a given key.\"\"\"\n if key not in self._dtypes:\n self.read_types()\n if key not in self._dtypes:\n name = serialization.name(value)\n serializer = serialization.serializer(name)\n deserializer = serialization.deserializer(name)\n self._dtypes[key] = (name, serializer, deserializer)\n with self.db:\n self.db.execute(\"replace into value_types (key, value_type) values (?, ?)\", (key, name))\n return self._dtypes[key][1](value)\n\n def recover(self, key, value):\n \"\"\"Get the deserialized value for a given key, and the serialized version.\"\"\"\n if key not in self._dtypes:\n self.read_types()\n if key not in self._dtypes:\n raise ValueError(\"Unknown datatype for {} and {}\".format(key, value))\n return self._dtypes[key][2](value)\n\n def flush_cache(self):\n '''\n Use a cache to save state changes to avoid opening a session for every change.\n The cache will be flushed at the end of the simulation, and when history is accessed.\n '''\n logger.debug('Flushing cache {}'.format(self.db_path))\n with self.db:\n for rec in self._tups:\n self.db.execute(\"replace into history(agent_id, t_step, key, value) values (?, ?, ?, ?)\", (rec.agent_id, rec.t_step, rec.key, rec.value))\n self._tups = list()\n\n def to_tuples(self):\n self.flush_cache()\n with self.db:\n res = self.db.execute(\"select agent_id, t_step, key, value from history \").fetchall()\n for r in res:\n agent_id, t_step, key, value = r\n value = self.recover(key, value)\n yield agent_id, t_step, key, value\n\n def read_types(self):\n with self.db:\n res = self.db.execute(\"select key, value_type from value_types \").fetchall()\n for k, v in res:\n serializer = serialization.serializer(v)\n deserializer = serialization.deserializer(v)\n self._dtypes[k] = (v, serializer, deserializer)\n\n def __getitem__(self, key):\n self.flush_cache()\n key = Key(*key)\n agent_ids = [key.agent_id] if key.agent_id is not None else []\n t_steps = [key.t_step] if key.t_step is not None else []\n keys = [key.key] if key.key is not None else []\n\n df = self.read_sql(agent_ids=agent_ids,\n t_steps=t_steps,\n keys=keys)\n r = Records(df, filter=key, dtypes=self._dtypes)\n if r.resolved:\n return r.value()\n return r\n\n def read_sql(self, keys=None, agent_ids=None, t_steps=None, convert_types=False, limit=-1):\n\n self.read_types()\n\n def escape_and_join(v):\n if v is None:\n return\n return \",\".join(map(lambda x: \"\\'{}\\'\".format(x), v))\n\n filters = [(\"key in ({})\".format(escape_and_join(keys)), keys),\n (\"agent_id in ({})\".format(escape_and_join(agent_ids)), agent_ids)\n ]\n filters = list(k[0] for k in filters if k[1])\n\n last_df = None\n if t_steps:\n # Look for the last value before the minimum step in the query\n min_step = min(t_steps)\n last_filters = ['t_step < {}'.format(min_step),]\n last_filters = last_filters + filters\n condition = ' and '.join(last_filters)\n\n last_query = '''\n select h1.*\n from history h1\n inner join (\n select agent_id, key, max(t_step) as t_step\n from history\n where {condition}\n group by agent_id, key\n ) h2\n on h1.agent_id = h2.agent_id and\n h1.key = h2.key and\n h1.t_step = h2.t_step\n '''.format(condition=condition)\n last_df = pd.read_sql_query(last_query, self.db)\n\n filters.append(\"t_step >= '{}' and t_step <= '{}'\".format(min_step, max(t_steps)))\n\n condition = ''\n if filters:\n condition = 'where {} '.format(' and '.join(filters))\n query = 'select * from history {} limit {}'.format(condition, limit)\n df = pd.read_sql_query(query, self.db)\n if last_df is not None:\n df = pd.concat([df, last_df])\n\n df_p = df.pivot_table(values='value', index=['t_step'],\n columns=['key', 'agent_id'],\n aggfunc='first')\n\n for k, v in self._dtypes.items():\n if k in df_p:\n dtype, _, deserial = v\n df_p[k] = df_p[k].fillna(method='ffill').astype(dtype)\n if t_steps:\n df_p = df_p.reindex(t_steps, method='ffill')\n return df_p.ffill()\n\n def __getstate__(self):\n state = dict(**self.__dict__)\n del state['_db']\n del state['_dtypes']\n return state\n\n def __setstate__(self, state):\n self.__dict__ = state\n self._dtypes = {}\n self._db = None\n\n def dump(self, f):\n self._close()\n for line in open_or_reuse(self.db_path, 'rb'):\n f.write(line)\n\n\nclass Records():\n\n def __init__(self, df, filter=None, dtypes=None):\n if not filter:\n filter = Key(agent_id=None,\n t_step=None,\n key=None)\n self._df = df\n self._filter = filter\n self.dtypes = dtypes or {}\n super().__init__()\n\n def mask(self, tup):\n res = ()\n for i, k in zip(tup[:-1], self._filter):\n if k is None:\n res = res + (i,)\n res = res + (tup[-1],)\n return res\n\n def filter(self, newKey):\n f = list(self._filter)\n for ix, i in enumerate(f):\n if i is None:\n f[ix] = newKey\n self._filter = Key(*f)\n\n @property\n def resolved(self):\n return sum(1 for i in self._filter if i is not None) == 3\n\n def __iter__(self):\n for column, series in self._df.iteritems():\n key, agent_id = column\n for t_step, value in series.iteritems():\n r = Record(t_step=t_step,\n agent_id=agent_id,\n key=key,\n value=value)\n yield self.mask(r)\n\n def value(self):\n if self.resolved:\n f = self._filter\n try:\n i = self._df[f.key][str(f.agent_id)]\n ix = i.index.get_loc(f.t_step, method='ffill')\n return i.iloc[ix]\n except KeyError as ex:\n return self.dtypes[f.key][2]()\n return list(self)\n\n def df(self):\n return self._df\n\n def __getitem__(self, k):\n n = copy.copy(self)\n n.filter(k)\n if n.resolved:\n return n.value()\n return n\n\n def __len__(self):\n return len(self._df)\n\n def __str__(self):\n if self.resolved:\n return str(self.value())\n return ''.format(self._filter)\n\nKey = namedtuple('Key', ['agent_id', 't_step', 'key'])\nRecord = namedtuple('Record', 'agent_id t_step key value')\n","sub_path":"soil/history.py","file_name":"history.py","file_ext":"py","file_size_in_byte":10291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"73049117","text":"from GameParams import *\n\n\nclass Bomb:\n def __init__(self, x, y, start, owner):\n self.x = x\n self.y = y\n self.exploded = False\n self.start = start\n self.owner = owner\n self.boom_range = bomb_range\n\n def boom(self):\n self.exploded = True\n\n def get_cords_in_range(self):\n \"\"\"\n :return: List of tuples with coordinates fields in bomb range.\n \"\"\"\n ret = []\n for x in range(self.x - self.boom_range, self.x + self.boom_range + 1):\n ret.append((x, self.y))\n for y in range(self.y - self.boom_range, self.y + self.boom_range + 1):\n ret.append((self.x, y))\n\n return ret\n","sub_path":"Bomb.py","file_name":"Bomb.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"210071562","text":"# A script for converting CSV to JSON\n# taken from http://stackoverflow.com/questions/7520165/python-convert-csv-file-to-json\n# with modifications from http://stackoverflow.com/questions/12309269/write-json-data-to-file-in-python\n\nimport json\n\nf = open('CrosswalkMasterListV2Lauren-google.csv', 'r')\n\narr = []\nheaders = []\n\nfor header in f.readline().split(','):\n\theaders.append(header)\n\nfor line in f.readlines():\n\tlineItems = {}\n\tfor i,item in enumerate(line.split(',')):\n\t\tlineItems[headers[i]] = item\n\tarr.append(lineItems)\n\nf.close()\n\nwith open('JEN-google.txt', 'w') as outfile:\n\tjson.dump(arr, outfile)\n\n#jsonText = json.dumps(arr)\n\n#print jsonText","sub_path":"data/junk/csv_to_json_script_4.py","file_name":"csv_to_json_script_4.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"362499582","text":"from flask import jsonify\n\nfrom Handler import DictionaryBuilder as Dic\nfrom DAO.ChatDAO import ChatDAO\n\ndao = ChatDAO()\n\n\ndef getAllChats():\n # This method will return all the chats\n chat_lists = dao.getAllChats()\n if not chat_lists:\n return jsonify(Error=\"No Chats Found\")\n result_list = []\n\n for row in chat_lists:\n result = Dic.build_chat_dict(row)\n result_list.append(result)\n return jsonify(Chat=result_list)\n\ndef getChatByID(cID):\n # This method will return the determined chat by its ID\n desired_chat = dao.getChatByID(cID)\n if not desired_chat:\n return jsonify(Error=\" Chat not found\"), 404\n\n chat = Dic.build_chat_dict(desired_chat)\n return jsonify(Chat=chat)\n\ndef getParticipantsByChatID(cID):\n # This method returns the list of participants in a determined chat\n chat_participants = dao.getChatParticipants(cID)\n if not chat_participants:\n return jsonify(Error=\"No Participants Found\")\n result_list = []\n for row in chat_participants:\n result = Dic.build_participants_dict(row)\n result_list.append(result)\n return jsonify(Participants=result_list)\n\n\ndef getChatByUserID(uID):\n # This method will return the chats on which the user are part of\n chats = dao.getChatByUserID(uID)\n if not chats:\n return jsonify(Error=\"No Chats Found\")\n result_list = []\n for row in chats:\n result = Dic.build_chat_dict(row)\n result_list.append(result)\n return jsonify(Chats=result_list)\n\n\ndef getALlParticipants():\n # This method will return all the participants on the application\n participants = dao.getAllParticipants()\n if not participants:\n return jsonify(Error=\"No Participants Found\")\n result_list = []\n for row in participants:\n result = Dic.build_participants_dict(row)\n result_list.append(result)\n return jsonify(Participants=result_list)\n\n\ndef getChatAsAdmin(uID):\n result = dao.getChatsAsAdmin(uID)\n if not result:\n return jsonify(Error=\"No Chats Found\")\n mapped_result = []\n for r in result:\n mapped_result.append(Dic.build_chat_dict(r))\n return jsonify(AdminChats=mapped_result)\n\n\ndef getChatAsMember(uID):\n result = dao.getChatAsMember(uID)\n if not result:\n return jsonify(Error=\"No Chats Found\")\n mapped_result = []\n for r in result:\n mapped_result.append(Dic.build_participants_dict(r))\n return jsonify(MemberChats=mapped_result)\n\ndef getGroupChats():\n groupChats = dao.getGroupChats()\n if not groupChats:\n return jsonify(Error= \"No group chats in the system\")\n result_list = []\n\n for row in groupChats:\n results = Dic.build_chat_dict(row)\n print(row)\n result_list.append(results)\n return jsonify(GroupChats = result_list)\n\ndef getAllActiveChats():\n result = dao.getAllActiveChats()\n if not result:\n return jsonify(Error=\"No Chats Found\")\n mapped_result = []\n for r in result:\n mapped_result.append(Dic.build_chat_dict(r))\n return jsonify(Chats=mapped_result)\ndef getChatInfo(CID):\n result = dao.getChatInfo(CID)\n# def removeChatGroup(self,cID):\n# #THis method will remove a chat\n# dao = ReadChatDAO()\n# if not dao.getChatInfo(cID):\n# return jsonify(Error = \"Chat not found\"), 404\n# else:\n# #CHECKKKKKKKKKKKK!!-!-!_!_!_!_1!_!!-!:D\n# dao.getAllChats().__getitem__(cID).insert(5, False)\n return[]\n\n\ndef insertChat(json):\n if len(json) != 3:\n\n return jsonify(Error = \" Malformed post request, missing or extra data\")\n else:\n print('working handler')\n cName = json['cname']\n isGroupChat = json['isGroupChat']\n uid = json['uid']\n\n if cName and isGroupChat and uid:\n cid = dao.insertChat(cName,isGroupChat,uid)\n if cid:\n return jsonify(Chat = \"Chat created!\")\n else:\n return jsonify(ERROR = 'Could not create group')\n else:\n return jsonify(Error = 'Unexpected attributes in post request'), 400\n\n\ndef insertParticipant(json):\n if len(json) != 3:\n return jsonify(Error = \"Malformed post request, missing or extra data\")\n\n else:\n cid = json['cid']\n uid = json['uid']\n contact = json['contact']\n\n if cid and uid and contact:\n test = dao.getChatParticipant(cid, contact)\n if test:\n return jsonify(Error=\"User already a participant\")\n ptime = dao.insertParticipant(cid,uid,contact)\n print(ptime)\n\n if ptime:\n result = Dic.build_participants_dict([cid,uid,ptime[0]])\n return jsonify(Participant = result)\n else:\n return jsonify(Error = \"Could not insert the participant\")\n else:\n return jsonify(Error='Unexpected attributes in post request'), 400\n\n\ndef removeParticipant(json):\n print(json)\n if len(json)!=3:\n return jsonify(Error=\"Missing arguments to delete participant\")\n else:\n cid = json['cid']\n uid = json['uid']\n admin = json['admin']\n if uid and cid and admin:\n print(\"gonna call DAO\")\n result = dao.deleteParticipant(cid,uid,admin)\n if result:\n return jsonify(\"Participant removed\")\n else:\n return jsonify(Error=\"Could not remove participant becuase admin is not granting permission\")\n else:\n return jsonify(Error= \"Could not remove participant due to missing arguments\")\n","sub_path":"Handler/Chat.py","file_name":"Chat.py","file_ext":"py","file_size_in_byte":5592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"20016595","text":"import numpy as np\nfrom rllab.algos.vpg import VPG\nfrom rllab.baselines.linear_feature_baseline import LinearFeatureBaseline\nfrom rllab.envs.box2d.cartpole_env import CartpoleEnv\nfrom rllab.envs.normalized_env import normalize\nfrom rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy\n\nfrom rllab.misc import logger\n\n\nresults = []\n\nfor rep in range(5):\n logfile = '/tmp/rllab/vpg/log'+str(rep)+'.csv'\n logger.add_tabular_output(logfile)\n\n env = normalize(CartpoleEnv())\n\n policy = GaussianMLPPolicy(\n env_spec=env.spec,\n hidden_sizes=(100, 50, 25) # section 5\n )\n\n baseline = LinearFeatureBaseline(env_spec=env.spec)\n\n algo = VPG(\n env=env,\n policy=policy,\n baseline=baseline,\n max_path_length=500, # table 2\n n_itr=500, # table 2\n batch_size=50000, # table 2 -- the rllab codebase measures batchsize in timesteps, no matter how many episodes there are in that time frame\n discount=0.99, # table 2\n step_size=0.05, # table 3\n )\n algo.train()\n\n log_table = np.genfromtxt(logfile, delimiter=',', skip_header=1)\n average_returns = log_table[:,2]\n mean_avg_return_all_iterations = np.mean(average_returns)\n print(mean_avg_return_all_iterations)\n results.append(mean_avg_return_all_iterations)\n\nprint(('final avg', np.mean(results)))\n\n","sub_path":"examples/vpg.py","file_name":"vpg.py","file_ext":"py","file_size_in_byte":1365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"149906729","text":"# method 3: Java use treemap\n\n\n\n# method 2: sliding window, using bucket (dictionary)\n# time O(n), space O(k)\nclass Solution2(object):\n def containsNearbyAlmostDuplicate(self, nums, k, t):\n \"\"\"\n :type nums: List[int]\n :type k: int\n :type t: int\n :rtype: bool\n \"\"\"\n if t < 0 or k < 1:\n return False\n \n window = {} # {buket id: value}, the window\n # if values are assigned to the same bucket, then their difference are no more than t\n bucket_size = t + 1 \n for right in range(len(nums)):\n bucket = nums[right]/bucket_size\n if bucket in window:\n return True\n if bucket - 1 in window and abs(window[bucket - 1] - nums[right]) <= t:\n return True\n if bucket + 1 in window and abs(window[bucket + 1] - nums[right]) <= t:\n return True\n window[bucket] = nums[right] # window[bucket] must be empty when reaching here\n if right - k >= 0:\n left = right - k\n del window[nums[left]/bucket_size]\n return False\n\n\n# Brute force, Time limit exceeded\n# Time: O(n*k), space O(1)\nclass Solution(object):\n def containsNearbyAlmostDuplicate(self, nums, k, t):\n n = len(nums)\n for i in range(n - 1): # mistake: range(n - k)\n for j in range(i + 1, i + 1 + k):\n if j < n and abs(nums[i] - nums[j]) <= t:\n return True\n return False\n\n\n\"\"\"\nGiven an array of integers, find out whether there are two distinct indices \ni and j in the array such that the absolute difference between nums[i] \nand nums[j] is at most t and the absolute difference between i and j \nis at most k.\n\nExample 1:\n\nInput: nums = [1,2,3,1], k = 3, t = 0\nOutput: true\nExample 2:\n\nInput: nums = [1,0,1,1], k = 1, t = 2\nOutput: true\nExample 3:\n\nInput: nums = [1,5,9,1,5,9], k = 2, t = 3\nOutput: false\n\"\"\"\n","sub_path":"0220. Contains Duplicate III.py","file_name":"0220. Contains Duplicate III.py","file_ext":"py","file_size_in_byte":1962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"275750532","text":"import sys\r\nfrom time import strftime\r\nfrom PyQt5.QtCore import QSize, Qt\r\nfrom pysnmp.hlapi import SnmpEngine, CommunityData, UdpTransportTarget, ContextData, ObjectType, ObjectIdentity, nextCmd\r\nfrom PyQt5.QtWidgets import QApplication, QMainWindow, QGridLayout, QWidget, QTableWidget, QTableWidgetItem, \\\r\n QDesktopWidget, QPushButton\r\nfrom PyQt5.QtGui import QColor, QIcon\r\n\r\n\r\nclass SNMP:\r\n def __init__(self):\r\n \"\"\"\r\n Define snmp settings\r\n \"\"\"\r\n self.password = 'public'\r\n self.oid = '1.3.6.1.4.1.248.14.5.1.1.7'\r\n with open('id_ip.txt', 'r') as file:\r\n self.hosts = {line.split('=')[0].strip(): line.split('=')[1].strip() for line in file}\r\n\r\n\r\nclass MainForm(QMainWindow):\r\n def __init__(self):\r\n super().__init__()\r\n self.initUI()\r\n\r\n def initUI(self):\r\n \"\"\"\r\n Create user interface\r\n \"\"\"\r\n self.setWindowTitle('Hyper Rings')\r\n self.setWindowIcon(QIcon('icon.ico'))\r\n self.setGeometry(500, 400, 350, 400)\r\n self.setMinimumSize(QSize(333, 500))\r\n central_widget = QWidget(self)\r\n self.setCentralWidget(central_widget)\r\n grid = QGridLayout()\r\n central_widget.setLayout(grid)\r\n table = QTableWidget(self)\r\n btn = QPushButton('Request', self)\r\n btn.clicked.connect(lambda: self.fill_table(table))\r\n grid.addWidget(table, 0, 0)\r\n grid.addWidget(btn, 1, 0)\r\n self.center()\r\n self.show()\r\n\r\n def fill_table(self, table):\r\n \"\"\"\r\n Insert received data into table\r\n \"\"\"\r\n snmp = SNMP()\r\n data = get_data(snmp)\r\n table.clear()\r\n table.setEditTriggers(QTableWidget.NoEditTriggers)\r\n table.setColumnCount(4)\r\n table.setHorizontalHeaderLabels(['Time', 'Name', 'IP', 'Status'])\r\n table.setRowCount(len(data))\r\n for i, rows in enumerate(data):\r\n for j, val in enumerate(rows):\r\n table.setItem(i, j, QTableWidgetItem(val))\r\n color = QColor(255, 255, 255)\r\n if j == len(rows) - 1:\r\n if val == '3':\r\n val = 'ON'\r\n color = QColor(0, 125, 0)\r\n else:\r\n val = 'OFF'\r\n color = QColor(125, 0, 0)\r\n table.setItem(i, j, QTableWidgetItem(val))\r\n table.item(i, j).setBackground(color)\r\n table.resizeColumnsToContents()\r\n\r\n def center(self):\r\n \"\"\"\r\n Put created window into center display\r\n \"\"\"\r\n window = self.frameGeometry()\r\n monitor = QDesktopWidget().availableGeometry().center()\r\n window.moveCenter(monitor)\r\n self.move(window.topLeft())\r\n\r\n\r\ndef get_data(snmp):\r\n \"\"\"\r\n Get states ring manager of all RS's\r\n \"\"\"\r\n link_states = []\r\n for rs_name in snmp.hosts:\r\n ip = snmp.hosts[rs_name]\r\n for (_, _, _, values) in nextCmd(SnmpEngine(),\r\n CommunityData(snmp.password),\r\n UdpTransportTarget((ip, 161)),\r\n ContextData(),\r\n ObjectType(ObjectIdentity(snmp.oid)),\r\n lookupMib=False,\r\n lexicographicMode=False):\r\n resolve = [value[1] for value in values][0]\r\n link_states.append([strftime('%Y-%m-%d %H:%M:%S'), rs_name, ip, str(resolve).strip()])\r\n return link_states\r\n\r\n\r\ndef main():\r\n app = QApplication(sys.argv)\r\n mf = MainForm()\r\n sys.exit(app.exec_())\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n","sub_path":"projects/ring control/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"52444600","text":"#!/usr/bin/env python\n\nimport ConfigParser\nimport logging\nimport tempfile\nimport unittest\n\nimport alerts\n\nclass ConfigureLoggingTest(unittest.TestCase):\n @staticmethod\n def _config(**data):\n config = ConfigParser.RawConfigParser()\n config.add_section('logging')\n for k, v in data.iteritems():\n config.set('logging', k, v)\n return config\n\n def test_get_logging_handler(self):\n def assertHandler(mode, expected_class):\n with tempfile.NamedTemporaryFile() as f:\n config = self._config(mode=mode, file=f.name,\n syslog_addr='/dev/log')\n self.assertTrue(\n isinstance(\n alerts._get_logging_handler(config), expected_class))\n\n assertHandler('file', logging.FileHandler)\n assertHandler('stderr', logging.StreamHandler)\n # we can count on the alerts module importing logging.handlers\n assertHandler('syslog', logging.handlers.SysLogHandler)\n\n self.assertRaises(ValueError, assertHandler, 'asdf', None)\n\n def test_get_logging_formatter(self):\n f = alerts._get_logging_formatter(self._config(mode='syslog'))\n self.assertFalse(isinstance(f, alerts.StreamLoggingFormatter))\n f = alerts._get_logging_formatter(self._config(mode='not-syslog'))\n self.assertTrue(isinstance(f, alerts.StreamLoggingFormatter))\n\n def test_get_logging_level(self):\n config = self._config()\n self.assertEquals(logging.INFO, alerts._get_logging_level(config))\n config = self._config(level='DEBUG')\n self.assertEquals('DEBUG', alerts._get_logging_level(config))\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"alerts_test.py","file_name":"alerts_test.py","file_ext":"py","file_size_in_byte":1741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"204332128","text":"from flask import Blueprint, request, jsonify\nfrom ..decorators import crossdomain, requires_auth\nfrom .model import Post\nfrom math import ceil\nposts = Blueprint('posts', __name__)\n\nclass Pagination(object):\n def __init__(self, page, per_page, total_count):\n self.page = page\n self.per_page = per_page\n self.total_count = total_count\n\n @property\n def pages(self):\n return int(ceil(self.total_count / float(self.per_page)))\n\n @property\n def has_prev(self):\n return self.page > 1\n\n @property\n def has_next(self):\n return self.page < self.pages\n\n def iter_pages(self, left_edge=2, left_current=2,\n right_current=5, right_edge=2):\n last = 0\n for num in xrange(1, self.pages + 1):\n if num <= left_edge or \\\n (num > self.page - left_current - 1 and \\\n num < self.page + right_current) or \\\n num > self.pages - right_edge:\n if last + 1 != num:\n yield None\n yield num\n last = num\n\n\n@posts.route('//', methods=['GET', 'OPTIONS'])\n@crossdomain(origin='*', headers=['Authorization', 'Content-Type'])\ndef get_all_post(zid, page):\n posts = Post.getAll(zid)\n pages = int(ceil(len(posts) / float(20)))\n if(page > pages):\n return '', 404\n low_limit = 20*(page-1)\n hi_limit = 20 + low_limit\n return jsonify({\n \"posts\": posts[low_limit : hi_limit],\n \"pages\": pages\n })\n\n\n@posts.route('/', methods=['POST', 'OPTIONS'])\n@crossdomain(origin='*', headers=['Authorization','Content-Type'])\ndef make_post(zid):\n assert request.json\n post = Post(request.json)\n post.insert()\n return '', 201\n\n\n@posts.route('//', methods=['DELETE', 'OPTIONS'])\n@crossdomain(origin='*', headers=['Authorization'])\n@requires_auth()\ndef delete_post(zid, id):\n Post({\"id\": id}).delete()\n return '', 200\n\n\n@posts.route('//post/', methods=['GET', 'OPTIONS'])\n@crossdomain(origin='*', headers=['Authorization'])\ndef get_post(zid, pid):\n post = Post.get(pid)\n if not post:\n return 'NOT FOUND!', 404\n return jsonify(post)\n","sub_path":"api/app/posts/controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":2242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"525692167","text":"#!/usr/bin/env python\nfrom google.cloud import pubsub_v1\nfrom google.oauth2 import service_account\n\nimport redis\nimport settings\nimport sys\nimport json\nfrom pprint import pprint\n\nr = redis.StrictRedis(**settings.REDIS_CONNECT_KWARGS)\n\n\n\ncredentials = service_account.Credentials.from_service_account_file(\"google-cloud-credentials.json\")\npublisher = pubsub_v1.PublisherClient(credentials=credentials)\ntopic_path = publisher.topic_path(credentials.project_id, \"ingestion\")\n\nif len(sys.argv) != 2:\n print(\"no device\")\n sys.exit(1)\n\ndevice_id = sys.argv[1]\n\ndevice = r.hgetall(device_id)\nif len(device) == 0:\n print(\"unknown device\")\n sys.exit(1)\n\ndevice[\"lat\"] = round(float(device[\"lat\"]), 7)\ndevice[\"lng\"] = round(float(device[\"lng\"]), 7)\ndevice[\"tempc\"] = round(float(device[\"tempc\"]), 2)\ndevice[\"seq_num\"] = int(device[\"seq_num\"])\n\ndef _callback(_):\n r.hincrby(device_id, \"seq_num\")\n print(\"sent..\")\n pprint(device)\n\nfuture = publisher.publish(topic_path, json.dumps(device).encode(\"utf-8\"), contentType=\"text/json\")\nfuture.add_done_callback(_callback)\n\n","sub_path":"testclient/sendone.py","file_name":"sendone.py","file_ext":"py","file_size_in_byte":1077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"196566357","text":"from django.shortcuts import render,redirect\nfrom .models import OrderItem,Order,OrderUpdate\nfrom .forms import OrderCreateForm\nfrom django.urls import reverse\nfrom cart.cart import Cart\nfrom django.views.decorators.csrf import csrf_exempt \nimport stripe\nfrom django.contrib.auth.models import User\nstripe.api_key = \"sk_test_51HqIOnLC1dFeExGNo52wPrsiZrIqvMfDefLTv1Um1jDodhCZwocdMhybjWAME6BsKnpuQbxhMU1H6dntx4bYbT2k00PsDY29ie\"\napp_name = \"orders\"\n# Create your views here.\n\ndef order_create(request):\n cart = Cart(request)\n if request.method == 'POST':\n form = OrderCreateForm(request.POST)\n if form.is_valid():\n order = form.save()\n for item in cart:\n OrderItem.objects.create(order=order,product=item['product'],price=item['price'],quantity=item['quantity'],username=request.user)\n \n cart.clear()\n return render(request,'orders/order/created.html',{'order':order})\n else:\n form = OrderCreateForm()\n return render(request,'orders/order/create.html',{'cart':cart,'form':form})\n\ndef sindex(request,id):\n order = Order.objects.get(pk=id)\n return render(request, 'orders/payment_index.html',{'item':order})\n\n\ndef scharge(request,id):\n order = Order.objects.get(pk=id)\n amount= order.get_total_cost()\n if request.method== 'POST':\n print('Data:', request.POST)\n customer = stripe.Customer.create(\n email=request.POST['email'],\n name=request.POST['name'],\n source=request.POST['stripeToken'],\n )\n charge = stripe.Charge.create(\n customer=customer,\n amount=amount*100,\n currency='inr',\n description=\"shop Payment\"\n\n )\n OrderUpdate.objects.create(order=order)\n return redirect(reverse('ssuccess', args=[amount]))\n\n\ndef ssuccess(request, args):\n amount = args\n return render(request, 'orders/success.html',{'amount':amount})\n\n","sub_path":"orders/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"60866465","text":"# coding=utf-8\n\"\"\"\n 扑克牌\n\"\"\"\nfrom tkinter import *\nfrom tkinter import messagebox\n\nclass Puke(Frame):\n def __init__(self, master=None):\n super().__init__(master)\n self.master = master\n self.pack()\n self.show_puke()\n\n def show_puke(self):\n \"\"\"展示\"\"\"\n self.photos = [PhotoImage(file=\"GIF/puke\"+str(i+1)+\".gif\")for i in range(10)]\n self.labels = [Label(self.master, image=self.photos[i])for i in range(10)]\n for i in range(10):\n self.labels[i].place(x=30*i+10, y=50)\n self.labels[0].bind_class(\"Label\", \"<1>\", self.action_puke)\n def action_puke(self, event):\n \"\"\"扑克牌的点击操作\"\"\"\n # print(event.widget.winfo_geometry())\n # print(event.widget.winfo_y())\n if event.widget.winfo_y() == 50:\n event.widget.place(y=30)\n else:\n event.widget.place(y=50)\n\n\nif __name__ == \"__main__\":\n root = Tk()\n root.title(\"扑克牌\")\n root.geometry(\"450x300+200+100\")\n app = Puke(master=root)\n root.mainloop()\n","sub_path":"mypy/mypy08.py","file_name":"mypy08.py","file_ext":"py","file_size_in_byte":1063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"334136268","text":"\"\"\"Module containing functions for downloading data\n\nFunctions:\n download_record -- downloads specified records of a user.\n\"\"\"\n\nimport csv\nfrom pathlib import Path\nfrom flask_login import current_user\nfrom flask import send_file\nfrom healthapp.encryption import decrypt_post, decrypt_medical_record\nfrom healthapp.models import User, Post, Weight, BloodPressure\nfrom healthapp import db\n\n\ndef download_record(user_email, record_type):\n \"\"\"Downloads data of the given user and record.\n\n Args:\n user_email -- the email of the user whose data is to be downloaded.\n record_type - the type of record to be downloaded.\n \"\"\"\n path = Path(__file__).parent / \"../ExportedData.csv\" # path csv is temporarily saved to.\n user = User.query.filter_by(email=user_email).first() # user that owns the record\n\n if record_type == 'Posts':\n # pulls all post between the specified user and the current user from the database.\n encrypted_posts = db.session.query(Post) \\\n .where(((Post.user_id == user.id) & (Post.recipient == current_user.email))\n | ((Post.user_id == current_user.id) & (Post.recipient == user.email))) \\\n .order_by(Post.date_posted.desc()).all()\n\n posts = [] # empty list that decrypted posts are appended to.\n\n for post in encrypted_posts:\n post_list = [post] # decrypt_posts() requires a list as an argument.\n\n if post_list[0].recipient == current_user.email:\n # if current user is post recipient then their key is used to decrypt.\n posts.append(decrypt_post(post_list, current_user.key)[0])\n\n if post_list[0].recipient == user.email:\n # if specified user is post recipient then their key is used to decrypt.\n posts.append(decrypt_post(post_list, user.key)[0])\n\n with open(path, 'w') as csvfile:\n # writes the decrypted posts to the csv at the path.\n # field names are column names.\n writer = csv.DictWriter(\n csvfile,\n fieldnames=['id', 'author', 'recipient', 'date_posted', 'title', 'content']\n )\n\n writer.writeheader()\n\n for post in posts:\n writer.writerow(post)\n\n elif record_type == 'Blood Pressure':\n # pulls all blood pressure records for the given user from the database.\n encrypted_bp = BloodPressure.query.filter_by(user_id=user.id).all()\n\n # decrypts records using the specified user's key\n posts = decrypt_medical_record(encrypted_bp, user.key)\n\n with open(path, 'w') as csvfile:\n # writes the decrypted posts to the csv at the path.\n # field names are column names.\n writer = csv.DictWriter(csvfile, fieldnames=['id', 'author', 'date_posted', 'record'])\n writer.writeheader()\n for post in posts:\n writer.writerow(post)\n\n elif record_type == 'Weight':\n # pulls all weight records for the given user from the database.\n encrypted_weight = Weight.query.filter_by(user_id=user.id).all()\n\n # decrypts records using the specified user's key\n posts = decrypt_medical_record(encrypted_weight, user.key)\n\n with open(path, 'w') as csvfile:\n # writes the decrypted posts to the csv at the path.\n # field names are column names.\n writer = csv.DictWriter(csvfile, fieldnames=['id', 'author', 'date_posted', 'record'])\n writer.writeheader()\n for post in posts:\n writer.writerow(post)\n\n return send_file(path, as_attachment=True) # sends csv file to be downloaded.\n","sub_path":"healthapp/webapp/downloads.py","file_name":"downloads.py","file_ext":"py","file_size_in_byte":3702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"293864259","text":"#Redo the frequency distribution of alice_in_wonderland.txt and save your result in a dictionary.\n\nfilename = \"alice_in_wonderland.txt\"\n\ndef alice_letter_counter_dict():\n\twith open(filename) as file:\n\t\traw = file.read()\n\t\traw = raw.lower()\n\t\ttext = []\n\t\tfor n in range(len(raw)):\n\t\t\tif raw[n].isalpha():\n\t\t\t\ttext.append(raw[n])\n\t\tjointtext = ('').join(text)\n\n\n\tfreq = 0\n\talphabet = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\n\talice_dict = {}\n\tfor i in range(len(alphabet)):\n\t\talice_dict[alphabet[i]] = str(raw.count(alphabet[i]))\n\tprint(alice_dict)\n\treturn alice_dict\n\nalice_letter_counter_dict()","sub_path":"summer-of-code/week-02/wk2-homework-submissions/alice_in_wonderland_dict.py","file_name":"alice_in_wonderland_dict.py","file_ext":"py","file_size_in_byte":685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"568010","text":"# Definition for an interval.\n# class Interval:\n# def __init__(self, s=0, e=0):\n# self.start = s\n# self.end = e\n\nclass Solution:\n def merge(self, intervals):\n \"\"\"\n :type intervals: List[Interval]\n :rtype: List[Interval]\n \"\"\"\n intervals.sort(key=lambda x: (x.start, x.end))\n res = []\n \n i = 0\n while i < len(intervals):\n start = intervals[i].start\n end = intervals[i].end\n while i+1 < len(intervals) and end >= intervals[i+1].start:\n end = max(end, intervals[i+1].end)\n i += 1\n res.append([start, end])\n i += 1\n return res\n","sub_path":"56/1001.py","file_name":"1001.py","file_ext":"py","file_size_in_byte":700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"90493235","text":"from models import Requests\n\n\nclass RequestsRecording(object):\n\n def process_request(self, request):\n\n if request.is_ajax() and 'request' in request.path or \\\n '/static/' in request.path or '/media/' in request.path:\n return\n # record the request to the db\n Requests.objects.create(\n path=request.path,\n method=request.META['REQUEST_METHOD'],\n )\n","sub_path":"apps/hello/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"557131910","text":"import json\nimport random\nimport collections\nimport itertools\nimport math\n\n\nDIRECTIONS = {\"u\": \"up\", \"d\": \"down\", \"l\": \"left\", \"r\": \"right\"}\nMAX_LEVEL = 5\nX_CHANGE = {\"u\": 0, \"d\": 0, \"l\": -1, \"r\": 1}\nY_CHANGE = {\"u\": -1, \"d\": 1, \"l\": 0, \"r\": 0}\nSCORES={\"fruit\": 10, \"empty\": 1, \"wall\": 1, \"self\": 2, \"other_snake_dead\": 2, \"collision_other\": 2, \"starve\": 1}\n\ndef get_move(data):\n tree = generate_tree(data)\n with open('tree.json', 'w') as f:\n f.write(json.dumps(tree, indent=2))\n tree = evaluate_tree(data, tree)\n with open('minmaxtree.json', 'w') as f:\n f.write(json.dumps(tree, indent=2))\n direction = max(DIRECTIONS.keys(), key=lambda x: tree[x][\"value\"])\n return DIRECTIONS[direction]\n\ndef generate_tree(data):\n tree = lambda: collections.defaultdict(tree)\n root = tree()\n all_combos = itertools.product(DIRECTIONS.keys(), repeat=MAX_LEVEL)\n for combo in all_combos:\n current = root\n for idx in range(len(combo)-1):\n direction = combo[idx]\n current = current[direction]\n current[\"value\"] = 0\n current[\"is_min\"] = bool(idx % 2)\n current[\"is_leaf\"] = False\n else:\n current[direction] = dict(value=0, is_min=bool((idx+1) % 2), is_leaf=True)\n root.update(dict(value=0, is_min=False, is_leaf=False))\n return json.loads(json.dumps(root))\n\ndef evaluate_tree(data, tree, head=None):\n head = head or data[\"you\"][\"body\"][0]\n if tree[\"is_leaf\"]:\n if data[\"you\"][\"health\"] - MAX_LEVEL <= 0:\n tree[\"value\"] = SCORES[\"starve\"] * (data[\"you\"][\"health\"] - MAX_LEVEL)\n return tree\n if head[\"x\"] < 0:\n tree[\"value\"] = SCORES[\"wall\"] * head[\"x\"]\n return tree\n if head[\"y\"] < 0:\n tree[\"value\"] = SCORES[\"wall\"] * head[\"y\"]\n return tree\n if head[\"x\"] >= data[\"board\"][\"width\"]:\n tree[\"value\"] = -SCORES[\"wall\"] * head[\"x\"]\n return tree\n if head[\"y\"] >= data[\"board\"][\"height\"]:\n tree[\"value\"] = -SCORES[\"wall\"] * head[\"y\"]\n return tree\n if head in data[\"you\"][\"body\"]:\n tree[\"value\"] = -SCORES[\"self\"]\n return tree\n if head in data[\"board\"][\"food\"]:\n tree[\"value\"] = SCORES[\"fruit\"] - math.sqrt((head[\"x\"] - data[\"you\"][\"body\"][0][\"x\"])**2 + (head[\"y\"] - data[\"you\"][\"body\"][0][\"y\"])**2)\n return tree\n if head in [snake[\"body\"][0] for snake in data[\"board\"][\"snakes\"] if (len(snake[\"body\"]) < len(data[\"you\"][\"body\"]))]:\n tree[\"value\"] = SCORES[\"other_snake_dead\"]\n return tree\n if head in [item for snake in data[\"board\"][\"snakes\"] for item in snake[\"body\"]]:\n tree[\"value\"] = -SCORES[\"collision_other\"]\n return tree\n tree[\"value\"] = SCORES[\"empty\"]\n return tree\n\n assert not tree[\"is_leaf\"], \"Leaf node has not actually returned\"\n for direction in [d for d in DIRECTIONS.keys() if tree.get(d)]:\n new_head=dict(x=head[\"x\"] + X_CHANGE[direction], y=head[\"y\"] + Y_CHANGE[direction])\n evaluate_tree(data, tree[direction], new_head)\n eval_func = min if tree[\"is_min\"] else max\n best_direction = eval_func([d for d in DIRECTIONS.keys() if tree.get(d)], key=lambda x: tree[x][\"value\"])\n tree[\"value\"] = tree[best_direction][\"value\"]\n return tree\n","sub_path":"app/minimax.py","file_name":"minimax.py","file_ext":"py","file_size_in_byte":3374,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"577456016","text":"from tkinter import *\n\n\nclass MyPaint:\n def __init__(self,root):\n self.COLOR = 'red'\n self.c = Canvas(root, width=750, height=650, bg='black', cursor='hand2')\n self.f = Frame(root, width=150, height=650, bg='white', cursor='arrow')\n\n self.var = IntVar()\n\n self.red = Radiobutton(self.f, text='Red', font=('Constantia',20,'bold italic'), value=1, variable=self.var, command=self.brush_color,bg='gray')\n self.green = Radiobutton(self.f, text='Green', font=('Constantia',20,'bold italic'), value=2, variable=self.var, command=self.brush_color,bg='gray')\n self.blue = Radiobutton(self.f, text='Blue', font=('Constantia',20,'bold italic'), value=3, variable=self.var, command=self.brush_color,bg='gray')\n\n self.red.select()\n\n self.c.bind('', self.draw_pixel)\n self.c.bind('', self.clear_canvas)\n\n self.red.pack(side=TOP,fill=X)\n self.green.pack(side=TOP,fill=X)\n self.blue.pack(side=TOP,fill=X)\n\n self.c.pack(side=LEFT)\n\n self.f.pack(side=LEFT)\n\n def draw_pixel(self,event):\n x = event.x\n y = event.y\n self.c.create_line(x - 5, y - 5, x, y, x + 5, y + 1, width=3, fill=self.COLOR)\n\n def clear_canvas(self,event):\n self.c.delete(\"all\")\n\n def brush_color(self):\n if self.var.get() == 1:\n self.COLOR = 'red'\n\n if self.var.get() == 2:\n self.COLOR = 'green'\n\n if self.var.get() == 3:\n self.COLOR = 'blue'\n\n\nroot = Tk()\n\npaint = MyPaint(root)\n\nroot.geometry('900x650')\n\nroot.title('Paint')\n\nroot.mainloop()\n\n","sub_path":"Python_tkinter/Python_Paint.py","file_name":"Python_Paint.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"67437965","text":"\"\"\"\nCopied from https://github.com/openai/spinningup/blob/master/spinup/algos/pytorch/vpg/vpg.py\n\"\"\"\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.optim import Adam\nimport time\n\nimport gym\nfrom gym.spaces import Box, Discrete\nimport wandb\nfrom spinup.utils.logx import EpochLogger\nfrom spinup.utils.mpi_pytorch import setup_pytorch_for_mpi, sync_params, mpi_avg_grads\nfrom spinup.utils.mpi_tools import mpi_fork, mpi_avg, proc_id, mpi_statistics_scalar, num_procs\n\nimport hca.core as core\n\n\n# if gpu is to be used\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(\"\\n ******** Number of GPUs:\", torch.cuda.device_count())\n\nclass VPGBuffer:\n \"\"\"\n A buffer for storing trajectories experienced by a VPG agent interacting\n with the environment, and using Generalized Advantage Estimation (GAE-Lambda)\n for calculating the advantages of state-action pairs.\n \"\"\"\n\n def __init__(self, obs_dim, act_dim, size, gamma=0.99, lam=0.95):\n self.obs_buf = np.zeros(core.combined_shape(size, obs_dim), dtype=np.float32)\n self.act_buf = np.zeros(size, dtype=np.float32) # assume discrete action space\n self.adv_buf = np.zeros(size, dtype=np.float32)\n self.rew_buf = np.zeros(size, dtype=np.float32)\n self.ret_buf = np.zeros(size, dtype=np.float32)\n self.val_buf = np.zeros(size, dtype=np.float32)\n self.logp_buf = np.zeros(size, dtype=np.float32)\n self.gamma, self.lam = gamma, lam\n self.ptr, self.path_start_idx, self.max_size = 0, 0, size\n\n def store(self, obs, act, rew, val, logp):\n \"\"\"\n Append one timestep of agent-environment interaction to the buffer.\n \"\"\"\n assert self.ptr < self.max_size # buffer has to have room so you can store\n self.obs_buf[self.ptr] = obs\n self.act_buf[self.ptr] = act\n self.rew_buf[self.ptr] = rew\n self.val_buf[self.ptr] = val\n self.logp_buf[self.ptr] = logp\n self.ptr += 1\n\n def finish_path(self, last_val=0):\n \"\"\"\n Call this at the end of a trajectory, or when one gets cut off\n by an epoch ending. This looks back in the buffer to where the\n trajectory started, and uses rewards and value estimates from\n the whole trajectory to compute advantage estimates with GAE-Lambda,\n as well as compute the rewards-to-go for each state, to use as\n the targets for the value function.\n\n The \"last_val\" argument should be 0 if the trajectory ended\n because the agent reached a terminal state (died), and otherwise\n should be V(s_T), the value function estimated for the last state.\n This allows us to bootstrap the reward-to-go calculation to account\n for timesteps beyond the arbitrary episode horizon (or epoch cutoff).\n \"\"\"\n\n path_slice = slice(self.path_start_idx, self.ptr)\n rews = np.append(self.rew_buf[path_slice], last_val)\n vals = np.append(self.val_buf[path_slice], last_val)\n \n # the next two lines implement GAE-Lambda advantage calculation\n deltas = rews[:-1] + self.gamma * vals[1:] - vals[:-1]\n self.adv_buf[path_slice] = core.discount_cumsum(deltas, self.gamma * self.lam)\n \n # the next line computes rewards-to-go, to be targets for the value function\n self.ret_buf[path_slice] = core.discount_cumsum(rews, self.gamma)[:-1]\n \n self.path_start_idx = self.ptr\n\n def get(self):\n \"\"\"\n Call this at the end of an epoch to get all of the data from\n the buffer, with advantages appropriately normalized (shifted to have\n mean zero and std one). Also, resets some pointers in the buffer.\n \"\"\"\n assert self.ptr == self.max_size # buffer has to be full before you can get\n self.ptr, self.path_start_idx = 0, 0\n # the next two lines implement the advantage normalization trick\n adv_mean, adv_std = mpi_statistics_scalar(self.adv_buf)\n #adv_mean, adv_std = mpi_statistics_scalar(self.adv_buf)\n self.adv_buf = (self.adv_buf - adv_mean) / adv_std\n data = dict(obs=self.obs_buf, act=self.act_buf, ret=self.ret_buf,\n adv=self.adv_buf, logp=self.logp_buf)\n return {k: torch.as_tensor(v, dtype=torch.float32, device=device) for k,v in data.items()}\n\n\n\ndef vpg(env_fn, actor_critic=core.MLPActorCritic, ac_kwargs=dict(), seed=0, \n steps_per_epoch=4000, epochs=50, gamma=0.99, pi_lr=3e-4,\n vf_lr=1e-3, train_v_iters=80, lam=0.97, max_ep_len=1000,\n logger_kwargs=dict(), save_freq=10, n_test_episodes=100):\n \"\"\"\n Vanilla Policy Gradient \n\n (with GAE-Lambda for advantage estimation)\n\n Args:\n env_fn : A function which creates a copy of the environment.\n The environment must satisfy the OpenAI Gym API.\n\n actor_critic: The constructor method for a PyTorch Module with a \n ``step`` method, an ``act`` method, a ``pi`` module, and a ``v`` \n module. The ``step`` method should accept a batch of observations \n and return:\n\n =========== ================ ======================================\n Symbol Shape Description\n =========== ================ ======================================\n ``a`` (batch, act_dim) | Numpy array of actions for each \n | observation.\n ``v`` (batch,) | Numpy array of value estimates\n | for the provided observations.\n ``logp_a`` (batch,) | Numpy array of log probs for the\n | actions in ``a``.\n =========== ================ ======================================\n\n The ``act`` method behaves the same as ``step`` but only returns ``a``.\n\n The ``pi`` module's forward call should accept a batch of \n observations and optionally a batch of actions, and return:\n\n =========== ================ ======================================\n Symbol Shape Description\n =========== ================ ======================================\n ``pi`` N/A | Torch Distribution object, containing\n | a batch of distributions describing\n | the policy for the provided observations.\n ``logp_a`` (batch,) | Optional (only returned if batch of\n | actions is given). Tensor containing \n | the log probability, according to \n | the policy, of the provided actions.\n | If actions not given, will contain\n | ``None``.\n =========== ================ ======================================\n\n The ``v`` module's forward call should accept a batch of observations\n and return:\n\n =========== ================ ======================================\n Symbol Shape Description\n =========== ================ ======================================\n ``v`` (batch,) | Tensor containing the value estimates\n | for the provided observations. (Critical: \n | make sure to flatten this!)\n =========== ================ ======================================\n\n ac_kwargs (dict): Any kwargs appropriate for the ActorCritic object \n you provided to VPG.\n\n seed (int): Seed for random number generators.\n\n steps_per_epoch (int): Number of steps of interaction (state-action pairs) \n for the agent and the environment in each epoch.\n\n epochs (int): Number of epochs of interaction (equivalent to\n number of policy updates) to perform.\n\n gamma (float): Discount factor. (Always between 0 and 1.)\n\n pi_lr (float): Learning rate for policy optimizer.\n\n vf_lr (float): Learning rate for value function optimizer.\n\n train_v_iters (int): Number of gradient descent steps to take on \n value function per epoch.\n\n lam (float): Lambda for GAE-Lambda. (Always between 0 and 1,\n close to 1.)\n\n max_ep_len (int): Maximum length of trajectory / episode / rollout.\n\n logger_kwargs (dict): Keyword args for EpochLogger.\n\n save_freq (int): How often (in terms of gap between epochs) to save\n the current policy and value function.\n\n n_test_episodes (int): Number of episodes for test agent evaluation at\n the end of each epoch.\n \"\"\"\n\n # Special function to avoid certain slowdowns from PyTorch + MPI combo.\n #setup_pytorch_for_mpi()\n\n # Set up logger and save configuration\n logger = EpochLogger(**logger_kwargs)\n logger.save_config(locals())\n\n # Random seed\n seed += 10000 * proc_id()\n torch.manual_seed(seed)\n np.random.seed(seed)\n\n # Instantiate environment\n env = env_fn()\n def space_dim(space):\n if isinstance(space, Box):\n return space.shape\n elif isinstance(space, Discrete):\n return space.n\n else:\n raise ValueError\n\n obs_dim = space_dim(env.observation_space)\n act_dim = space_dim(env.action_space)\n\n # Create actor-critic module\n ac = actor_critic(env.observation_space, env.action_space, **ac_kwargs)\n # if torch.cuda.device_count() > 1:\n # ac.pi = nn.DataParallel(ac.pi)\n # ac.v = nn.DataParallel(ac.v)\n\n ac.to(device)\n\n # Sync params across processes\n #sync_params(ac)\n\n # Count variables\n var_counts = tuple(core.count_vars(module) for module in [ac.pi, ac.v])\n logger.log('\\nNumber of parameters: \\t pi: %d, \\t v: %d\\n'%var_counts)\n\n # Set up experience buffer\n local_steps_per_epoch = steps_per_epoch\n #local_steps_per_epoch = int(steps_per_epoch / num_procs())\n buf = VPGBuffer(obs_dim, act_dim, local_steps_per_epoch, gamma, lam)\n\n # Set up function for computing VPG policy loss\n def compute_loss_pi(data):\n obs, act, adv, logp_old = data['obs'], data['act'], data['adv'], data['logp']\n\n # Policy loss\n pi, logp = ac.pi(obs, act)\n loss_pi = -(logp * adv).mean()\n\n # Useful extra info\n approx_kl = (logp_old - logp).mean().item()\n ent = pi.entropy().mean().item()\n pi_info = dict(kl=approx_kl, ent=ent)\n\n return loss_pi, pi_info\n\n # Set up function for computing value loss\n def compute_loss_v(data):\n obs, ret = data['obs'], data['ret']\n return ((ac.v(obs) - ret)**2).mean()\n\n # Set up optimizers for policy and value function\n pi_optimizer = Adam(ac.pi.parameters(), lr=pi_lr)\n vf_optimizer = Adam(ac.v.parameters(), lr=vf_lr)\n\n # Set up model saving\n logger.setup_pytorch_saver(ac)\n\n def update():\n data = buf.get()\n\n # Get loss and info values before update\n pi_l_old, pi_info_old = compute_loss_pi(data)\n pi_l_old = pi_l_old.item()\n v_l_old = compute_loss_v(data).item()\n\n # Train policy with a single step of gradient descent\n pi_optimizer.zero_grad()\n loss_pi, pi_info = compute_loss_pi(data)\n loss_pi.backward()\n #mpi_avg_grads(ac.pi) # average grads across MPI processes\n pi_optimizer.step()\n\n # Value function learning\n for i in range(train_v_iters):\n vf_optimizer.zero_grad()\n loss_v = compute_loss_v(data)\n loss_v.backward()\n #mpi_avg_grads(ac.v) # average grads across MPI processes\n vf_optimizer.step()\n\n # Log changes from update\n kl, ent = pi_info['kl'], pi_info_old['ent']\n logger.store(LossPi=pi_l_old, LossV=v_l_old,\n KL=kl, Entropy=ent,\n DeltaLossPi=(loss_pi.item() - pi_l_old),\n DeltaLossV=(loss_v.item() - v_l_old))\n\n def test_agent():\n test_env = env_fn()\n o, test_ep_ret, test_ep_len = test_env.reset(), 0, 0\n\n num_episodes = 0\n while num_episodes < n_test_episodes:\n a, _, _ = ac.step(torch.as_tensor(o, dtype=torch.float32, device=device))\n o2, r, d, _ = env.step(a)\n test_ep_ret += r\n test_ep_len += 1\n\n o = o2\n timeout = ep_len == max_ep_len\n terminal = d or timeout\n\n if timeout or terminal:\n logger.store(TestEpRet=test_ep_ret)\n num_episodes += 1\n o, test_ep_ret, test_ep_len = test_env.reset(), 0, 0\n\n # Prepare for interaction with environment\n start_time = time.time()\n o, ep_ret, ep_len = env.reset(), 0, 0\n\n # Main loop: collect experience in env and update/log each epoch\n for epoch in range(epochs):\n for t in range(local_steps_per_epoch):\n a, v, logp = ac.step(torch.as_tensor(o, dtype=torch.float32, device=device))\n\n next_o, r, d, _ = env.step(a)\n ep_ret += r\n ep_len += 1\n\n # save and log\n buf.store(o, a, r, v, logp)\n logger.store(VVals=v)\n\n # Update obs (critical!)\n o = next_o\n\n timeout = ep_len == max_ep_len\n terminal = d or timeout\n epoch_ended = t==local_steps_per_epoch-1\n\n if terminal or epoch_ended:\n if epoch_ended and not(terminal):\n print('Warning: trajectory cut off by epoch at %d steps.'%ep_len, flush=True)\n # if trajectory didn't reach terminal state, bootstrap value target\n if timeout or epoch_ended:\n _, v, _ = ac.step(torch.as_tensor(o, dtype=torch.float32, device=device))\n else:\n v = 0\n buf.finish_path(v)\n if terminal:\n # only save EpRet / EpLen if trajectory finished\n logger.store(EpRet=ep_ret, EpLen=ep_len)\n o, ep_ret, ep_len = env.reset(), 0, 0\n\n\n # Save model\n if (epoch % save_freq == 0) or (epoch == epochs-1):\n logger.save_state({'env': env}, None)\n\n # Perform VPG update!\n update()\n\n test_agent()\n\n # Log info about epoch\n logger.log_tabular('Epoch', epoch)\n logger.log_tabular('EpRet', with_min_and_max=True)\n logger.log_tabular('EpLen', average_only=True)\n logger.log_tabular('TestEpRet', with_min_and_max=True)\n logger.log_tabular('VVals', with_min_and_max=True)\n logger.log_tabular('TotalEnvInteracts', (epoch+1)*steps_per_epoch)\n logger.log_tabular('LossPi', average_only=True)\n logger.log_tabular('LossV', average_only=True)\n logger.log_tabular('DeltaLossPi', average_only=True)\n logger.log_tabular('DeltaLossV', average_only=True)\n logger.log_tabular('Entropy', average_only=True)\n logger.log_tabular('KL', average_only=True)\n logger.log_tabular('Time', time.time()-start_time)\n wandb.log(logger.log_current_row, step=epoch)\n logger.dump_tabular()\n\nif __name__ == '__main__':\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('--env', type=str, default='CartPole-v1')\n parser.add_argument('--hid', type=int, default=256)\n parser.add_argument('--l', type=int, default=2)\n parser.add_argument('--gamma', type=float, default=0.99)\n parser.add_argument('--seed', '-s', type=int, default=0)\n parser.add_argument('--cpu', type=int, default=4)\n parser.add_argument('--steps', type=int, default=4000)\n parser.add_argument('--epochs', type=int, default=200)\n args = parser.parse_args()\n\n wandb.init(project=\"vpg\", config=vars(args), tags=['CartPole-v1', 'spinup'])\n logger_kwargs={'exp_name': 'vpg', 'output_dir': wandb.run.dir}\n\n #mpi_fork(args.cpu) # run parallel code with mpi\n\n vpg(lambda : gym.make(args.env), actor_critic=core.MLPActorCritic,\n ac_kwargs=dict(hidden_sizes=[args.hid]*args.l), gamma=args.gamma, \n seed=args.seed, steps_per_epoch=args.steps, epochs=args.epochs,\n logger_kwargs=logger_kwargs)\n","sub_path":"HCA/hca/spinup_vpg.py","file_name":"spinup_vpg.py","file_ext":"py","file_size_in_byte":16506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"262046456","text":"#Zach Wu 2016\n#Note: NK Testing does not account for stop codons or deletions\n\n#Add stipulation for unique mutations?\n#(ie: if one site is mutated already, do not mutate again)\n\nimport numpy as np\nimport sys\nimport random\nimport NK_Landscape\nfrom scipy.stats import poisson\nfrom scipy.stats import pearsonr\nfrom scipy import stats\n\nimport warnings\nwarnings.simplefilter('once')\n\nsample_parent = [random.randint(0,19) for i in range(10)]\nnum_AA = 20\n\ndef EPM_Poisson_countd(mu, library_size):\n #returns the Poisson mutation rate distribution for a given library size\n\n probs_list = []\n mut_list = []\n alpha = 1-1/(library_size*10)\n a,b = poisson.interval(alpha, mu, loc=0)\n a = int(a)\n b = int(b)\n for k in range(a,b+1):\n k_count = int(round(poisson.pmf(k,mu)*library_size,0))\n if k_count != 0:\n probs_list.append(k_count)\n mut_list.append(k)\n dif = sum(probs_list) - library_size\n# print(dif)\n mutation_list = [i for i in range(a,b+1)]\n index = mutation_list.index(mu)\n probs_list[index] -= dif\n\n# print(probs_list)\n# print(sum(probs_list))\n return probs_list, mut_list\n\ndef NK_Featurize(seqList):\n featurized_seq = []\n for i, seq in enumerate(seqList):\n temp_seq = [0] * len(seq) * num_AA\n for j, AA in enumerate(seq):\n temp_seq[j*20 + AA] = 1\n featurized_seq.append(temp_seq)\n return featurized_seq\n\ndef mutate(p2, n):\n '''mutates parent n times\n '''\n current_seq2 = p2\n\n for i in range(n):\n #print(current)\n mut_index = random.randint(0,len(p2)-1)\n curr_AA = current_seq2[mut_index]\n poss_AA = set([j for j in range(num_AA)])\n poss_AA.remove(curr_AA)\n mut_AA = list(poss_AA)[random.randint(0,len(poss_AA)-1)]\n current_seq2[mut_index] = mut_AA\n\n return current_seq2\n\ndef epm_library(parent, library_size, rate = 1):\n '''parent sequence should be in [19,3,0,1] format\n rate is the average number of mutations\n I'm just going to assume a poisson distribution for now\n check http://www.ncbi.nlm.nih.gov/pubmed/15939434\n\n -Fox assumes gamma distribution\n '''\n dist_countL, num_mutL = EPM_Poisson_countd(rate, library_size)\n\n seq_list = []\n seq_list.append(parent[:])\n for i, num_mut in enumerate(num_mutL):\n for j in range(dist_countL[i]):\n seq_list.append(mutate(parent[:], num_mut)[:])\n\n return seq_list\n\ndef single_mutant_library(parent, n):\n ''' sequence should be in [19,3,0,1] format\n n is length of walk\n\n returns single mutant walk library (closer to what we do in directed evolution)\n '''\n current_seq = list(parent)[:]\n seq_list = []\n seq_list.append(parent[:])\n for i in range(n):\n temp = current_seq[:]\n temp = mutate(current_seq, 1)\n\n current_seq = temp[:]\n seq_list.append(temp)\n return seq_list\n\n# def random_library(parent, n):\n# ''' sequence should be in [19,3,0,1] format\n# n is length of walk\n\n# returns completely random library (closer to a broad representation of sequence space)\n# '''\n# current_seq = list(parent)[:]\n# seq_list = []\n# seq_list.append(parent[:])\n\n# poss_AA = [j for j in range(num_AA)]\n\n# for i in range(n):\n# j +=0\n\n\n\n###############################################################\n###############################################################\n###############################################################\n\nimport time\n\n#run models\nimport PartialLeastSquares as PLS\n\n\nK_list = [0,1,2,3]\nn = 20 #Sample length = 40\nlibrary_size = 3*n #Library size = 3*n\n\n\n#whooo\nf = open('NK_Model_Testing(PLS).txt', 'w')\nf2 = open('NK_Model_Correlations(PLS).txt','w')\n\npredict_master_list = []\ntrue_master_list = []\n\n#Specify parent sequence and library\nparent = [np.random.randint(num_AA) for i in range(n)]\nepm_lib = single_mutant_library(parent, library_size) #Currently only looking at single mutant walk\n #repeat for random mutants?\n\n#Featurize\nlib_features = NK_Featurize(epm_lib)\n\n#Run Models\nbeg = time.clock()\nfor K in K_list:\n print('\\n\\nK = ' + str(K))\n\n f.write('\\n##########\\nNew Fitness Landscape \\nK = ' + str(K) + '\\n##########\\n')\n f2.write('K Value = ' + str(K) + '\\n------\\n')\n #Make NK_Landscape\n landscape = NK_Landscape.NKLandscape(n,K, savespace = False, epi_dist = 'gamma')\n interactions_list = landscape.nk_interactions()\n epistatic_list = landscape.nk_epistatic()\n\n #Determine Fitnesses\n lib_fitnessL = []\n for i in range(len(lib_features)):\n fitness = landscape.get_Energy(epm_lib[i])\n lib_fitnessL.append(fitness)\n\n predict_list_by_clf = []\n true_list_by_clf = []\n\n\n f.write('\\nNew CLF:\\n' + 'PLS' + '\\n---------\\n')\n\n predict_list = []\n true_list = []\n #Fit to model and save fitnesses:\n for j in range(len(lib_features)):\n X_test, Y_test = [lib_features[j]], [lib_fitnessL[j]]\n X_train = lib_features[0:j] + lib_features[j+1:]\n Y_train = lib_fitnessL[0:j] + lib_fitnessL[j+1:]\n\n np.savetxt('X_temp.csv', X_train, delimiter = ',')\n np.savetxt('Y_temp.csv', Y_train, delimiter = ',')\n\n pls = PLS.PartialLeastSquares(XMatrix_file = 'X_temp.csv', YMatrix_file = 'Y_temp.csv', epsilon = 0.001)\n pls.get_XMatrix_from_csv()\n pls.get_YMatrix_from_csv()\n B = pls.PLS()\n\n temp = X_test * B\n print('--')\n print(temp)\n print(X_test)\n print(B)\n\n Y_predicted = temp[0,0]\n #Y_predicted = np.dot(X_test,B)\n\n print(type(Y_predicted))\n predict_list.append(Y_predicted)\n true_list.append(Y_test[0])\n\n f.write(str(Y_predicted) + ',' + str(Y_test[0]) + '\\n')\n\n #Determine Pearson's R\n #r = np.corrcoef(predict_list, true_list)\n slope, intercept, r_value, p_value, std_err = stats.linregress(predict_list, true_list)\n r = r_value\n write_me = 'Rsquared : ' + str(r**2) + '\\n'\n print(write_me)\n f.write(write_me)\n f2.write(str(r**2) + ',')\n predict_list_by_clf.append(predict_list)\n true_list_by_clf.append(true_list)\n\nf.close()\nf2.close()\n\nprint('end')\nprint('runtime = ' + str(time.clock() - beg))\n","sub_path":"NK_Testing2_PLS.py","file_name":"NK_Testing2_PLS.py","file_ext":"py","file_size_in_byte":6330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"172335773","text":"import keras.optimizers\nimport keras.callbacks\nimport keras.optimizers\nimport keras.backend as K\nfrom hyperopt import STATUS_OK\n\nfrom createParametricBCNN import createParametricBCNN\n\n\n\n\n#Clears the model from GPU memory, only returns information needed for hyperparameter tuning using hyperopt.\ndef runOptimisingTrial(optimisingParameters,nonOptimisingModelParameters,nonOptimisingTrainParameters,nonOptimisingF1Parameters):\n model,history,f1Score=runTrial(optimisingParameters,nonOptimisingModelParameters,nonOptimisingTrainParameters,nonOptimisingF1Parameters)\n \n #The GPUs are cleared of the model so they are ready for the next one; the model must be saved beforehand to provent the weights from being reset.\n K.clear_session()\n return {\"loss\":-f1Score,\"status\":STATUS_OK}\n \n \ndef runTrial(optimisingParameters,nonOptimisingModelParameters,nonOptimisingTrainParameters,nonOptimisingF1Parameters): \n dropoutFraction,convolutionLayersPerBlock,extraFirstBlock,initalLayerFilterCount,filterCountBlockMultiplicativeFactor,initalLayerKernalSize,kernalSizeBlockMultiplicitiveFactor,learningRate=optimisingParameters\n inputShape,outputLayerNames,objectTypePossibleLabelSets,gpuQuantity=nonOptimisingModelParameters\n xTrain,xValidation,yTrain,yValidation,batchSize,epochNumber,trainingLossWeight,earlyStoppingMinDelta,earlyStoppingPatience=nonOptimisingTrainParameters\n validationObjectIntegerLabelList,objectHierarchyDepth=nonOptimisingF1Parameters\n \n print(\"\\n\")\n print(\"Begining training run, parameters used are:\")\n print(\" dropoutFraction: \"+str(dropoutFraction))\n print(\" extraFirstBlock: \"+str(extraFirstBlock))\n print(\" convolutionLayersPerBlock: \"+str(convolutionLayersPerBlock))\n print(\" initalLayerFilterCount: \"+str(initalLayerFilterCount))\n print(\" filterCountBlockMultiplicativeFactor: \"+str(filterCountBlockMultiplicativeFactor))\n print(\" initalLayerKernalSize: \"+str(initalLayerKernalSize))\n print(\" kernalSizeBlockMultiplicitiveFactor: \"+str(kernalSizeBlockMultiplicitiveFactor))\n print(\" learningRate: \"+str(learningRate))\n \n model,gpuModel=createParametricBCNN(optimisingParameters=optimisingParameters,inputShape=inputShape,outputLayerNames=outputLayerNames,\n objectTypePossibleLabelSets=objectTypePossibleLabelSets,gpuQuantity=gpuQuantity)\n \n print(\"\\n\")\n print(model.summary())\n history=trainBCNN(model=gpuModel,xTrain=xTrain,xValidation=xValidation,yTrain=yTrain,yValidation=yValidation,\n batchSize=batchSize,epochsPerHierarchyLevel=epochNumber,trainingLossWeight=trainingLossWeight,\n learningRate=learningRate,outputLayerNames=outputLayerNames,\n earlyStoppingMinDelta=earlyStoppingMinDelta,earlyStoppingPatience=earlyStoppingPatience) \n \n f1Score=getF1ScoreOfValidationData(model=model,xValidation=xValidation,\n validationObjectIntegerLabelList=validationObjectIntegerLabelList,\n batchSize=batchSize,objectHierarchyDepth=objectHierarchyDepth)\n \n print(\"Training run complete, f1 score of the validation data for this training run is \"+str(f1Score)) \n return model,history,f1Score\n\n\n#Trains a B-CNN by training mainly on the loss of each output successively. outputLayerNames needs to be a list of the output layers in sequential order from\n#the output closest to the input layer.\ndef trainBCNN(model,xTrain,xValidation,yTrain,yValidation,batchSize,epochsPerHierarchyLevel,trainingLossWeight,learningRate,outputLayerNames,earlyStoppingMinDelta,earlyStoppingPatience):\n #For each layer in the obejct hierarchy that is trainined in the CNN, that layer has a higher loss weight than the othher layers in the hierarchy.\n #This is so then training is focused on only one level of the hierarchy at a time.\n lossWeights=[]\n for currentTrainingRun in range(0,len(outputLayerNames)):\n nonTrainingLossWeight=(1.0-trainingLossWeight)/float(len(outputLayerNames)-1) #Loss weight applied to outputs not being trained on; it is done this way so trainingLossWeight corresponds to a fraction between 0 and 1.\n currentWeightValues=[trainingLossWeight if(currentIndex==currentTrainingRun) else nonTrainingLossWeight for currentIndex in range(0,len(outputLayerNames))]\n currentWeights=dict(zip(outputLayerNames,currentWeightValues))\n lossWeights.append(currentWeights)\n \n \n \n print(\"Training of B-CNN model will now begin, there are \"+str(len(outputLayerNames))+\" hierarchy levels; each of which will be trained for \"+str(epochsPerHierarchyLevel)+\" epochs\"+\"\\n\")\n \n modelHistory=None #Stores the history for all the training runs.\n currentOptimizer=keras.optimizers.Adam(lr=learningRate) #Done so the optimizer does not lose it's state when the model is recompiled.\n\n for i,currentLossWeights in enumerate(lossWeights):\n print(\"Training hierarchy level \"+str(i+1)+\", loss weights currently: \"+str(list(currentLossWeights.values()))+\"\\n\")\n model.compile(optimizer=currentOptimizer,loss=\"categorical_crossentropy\",metrics=[\"accuracy\"],loss_weights=currentLossWeights)\n \n earlyStoppingCallbackMonitorString=\"val_\"+outputLayerNames[i]+\"_loss\" #The loss of the current model output that is being trained is monitored for early stopping.\n earlyStoppingCallback=keras.callbacks.EarlyStopping(monitor=earlyStoppingCallbackMonitorString,min_delta=earlyStoppingMinDelta,patience=earlyStoppingPatience,mode=\"auto\",restore_best_weights=True) #Allows the model to stop training the current output if it is not improving; if so the weights will be reverted to the that the current monitored loss was at it's best value. \n currentModelFit=model.fit(x=xTrain,y=yTrain,batch_size=batchSize,validation_data=(xValidation,yValidation),epochs=epochsPerHierarchyLevel,verbose=2,callbacks=[earlyStoppingCallback])\n print(\"\\n\"+\"Training of hierarchy level \"+str(i+1)+\" completed\")\n \n \n currentModelHistory=currentModelFit.history\n \n if(modelHistory==None):\n modelHistory=currentModelHistory #The model history for the first training run.\n else:\n #The history for this training run is added to the combined history of the previous training run/s.\n modelHistory={currentKey:modelHistory[currentKey]+currentModelHistory[currentKey] for currentKey in modelHistory.keys()}\n \n return modelHistory\n\n\n\ndef getF1ScoreOfValidationData(model,xValidation,validationObjectIntegerLabelList,batchSize,objectHierarchyDepth):\n predictedProbabilities=model.predict(x=xValidation,batch_size=batchSize)\n numberOfValidationObjects=xValidation.shape[0]\n predictedClasses=[] #Holds a list of the labels for each object.\n for i in range(0,numberOfValidationObjects): #Loops through each validation object.\n currentObjectPredictedProbabilities=[currentLevelPredictedProbabilies[i,:] for currentLevelPredictedProbabilies in predictedProbabilities]\n currentObjectPredictedClasses=[currentProbabilites.argmax() for currentProbabilites in currentObjectPredictedProbabilities] #Represents the most likely labels using integers.\n predictedClasses.append(currentObjectPredictedClasses)\n \n \n possibleClassSets=[] #A list that holds all of the possible sets of true labels. Stores data as a string to make comparisons easier.\n trueClasses=[] #Similar to predictedClasses but for the true labels of the validation objects.\n for currentObjectIndex in range(0,numberOfValidationObjects):\n currentObjectTrueClasses=[validationObjectIntegerLabelList[currentHierarchyLevel][currentObjectIndex] for currentHierarchyLevel in range(0,objectHierarchyDepth)]\n trueClasses.append(currentObjectTrueClasses)\n \n currentObjectTrueClassesString=str(currentObjectTrueClasses)\n if(not (currentObjectTrueClassesString in possibleClassSets)): #If the current object has a new unique set of object labels.\n possibleClassSets.append(currentObjectTrueClassesString)\n \n\n \n #Dictionaries that hold the results for every real type of object; they are uses to create f1 scores for each real type of object.\n truePositives={currentPossibleClassSet:0.0 for currentPossibleClassSet in possibleClassSets}\n falsePositives={currentPossibleClassSet:0.0 for currentPossibleClassSet in possibleClassSets}\n falseNegatives={currentPossibleClassSet:0.0 for currentPossibleClassSet in possibleClassSets}\n\n for currentObjectIndex in range(0,numberOfValidationObjects): #Loops through all validation objects.\n currentObjectTrueClasses=trueClasses[currentObjectIndex]\n currentObjectPredictedClasses=predictedClasses[currentObjectIndex]\n \n currentObjectTrueClassesString=str(currentObjectTrueClasses)\n currentObjectPredictedClassesString=str(currentObjectPredictedClasses)\n \n if(currentObjectTrueClassesString==currentObjectPredictedClassesString):\n truePositives[currentObjectTrueClassesString]+=1.0 #The object has been identified correctly.\n else:\n if(currentObjectPredictedClassesString in possibleClassSets): #If the predicted classes corresponds to a real type of object. \n falsePositives[currentObjectPredictedClassesString]+=1.0 #The current object has meed misidentified as the type currentObjectPredictedClassesString.\n \n falseNegatives[currentObjectTrueClassesString]+=1.0 #The object has failed to be identified as being of the type currentObjectTrueClassesString. \n\n \n resultantF1Score=0.0\n for currentClassSetString in possibleClassSets:\n currentTruePositives=truePositives[currentClassSetString]\n currentFalseNegatives=falseNegatives[currentClassSetString]\n currentFalsePositives=falsePositives[currentClassSetString]\n \n currentF1Score=(2*currentTruePositives)/((2*currentTruePositives)+currentFalsePositives+currentFalseNegatives)\n resultantF1Score+=currentF1Score/float(len(possibleClassSets)) #The total f1 score is an average of the f1 scores for each real object type.\n \n return resultantF1Score\n","sub_path":"trainBCNN.py","file_name":"trainBCNN.py","file_ext":"py","file_size_in_byte":10311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"452498014","text":"# import selenium drivers\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.common.exceptions import TimeoutException\nimport os\nimport pandas as pd\nfrom time import time\nimport re\n\n\nclass Covid19Scraper():\n '''Class to scrape covid data from \"https://www.worldometers.info/coronavirus/#main_table\"\n '''\n\n def __init__(self, webdriver_path: str, headless: bool, save_path: str, url: str, delay: int):\n '''\n Parameters\n ----------\n webdriver_path: str, path to chrome webdriver\n headless: bool, whther to run the scraper headlessly\n num_pages: int, number of pages to scrape\n save_path: str, dataset save location (include file name)\n url: str, url to start scraping from\n delay: int, number of seconds to wait for page to load\n '''\n # check parameter types\n if type(webdriver_path) != str or type(headless) != bool or type(save_path) != str or type(url) != str or type(delay) != int:\n print(\"Type Error: Invalid type in arguements\")\n exit(0)\n if not os.path.exists(webdriver_path):\n print('Webdriver not found')\n exit(0)\n\n self.webdriver_path = webdriver_path\n self.headless = headless\n self.save_path = save_path\n self.url = url\n self.delay = delay\n\n def wait_for_element(self, classname: str) -> bool:\n try:\n element_present = EC.presence_of_element_located((By.CSS_SELECTOR, classname))\n WebDriverWait(self.driver, self.delay).until(element_present)\n except TimeoutException:\n print(\"Timed out waiting for element\")\n return False\n return True\n\n def clean_data(self, df):\n print('Cleaning data')\n num_cols = list(df.columns)\n num_cols.remove('region')\n\n for idx, row in df.iterrows():\n for col in num_cols:\n if row[col] is not None:\n row[col] = re.sub(r'[^\\d]+', '', str(row[col]))\n df.loc[idx, col] = int(row[col]) if row[col].isdigit() else None\n return df\n\n def save_data(self, data: list) -> None:\n df = pd.DataFrame(data)\n df = self.clean_data(df)\n print('Saving data')\n df.to_csv(self.save_path, index=False)\n print('Data saved successfully.')\n\n def scrape(self) -> None:\n print('Scraping Data')\n start = time()\n options = Options()\n if self.headless:\n options.add_argument('--headless')\n self.driver = webdriver.Chrome(self.webdriver_path, options=options)\n\n data = []\n\n try:\n self.driver.get(self.url)\n self.wait_for_element('a.mt_a')\n rows = self.driver.find_elements_by_xpath('//tr[@role=\"row\"]')\n print(len(rows))\n for row in rows:\n cols = row.find_elements_by_xpath('.//td')\n if len(cols) >= 5 and cols[1].text != '':\n record = {}\n record['region'] = cols[1].text\n record['total_cases'] = cols[2].text\n record['total_deaths'] = cols[4].text\n record['total_recovered'] = cols[6].text\n record['active_cases'] = cols[7].text\n record['total_cases/1M_population'] = cols[9].text\n record['deaths/1M_population'] = cols[10].text\n record['total_tests'] = cols[11].text\n record['tests/1M_population'] = cols[12].text\n record['population'] = cols[13].text\n data.append(record)\n\n finally:\n # Close the webdriver\n self.driver.quit()\n\n self.save_data(data)\n print(f'Time elapsed: {(time()-start): .2f} seconds')\n\n\nif __name__ == '__main__':\n # Constants and Parameters\n URL = 'https://www.worldometers.info/coronavirus/?utm_campaign=homeAdvegas1?'\n DRIVER_PATH = os.getcwd() + '/chromedriver'\n SAVE_PATH = './covid19_data.csv'\n HEADLESS = True\n NUM_PAGES = 10\n DELAY = 2\n\n cs = Covid19Scraper(DRIVER_PATH, HEADLESS, SAVE_PATH, URL, DELAY)\n cs.scrape()\n","sub_path":"DataScience/Projects/Covid19Cases/selenium_scraper.py","file_name":"selenium_scraper.py","file_ext":"py","file_size_in_byte":4399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"557171090","text":"from __future__ import print_function\nimport c4_questionAnswering.predict as question_answerer\nimport c3_nlpMappingGenerator.predict as mapping_generator\nimport c2_imageCaptionGenerator.predict as caption_generator\nimport c1_speechRecognition.predict as speech_recognizer\nimport c0_entryContainer.predict as entry_container\nfrom timeit import default_timer as timer\nfrom multiprocessing import Pool\nimport rpc\nimport os\nimport sys\nimport numpy as np\n\nIMPORT_ERROR_RETURN_CODE = 3\n\n################### From main.py #######################################\n\n\nprint(\"---Modules successfully imported---\")\n\n\ndef run_speech_recognition(input_index):\n speech_text = speech_recognizer.predict(input_index)\n return speech_text\n\n\ndef generate_image_caption(input_index):\n captions = caption_generator.predict(input_index)\n return captions\n\n\ndef run(input_index_list_format):\n\n print(\"input format:\" + str(input_index_list_format))\n\n input_index = int(input_index_list_format[0])\n\n # CONTAINER 0\n input_index = entry_container.predict(input_index)\n\n # CONTAINER 1, 2: Multi Threading\n p = Pool(1) # use only one subprocess, run TF session in main process\n returned_result1 = p.apply_async(\n run_speech_recognition, args=(input_index,))\n # returned_result2 = p.apply_async(generate_image_caption, args=(input_index,))\n result2 = generate_image_caption(input_index)\n p.close()\n p.join() # p.join()方法会等待所有子进程执行完毕\n\n # CONTAINER 3\n result1 = returned_result1.get()[0]\n text = result1 + \"|\" + result2\n result3 = mapping_generator.predict(text)\n\n # Container 4\n question = \"Verb\"\n result4 = question_answerer.predict(result3)\n\n print(\"c1: \" + str(result1))\n print(\"c2: \" + str(result2))\n print(\"c3: \" + str(result3))\n print(\"c4: \" + str(result4))\n return [str(result4), str(result4)]\n\n############################################################################\n\n\nclass PythonContainer(rpc.ModelContainerBase):\n def __init__(self, input_type):\n self.input_type = rpc.string_to_input_type(input_type)\n # modules_folder_path = \"{dir}/modules/\".format(dir=path)\n # sys.path.append(os.path.abspath(modules_folder_path))\n # predict_fname = \"func.pkl\"\n # predict_path = \"{dir}/{predict_fname}\".format(\n # dir=path, predict_fname=predict_fname)\n self.predict_func = run\n\n def predict_ints(self, inputs):\n preds = self.predict_func(inputs)\n return [str(p) for p in preds]\n\n def predict_floats(self, inputs):\n preds = self.predict_func(inputs)\n return [str(p) for p in preds]\n\n def predict_doubles(self, inputs):\n preds = self.predict_func(inputs)\n return [str(p) for p in preds]\n\n def predict_bytes(self, inputs):\n preds = self.predict_func(inputs)\n return [str(p) for p in preds]\n\n def predict_strings(self, inputs):\n preds = self.predict_func(inputs)\n return [str(p) for p in preds]\n\n\nif __name__ == \"__main__\":\n print(\"Starting Python Closure container\")\n rpc_service = rpc.RPCService()\n try:\n model = PythonContainer(rpc_service.get_input_type())\n sys.stdout.flush()\n sys.stderr.flush()\n except ImportError:\n sys.exit(IMPORT_ERROR_RETURN_CODE)\n rpc_service.start(model)\n","sub_path":"applications/imagequery_clipper/container/closure.py","file_name":"closure.py","file_ext":"py","file_size_in_byte":3348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"451203109","text":"from flask_sqlalchemy import SQLAlchemy\nimport requests\nimport time\nfrom flask import Flask, render_template, request\nfrom datetime import datetime\nfrom multiprocessing import Process, Value\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///new.db'\ndb = SQLAlchemy(app)\t\n\nmoistURL = 'https://api.particle.io/v1/devices/350025000b47363339343638/moisturePercentage?access_token=1aa09ff2c1a069ca69c16e6822b0602412a8c1c4'\ntempURL = 'https://api.particle.io/v1/devices/350025000b47363339343638/temperature?access_token=1aa09ff2c1a069ca69c16e6822b0602412a8c1c4'\nhumidURL = 'https://api.particle.io/v1/devices/350025000b47363339343638/humidity?access_token=1aa09ff2c1a069ca69c16e6822b0602412a8c1c4'\n\ndef getjson(url):\n\tr = requests.get(url)\n\treturn r.json()['result']\n\t\nclass Plant(db.Model):\n\tid = db.Column(db.Integer, primary_key=True)\n\ttime = db.Column(db.String, nullable=False, default = datetime.now().strftime(\"%A, %B %d, %I:%M %p\"))\n\tmoisture = db.Column(db.Integer)\n\ttemperature = db.Column(db.Float)\n\thumidity = db.Column(db.Float)\ndef __repr__(self):\n\treturn f\"Plant('{self.time}','{self.moisture}','{self.temperature}','{self.humidity}')\"\ndb.create_all()\n\nxvalues = Plant.query.with_entities(Plant.time).all()\nytemperature = Plant.query.with_entities(Plant.temperature).all()\nymoisture = Plant.query.with_entities(Plant.moisture).all()\nyhumidity = Plant.query.with_entities(Plant.humidity).all()\nidvalues = Plant.query.with_entities(Plant.id).all()\nreallyxvalues=['']\nreallyidvalues=[]\nreallytemperature=[]\nreallyhumidity = []\nreallymoisture = []\nreallytimes = []\nfor i in range(10,(len(xvalues))):\n\treallyxvalues.append(xvalues[i][0])\nfor i in range(1,(len(ytemperature))):\n\treallytemperature.append(ytemperature[i][0])\nfor i in range(1,(len(yhumidity))):\n\treallyhumidity.append(yhumidity[i][0])\nfor i in range(1,(len(ymoisture))):\n\treallymoisture.append(ymoisture[i][0])\nfor i in range(1,(len(idvalues))):\n\treallyidvalues.append(idvalues[i][0])\nclass values():\n\tglobalmoist = reallymoisture[-1]\n\tglobalhumid = reallyhumidity[-1]\n\tglobaltemp = reallytemperature[-1]\nv = values()\ndef record_loop(loop_on):\n\ttry:\n\t\twhile True:\n\t\t\tif loop_on.value == True:\n\t\t\t\tprint(\"loop running\")\n\t\t\t\tv.globalmoist = getjson(moistURL)\n\t\t\t\ttime.sleep(3)\n\t\t\t\tv.globalhumid = getjson(humidURL)\n\t\t\t\ttime.sleep(3)\n\t\t\t\tv.globaltemp = getjson(tempURL)\n\t\t\t\ttime.sleep(3)\n\t\t\t\tnewReading = Plant(moisture = v.globalmoist, temperature = v.globaltemp, humidity = v.globalhumid)\n\t\t\t\tdb.session.add(newReading)\n\t\t\t\tPlant.query.filter(Plant.humidity >= 100).delete()\n\t\t\t\tdb.session.commit()\n\t\t\t\ttime.sleep(1)\n\texcept KeyError:\n\t\tprint('Box disconnected!')\n\nallvalues= {'idvalues':reallyidvalues, 'xvalues':reallyxvalues,'ytemperature':reallytemperature,'ymoisture':reallymoisture,'yhumidity':reallyhumidity, 'currenttemperature':v.globaltemp, 'currenthumidity':v.globalhumid,'currentmoisture':v.globalmoist}\n\n@app.route('/')\ndef index():\n\treturn render_template(\"websiteinprogress.html\", allvalues=allvalues)\n\nif __name__ == \"__main__\":\n\trecording_on = Value('b', True)\n\tp = Process(target=record_loop, args=(recording_on,))\n\tp.start() \n\tapp.run(use_reloader=False)\n\tp.join()\n\n\n","sub_path":"deploy.py","file_name":"deploy.py","file_ext":"py","file_size_in_byte":3231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"179375533","text":"# -*- coding: utf-8 -*-\n\"\"\"This file includes all required openEO response schemas\n\"\"\"\nimport re\nfrom datetime import datetime\nfrom flask import make_response\nfrom typing import Dict\nfrom openeo_grass_gis_driver.models.schema_base import JsonableObject\nfrom openeo_grass_gis_driver.models.process_graph_schemas import \\\n ProcessGraphNode\nfrom openeo_grass_gis_driver.models.error_schemas import ErrorSchema\n\n__author__ = \"Anika Bettge\"\n__copyright__ = \"Copyright 2018, mundialis\"\n__maintainer__ = \"Anika Bettge\"\n\n\nclass Service(JsonableObject):\n \"\"\"\tThe base data for the secondary web service to create\n\n id:\n required\n string (service_id) ^[A-Za-z0-9_\\-\\.~]+$\n Unique identifier of a secondary web service that is generated by the\n back-end during creation. MUST match the specified pattern.\n\n title:\n string (title) Nullable\n A short description to easily distinguish entities.\n\n description:\n string (description) Nullable\n Detailed description to fully explain the entity.\n CommonMark 0.28 syntax MAY be used for rich text representation.\n\n process_graph:\n required\n object (Process Graph)\n A process graph defines a graph-like structure as a connected set\n of executable processes. Each key is a unique identifier (node id)\n that is used to refer to the process in the graph.\n\n url:\n required\n string (service_url)\n URL at which the secondary web service is accessible. Doesn't\n necessarily need to be located within the API.\n\n type\n required\n string (service_type)\n Definintion of the service type to access result data. All available\n service types can be retrieved via GET /service_types. Service types\n MUST be accepted case insensitive.\n\n enabled:\n boolean (service_enabled)\n Default: true\n Describes whether a secondary web service is responding to requests\n (true) or not (false). Defaults to true. Disabled services don't\n produce any costs.\n\n parameters:\n object (Service Parameters)\n List of arguments, i.e. the parameter names supported by the\n secondary web service combined with actual values. See GET\n /service_types for supported parameters and valid arguments.\n For example, this could specify the required version of the\n service, visualization details or any other service dependant\n configuration.\n\n attributes:\n required\n object (Secondary Web Service Attributes)\n Additional attributes of the secondary web service, e.g. available\n layers for a WMS based on the bands in the underlying GeoTiff. See\n GET /service_types for supported attributes.\n\n submitted:\n string (submitted)\n Date and time of creation, formatted as a RFC 3339 date-time.\n\n plan:\n string (billing_plan_defaultable) Nullable\n The billing plan to process and charge the job with.\n The plans and the default plan can be retrieved by calling GET /.\n Billing plans MUST be accepted case insensitive. Billing plans not on\n the list of available plans MUST be rejected with openEO error\n BillingPlanInvalid.\n If no billing plan is specified by the client, the server MUST default\n to the default billing plan in GET /. If the default billing plan of\n the provider changes, the job or service MUST not be affected by the\n change, i.e. the default plan which is valid during job or service\n creation must be permanently assigned to the job or service until the\n client requests to change it.\n\n costs:\n number (money)\n An amount of money or credits. The value MUST be specified in the\n currency the back-end is working with. The currency can be retrieved\n by calling GET /.\n\n budget:\n number (budget) Nullable\n Default: null\n Maximum amount of costs the user is allowed to produce. The value MUST\n be specified in the currency the back-end is working with. The currency\n can be retrieved by calling GET /. If possible, back-ends SHOULD\n reject jobs with openEO error PaymentRequired if the budget is too low\n to process the request completely. Otherwise, when reaching the budget\n jobs MAY try to return partial results if possible. Otherwise the\n request and results are discarded. Users SHOULD be warned by clients\n that reaching the budget MAY discard the results and that setting this\n value should be well-wrought. Setting the buget to null means there is\n no specified budget.\n\n \"\"\"\n\n def __init__(self,\n process_graph: ProcessGraphNode,\n url: str, type: str, parameters: Dict,\n attributes: Dict, submitted: str = None,\n title: str = None, description: str = None,\n enabled: bool = True,\n plan: str = None, costs: float = None, budget: float = None):\n\n self.title = title\n self.description = description\n self.process_graph = process_graph\n self.url = url\n self.type = type\n self.enabled = enabled\n self.parameters = parameters\n self.attributes = attributes\n self.submitted = submitted\n self.plan = plan\n self.costs = costs\n self.budget = budget\n\n # Test id in pattern\n pattern = r\"^[A-Za-z0-9_\\-\\.~]+$\"\n x = re.search(pattern, id)\n if not x:\n es = ErrorSchema(\n id=str(\n datetime.now()),\n code=400,\n message=\"The id MUST match the following pattern: %s\" %\n pattern)\n return make_response(es.to_json(), 400)\n self.id = id\n","sub_path":"src/openeo_grass_gis_driver/models/service_schemas.py","file_name":"service_schemas.py","file_ext":"py","file_size_in_byte":5890,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"565400045","text":"# -*- coding:utf-8 -*-\nfrom __future__ import print_function\n\nimport unittest\nimport wireless\nimport wireless.util.hashes as hashes\nimport wireless.codes.spinal as spinal\nimport wireless.codes.spinal.reference as reference\nimport numpy\n\nPACKET_LENGTH_BITS = 192\nNUM_PASSES = 5\nNUM_LAST_CODE_STEP_SYMBOLS = 2\n\n\nclass CodeParams(object):\n def __init__(self, salsaNumRounds, hashWordSize, numBitsPerCodingStep, symbolSizeBits, transmittedPointPrecisionBits, lookaheadDepth):\n self.salsaNumRounds = salsaNumRounds\n self.hashWordSize = hashWordSize\n self.numBitsPerCodingStep = numBitsPerCodingStep\n self.symbolSizeBits =symbolSizeBits\n self.transmittedPointPrecisionBits = transmittedPointPrecisionBits\n self.lookaheadDepth = lookaheadDepth\n\n\ncodeParams = CodeParams(\n salsaNumRounds = 12,\n hashWordSize = 64,\n numBitsPerCodingStep = 4,\n symbolSizeBits = 16,\n transmittedPointPrecisionBits = 16,\n lookaheadDepth = 0)\n\n# get packet\npacket = numpy.random.bytes(PACKET_LENGTH_BITS / 8)\n\nassert(PACKET_LENGTH_BITS % codeParams.numBitsPerCodingStep == 0)\n\n# decide on symbol schedule\nnumCodingSteps = PACKET_LENGTH_BITS / codeParams.numBitsPerCodingStep\nnumSymbolsForCodingStep = numpy.ones([numCodingSteps], dtype=numpy.uint32)\nnumSymbolsForCodingStep *= NUM_PASSES\nnumSymbolsForCodingStep[-1] *= NUM_LAST_CODE_STEP_SYMBOLS\n\n# Create C++ encoder\ncppEncoder = spinal.CodeFactory(\n codeParams.numBitsPerCodingStep,\n numCodingSteps,\n codeParams.symbolSizeBits) \\\n .salsa().encoder()\ncppSymbols = wireless.vectorus()\n\n# Convert symbol schedule to spine value indices array\ncppSpineValueIndices = wireless.vectorus()\nfor codeStep in xrange(numCodingSteps):\n for i in xrange(numSymbolsForCodingStep[codeStep]):\n cppSpineValueIndices.push_back(codeStep)\n\n# Encode with c++ encoder\ncppEncoder.setPacket(packet)\ncppEncoder.encode(cppSpineValueIndices,\n cppSymbols)\n\n# Encode with python encoder\npyEncoder = reference.Encoder(codeParams)\npySymbols = pyEncoder.encode(packet, numSymbolsForCodingStep)\n\n# Check the two encoders produced the same symbols\nprint(pySymbols == list(cppSymbols))","sub_path":"codesTest.py","file_name":"codesTest.py","file_ext":"py","file_size_in_byte":2184,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"572369856","text":"import numpy as np \nimport math\nimport argparse\n\ndef get_parser():\n\tparser = argparse.ArgumentParser(conflict_handler='resolve')\n\n\t# --------------- specify yml config -------------------------------\n\n\t# parser.add_argument(\"--yaml_config\", action=\"store\", dest=\"yaml_config\", default=None, type=str,\n\t# help=\"Optionally specify parameters with a yaml file. YAML file overrides command line args\")\n\n\t# --------------- specify model architecture -------------------------------\n\n\tparser.add_argument(\"--ae\", action='store_true', dest=\"ae\", default=False, help=\"True for ae [False]\")\n\tparser.add_argument(\"--svr\", action='store_true', dest=\"svr\", default=False, help=\"True for svr [False]\")\n\n\t# --------------- specify model mode -------------------------------\n\n\tparser.add_argument(\"--train\", action='store_true', dest=\"train\", default=False,\n\t\t\t\t\t\thelp=\"True for training, False for testing\")\n\tparser.add_argument(\"--getz\", action='store_true', dest=\"getz\", default=False,\n\t\t\t\t\t\thelp=\"True for getting latent codes\")\n\tparser.add_argument(\"--interpol\", action='store_true', dest=\"interpol\", default=False,\n\t\t\t\t\t\thelp=\"True for interpolation\")\n\tparser.add_argument(\"--deepdream\", action='store_true', dest=\"deepdream\", default=False,\n\t\t\t\t\t\thelp=\"True for deepdream\")\n\n\t# --------------- training -------------------------------\n\n\tparser.add_argument(\"--sample_vox_size\", action=\"store\", dest=\"sample_vox_size\", default=64, type=int,\n\t\t\t\t\t\thelp=\"Voxel resolution for coarse-to-fine training [64]\")\n\tparser.add_argument(\"--epoch\", action=\"store\", dest=\"epoch\", default=0, type=int, help=\"Epoch to train [0]\")\n\tparser.add_argument(\"--iteration\", action=\"store\", dest=\"iteration\", default=0, type=int,\n\t\t\t\t\t\thelp=\"Iteration to train. Either epoch or iteration need to be zero [0]\")\n\tparser.add_argument(\"--learning_rate\", action=\"store\", dest=\"learning_rate\", default=0.00005, type=float,\n\t\t\t\t\t\thelp=\"Learning rate for adam [0.00005]\")\n\tparser.add_argument(\"--beta1\", action=\"store\", dest=\"beta1\", default=0.5, type=float,\n\t\t\t\t\t\thelp=\"Momentum term of adam [0.5]\")\n\n\t# --------------- testing -------------------------------\n\n\tparser.add_argument(\"--start\", action=\"store\", dest=\"start\", default=0, type=int,\n\t\t\t\t\t\thelp=\"In testing, output shapes [start:end]\")\n\tparser.add_argument(\"--end\", action=\"store\", dest=\"end\", default=16, type=int,\n\t\t\t\t\t\thelp=\"In testing, output shapes [start:end]\")\n\n\t# --------------- Data and Directories -------------------------------\n\n\tparser.add_argument(\"--R2N2_dir\", action=\"store\", dest=\"R2N2_dir\", default=\"/shapenet\",\n\t\t\t\t\t\thelp=\"R2N2_dir directory\")\n\tparser.add_argument(\"--dataset\", action=\"store\", dest=\"dataset\", default=\"all_vox256_img\",\n\t\t\t\t\t\thelp=\"The name of dataset\")\n\tparser.add_argument(\"--splitfile_dir\", action=\"store\", dest=\"splitfile\",\n\t\t\t\t\t\tdefault=\"data/metadata/all_vox256_img_test.txt\",\n\t\t\t\t\t\thelp=\"The name of dataset\")\n\tparser.add_argument(\"--checkpoint_dir\", action=\"store\", dest=\"checkpoint_dir\", default=\"checkpoint\",\n\t\t\t\t\t\thelp=\"Directory name to save the checkpoints [checkpoint]\")\n\tparser.add_argument(\"--data_dir\", action=\"store\", dest=\"data_dir\", default=\"./data/all_vox256_img/\",\n\t\t\t\t\t\thelp=\"Root directory of dataset [data]\")\n\tparser.add_argument(\"--sample_dir\", action=\"store\", dest=\"sample_dir\", default=\"./samples/\",\n\t\t\t\t\t\thelp=\"Directory name to save the image samples [samples]\")\n\tparser.add_argument(\"--interpol_directory\", action=\"store\", dest=\"interpol_directory\", default=None,\n\t\t\t\t\t\thelp=\"First Interpolation latent vector\")\n\n\t# --------------- Interpolation -------------------------------\n\n\tparser.add_argument(\"--interpol_z1\", action=\"store\", dest=\"interpol_z1\", type=int, default=0,\n\t\t\t\t\t\thelp=\"First Interpolation latent vector\")\n\tparser.add_argument(\"--z1_im_view\", action=\"store\", dest=\"z1_im_view\", type=int, default=23,\n\t\t\t\t\t\thelp=\"First image number to deep dream with\")\n\tparser.add_argument(\"--interpol_z2\", action=\"store\", dest=\"interpol_z2\", type=int, default=1,\n\t\t\t\t\t\thelp=\"Second Interpolation latent vector\")\n\tparser.add_argument(\"--z2_im_view\", action=\"store\", dest=\"z2_im_view\", type=int, default=23,\n\t\t\t\t\t\thelp=\"Second image number to deep dream with\")\n\tparser.add_argument(\"--interpol_steps\", action=\"store\", dest=\"interpol_steps\", type=int, default=5,\n\t\t\t\t\t\thelp=\"number of steps to take between values\")\n\n\t# --------------- deepdream -------------------------------\n\n\t# dreaming uses the interpolation targets from interpolation as well as the number of steps.\n\n\tparser.add_argument(\"--layer_num\", action=\"store\", dest=\"layer_num\", default=3, type=int,\n\t\t\t\t\t\thelp=\"activation layer to maximize\")\n\tparser.add_argument(\"--dream_rate\", action=\"store\", dest=\"dream_rate\", default=.01, type=float,\n\t\t\t\t\t\thelp=\"dream update rate\")\n\tparser.add_argument(\"--beta\", action=\"store\", dest=\"beta\", default=1e-7, type=float,\n\t\t\t\t\t\thelp=\"style loss factor (gram matrix)\")\n\tparser.add_argument(\"--annealing_rate\", action=\"store\", dest=\"annealing_rate\", default=1, type=int,\n\t\t\t\t\t\thelp=\"annealing rate\")\n\n\n\treturn parser\n\ndef write_ply_point(name, vertices):\n\tfout = open(name, 'w')\n\tfout.write(\"ply\\n\")\n\tfout.write(\"format ascii 1.0\\n\")\n\tfout.write(\"element vertex \"+str(len(vertices))+\"\\n\")\n\tfout.write(\"property float x\\n\")\n\tfout.write(\"property float y\\n\")\n\tfout.write(\"property float z\\n\")\n\tfout.write(\"end_header\\n\")\n\tfor ii in range(len(vertices)):\n\t\tfout.write(str(vertices[ii,0])+\" \"+str(vertices[ii,1])+\" \"+str(vertices[ii,2])+\"\\n\")\n\tfout.close()\n\n\ndef write_ply_point_normal(name, vertices, normals=None):\n\tfout = open(name, 'w')\n\tfout.write(\"ply\\n\")\n\tfout.write(\"format ascii 1.0\\n\")\n\tfout.write(\"element vertex \"+str(len(vertices))+\"\\n\")\n\tfout.write(\"property float x\\n\")\n\tfout.write(\"property float y\\n\")\n\tfout.write(\"property float z\\n\")\n\tfout.write(\"property float nx\\n\")\n\tfout.write(\"property float ny\\n\")\n\tfout.write(\"property float nz\\n\")\n\tfout.write(\"end_header\\n\")\n\tif normals is None:\n\t\tfor ii in range(len(vertices)):\n\t\t\tfout.write(str(vertices[ii,0])+\" \"+str(vertices[ii,1])+\" \"+str(vertices[ii,2])+\" \"+str(vertices[ii,3])+\" \"+str(vertices[ii,4])+\" \"+str(vertices[ii,5])+\"\\n\")\n\telse:\n\t\tfor ii in range(len(vertices)):\n\t\t\tfout.write(str(vertices[ii,0])+\" \"+str(vertices[ii,1])+\" \"+str(vertices[ii,2])+\" \"+str(normals[ii,0])+\" \"+str(normals[ii,1])+\" \"+str(normals[ii,2])+\"\\n\")\n\tfout.close()\n\n\ndef write_ply_triangle(name, vertices, triangles):\n\tfout = open(name, 'w')\n\tfout.write(\"ply\\n\")\n\tfout.write(\"format ascii 1.0\\n\")\n\tfout.write(\"element vertex \"+str(len(vertices))+\"\\n\")\n\tfout.write(\"property float x\\n\")\n\tfout.write(\"property float y\\n\")\n\tfout.write(\"property float z\\n\")\n\tfout.write(\"element face \"+str(len(triangles))+\"\\n\")\n\tfout.write(\"property list uchar int vertex_index\\n\")\n\tfout.write(\"end_header\\n\")\n\tfor ii in range(len(vertices)):\n\t\tfout.write(str(vertices[ii,0])+\" \"+str(vertices[ii,1])+\" \"+str(vertices[ii,2])+\"\\n\")\n\tfor ii in range(len(triangles)):\n\t\tfout.write(\"3 \"+str(triangles[ii,0])+\" \"+str(triangles[ii,1])+\" \"+str(triangles[ii,2])+\"\\n\")\n\tfout.close()\n\n\ndef sample_points_triangle(vertices, triangles, num_of_points):\n\tepsilon = 1e-6\n\ttriangle_area_list = np.zeros([len(triangles)],np.float32)\n\ttriangle_normal_list = np.zeros([len(triangles),3],np.float32)\n\tfor i in range(len(triangles)):\n\t\t#area = |u x v|/2 = |u||v|sin(uv)/2\n\t\ta,b,c = vertices[triangles[i,1]]-vertices[triangles[i,0]]\n\t\tx,y,z = vertices[triangles[i,2]]-vertices[triangles[i,0]]\n\t\tti = b*z-c*y\n\t\ttj = c*x-a*z\n\t\ttk = a*y-b*x\n\t\tarea2 = math.sqrt(ti*ti+tj*tj+tk*tk)\n\t\tif area2100:\n\t\t\tprint(\"infinite loop here!\")\n\t\t\treturn point_normal_list\n\t\tfor i in range(len(triangle_index_list)):\n\t\t\tif count>=num_of_points: break\n\t\t\tdxb = triangle_index_list[i]\n\t\t\tprob = sample_prob_list[dxb]\n\t\t\tprob_i = int(prob)\n\t\t\tprob_f = prob-prob_i\n\t\t\tif np.random.random()=1:\n\t\t\t\t\tu_x = 1-u_x\n\t\t\t\t\tv_y = 1-v_y\n\t\t\t\tppp = u*u_x+v*v_y+base\n\t\t\t\t\n\t\t\t\tpoint_normal_list[count,:3] = ppp\n\t\t\t\tpoint_normal_list[count,3:] = normal_direction\n\t\t\t\tcount += 1\n\t\t\t\tif count>=num_of_points: break\n\n\treturn point_normal_list","sub_path":"DeepDream3D/ModelDefinition/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":9019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"583633185","text":"import re\n\nwith open('quotes.txt', 'r') as f:\n src = f.read()\n\nquoteCheck = '(?<=

\\s)[\\s\\S]*?(?=\\s

)'\nauthCheck = '(?<=>)[^<]*?(?=

)'\nrefCheck = '(?<=

)'\n\nquotes = re.findall(quoteCheck, src)\nauthors = re.findall(authCheck, src)\nreferences = re.findall(refCheck, src)\n\nwith open('parsedquotes.txt', 'w') as file:\n for i in range(len(quotes)):\n file.write('{\\n\\t\"text\": \"'+quotes[i]+'\",\\n\\t\"author\": \"'+authors[i]+'\",\\n\\t\"ref\": \"'+references[i]+'\"\\n},\\n')\n","sub_path":"pset8/homepage/data/script.1.py","file_name":"script.1.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"106941612","text":"#!/usr/bin/env python\n\n\"\"\"\n================================================\nABElectronics RTC Pi | RTC memory integer demo\n\nRequires python smbus to be installed\nFor Python 2 install with: sudo apt-get install python-smbus\nFor Python 3 install with: sudo apt-get install python3-smbus\n\nrun with: python demo_rtcmemory_int.py\n================================================\n\nThis demo shows how to write to and read from the internal battery\nbacked memory on the DS1307 RTC chip\n\"\"\"\n\nfrom __future__ import absolute_import, division, print_function, \\\n unicode_literals\n\ntry:\n from RTCPi import RTC\nexcept ImportError:\n print(\"Failed to import RTCPi from python system path\")\n print(\"Importing from parent folder instead\")\n try:\n import sys\n sys.path.append('..')\n from RTCPi import RTC\n except ImportError:\n raise ImportError(\n \"Failed to import library from parent folder\")\n\n\ndef int_to_array(val):\n '''\n convert an integer into a four byte array\n '''\n arraybytes = [0, 0, 0, 0]\n arraybytes[3] = val & 0xFF\n val >>= 8\n arraybytes[2] = val & 0xFF\n val >>= 8\n arraybytes[1] = val & 0xFF\n val >>= 8\n arraybytes[0] = val & 0xFF\n return arraybytes\n\n\ndef array_to_int(arraybytes):\n '''\n convert a four byte array into an integer\n '''\n val = (arraybytes[0] << 24) + (arraybytes[1] << 16) + \\\n (arraybytes[2] << 8) + arraybytes[3]\n return val\n\n\ndef main():\n '''\n Main program function\n '''\n\n # create a new instance of the RTC class\n rtc = RTC()\n\n # integer to be written to the RTC memory\n writeval = 176247\n print(\"Writing to memory: \", writeval)\n\n # convert the integer into an array of bytes\n writearray = int_to_array(writeval)\n\n # write the array to the RTC memory\n rtc.write_memory(0x08, writearray)\n\n # read four bytes from the RTC memory into an array\n readarray = rtc.read_memory(0x08, 4)\n\n # combine the array values into an integer and print it\n print(\"Reading from memory: \", array_to_int(readarray))\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"reinvent-2019/rhythm-cloud/lib/ABElectronics_Python_Libraries/RTCPi/demos/demo_rtcmemory_int.py","file_name":"demo_rtcmemory_int.py","file_ext":"py","file_size_in_byte":2160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"366031536","text":"from Datasets.BCI.BCIDatasets import *\nfrom Datasets.BCI.SignalTarget import SignalAndTarget\nfrom Estimators.Classifiers.EEGClassifiers.CSPmodel import CSSP\nimport random\n\n\n\n\n\ndb = BCIC3_3a()\ndb.preprocess_standard()\ndb.load_data()\n\nsignal_and_target = SignalAndTarget(dataset=db,subject_ids=[0])\nrandom.seed(2)\ntrain_set,test_set = signal_and_target.split_into_two_sets(n_first_set=80,shuffle=False)\n\n\nmodel_const = {\n 'fs':db.fs,\n 'time_steps':db.time_steps\n}\nfit_const = {\n 'train_set':train_set,\n 'valid_set':None,\n 'test_set':test_set,\n 'fitter':'InstantFitter'\n}\n\n#suppose we hava find the best parameter for the dataset\nmodel_hyper = {\n 'T':5,\n 'low_cut_hz':4,\n 'high_cut_hz':38,\n 'window_start':448,\n 'window_length':448,\n 'n_csp_component':2,\n 'filt_order':2\n}\n\n\nmodel = CSSP(model_const=model_const,model_hyper=model_hyper)\nmodel.compile(fit_const=fit_const,optimizer_hyper=None,loss_hyper=None)\nans = model.fitter.run()\n\n","sub_path":"tp_script_4.py","file_name":"tp_script_4.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"396817745","text":"import requests\nfrom bs4 import BeautifulSoup as BS\n\nurl = 'http://teh-treid.com/catalog-zapchasti/zapchasti-k-dvigatelyam-honda/zapchasti-honda-gxh50/'\n\nsoup = BS(requests.get(url).content, 'html.parser')\n\ncategory_url = soup.find('div', class_='breadcrumbs')\n\nmain_category = category_url.find_all('a')[2].text\ncategory = category_url.find('div', class_='wire').text.split('|')[-1]\n\nprint(category_url)","sub_path":"src/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"430531544","text":"import matplotlib.pyplot as plt\r\nimport json\r\n\r\nwith open('league_data.json') as data_file:\r\n data = json.load(data_file)\r\nkda = []\r\npercent = []\r\nhours = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]\r\nfor i in range(24):\r\n hour = str(i)\r\n if i < 10:\r\n hour = '0' + hour\r\n kda.append(data['hours'][hour]['kda'])\r\n percent.append(data['hours'][hour]['percent'])\r\nplt.plot(hours, kda, label='KDA', linewidth='1.5', color='r')\r\nplt.plot(hours, percent, label='Percent Win', linewidth='1.5', color='g')\r\nplt.xlabel('Time of day (Hours 0-23)')\r\nplt.ylabel('KDA/Percent Win')\r\nplt.title('LoL Skill Based On Time of Day')\r\nplt.legend()\r\nleg = plt.legend(loc=9, ncol=2, prop={'size': 12})\r\nleg.get_frame().set_alpha(0.4)\r\nplt.grid(linestyle='-')\r\nplt.show()\r\n","sub_path":"LoLAnalysis/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":814,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"62479945","text":"import pandas as pd\r\nimport random\r\nimport numpy as np\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.ensemble import GradientBoostingClassifier\r\nfrom sklearn.model_selection import train_test_split\r\n\r\ndef load_data(filename,x_label,y_label):\r\n df = pd.read_csv(filename, encoding='GBK')\r\n df.fillna(0)\r\n # 619*19\r\n raw_x = df[x_label]\r\n for i in range(len(df['sim'])):\r\n if df['sim'][i] != 4:\r\n df['sim'][i] = 1\r\n # print(df['sim'].value_counts())\r\n # 类别4:398，占比64.3%\r\n # 类别1:221\r\n raw_y = df[y_label][y_label]\r\n return raw_x,raw_y\r\n\r\ndef balance_sample(feature,label):\r\n # 本函数用于解决样本分别不均匀的问题\r\n # print('feature',type(feature),feature.shape,feature.index)\r\n # print('label',type(label),label.shape,label.index)\r\n index_1 = []\r\n index_4 = []\r\n for i in range(len(label)):\r\n # print(label['sim'][i])\r\n if label['sim'][i]==1:\r\n index_1.append(i)\r\n else:\r\n index_4.append(i)\r\n min_count = min(len(index_1),len(index_4))\r\n index_1 = random.sample(index_1,min_count)\r\n index_4 = random.sample(index_4,min_count)\r\n index_1.extend(index_4)\r\n select_index = index_1\r\n select_index.sort()\r\n\r\n # random.shuffle(select_index)\r\n # print(select_index,len(select_index))\r\n\r\n for i in range(len(label)):\r\n if i not in select_index:\r\n feature.drop(i,inplace=True,axis=0)\r\n label.drop(i,inplace=True,axis=0)\r\n\r\n return feature,label\r\n\r\ndef get_quality(test_labels,predict_labels):\r\n # print (test_labels)\r\n # print type(test_labels)\r\n # print (predict_labels)\r\n # print type(predict_labels)\r\n num = len(test_labels)\r\n right_num = 0\r\n true_p = 0\r\n true_n = 0\r\n false_p = 0\r\n false_n = 0\r\n test_labels_list = []\r\n for i in test_labels['sim']:\r\n # print(i)\r\n test_labels_list.append(i)\r\n\r\n for i in range(len(predict_labels)):\r\n if test_labels_list[i] == 1 and predict_labels[i] == 1:\r\n right_num += 1\r\n true_p += 1\r\n elif test_labels_list[i] == 1 and predict_labels[i] == 4:\r\n false_n += 1\r\n elif test_labels_list[i] == 4 and predict_labels[i] == 1:\r\n false_p += 1\r\n elif test_labels_list[i] == 4 and predict_labels[i] == 4:\r\n right_num += 1\r\n true_n += 1\r\n # print ('num',num)\r\n # print ('right_num',right_num)\r\n # print ('true_p',true_p)\r\n # print ('false_p',false_p)\r\n\r\n accuracy = right_num / float(num)\r\n\r\n precision = true_p / float(true_p + false_p)\r\n sensitivity = true_p / float(true_p + false_n)\r\n specificity = true_n / float(true_n + false_p)\r\n return sensitivity,precision\r\n # return accuracy,true_p,true_n,false_p,false_n\r\n\r\ndef use_model(model,modelname,feature,label):\r\n sum_acc = 0\r\n sum_sensitivity = 0\r\n sum_precison = 0\r\n split_count=5\r\n for j in range(split_count):\r\n x_train, x_test, y_train, y_test = train_test_split(feature, label, test_size=0.25)\r\n model.fit(X=x_train, y=y_train)\r\n single_acc = model.score(X=x_test, y=y_test)\r\n predict_labels = model.predict(X=x_test)\r\n sensitivity, precision = get_quality(test_labels=y_test, predict_labels=predict_labels)\r\n # print('单次准确率：',single_acc,'类别1查全率：',sensitivity,'类别1查准率',precision)\r\n sum_acc += single_acc\r\n sum_precison += precision\r\n sum_sensitivity += sensitivity\r\n mean_acc = sum_acc / split_count\r\n mean_sensitivity = sum_sensitivity / split_count\r\n mean_precision = sum_precison / split_count\r\n print(modelname,'的平均准确率', mean_acc, '平均查全率', mean_sensitivity, '平均查准率', mean_precision)\r\n\r\n\r\nif __name__ =='__main__':\r\n filename = 'train.csv'\r\n x_label = ['经营范围lda', '专利标题lda', '专利内容lda', '经营范围cos', '专利标题cos',\r\n '专利内容cos', '所属行业']\r\n y_label = ['sim']\r\n feature,label = load_data(filename = filename,x_label=x_label,y_label=y_label)\r\n feature,label = balance_sample(feature=feature,label=label)\r\n # print(feature,label)\r\n print(np.shape(feature),np.shape(label))","sub_path":"Django_Projects/wotou/wotouml/wotouml/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":4281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"541577534","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.svm import SVC\nimport math\nfrom sklearn.metrics import classification_report, confusion_matrix\n\n\n# find score of a classifier\n# compares predicted traffic volume to real traffic volume, if within 5% of real, it is deemed correct\ndef get_score(sv_classifier, xtest, ytest, error):\n y_pred = sv_classifier.predict(xtest)\n count = 0\n for i in range(0, len(y_pred)):\n delta = ytest.iloc[i] * error\n if ytest.iloc[i] - delta < y_pred[i] < ytest.iloc[i] + delta:\n count = count + 1\n\n return count / len(y_pred)\n\n\ninput_data = pd.read_csv(\"CleanedData_new.csv\")\n# Preview the first 5 lines of the loaded data\ndatasize = 2500\n\npreX = input_data.drop('traffic_volume', axis=1).drop('date', axis=1).drop('date_time', axis=1).drop('weather_main',\n axis=1).drop(\n 'weather_description', axis=1)\nX = preX.iloc[0:datasize]\n\npreY = input_data['traffic_volume']\nY = preY.iloc[0:datasize]\n\nX_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.80)\n\n# run k fold cross validation on C value and svc kernel functions 'linear' and 'rbf'\nK = 10\nClist = np.power(10, np.linspace(-3, 9, num=7))\nk_rankings = np.zeros((K, 3))\nerror_range = 0.10\nrbf = 0\nlinear = 1\nfor k in range(0, K):\n x_k_train, x_k_test, y_k_train, y_k_test = train_test_split(X_train, y_train, test_size=0.80)\n max = -math.inf\n bestfun = 0\n bestC = 0\n for fun in range(0, 2):\n for c in range(0, len(Clist)):\n if fun == rbf:\n k_svclassifier = SVC(kernel='rbf', C=Clist[c], gamma='auto')\n else:\n k_svclassifier = SVC(kernel='linear', C=Clist[c], gamma='auto')\n\n k_svclassifier.fit(x_k_train, y_k_train)\n score = get_score(k_svclassifier, x_k_test, y_k_test, error_range)\n if score > max:\n max = score\n bestfun = fun\n bestC = Clist[c]\n\n k_rankings[k, 0] = max\n k_rankings[k, 1] = bestfun\n k_rankings[k, 2] = bestC\n\n# find best C and function\nbest_k = np.argmax(k_rankings[:, 0])\nif k_rankings[best_k, 1] == rbf:\n svclassifier = SVC(kernel='rbf', C=k_rankings[best_k, 2], gamma='auto')\nelse:\n svclassifier = SVC(kernel='linear', C=k_rankings[best_k, 2], gamma='auto')\n\nsvclassifier.fit(X_train, y_train)\nprint(\"score \" + str(get_score(svclassifier, X_test, y_test, error_range)))\nprint(\"best function \" + str(k_rankings[best_k, 1]))\nprint(\"best C \" + str(k_rankings[best_k, 2]))\n","sub_path":"eece5644_finalproj_svm.py","file_name":"eece5644_finalproj_svm.py","file_ext":"py","file_size_in_byte":2650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"517559155","text":"#!/bin/python\nimport numpy as np\n\nbits = 12\nsamples = np.power(2, 9)\nvariable_name = 'sine'\n\ndef twos_comp(value, bits):\n if value < 0:\n value = ( 1<= th], dtype=means.dtype)\n covs = np.array([covs[k] for k in xrange(len(weights)) if weights[k] >= th], dtype=covs.dtype)\n weights = np.array([weights[k] for k in xrange(len(weights)) if weights[k] >= th], dtype=weights.dtype)\n else:\n sys.stdout.write(\"npy filename:\")\n npy_filename = raw_input()\n descriptors = np.load(\"{}/{}\".format(root, npy_filename))\n print(\"Training GMM of size\", n)\n means, covs, weights = sklearn_dictionary(descriptors, n)\n np.save(\"%s/means_gmm.npy\" % root, means)\n np.save(\"%s/covs_gmm.npy\" % root, covs)\n np.save(\"%s/weights_gmm.npy\" % root, weights)\n return means, covs, weights\n\n\ndef get_fisher_vectors_from_folder(folder, gmm):\n filenames = glob.glob(\"%s/*.jpg\" % folder)\n return np.array([fisher_vector(image_descriptors(filename), *gmm) for filename in filenames], dtype=np.float64)\n\n\ndef fisher_features(folder, gmm):\n folders = glob.glob(\"%s/*\" % folder)\n features = {f: get_fisher_vectors_from_folder(f, gmm) for f in folders}\n return features\n\n\ndef train(features):\n x = np.concatenate(features.values())\n y = np.concatenate([[i] * len(features.values()[i]) for i in xrange(len(features))])\n clf = svm.SVC()\n clf.fit(x, y)\n return clf\n\n\ndef success_rate(classifier, features):\n print(\"Applying the classifier...\")\n x = np.concatenate(np.array(features.values()))\n y = np.concatenate([[i] * len(features.values()[i]) for i in xrange(len(features))])\n res = float(sum([classifier.predict(x)[i] == y[i] for i in xrange(len(y))])) / len(y)\n return res\n\n\ndef load_gmm(folder=\".\"):\n filenames = [\"{}/{}\".format(folder, filename) for filename in (\"means_gmm.npy\", \"covs_gmm.npy\", \"weights_gmm.npy\")]\n return tuple(np.load(filename) for filename in filenames)\n\n\ndef image_classification(directory=\".\", load=False, number=5):\n gmm = load_gmm(directory) if load else generate_gmm(directory, number)\n features = fisher_features(directory, gmm)\n # TBD, split the features into training and validation\n classifier = train(features)\n rate = success_rate(classifier, features)\n print(\"Success rate is\", rate)\n\n\ndef generate_vector(directory=\".\", load=False, number=1):\n gmm = load_gmm(directory) if load else generate_gmm(directory, number, mode=\"Word2Vec\")\n sys.stdout.write(\"npy filename:\")\n npy_filename = raw_input()\n sys.stdout.write(\"text filename:\")\n text_filename = raw_input()\n data = np.load(\"{}/{}\".format(directory, npy_filename))\n with open(\"{}/{}\".format(directory, text_filename)) as f:\n index = np.array(eval(f.read()), dtype=np.int)\n print(\"Number of vectors is\", len(index) * 2)\n if number != 1:\n output = np.empty((len(index) * 2, (data.shape[1] * 2 + 1) * number))\n else:\n output = np.empty((len(index) * 2, (data.shape[1] * 2 )))\n for i in xrange(len(index)):\n output[i * 2] = fisher_vector(data[index[i,0]:index[i,1]], *gmm)\n output[i * 2 + 1] = fisher_vector(data[index[i, 2]:index[i, 3]], *gmm)\n np.save(\"fv.npy\", output)\n\n\ndef _main(argv=None):\n if argv is None:\n argv = sys.argv[1:]\n parser = argparse.ArgumentParser()\n parser.add_argument('-m', \"--mode\", help=\"SIFT descriptors or Word2Vec\", nargs=\"?\",\n const=\"Word2Vec\", default=\"SIFT\", choices=[\"Word2Vec\", \"SIFT\"])\n parser.add_argument('-d', \"--directory\", help=\"Directory with images\", default=\".\")\n parser.add_argument(\"-l\", \"--load\", help=\"Load Gmm dictionary\", action='store_true', default=False)\n parser.add_argument('-n', \"--number\", help=\"Number of words in dictionary\", default=5, type=int)\n try:\n args = parser.parse_args(argv)\n except argparse.ArgumentError as msg:\n print(repr(msg), file=sys.stderr)\n return 1\n else:\n if args.mode == \"SIFT\":\n image_classification(args.directory, args.load, args.number)\n else:\n generate_vector(args.directory, args.load, args.number)\n return 0\n\n\nif __name__ == \"__main__\":\n sys.exit(_main())\n","sub_path":"fisher_vector.py","file_name":"fisher_vector.py","file_ext":"py","file_size_in_byte":7363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"636122812","text":"from tkinter import *\nfrom tkinter import ttk\nfrom tkinter.messagebox import *\n\nfrom demopanels import MsgPanel, SeeDismissPanel\n\n\nclass NestedPanesDemo(ttk.Frame):\n\n def __init__(self, isapp=True, name='nestedpanesdemo'):\n ttk.Frame.__init__(self, name=name)\n self.pack(expand=Y, fill=BOTH)\n self.master.title('Nested Panes Demo')\n self.isapp = isapp\n self._create_widgets()\n\n def _create_widgets(self):\n if self.isapp:\n MsgPanel(self,\n [\"This demonstration shows off a nested set of themed paned \",\n \"windows. Their sizes can be changed by grabbing the area \",\n \"between each contained pane and dragging the divider.\"])\n\n SeeDismissPanel(self)\n\n self._create_demo_panel()\n\n def _create_demo_panel(self):\n demoPanel = ttk.Frame(self, name='demo')\n demoPanel.pack(side=TOP, fill=BOTH, expand=Y)\n\n self._create_wnd_struct(demoPanel)\n self._fill_button_pane()\n self._fill_clock_pane()\n self._fill_progress_pane()\n self._fill_text_pane()\n\n def _create_wnd_struct(self, parent):\n outer = ttk.PanedWindow(parent, orient=HORIZONTAL, name='outer')\n outer.pack(expand=Y, fill=BOTH, padx=10, pady=(6, 10))\n\n left = ttk.PanedWindow(outer, orient=VERTICAL, name='left')\n right = ttk.PanedWindow(outer, orient=VERTICAL, name='right')\n outer.add(left)\n outer.add(right)\n\n ltop = ttk.LabelFrame(left, text='Button', padding=3, name='ltop')\n lbot = ttk.LabelFrame(left, text='Clocks', padding=3, name='lbot')\n left.add(ltop)\n left.add(lbot)\n\n rtop = ttk.LabelFrame(right, text='Progress', padding=3, name='rtop')\n rbot = ttk.LabelFrame(right, text='Text', padding=3, name='rbot')\n right.add(rtop)\n right.add(rbot)\n\n def _fill_button_pane(self):\n # create and add button\n ltop = self.nametowidget('demo.outer.left.ltop')\n b = ttk.Button(ltop, text='Press Me', command=self._say_ouch)\n b.pack(padx=2, pady=3)\n\n def _say_ouch(self):\n # triggered when the button is pressed\n showinfo(title='Button Pressed', message='Ouch!',\n detail='That hurt...', parent=self)\n\n def _fill_progress_pane(self):\n # create and add progress bar\n rtop = self.nametowidget('demo.outer.right.rtop')\n pb = ttk.Progressbar(rtop, mode='indeterminate')\n pb.pack(fill=BOTH, expand=Y, padx=2, pady=5)\n pb.start()\n\n def _fill_text_pane(self):\n # create and add text widget\n rbot = self.nametowidget('demo.outer.right.rbot')\n f = ttk.Frame(rbot)\n f.pack(expand=Y, fill=BOTH)\n\n txt = Text(f, wrap=WORD, width=30, bd=0)\n vscroll = ttk.Scrollbar(f, orient=VERTICAL, command=txt.yview)\n txt['yscroll'] = vscroll.set\n vscroll.pack(side=RIGHT, fill=Y)\n txt.pack(fill=BOTH, expand=Y)\n\n def _fill_clock_pane(self):\n # create and add clock labels\n # makes use of the Tcl/Tk 'clock' functions\n zones = (':Europe/Berlin',\n ':America/Argentina/Buenos_Aires',\n ':Africa/Johannesburg',\n ':Europe/London',\n ':America/Los_Angeles',\n ':Europe/Moscow',\n ':America/New_York',\n ':Asia/Singapore',\n ':Australia/Sydney',\n ':Asia/Tokyo')\n\n # Force a pre-load of all the timezones\n # needed to avoid sync problems\n for z in zones:\n c = self.tk.call('clock', 'format', 0, '-timezone', z)\n\n lbot = self.nametowidget('demo.outer.left.lbot')\n for z in zones:\n # extract and format city name\n city = z.split('/')[-1]\n city = [c if c != '_' else ' ' for c in city]\n\n # create two labels for each city; one with the name\n # the other to hold the city's time\n lbl = ttk.Label(lbot, text=''.join(city), anchor=W)\n time = ttk.Label(lbot, textvariable=StringVar(), anchor=W)\n lbl.pack(fill=X)\n time.pack(fill=X)\n ttk.Separator(lbot).pack(fill=X)\n\n # set the current time for the indicated zone\n self._tick(time, z)\n\n def _tick(self, time, zone):\n # clock label helper function\n # sets the time immediately and updates it every 1000ms\n # (makes use of Tcl/Tk time handling routines)\n #\n # time - one of the 'clock' labels\n # zone - the associated timezone\n\n cs = self.tk.call('clock', 'seconds') # current clock time in seconds\n c = self.tk.call('clock', 'format', cs, '-timezone', zone, '-format', '%T')\n\n varname = time.cget('textvariable')\n time.setvar(varname, c)\n\n self.after(1000, self._tick, *(time, zone))\n\n\nif __name__ == '__main__':\n NestedPanesDemo().mainloop()","sub_path":"panedwindowtest.py","file_name":"panedwindowtest.py","file_ext":"py","file_size_in_byte":4977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"403106485","text":"# Nick Anway\n\n# import packages\n\nimport json\nimport csv\n\n# read json file to superheroes variable\n\nwith open('superheroes.json', 'r') as f:\n\tsuperheroes = json.load(f)\n\n# write the header row into a new csv file\n\nwith open('superheroes.csv', 'w') as f:\n\twriter = csv.writer(f)\n\theaders = [\"name\", \"age\", \"secretIdentity\", \"powers\", \"squadName\", \"homeTown\", \"formed\", \"secretBase\", \"active\"]\n\twriter.writerow(headers)\n\n# iterate over members\n\n\tmembers = superheroes['members']\n\tfor member in members:\n\t\tpowers = member[\"powers\"]\n\n# iterate over powers\n\n\t\tfor power in powers:\n\t\t\tpowerSingle = power \n\t\t\tname = member[\"name\"]\n\t\t\tage = member[\"age\"]\n\t\t\tsecretIdentity = member[\"secretIdentity\"]\n\t\t\tpowers = member[\"powers\"]\n\t\t\tsquadName = superheroes[\"squadName\"]\n\t\t\thomeTown = superheroes[\"homeTown\"]\n\t\t\tformed = superheroes[\"formed\"]\n\t\t\tsecretBase = superheroes[\"secretBase\"]\n\t\t\tactive = superheroes[\"active\"]\n\n# write one row for each power\n\n\t\t\trow = [name, age, secretIdentity, powerSingle, squadName, homeTown, formed, secretBase, active]\n\t\t\twriter.writerow(row)","sub_path":"superheroes.py","file_name":"superheroes.py","file_ext":"py","file_size_in_byte":1064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"165089175","text":"###################################################################\n# FAULT.PY\n#\n# A library of functions, constants and more that are \n# related to Power-System-Faults in Electrical Engineering.\n#\n# March 18, 2019\n#\n# Written by Joe Stanley\n#\n# Special Thanks To:\n# Dr. Yacine Chakhchoukh - Faculty University of Idaho\n#\n# Included Functions:\n# - Single Line to Ground phs1g\n# - Double Line to Ground phs2g\n# - Line to Line phs2\n# - Three-Phase Fault phs3\n# - Faulted Bus Voltage busvolt\n# - CT Saturation Function ct_saturation\n# - CT C-Class Calculator ct_cclass\n# - CT Sat. V at rated Burden ct_satratburden\n# - CT Voltage Peak Formula ct_vpeak\n# - CT Time to Saturation ct_timetosat\n# - Transient Recovery Voltage Calc. pktransrecvolt\n# - TRV Reduction Resistor trvresistor\n# - Natural Frequency Calculator natfreq\n# - TOC Trip Time toctriptime\n# - TOC Reset Time tocreset\n# - Pickup Setting Assistant pickup\n# - Radial TOC Coordination Tool tdcoordradial\n####################################################################\n\n# Import Necessary Libraries\nimport numpy as np\nfrom scipy.optimize import fsolve\n\n# Import Local Dependencies\nfrom .__init__ import Aabc, A012\n\n# Define Single Line to Ground Fault Function\ndef phs1g(Vsrc,Xseq,Rf=0,load=None,sequence=True):\n \"\"\"\n PHS1G Function\n \n Purpose:\n --------\n This function will evaluate the 0, Positive, and Negative\n sequence currents for a single-line-to-ground fault with the\n option of calculating with or without a load.\n \n Required Arguments:\n -------------------\n Vsrc: The Source Voltage\n Xseq: Tupple of sequence reactances as (X0, X1, X2)\n \n Optional Arguments:\n -------------------\n Rf: The fault resistance, default=0\n load: The load conditions, default=None\n sequence: Control argument to force return into ABC-Domain Currents\n \n Returns:\n --------\n Ifault: The Fault Current, equal for 0, pos., and neg. seq.\n \"\"\"\n # Decompose Reactance Tuple\n X0, X1, X2 = Xseq\n # Load Condition\n if(load!=None):\n print(\"nothing yet\")\n else:\n # Ensure that X-components are imaginary\n if(not isinstance(X0, complex)): X0 *= 1j\n if(not isinstance(X1, complex)): X1 *= 1j\n if(not isinstance(X2, complex)): X2 *= 1j\n # Calculate Fault Current\n Ifault = Vsrc / (X0 + X1 + X2 + 3*Rf)\n Ifault = np.array([ Ifault, Ifault, Ifault ])\n # Prepare Value for return\n if not sequence:\n Ifault = A012.dot( Ifault ) # Convert to ABC-Domain\n # Return Value\n return(Ifault)\n \n# Define Double Line to Ground Fault Current Calculator\ndef phs2g(Vsrc,Xseq,Rf=0,load=None,sequence=True):\n \"\"\"\n PHS2G Function\n \n Purpose:\n --------\n This function will evaluate the 0, Positive, and Negative\n sequence currents for a double-line-to-ground fault with the\n option of calculating with or without a load.\n \n Required Arguments:\n -------------------\n Vsrc: The Source Voltage\n Xseq: Tupple of sequence reactances as (X0, X1, X2)\n \n Optional Arguments:\n -------------------\n Rf: The fault resistance, default=0\n load: The load conditions, default=None\n sequence: Control argument to force return into ABC-Domain Currents\n \n Returns:\n --------\n Ifault: The Array of Fault Currents as (If0, If1, If2)\n \"\"\"\n # Decompose Reactance Tuple\n X0, X1, X2 = Xseq\n # Load Condition\n if(load!=None):\n print(\"nothing yet\")\n else:\n # Ensure that X-components are imaginary\n if(not isinstance(X0, complex)): X0 *= 1j\n if(not isinstance(X1, complex)): X1 *= 1j\n if(not isinstance(X2, complex)): X2 *= 1j\n # Calculate Fault Currents\n If1 = Vsrc / (X1 + (X2*(X0+3*Rf))/(X0+X2+3*Rf))\n If2 = -(Vsrc - X1*If1)/X2\n If0 = -(Vsrc - X1*If1)/(X0+3*Rf)\n faults = np.array([If0, If1, If2])\n # Return Currents\n if not sequence:\n faults = A012.dot(faults.T)\n return(faults)\n\n# Define Phase-to-Phase Fault Current Calculator\ndef phs2(Vsrc,Xseq,Rf=0,load=None,sequence=True):\n \"\"\"\n PHS2 Function\n \n Purpose:\n --------\n This function will evaluate the 0, Positive, and Negative\n sequence currents for a phase-to-phase fault with the\n option of calculating with or without a load.\n \n Required Arguments:\n -------------------\n Vsrc: The Source Voltage\n Xseq: Tupple of sequence reactances as (X0, X1, X2)\n \n Optional Arguments:\n -------------------\n Rf: The fault resistance, default=0\n load: The load conditions, default=None\n sequence: Control argument to force return into ABC-Domain Currents\n \n Returns:\n --------\n Ifault: The Array of Fault Currents as (If0, If1, If2)\n \"\"\"\n # Decompose Reactance Tuple\n X0, X1, X2 = Xseq\n # Load Condition\n if(load!=None):\n print(\"nothing yet\")\n else:\n # Ensure that X-components are imaginary\n if(not isinstance(X0, complex)): X0 *= 1j\n if(not isinstance(X1, complex)): X1 *= 1j\n if(not isinstance(X2, complex)): X2 *= 1j\n # Calculate Fault Currents\n If0 = 0\n If1 = Vsrc / (X1 + X2 + Rf)\n If2 = -If1\n faults = np.array([If0, If1, If2])\n # Return Currents\n if not sequence:\n faults = A012.dot(faults.T)\n return(faults)\n\n# Define Three-Phase Fault Current Calculator\ndef phs3(Vsrc,Xseq,Rf=0,load=None,sequence=True):\n \"\"\"\n PHS3 Function\n \n Purpose:\n --------\n This function will evaluate the 0, Positive, and Negative\n sequence currents for a three-phase fault with the\n option of calculating with or without a load.\n \n Required Arguments:\n -------------------\n Vsrc: The Source Voltage\n Xseq: Tupple of sequence reactances as (X0, X1, X2)\n \n Optional Arguments:\n -------------------\n Rf: The fault resistance, default=0\n load: The load conditions, default=None\n sequence: Control argument to force return into ABC-Domain Currents\n \n Returns:\n --------\n Ifault: The Fault Current, equal for 0, pos., and neg. seq.\n \"\"\"\n # Decompose Reactance Tuple\n X0, X1, X2 = Xseq\n # Load Condition\n if(load!=None):\n print(\"nothing yet\")\n else:\n # Ensure that X-components are imaginary\n if(not isinstance(X1, complex)): X1 *= 1j\n # Calculate Fault Currents\n Ifault = Vsrc/(X1 + Rf)\n Ifault = np.array([ 0, Ifault, 0 ])\n # Prepare to Return Value\n if not sequence:\n Ifault = A012.dot( Ifault ) # Convert to ABC-Domain\n return(Ifault)\n\n\n# Define Faulted Bus Voltage Calculator\ndef busvolt(k,n,Vpf,Z0,Z1,Z2,If,sequence=True):\n \"\"\"\n BUSVOLT Function\n \n Purpose:\n --------\n This function is designed to calculate the bus voltage(s)\n given a specific set of fault characteristics.\n \n Required Arguments:\n -------------------\n k: Bus at which to calculate faulted voltage\n n: Bus at which fault occurred\n Vpf: Voltage Pre-Fault, Singular Number\n Z0: Zero-Sequence Impedance Matrix\n Z1: Positive-Sequence Impedance Matrix\n Z2: Negative-Sequence Impedance Matrix\n If: Sequence Fault Current Evaluated at Bus *n*\n \n Optional Arguments:\n -------------------\n sequence: Control argument to force return into ABC-Domain Currents\n \n Returns:\n --------\n Vf: The Fault Voltage, set of sequence or phase voltages as\n specified by *sequence*\n \"\"\"\n # Condition Inputs\n k = k-1\n n = n-1\n Z0 = np.asarray(Z0)\n Z1 = np.asarray(Z1)\n Z2 = np.asarray(Z2)\n If = np.asarray(If)\n # Generate Arrays For Calculation\n Vfmat = np.array([0, Vpf, 0]).T\n Zmat = np.array([[Z0[k,n], 0, 0],\n [0, Z1[k,n], 0],\n [0, 0, Z2[k,n]]])\n # Perform Calculation\n Vf = Vfmat - Zmat.dot(If)\n if not sequence:\n Vf = A012.dot( Vf ) # Convert to ABC-Domain\n return(Vf)\n\n\n# Define CT Saturation Function\ndef ct_saturation(XR,Imag,Vrated,Irated,CTR,Rb,Xb,remnance=0,freq=60,ALF=20):\n \"\"\"\n ct_saturation Function\n \n A function to determine the saturation value and a boolean indicator\n showing whether or not CT is -in fact- saturated.\n \n Parameters\n ----------\n XR: float\n The X-over-R ratio of the system.\n Imag: float\n The (maximum) current magnitude to use for calculation,\n typically the fault current.\n Vrated: float\n The rated voltage (accompanying the C-Class value) of\n the CT.\n Irated: float\n The rated secondary current for the CT.\n CTR: float\n The CT Ratio (primary/secondary, N) to be used.\n Rb: float\n The total burden resistance in ohms.\n Xb: float\n The total burden reactance in ohms.\n remnance: float, optional\n The system flux remnance, default=0.\n freq: float, optional\n The system frequency in Hz, default=60.\n ALF: float, optional\n The Saturation Constant which must be satisfied,\n default=20.\n \n Returns\n -------\n result: float\n The calculated Saturation value.\n saturation: bool\n Boolean indicator to mark presence of saturation.\n \"\"\"\n # Define omega\n w = 2*np.pi*freq\n # Find Lb\n Lb = Xb/w\n # Re-evaluate Vrated\n Vrated = Vrated*(1-remnance)\n # Calculate each \"term\" (multiple)\n t1 = (1+XR)\n t2 = (Imag/(Irated*CTR))\n t3 = abs(Rb+1j*w*Lb)*100/Vrated\n # Evaluate\n result = t1*t2*t3\n # Test for saturation\n saturation = result >= ALF\n # Return Results\n return(result,saturation)\n\n\n# Define C-Class Calculator\ndef ct_cclass(XR,Imag,Irated,CTR,Rb,Xb,remnance=0,freq=60,ALF=20):\n \"\"\"\n ct_cclass Function\n \n A function to determine the C-Class rated voltage for a CT.\n \n Parameters\n ----------\n XR: float\n The X-over-R ratio of the system.\n Imag: float\n The (maximum) current magnitude to use for calculation,\n typically the fault current.\n Irated: float\n The rated secondary current for the CT.\n CTR: float\n The CT Ratio (primary/secondary, N) to be used.\n Rb: float\n The total burden resistance in ohms.\n Xb: float\n The total burden reactance in ohms.\n remnance: float, optional\n The system flux remnance, default=0.\n freq: float, optional\n The system frequency in Hz, default=60.\n ALF: float, optional\n The Saturation Constant which must be satisfied,\n default=20.\n \n Returns\n -------\n c_class: float\n The calculated C-Class rated voltage.\n \"\"\"\n # Define omega\n w = 2*np.pi*freq\n # Find Lb\n Lb = Xb/w\n # Calculate each \"term\" (multiple)\n t1 = (1+XR)\n t2 = (Imag/(Irated*CTR))\n t3 = abs(Rb+1j*w*Lb)*100/ALF\n # Evaluate\n Vr_w_rem = t1*t2*t3\n c_class = Vr_w_rem/(1-remnance)\n # Return Result\n return(c_class)\n\n\n# Define Saturation Voltage at Rated Burden\ndef ct_satratburden(Inom,VArat=None,ANSIv=None,ALF=20,):\n \"\"\"\n ct_satratburden Function\n \n A function to determine the Saturation at rated burden.\n \n Parameters\n ----------\n Inom: float\n Nominal Current\n VArat: float, optional, exclusive\n The apparent power (VA) rating of the CT.\n ANSIv: float, optional, exclusive\n The ANSI voltage requirement to meet.\n ALF: float, optional\n Accuracy Limit Factor, default=20.\n \n Returns\n -------\n Vsat: float\n The saturated voltage.\n \"\"\"\n # Validate Inputs\n if VArat == None and ANSIv == None:\n raise ValueError(\"VArat or ANSIv must be specified.\")\n elif VArat==None:\n # Calculate VArat from ANSIv\n Zrat = ANSIv/(20*Inom)\n VArat = Inom**2 * Zrat\n # Determine Vsaturation\n Vsat = ALF * VArat/Inom\n return(Vsat)\n\n\n# Define CT Vpeak Formula\ndef ct_vpeak(Zb,Ip,N):\n \"\"\"\n ct_vpeak Function\n \n Simple formula to calculate the Peak Voltage of a CT.\n \n Parameters\n ----------\n Zb: float\n The burden impedance magnitude (in ohms).\n Ip: float\n The peak current for the CT.\n N: float\n The CTR turns ratio of the CT.\n \n Returns\n -------\n Vpeak: float\n The peak voltage.\n \"\"\"\n return(np.sqrt(3.5*Zb*Ip*N))\n\n\n# Define Saturation Time Calculator\ndef ct_timetosat(Vknee,XR,Rb,CTR,Imax,ts=None,npts=100,freq=60,plot=False):\n \"\"\"\n ct_timetosat Function\n \n Function to determine the \"time to saturate\" for an underrated C-Class\n CT using three standard curves described by Juergen Holbach.\n \n Parameters\n ----------\n Vknee: float\n The knee-voltage for the CT.\n XR: float\n The X-over-R ratio of the system.\n Rb: float\n The total burden resistance in ohms.\n CTR: float\n The CT Ratio (primary/secondary, N) to be used.\n Imax: float\n The (maximum) current magnitude to use for calculation,\n typically the fault current.\n ts: numpy.ndarray or float, optional\n The time-array or particular (floatint point) time at which\n to calculate the values. default=np.linspace(0,0.1,freq*npts)\n npts: float, optional\n The number of points (per cycle) to calculate if ts is not\n specified, default=100.\n freq: float, optional\n The system frequency in Hz, default=60.\n plot: bool, optional\n Control argument to enable plotting of calculated curves,\n default=False.\n \"\"\"\n # Calculate omega\n w = 2*np.pi*freq\n # Calculate Tp\n Tp = XR/w\n # If ts isn't specified, generate it\n if ts==None:\n ts = np.linspace(0,0.1,freq*npts)\n # Calculate inner term\n term = -XR*(np.exp(-ts/Tp)-1)\n # Calculate Vsaturation terms\n Vsat1 = Imax*Rb*(term+1)\n Vsat2 = Imax*Rb*(term-np.sin(w*ts))\n Vsat3 = Imax*Rb*(1-np.cos(w*ts))\n # If plotting requested\n if plot and isinstance(ts,np.ndarray):\n plt.plot(ts,Vsat1,label=\"Vsat1\")\n plt.plot(ts,Vsat2,label=\"Vsat2\")\n plt.plot(ts,Vsat3,label=\"Vsat3\")\n plt.axhline(Vknee,label=\"V-knee\",linestyle='--')\n plt.title(\"Saturation Curves\")\n plt.xlabel(\"Time (ts)\")\n plt.legend()\n plt.show()\n elif plot:\n print(\"Unable to plot a single point, *ts* must be a numpy-array.\")\n # Determine the crossover points for each saturation curve\n Vsat1c = Vsat2c = Vsat3c = 0\n if isinstance(ts,np.ndarray):\n for i in range(len(ts)):\n if Vsat1[i]>Vknee and Vsat1c==0:\n Vsat1c = ts[i-1]\n if Vsat2[i]>Vknee and Vsat2c==0:\n Vsat2c = ts[i-1]\n if Vsat3[i]>Vknee and Vsat3c==0:\n Vsat3c = ts[i-1]\n results = (Vsat1c,Vsat2c,Vsat3c)\n else:\n results = (Vsat1,Vsat2,Vsat3)\n return(results)\n\n# Define Function to Calculate TRV\ndef pktransrecvolt(C,L,R=0,VLL=None,VLN=None,freq=60):\n \"\"\"\n pktransrecvolt Function\n \n Peak Transient Recovery Voltage calculation\n function, evaluates the peak transient\n recovery voltage (restriking voltage) and\n the Rate-of-Rise-Recovery Voltage.\n \n Parameters\n ----------\n C: float\n Capacitance Value in Farads.\n L: float\n Inductance in Henries.\n R: float, optional\n The resistance of the system used for\n calculation, default=0.\n VLL: float, exclusive\n Line-to-Line voltage, exclusive\n optional argument.\n VLN: float, exclusive\n Line-to-Neutral voltage, exclusive\n optional argument.\n freq: float, optional\n System frequency in Hz.\n \n Returns\n -------\n Vcpk: float\n Peak Transient Recovery Voltage in volts.\n RRRV: float\n The RRRV (Rate-of-Rise-Recovery Voltage)\n calculated given the parameters in volts\n per second.\n \"\"\"\n # Evaluate alpha, omega-n, and fn\n alpha = R/(2*L)\n wn = 1/np.sqrt(L*C) - alpha\n fn = wn/(2*np.pi)\n # Evaluate Vm\n if VLL!=None:\n Vm = np.sqrt(2/3)*VLL\n elif VLN!=None:\n Vm = np.sqrt(2)*VLN\n else:\n raise ValueError(\"One voltage must be specified.\")\n # Evaluate Vcpk (worst case)\n Vcpk = wn**2/(wn**2-2*np.pi*freq)*Vm*2\n # Evaluate RRRV\n RRRV = 2*Vm*fn/0.5\n return(Vcpk,RRRV)\n\n# Define TRV Reduction Resistor Function\ndef trvresistor(C,L,reduction,Rd0=500,wd0=260e3,tpk0=10e-6):\n \"\"\"\n trvresistor Function\n \n Function to find the resistor value that\n will reduce the TRV by a specified\n percentage.\n \n Parameters\n ----------\n C: float\n Capacitance Value in Farads.\n L: float\n Inductance in Henries.\n reduction: float\n The percentage that the TRV\n should be reduced by.\n Rd0: float, optional\n Damping Resistor Evaluation Starting Point\n default=500\n wd0: float, optional\n Omega-d evaluation starting point, default=260*k\n tpk0: float, optional\n Time of peak voltage evaluation starting point,\n default=10*u\n \n Returns\n -------\n Rd: float\n Damping resistor value, in ohms.\n wd: float\n Omega-d\n tpk: float\n Time of peak voltage.\n \"\"\"\n # Evaluate omega-n\n wn = 1/np.sqrt(L*C)\n # Generate Constant Factor\n fctr = (1-reduction)*2 - 1\n # Define Function Set\n def equations(data):\n Rd, wd, tpk = data\n X = np.sqrt(wn**2-(1/(2*Rd*C))**2) - wd\n Y = np.exp(-tpk/(2*Rd*C))-fctr\n Z = wd*tpk - np.pi\n return(X,Y,Z)\n Rd, wd, tpk = fsolve(equations, (Rd0,wd0,tpk0))\n return(Rd, wd, tpk)\n\n# Define Natural Frequency/Resonant Frequency Calculator\ndef natfreq(C,L,Hz=True):\n \"\"\"\n natfreq Function\n \n Evaluates the natural frequency (resonant frequency)\n of a circuit given the circuit's C and L values. Defaults\n to returning values in Hz, but may also return in rad/sec.\n \n Parameters\n ----------\n C: float\n Capacitance Value in Farads.\n L: float\n Inductance in Henries.\n Hz: bool, optional\n Control argument to set return value in either\n Hz or rad/sec; default=True.\n \n Returns\n -------\n freq: float\n Natural (Resonant) frequency, will be in Hz if\n argument *Hz* is set True (default), or rad/sec\n if argument is set False.\n \"\"\"\n # Evaluate Natural Frequency in rad/sec\n freq = 1/np.sqrt(L*C)\n # Convert to Hz as requested\n if Hz:\n freq = freq / (2*np.pi)\n return(freq)\n\n# Define Time-Overcurrent Trip Time Function\ndef toctriptime(I,Ipickup,TD,curve=\"U1\"):\n \"\"\"\n toctriptime Function\n \n Time-OverCurrent Trip Time Calculator, evaluates the time\n to trip for a specific TOC (51) element given the curve\n type, current characteristics and time-dial setting.\n \n Parameters\n ----------\n I: float\n Measured Current in Amps\n Ipickup: float\n Fault Current Pickup Setting (in Amps)\n TD: float\n Time Dial Setting\n curve: string, optional\n Name of specified TOC curve, may be entry from set:\n {U1,U2,U3,U4,U5,C1,C2,C3,C4,C5}, default=U1\n \n Returns\n -------\n tt: float\n Time-to-Trip for characterized element.\n \"\"\"\n # Condition Inputs\n curve = curve.upper()\n # Define Dictionary of Constants\n const = { \"U1\" : {\"A\": 0.0104, \"B\": 0.2256, \"P\": 0.02},\n \"U2\" : {\"A\": 5.95, \"B\": 0.180, \"P\": 2.00},\n \"U3\" : {\"A\": 3.88, \"B\": 0.0963, \"P\": 2.00},\n \"U4\" : {\"A\": 5.67, \"B\": 0.352, \"P\": 2.00},\n \"U5\" : {\"A\": 0.00342, \"B\": 0.00262, \"P\": 0.02},\n \"C1\" : {\"A\": 0.14, \"B\":0, \"P\": 0.02},\n \"C2\" : {\"A\": 13.5, \"B\":0, \"P\": 2.00},\n \"C3\" : {\"A\": 80.0, \"B\":0, \"P\": 2.00},\n \"C4\" : {\"A\": 120.0, \"B\":0, \"P\": 2.00},\n \"C5\" : {\"A\": 0.05, \"B\":0, \"P\": 0.04}}\n # Load Constants\n A = const[curve][\"A\"]\n B = const[curve][\"B\"]\n P = const[curve][\"P\"]\n # Evaluate M\n M = I / Ipickup\n # Evaluate Trip Time\n tt = TD * (A/(M**P-1)+B)\n return(tt)\n\n# Define Time Overcurrent Reset Time Function\ndef tocreset(I,Ipickup,TD,curve=\"U1\"):\n \"\"\"\n tocreset Function\n \n Function to calculate the time to reset for a TOC\n (Time-OverCurrent, 51) element.\n \n Parameters\n ----------\n I: float\n Measured Current in Amps\n Ipickup: float\n Fault Current Pickup Setting (in Amps)\n TD: float\n Time Dial Setting\n curve: string, optional\n Name of specified TOC curve, may be entry from set:\n {U1,U2,U3,U4,U5,C1,C2,C3,C4,C5}, default=U1\n \n Returns\n -------\n tr: float\n Time-to-Reset for characterized element.\n \"\"\"\n # Condition Inputs\n curve = curve.upper()\n # Define Dictionary of Constants\n C = { \"U1\" : 1.08,\"U2\" : 5.95,\"U3\" : 3.88,\n \"U4\" : 5.67,\"U5\" : 0.323,\"C1\" : 13.5,\n \"C2\" : 47.3,\"C3\" : 80.0,\"C4\" : 120.0,\n \"C5\" : 4.85}\n # Evaluate M\n M = I / Ipickup\n # Evaluate Reset Time\n tr = TD * (C[curve]/(1-M**2))\n return(tr)\n\n# Define Pickup Current Calculation\ndef pickup(Iloadmax,Ifaultmin,scale=0,printout=False,units=\"A\"):\n \"\"\"\n pickup Function\n \n Used to assist in evaluating an optimal phase-over-current pickup\n setting. Uses maximum load and minimum fault current to provide\n user assistance.\n \n Parameters\n ----------\n Iloadmax: float\n The maximum load current in amps.\n Ifaultmin: float\n The minimum fault current in amps.\n scale: int, optional\n Control scaling to set number of significant figures.\n default=0\n printout: boolean, optional\n Control argument to enable printing of intermediate\n stages, default=False.\n units: string, optional\n String to be appended to any printed output denoting\n the units of which are being printed, default=\"A\"\n \n Returns\n -------\n setpoint: float\n The evaluated setpoint at which the function suggests\n the phase-over-current pickup setting be placed.\n \"\"\"\n IL2 = 2*Iloadmax\n IF2 = Ifaultmin/2\n exponent = len(str(IL2).split('.')[0])\n setpoint = np.ceil(IL2*10**(-exponent+1+scale))*10**(exponent-1-scale)\n if printout:\n print(\"Range Min:\",IL2,units,\"\\t\\tRange Max:\",IF2,units)\n if IF2 < setpoint:\n setpoint = IL2\n if IL2 > IF2:\n raise ValueError(\"Invalid Parameters.\")\n if printout:\n print(\"Current Pickup:\",setpoint,units)\n return(setpoint)\n\n# Define Time-Dial Coordination Function\ndef tdcoordradial(I,CTI,Ipu_up,Ipu_dn,TDdn,curve=\"U1\",scale=1,freq=60):\n \"\"\"\n tdcoordradial Function\n \n Function to evaluate the Time-Dial (TD) setting in radial schemes\n where the Coordinating Time Interval (CTI) and the up/downstream\n pickup settings are known along with the TD setting for the\n downstream protection.\n \n Parameters\n ----------\n I: float\n Measured fault current in Amps, typically set using the\n maximum fault current available.\n CTI: float\n Coordinating Time Interval in cycles.\n Ipu_up: float\n Pickup setting for upstream protection,\n specified in amps\n Ipu_dn: float\n Pickup setting for downstream protection,\n specified in amps\n TDdn: float\n Time-Dial setting for downstream protection,\n specified in seconds\n curve: string, optional\n Name of specified TOC curve, may be entry from set:\n {U1,U2,U3,U4,U5,C1,C2,C3,C4,C5}, default=U1\n scale: int, optional\n Scaling value used to evaluate a practical TD\n setting, default=1\n freq: float, optional\n System operating frequency, default=60\n \n Returns\n -------\n TD: float\n Calculated Time-Dial setting according to radial\n scheme logical analysis.\n \"\"\"\n # Condition Inputs\n curve = curve.upper()\n CTI = CTI/freq # Evaluate in seconds from cycles\n # Define Dictionary of Constants\n const = { \"U1\" : {\"A\": 0.0104, \"B\": 0.2256, \"P\": 0.02},\n \"U2\" : {\"A\": 5.95, \"B\": 0.180, \"P\": 2.00},\n \"U3\" : {\"A\": 3.88, \"B\": 0.0963, \"P\": 2.00},\n \"U4\" : {\"A\": 5.67, \"B\": 0.352, \"P\": 2.00},\n \"U5\" : {\"A\": 0.00342, \"B\": 0.00262, \"P\": 0.02},\n \"C1\" : {\"A\": 0.14, \"B\":0, \"P\": 0.02},\n \"C2\" : {\"A\": 13.5, \"B\":0, \"P\": 2.00},\n \"C3\" : {\"A\": 80.0, \"B\":0, \"P\": 2.00},\n \"C4\" : {\"A\": 120.0, \"B\":0, \"P\": 2.00},\n \"C5\" : {\"A\": 0.05, \"B\":0, \"P\": 0.04}}\n # Load Constants\n A = const[curve][\"A\"]\n B = const[curve][\"B\"]\n P = const[curve][\"P\"]\n # Evaluate M\n M = I / Ipu_dn\n # Evaluate Trip Time\n tpu_desired = TDdn * (A/(M**P-1)+B) + CTI\n # Re-Evaluate M\n M = I / Ipu_up\n # Calculate TD setting\n TD = tpu_desired / (A/(M**2-1)+B)\n # Scale and Round\n TD = np.ceil(TD*10**scale)/10**scale\n return(TD)\n\n\n# END OF FILE","sub_path":"build/lib/eepower/fault.py","file_name":"fault.py","file_ext":"py","file_size_in_byte":26936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"322251695","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Date : 2015-12-19 02:09:53\n# @Author : Gefu Tang (tanggefu@gmail.com)\n# @Link : https://github.com/primetang/pylsd\n# @Version : 0.0.1\n\nfrom .bindings.lsd_ctypes import *\nimport os\nimport sys\nfrom tempfile import NamedTemporaryFile\n\ndef lsd(src):\n rows, cols = src.shape\n src = src.reshape(1, rows * cols).tolist()[0]\n\n lens = len(src)\n src = (ctypes.c_double * lens)(*src)\n\n with NamedTemporaryFile(prefix='pylsd-', suffix='.ntl.txt', delete=False) as fp:\n fname = fp.name\n fname_bytes = bytes(fp.name) if sys.version_info < (3, 0) else bytes(fp.name, 'utf8')\n\n lsdlib.lsdGet(src, ctypes.c_int(rows), ctypes.c_int(cols), fname_bytes)\n\n with open(fname, 'r') as fp:\n output = fp.read()\n cnt = output.strip().split(' ')\n count = int(cnt[0])\n dim = int(cnt[1])\n lines = np.array([float(each) for each in cnt[2:]])\n lines = lines.reshape(count, dim)\n\n os.remove(fname)\n\n return lines\n","sub_path":"demo/requirement/lsd/pylsd/lsd.py","file_name":"lsd.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"195998652","text":"from typing import Generator\n\n\ndef gen() -> Generator[int, str, bool]:\n s = yield 1\n print(s)\n yield 2\n print('about to end')\n return False\n\n\nthing = gen()\nvalue = next(thing)\nprint(f'got from generator: {value}')\nvalue2 = thing.send('hello hello')\nprint(f'got from generator: {value2}')\n\nnext(thing)\n","sub_path":"sample_code/ep297/rev04/t.py","file_name":"t.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"133573620","text":"from datetime import datetime\n\nfrom django.core.validators import ValidationError\nfrom django.forms import ModelForm, CheckboxInput, ChoiceField, TypedChoiceField, TextInput, EmailInput, \\\n ModelMultipleChoiceField, MultipleChoiceField\nfrom django.utils.translation import gettext_lazy as _\n\nfrom .question_group import QuestionGroup\nfrom .widgets import UsaRadioSelect, UsaCheckboxSelectMultiple, CrtRadioArea, CrtDropdown, CrtMultiSelect, ComplaintSelect\nfrom .models import Report, ProtectedClass, HateCrimesandTrafficking\nfrom .model_variables import (\n ELECTION_CHOICES,\n RESPONDENT_TYPE_CHOICES,\n PROTECTED_CLASS_CHOICES,\n PROTECTED_CLASS_ERROR,\n PRIMARY_COMPLAINT_CHOICES,\n PRIMARY_COMPLAINT_CHOICES_TO_EXAMPLES,\n PRIMARY_COMPLAINT_CHOICES_TO_HELPTEXT,\n PUBLIC_OR_PRIVATE_EMPLOYER_CHOICES,\n EMPLOYER_SIZE_CHOICES,\n SECTION_CHOICES,\n STATES_AND_TERRITORIES,\n VIOLATION_SUMMARY_ERROR,\n WHERE_ERRORS,\n HATE_CRIMES_TRAFFICKING_CHOICES,\n PRIMARY_COMPLAINT_ERROR,\n SERVICEMEMBER_CHOICES,\n SERVICEMEMBER_ERROR,\n CORRECTIONAL_FACILITY_LOCATION_CHOICES,\n CORRECTIONAL_FACILITY_LOCATION_TYPE_CHOICES,\n POLICE_LOCATION_ERRORS,\n COMMERCIAL_OR_PUBLIC_PLACE_CHOICES,\n COMMERCIAL_OR_PUBLIC_PLACE_HELP_TEXT,\n PUBLIC_OR_PRIVATE_SCHOOL_CHOICES,\n STATUS_CHOICES,\n)\nfrom .phone_regex import phone_validation_regex\n\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n\nclass ContactA11y():\n def __init__(self):\n self.name_a11y_id = 'contact_name'\n self.contact_a11y_id = 'contact_info'\n\n def name_id(self):\n return self.name_a11y_id\n\n def contact_info_id(self):\n return self.contact_a11y_id\n\n\nclass Contact(ModelForm):\n class Meta:\n a11y = ContactA11y()\n model = Report\n fields = [\n 'contact_first_name', 'contact_last_name',\n 'contact_email', 'contact_phone', 'servicemember',\n ]\n widgets = {\n 'contact_first_name': TextInput(attrs={\n 'class': 'usa-input',\n 'aria-describedby': a11y.name_id\n }),\n 'contact_last_name': TextInput(attrs={\n 'class': 'usa-input',\n 'aria-describedby': a11y.name_id\n }),\n 'contact_email': EmailInput(attrs={\n 'class': 'usa-input',\n 'aria-describedby': a11y.contact_info_id\n }),\n 'contact_phone': TextInput(attrs={\n 'class': 'usa-input',\n 'aria-describedby': a11y.contact_info_id,\n 'pattern': phone_validation_regex,\n 'title': _('If you submit a phone number, please make sure to include between 7 and 15 digits. The characters \"+\", \")\", \"(\", \"-\", and \".\" are allowed. Please include country code if entering an international phone number.')\n }),\n }\n\n def __init__(self, *args, **kwargs):\n ModelForm.__init__(self, *args, **kwargs)\n\n a11y = ContactA11y()\n\n self.label_suffix = ''\n\n self.fields['contact_first_name'].label = _('First name')\n self.fields['contact_last_name'].label = _('Last name')\n self.fields['contact_email'].label = _('Email address')\n self.fields['contact_phone'].label = _('Phone number')\n self.fields['servicemember'] = TypedChoiceField(\n error_messages={'required': SERVICEMEMBER_ERROR},\n widget=UsaRadioSelect(),\n label=_('Are you now or have ever been an active duty service member?'),\n help_text=_('If you’re reporting on behalf of someone else, please select their status.'),\n empty_value=None,\n choices=SERVICEMEMBER_CHOICES,\n )\n self.question_groups = [\n QuestionGroup(\n self,\n ('contact_first_name', 'contact_last_name'),\n group_name=_('Your name'),\n help_text=_('Leave the fields blank if you\\'d like to file anonymously'),\n ally_id=a11y.name_id\n ),\n QuestionGroup(\n self,\n ('contact_email', 'contact_phone'),\n group_name=_('Contact information'),\n help_text=_('You are not required to provide contact information, but it will help us if we need to gather more information about the incident you are reporting or to respond to your submission'),\n ally_id=a11y.contact_info_id\n )\n ]\n\n\nclass PrimaryReason(ModelForm):\n class Meta:\n model = Report\n fields = [\n 'primary_complaint'\n ]\n widgets = {\n 'primary_complaint': CrtRadioArea\n }\n\n def __init__(self, *args, **kwargs):\n ModelForm.__init__(self, *args, **kwargs)\n self.fields['primary_complaint'] = ChoiceField(\n choices=PRIMARY_COMPLAINT_CHOICES,\n widget=CrtRadioArea(attrs={\n 'choices_to_examples': PRIMARY_COMPLAINT_CHOICES_TO_EXAMPLES,\n 'choices_to_helptext': PRIMARY_COMPLAINT_CHOICES_TO_HELPTEXT,\n }),\n required=True,\n error_messages={\n 'required': PRIMARY_COMPLAINT_ERROR\n },\n label=_('What is your primary reason for contacting the Civil Rights Division?'),\n help_text=_('Please choose the option below that best fits your situation. The examples listed in each are only a sampling of related issues. You will have space to explain in detail later.'),\n )\n\n\nclass HateCrimesTrafficking(ModelForm):\n class Meta:\n model = Report\n fields = [\n 'hatecrimes_trafficking'\n ]\n widgets = {\n 'hatecrimes_trafficking': UsaCheckboxSelectMultiple(attrs={\n 'aria-describedby': 'hatecrimes-help-text'\n }),\n }\n\n def __init__(self, *args, **kwargs):\n ModelForm.__init__(self, *args, **kwargs)\n\n self.fields['hatecrimes_trafficking'] = ModelMultipleChoiceField(\n queryset=HateCrimesandTrafficking.objects.filter(hatecrimes_trafficking_option__in=HATE_CRIMES_TRAFFICKING_CHOICES),\n widget=UsaCheckboxSelectMultiple(attrs={\n 'aria-describedby': 'hatecrimes-help-text'\n }),\n required=False,\n label=_('Please select if any that apply to your situation (optional)')\n )\n\n self.question_groups = [\n QuestionGroup(\n self,\n ('hatecrimes_trafficking',),\n group_name=_('Hate Crimes & Human Trafficking'),\n help_text=_('Hate crimes and human trafficking are considered criminal cases and go through a different process for investigation than other civil rights cases. If we determine your situation falls into these categories after submitting your concern, we will contact you with next steps.'),\n optional=False,\n cls=\"text-bold\",\n ally_id=\"hatecrimes-help-text\"\n )\n ]\n\n\nclass Details(ModelForm):\n class Meta:\n model = Report\n fields = [\n 'violation_summary'\n ]\n\n def __init__(self, *args, **kwargs):\n ModelForm.__init__(self, *args, **kwargs)\n\n self.fields['violation_summary'].widget.attrs['class'] = 'usa-textarea word-count-500'\n self.label_suffix = ''\n self.fields['violation_summary'].label = _('Tell us what happened')\n self.fields['violation_summary'].widget.attrs['aria-describedby'] = 'details-help-text'\n self.fields['violation_summary'].help_text = _(\"Please include any details you have about time, location, or people involved with the event, names of witnesses or any materials that would support your description\")\n self.fields['violation_summary'].error_messages = {'required': VIOLATION_SUMMARY_ERROR}\n\n\nclass LocationForm(ModelForm):\n class Meta:\n model = Report\n fields = [\n 'location_name',\n 'location_address_line_1',\n 'location_address_line_2',\n 'location_city_town',\n 'location_state',\n ]\n\n widgets = {\n 'location_name': TextInput(attrs={\n 'class': 'usa-input',\n 'aria-describedby': 'location-help-text'\n }),\n 'location_address_line_1': TextInput(attrs={\n 'class': 'usa-input',\n 'aria-describedby': 'location-help-text'\n }),\n 'location_address_line_2': TextInput(attrs={\n 'class': 'usa-input',\n 'aria-describedby': 'location-help-text'\n }),\n 'location_city_town': TextInput(attrs={\n 'class': 'usa-input',\n 'aria-describedby': 'location-help-text'\n }),\n 'location_state': CrtDropdown(attrs={\n 'aria-describedby': 'location-help-text'\n }),\n }\n\n def __init__(self, *args, **kwargs):\n ModelForm.__init__(self, *args, **kwargs)\n\n errors = dict(WHERE_ERRORS)\n\n self.fields['location_name'].label = 'Location name'\n self.fields['location_name'].help_text = 'Examples: Name of business, school, intersection, prison, polling place, website, etc.'\n self.fields['location_name'].error_messages = {\n 'required': errors['location_name']\n }\n self.fields['location_address_line_1'].label = 'Street address 1 (Optional)'\n self.fields['location_address_line_2'].label = 'Street address 2 (Optional)'\n self.fields['location_city_town'].label = 'City/town'\n self.fields['location_city_town'].error_messages = {\n 'required': errors['location_city_town']\n }\n self.fields['location_state'] = ChoiceField(\n choices=STATES_AND_TERRITORIES,\n widget=CrtDropdown,\n required=True,\n error_messages={\n 'required': errors['location_state']\n },\n label='State',\n help_text=\"Where did this happen?\"\n )\n self.fields['location_state'].widget.attrs['list'] = 'states'\n\n self.question_groups = [\n QuestionGroup(\n self,\n ('location_name', 'location_address_line_1', 'location_address_line_2'),\n group_name=_('Where did this happen?'),\n help_text=_('Please be as specific as possible. We will handle this information with sensitivity.'),\n optional=False,\n ally_id='location-help-text'\n ),\n ]\n\n\nclass ElectionLocation(LocationForm):\n class Meta:\n model = Report\n fields = LocationForm.Meta.fields + ['election_details']\n widgets = LocationForm.Meta.widgets\n\n def __init__(self, *args, **kwargs):\n LocationForm.__init__(self, *args, **kwargs)\n self.question_groups = [\n QuestionGroup(\n self,\n ('election_details',),\n group_name=_('What kind of election or voting activity was this related to?'),\n optional=False\n\n )\n ] + self.question_groups\n\n self.fields['election_details'] = TypedChoiceField(\n choices=ELECTION_CHOICES,\n empty_value=None,\n widget=UsaRadioSelect(attrs={\n 'help_text': {\n 'federal': _('Presidential or congressional'),\n 'state_local': _('Governor, state legislation, city position (mayor, council, local board)'),\n 'both': _('Federal & State/local')\n }\n }),\n required=True,\n error_messages={\n 'required': _('Please select the type of election or voting activity.')\n },\n label=''\n )\n\n\nclass WorkplaceLocation(LocationForm):\n class Meta:\n model = Report\n fields = LocationForm.Meta.fields + [\n 'public_or_private_employer',\n 'employer_size'\n ]\n widgets = LocationForm.Meta.widgets\n\n def __init__(self, *args, **kwargs):\n LocationForm.__init__(self, *args, **kwargs)\n self.question_groups = [\n QuestionGroup(\n self,\n ('public_or_private_employer',),\n group_name=_('Was this a public or private employer?'),\n optional=False\n ),\n QuestionGroup(\n self,\n ('employer_size',),\n group_name=_('How large is this employer?'),\n optional=False\n )\n ] + self.question_groups\n\n self.fields['public_or_private_employer'] = TypedChoiceField(\n choices=PUBLIC_OR_PRIVATE_EMPLOYER_CHOICES,\n widget=UsaRadioSelect(attrs={\n 'help_text': {\n 'public_employer': _('Funded by the government like a post office, fire department, courthouse, DMV, or public school. This could be at the local, state, or federal level'),\n 'private_employer': _('Businesses or non-profits not funded by the government such as retail stores, banks, or restaurants')\n }\n }),\n required=True,\n error_messages={\n 'required': _('Please select what type of employer this is.')\n },\n label=''\n )\n\n self.fields['employer_size'] = TypedChoiceField(\n choices=EMPLOYER_SIZE_CHOICES,\n widget=UsaRadioSelect,\n required=True,\n error_messages={\n 'required': _('Please select how large the employer is.')\n },\n label=''\n )\n\n\nclass CommercialPublicLocation(LocationForm):\n class Meta:\n model = Report\n fields = LocationForm.Meta.fields + ['commercial_or_public_place', 'other_commercial_or_public_place']\n widgets = LocationForm.Meta.widgets\n\n def __init__(self, *args, **kwargs):\n LocationForm.__init__(self, *args, **kwargs)\n\n self.name = 'CommericalPublicLocation'\n\n self.fields['commercial_or_public_place'] = TypedChoiceField(\n choices=COMMERCIAL_OR_PUBLIC_PLACE_CHOICES,\n empty_value=None,\n widget=UsaRadioSelect(attrs={\n 'help_text': COMMERCIAL_OR_PUBLIC_PLACE_HELP_TEXT\n }),\n required=True,\n error_messages={\n 'required': _('Please select the type of location. If none of these apply to your situation, please select \"Other\".')\n }\n )\n\n self.fields['other_commercial_or_public_place'].help_text = _('Please describe')\n self.fields['other_commercial_or_public_place'].widget = TextInput(\n attrs={'class': 'usa-input word-count-10'}\n )\n\n\nclass PoliceLocation(LocationForm):\n class Meta:\n model = Report\n fields = LocationForm.Meta.fields + ['inside_correctional_facility', 'correctional_facility_type']\n widgets = LocationForm.Meta.widgets\n\n def __init__(self, *args, **kwargs):\n LocationForm.__init__(self, *args, **kwargs)\n\n self.name = 'PoliceLocation'\n\n self.fields['inside_correctional_facility'] = TypedChoiceField(\n choices=CORRECTIONAL_FACILITY_LOCATION_CHOICES,\n widget=UsaRadioSelect,\n required=True,\n error_messages={\n 'required': POLICE_LOCATION_ERRORS['facility']\n },\n label=''\n )\n\n self.fields['correctional_facility_type'] = TypedChoiceField(\n choices=CORRECTIONAL_FACILITY_LOCATION_TYPE_CHOICES,\n widget=UsaRadioSelect,\n required=False,\n label=''\n )\n self.fields['correctional_facility_type'].widget.attrs['class'] = 'margin-bottom-0 padding-bottom-0 padding-left-1'\n self.fields['correctional_facility_type'].help_text = 'What type of prison or correctional facility?'\n\n def clean(self):\n inside_facility = self.cleaned_data.get('inside_correctional_facility')\n facility_type = self.cleaned_data.get('correctional_facility_type')\n\n if inside_facility == 'inside':\n if bool(facility_type) is False:\n msg = ValidationError(POLICE_LOCATION_ERRORS['facility_type'])\n self.add_error('correctional_facility_type', msg)\n\n if inside_facility == 'outside':\n self.cleaned_data['correctional_facility_type'] = None\n\n return self.cleaned_data\n\n\nclass EducationLocation(LocationForm):\n class Meta:\n model = Report\n fields = LocationForm.Meta.fields + ['public_or_private_school']\n widgets = LocationForm.Meta.widgets\n\n def __init__(self, *args, **kwargs):\n LocationForm.__init__(self, *args, **kwargs)\n\n self.question_groups = [\n QuestionGroup(\n self,\n ('public_or_private_school',),\n group_name=_('Did this happen at a public or a private school, educational program or activity?'),\n help_text=_('Includes schools, educational programs, or educational activities, like training programs, sports teams, clubs, or other school-sponsored activities'),\n optional=False,\n ally_id='education-location-help-text'\n ),\n ] + self.question_groups\n\n self.fields['public_or_private_school'] = TypedChoiceField(\n choices=PUBLIC_OR_PRIVATE_SCHOOL_CHOICES,\n widget=UsaRadioSelect(attrs={\n 'aria-describedby': 'education-location-help-text'\n }),\n label='',\n required=True,\n error_messages={\n 'required': _('Please select the type of school or educational program.')\n }\n )\n\n\nclass ProtectedClassForm(ModelForm):\n class Meta:\n model = Report\n fields = ['protected_class', 'other_class']\n\n # Overriding __init__ here allows us to provide initial data for 'protected_class' field\n def __init__(self, *args, **kwargs):\n ModelForm.__init__(self, *args, **kwargs)\n self.fields['protected_class'] = ModelMultipleChoiceField(\n error_messages={'required': PROTECTED_CLASS_ERROR},\n required=True,\n label=\"\",\n queryset=ProtectedClass.objects.filter(protected_class__in=PROTECTED_CLASS_CHOICES).order_by('form_order'),\n widget=UsaCheckboxSelectMultiple(attrs={\n 'aria-describedby': 'protected-class-help-text'\n }),\n )\n self.fields['other_class'].help_text = _('Please describe \"Other reason\"')\n self.fields['other_class'].widget = TextInput(\n attrs={'class': 'usa-input word-count-10'}\n )\n\n self.question_groups = [\n QuestionGroup(\n self,\n ('protected_class',),\n group_name=_('Do you believe any of these personal characteristics influenced why you were treated this way?'),\n help_text=_('Some civil rights laws protect people from discrimination, which include these protected classes. These are some of the most common classes that we see.'),\n optional=False,\n ally_id=\"protected-class-help-text\"\n )\n ]\n\n\nclass When(ModelForm):\n class Meta:\n model = Report\n fields = ['last_incident_month', 'last_incident_day', 'last_incident_year']\n widgets = {\n 'last_incident_month': TextInput(attrs={\n 'class': 'usa-input usa-input--small',\n 'required': True,\n 'type': 'number',\n }),\n 'last_incident_day': TextInput(attrs={\n 'class': 'usa-input usa-input--small',\n 'type': 'number',\n }),\n 'last_incident_year': EmailInput(attrs={\n 'class': 'usa-input usa-input--medium',\n 'required': True,\n 'type': 'number',\n }),\n }\n\n def __init__(self, *args, **kwargs):\n ModelForm.__init__(self, *args, **kwargs)\n\n self.fields['last_incident_month'].label = _('Month')\n self.fields['last_incident_month'].error_messages = {\n 'required': _('Please enter a month.'),\n }\n self.fields['last_incident_day'].label = _('Day')\n self.fields['last_incident_year'].label = _('Year')\n self.fields['last_incident_year'].error_messages = {\n 'required': _('Please enter a year.'),\n }\n\n def clean(self):\n \"\"\"Validating more than one field at a time can't be done in the model validation\"\"\"\n cleaned_data = super(When, self).clean()\n\n try:\n year = cleaned_data['last_incident_year']\n month = cleaned_data['last_incident_month']\n day = cleaned_data['last_incident_day'] or 1\n test_date = datetime(year, month, day)\n if test_date > datetime.now():\n self.add_error('last_incident_year', ValidationError(\n _('Date can not be in the future.'),\n params={'value': test_date.strftime('%x')},\n ))\n if year < 100:\n self.add_error('last_incident_year', ValidationError(\n _('Please enter four digits for the year.'),\n params={'value': test_date.strftime('%x')},\n ))\n if test_date < datetime(1899, 12, 31):\n self.add_error('last_incident_year', ValidationError(\n _('Please enter a year after 1900.'),\n params={'value': test_date.strftime('%x')},\n ))\n except ValueError:\n # a bit of a catch-all for all the ways people could make bad dates\n self.add_error('last_incident_year', ValidationError(\n _(f'Invalid date format {month}/{day}/{year}.'),\n params={'value': f'{month}/{day}/{year}'},\n ))\n except KeyError:\n # these will be caught by the built in error validation\n return cleaned_data\n\n return cleaned_data\n\n\nclass Who(ModelForm):\n respondent_type = TypedChoiceField(\n choices=RESPONDENT_TYPE_CHOICES, empty_value=None, widget=UsaRadioSelect, required=False\n )\n\n class Meta:\n model = Report\n fields = ['respondent_contact_ask', 'respondent_type', 'respondent_name', 'respondent_city', 'respondent_state']\n widgets = {\n 'respondent_contact_ask': CheckboxInput,\n }\n\n\nclass Filters(ModelForm):\n class Meta:\n model = Report\n fields = [\n 'assigned_section',\n 'contact_first_name',\n 'contact_last_name',\n 'location_city_town',\n 'location_state'\n ]\n widgets = {\n 'contact_first_name': TextInput(attrs={\n 'class': 'usa-input',\n 'name': 'contact_first_name'\n }),\n 'contact_last_name': TextInput(attrs={\n 'class': 'usa-input',\n 'name': 'contact_last_name'\n }),\n 'location_city_town': TextInput(attrs={\n 'class': 'usa-input',\n 'name': 'location_city_town'\n }),\n 'location_state': CrtDropdown(attrs={\n 'name': 'location_state'\n })\n }\n\n def __init__(self, *args, **kwargs):\n ModelForm.__init__(self, *args, **kwargs)\n\n self.fields['assigned_section'] = MultipleChoiceField(\n choices=SECTION_CHOICES,\n widget=CrtMultiSelect(attrs={\n 'classes': 'text-uppercase',\n 'name': 'assigned_section'\n }),\n required=False\n )\n self.fields['location_state'] = ChoiceField(\n choices=STATES_AND_TERRITORIES,\n widget=CrtDropdown(attrs={\n 'name': 'location_state'\n }),\n required=False,\n )\n\n self.fields['assigned_section'].label = _('View sections')\n self.fields['contact_first_name'].label = _('Contact first name')\n self.fields['contact_last_name'].label = _('Contact last name')\n self.fields['location_city_town'].label = _('Incident location city')\n\n self.fields['location_state'].label = _('Incident location state')\n self.fields['location_state'].widget.attrs['list'] = 'states'\n\n\nclass ComplaintActions(ModelForm):\n class Meta:\n model = Report\n fields = ['assigned_section', 'status']\n\n def __init__(self, *args, **kwargs):\n ModelForm.__init__(self, *args, **kwargs)\n\n self.fields['assigned_section'] = ChoiceField(\n widget=ComplaintSelect(label='Section', attrs={\n 'classes': 'text-uppercase'\n }),\n choices=SECTION_CHOICES,\n required=False\n )\n\n self.fields['status'] = ChoiceField(\n widget=ComplaintSelect(label='Status'),\n choices=STATUS_CHOICES,\n required=False\n )\n","sub_path":"crt_portal/cts_forms/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":25154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"169827099","text":"import os\nimport sys\nimport pytest\nimport configparser\nimport numpy as np\nsys.path.insert(0,'..')\n\nimport oect\n\n\n#most values are hardcoded - be careful if modifying cfg/txt files\n#some tests use different subfolders to avoid conflicts\n\nclass TestOECT:\n\n\toect.make_config(os.getcwd())\n\n\n\t#set_params\n\t############################################################################\n\n\t#test that parameters are read from config\n\tdef test_set_params(self):\n\t\ttest_oect = oect.OECT(folder='tests/test_device/01') #called in init\n\t\tassert (test_oect.params['W'] == 4000.0\n\t\t\tand test_oect.params['L'] == 20.0\n\t\t\tand test_oect.params['d'] == 4e-8\n\t\t\tand test_oect.params['Preread (ms)'] == 20000\n\t\t\tand test_oect.params['First Bias (ms)'] == 120000\n\t\t\tand test_oect.params['Vds (V)'] == 0\n\t\t\tand test_oect.params['output_Preread (ms)'] == 5000\n\t\t\tand test_oect.params['output_First Bias (ms)'] == 200\n\t\t\tand test_oect.params['Output Vgs'] == 2\n\t\t\tand test_oect.params['Vgs'] == [-0.5, -0.8])\n\n\t#test that options are read from config\n\tdef test_set_opts(self):\n\t\ttest_oect = oect.OECT(folder='tests/test_device/options_test') #called in init\n\t\tassert (test_oect.options['Reverse'] == True\n\t\t\tand test_oect.options['Average'] == False\n\t\t\tand test_oect.options['gm_method'] == 'method'\n\t\t\tand test_oect.options['V_low'] == 10)\n\n\t#test that additional parameters can be added from constructor\n\tdef test_set_params_add(self):\n\t\ttest_oect = oect.OECT(folder='tests/test_device/01', params={'test_param1': 100},\n\t\t\toptions={'test_option1': 200})\n\t\tassert (test_oect.params['test_param1'] == 100\n\t\t\tand test_oect.options['test_option1'] == 200)\n\n\t#test that if options are not in config, all settings are set to default\n\tdef test_set_params_defaults(self):\n\t\ttest_oect = oect.OECT(folder=os.getcwd())\n\t\ttest_oect.set_params({}, {}, {}, {}) #try defaults\n\t\tassert (test_oect.options['gm_method'] == 'sg' \n\t\t\tand test_oect.options['Reverse'] == True\n\t\t\tand test_oect.options['Average'] == False\n\t\t\tand test_oect.options['V_low'] == False\n\t\t\tand test_oect.options['overwrite'] == False)\n\n\t#test that TypeError is raised when parameters passed are not dicts\n\tdef test_set_params_not_dict(self):\n\t\ttest_oect = oect.OECT(folder=os.getcwd())\n\t\twith pytest.raises(IndexError) or pytest.raises(TypeError):\n\t\t\ttest_oect.set_params([1, 2, 3, 4, 5], [1, 2, 3, 4, 5], [1, 2, 3, 4, 5], [1, 2, 3, 4, 5])\n\n\n\t#filelist\n\t#####################################################################\n\n\t#test that txt filelist is correctly grabbed from path\n\tdef test_filelist(self):\n\t\ttest_oect = oect.OECT(folder='tests/test_device/01')\n\t\ttest_oect.filelist()\n\t\tassert (os.path.join('tests/test_device/01', 'uc1_kpf6_output_0.txt') in test_oect.files\n\t\t\tand os.path.join('tests/test_device/01', 'uc1_kpf6_output_1.txt') in test_oect.files\n\t\t\tand os.path.join('tests/test_device/01', 'uc1_kpf6_transfer_0.txt') in test_oect.files\n\t\t\tand test_oect.config[0] == os.path.join('tests/test_device/01', 'uc1_kpf6_config.cfg'))\n\n\t#test that config file is generated when folder starts with no cfg\n\t@pytest.mark.xfail\n\tdef test_filelist_noconfig(self):\n\t\ttest_oect = oect.OECT(folder='tests/test_device/no_config')\n\t\tconfig_check = 'config.cfg' in os.listdir('tests/test_device/no_config')\n\t\ttry:\n\t\t\tos.remove('tests/test_device/no_config/config.cfg')\n\t\texcept:\n\t\t\tpass\n\t\tassert config_check\n\n\t#get_metadata\n\t#####################################################################\n\t\n\t#test that metadata is correctly taken from file\n\tdef test_get_metadata(self):\n\t\ttest_oect = oect.OECT(folder=os.getcwd())\n\t\ttest_file = 'tests/test_device/metadata_test/uc1_kpf6_output_0.txt'\n\t\ttest_oect.get_metadata(test_file)\n\t\tassert (test_oect.Vg == -.5\n\t\t\tand test_oect.W == 2000\n\t\t\tand test_oect.L == 20)\n\n\t#test that metadata is correctly grabbed from data file if config doesn't exist\n\tdef test_get_metadata_no_config(self):\n\t\ttest_oect = oect.OECT(folder='tests/test_device/metadata_test')\n\t\ttest_oect.make_config = True\n\t\ttest_file = 'tests/test_device/metadata_test/uc1_kpf6_output_0.txt'\n\t\ttest_oect.get_metadata(test_file)\n\t\tassert (test_oect.Vg == -.5\n\t\t\tand test_oect.W == 4000\n\t\t\tand test_oect.L == 10)\n\n\t#transfer_curve\n\t######################################################################\n\t\n\t#test that KeyError raised when file not correctly formatted\n\tdef test_transfer_curve_wrong_col_names(self):\n\t\ttest_oect = oect.OECT(folder=os.getcwd())\n\t\twith pytest.raises(KeyError):\n\t\t\ttest_file = 'tests/test_device/broken/broken_uc1_kpf6_transfer_0.txt'\n\t\t\ttest_oect.get_metadata(test_file)\n\t\t\ttest_oect.transfer_curve(test_file)\n\n\t#output_curve\n\t###################################################################\n\n\t#test that KeyError raised when file not correctly formatted\n\tdef test_output_curve_wrong_col_names(self):\n\t\ttest_oect = oect.OECT(folder=os.getcwd())\n\t\twith pytest.raises(KeyError):\n\t\t\ttest_file = 'tests/test_device/01/uc1_kpf6_output_0.txt'\n\t\t\ttest_oect.get_metadata(test_file)\n\t\t\ttest_oect.transfer_curve(test_file)\n\n\t#all_outputs\n\t###################################################################\n\n\t#test that correct number of outputs were added\n\tdef test_all_outputs(self):\n\t\ttest_oect = oect.OECT(folder='tests/test_device/01') #called in init\n\t\tassert test_oect.num_outputs == 4\n\n\t#test that outputs are added to existing outputs\n\tdef test_all_outputs_append(self):\n\t\ttest_oect = oect.OECT(folder='tests/test_device/01') #called in init\n\t\ttest_oect.all_outputs() #call again\n\t\tassert test_oect.num_outputs == 8\n\n\t#all_transfers\n\t##################################################################\n\t\n\t#test that correct number of transfers were added\n\tdef test_all_transfers(self):\n\t\ttest_oect = oect.OECT(folder='tests/test_device/01') #called in init\n\t\tassert test_oect.num_transfers == 2\n\n\t#_reverse\n\t###################################################################\n\t\n\t#test that correct values returned when sweep was not performed\n\tdef test_reverse_no_sweep(self):\n\t\ttest_oect = oect.OECT(folder=os.getcwd())\n\t\tv = np.arange(start=-1, stop=.1, step=.1)\n\t\tassert len(v) // 2, False == test_oect._reverse(v)\n\n\t#test that correct values returned when sweep was performed\n\tdef test_reverse_with_sweep(self):\n\t\ttest_oect = oect.OECT(folder=os.getcwd())\n\t\ta = np.arange(start=-1, stop=1.1, step=.1)\n\t\tb = np.arange(start=.9, stop=-1.1, step=-.1)\n\t\tv = np.concatenate((a, b))\n\t\tassert len(v) // 2, True == test_oect._reverse(v)\n\n\n\t#update_config\n\t######################################################################\n\t\n\t#test that config file is updated to match oect attributes\n\tdef test_update_config(self):\n\t\ttest_oect = oect.OECT(folder='tests/test_device/no_config')\n\t\t#config will be auto generated in init\n\t\t#default config will be made with values:\n\t\t# [Dimensions]\n\t\t# Width (um) = 2000\n\t\t# Length (um) = 20\n\n\t\t# [Transfer]\n\t\t# Preread (ms) = 30000.0\n\t\t# First Bias (ms) = 120000.0\n\t\t# Vds (V) = -0.6\n\n\t\t# [Output]\n\t\t# Preread (ms) = 500.0\n\t\t# First Bias (ms) = 200.0\n\t\t# Output Vgs = 4\n\t\t# Vgs (V) 0 = -0.1\n\t\t# Vgs (V) 1 = -0.3\n\t\t# Vgs (V) 2 = -0.5\n\t\t# Vgs (V) 3 = -0.9\n\t\ttest_oect.W = 4000\n\t\ttest_oect.L = 10\n\t\ttest_oect.Vd = -1\n\t\ttest_oect.Vg_array = [0, .1, .2, .3, .4]\n\t\ttest_oect.update_config()\n\t\tconfig = configparser.ConfigParser()\n\t\tconfig.read(test_oect.config)\n\t\tassert (config['Dimensions']['Width (um)'] == '4000'\n\t\t\tand config['Dimensions']['Length (um)'] == '10'\n\t\t\tand config['Transfer']['Vds (V)'] == '-1'\n\t\t\tand config['Output']['Preread (ms)'] == '500.0'\n\t\t\tand config['Output']['First Bias (ms)'] == '200.0'\n\t\t\tand config['Output']['Vgs (V) 0'] == '0'\n\t\t\tand config['Output']['Vgs (V) 1'] == '0.1'\n\t\t\tand config['Output']['Vgs (V) 2'] == '0.2'\n\t\t\tand config['Output']['Vgs (V) 3'] == '0.3'\n\t\t\tand config['Output']['Vgs (V) 4'] == '0.4')\n\t\ttry:\n\t\t\tos.remove('tests/test_device/no_config/config.cfg')\n\t\texcept:\n\t\t\tpass\n\n\n\t#make_config\n\t######################################################################\n\n\t#test that FileNotFoundError thrown when provided with invalid path\n\t@pytest.mark.xfail\n\tdef test_make_config_invalid_path(self):\n\t\twith pytest.raises(FileNotFoundError):\n\t\t\toect.make_config('a_nonexistent_path')\n\n\t#config_file\n\t#############################################################\n\t\n\t#test that params exist when loaded from config\n\tdef test_config_file_params(self):\n\t\tparams, opts = oect.config_file('tests/config.cfg')\n\t\tassert bool(params) and not bool(opts)\n\n\t#test that options exist when loaded from config\n\tdef test_config_file_opts(self):\n\t\tparams, opts = oect.config_file('tests/test_device/options_test/uc1_kpf6_config.cfg')\n\t\tassert bool(params) and bool(opts)\n\t\t\n\t#tests that nothing is added when provided with invalid path\n\tdef test_config_file_invalid_file(self):\n\t\tparams, opts = oect.config_file('a_nonexistent_file')\n\t\tassert not bool(params)\n\n\t#test that configparser error thrown when provided with non-cfg file\n\tdef test_config_not_cfg(self):\n\t\twith pytest.raises(configparser.MissingSectionHeaderError):\n\t\t\tparams, opts = oect.config_file('tests/dummy_file.py')\n\n\n\n\t#questions/why I didn't write tests for these functions\n\t#loaddata - just consolidates a lot of functions\n\t#calc_gms - what would an expected gm would be?\n\t#_calc_gm - \"\"\n\t#quadrant - what does the self.gm_peaks.index indicate? what\n\t#\twould be an expected value?\n\t#thresh - confused about the zip calls, gm_peaks\n\t#_min_fit - what would an expected result be?\n\t#line_f - self-explanatory enough to not test\n\t#_find_peak - what would an expected result be?","sub_path":"tests/oect_test.py","file_name":"oect_test.py","file_ext":"py","file_size_in_byte":9467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"306008995","text":"#Alice’s Adventures in Wonderland, by Lewis Carroll\nimport random\nclass Alice:\n def getTotalNumberOfWords(book):\n words = {}\n f = open(book)\n\n for line in f:\n w = line.lower().split()\n for word in w:\n word = word.strip(\",.\\\"'-;!)(‘’:“”?\")\n if word not in words:\n words[word] = 1\n else:\n words[word] += 1\n \n return sum(words.values())\n\n def getTotalUniqueWords(book):\n words = {}\n f = open(book)\n\n for line in f:\n w = line.lower().split()\n for word in w:\n word = word.strip(\",.\\\"'-;!)(‘’:“”?\")\n if word not in words:\n words[word] = 1\n else:\n words[word] += 1\n\n return len(words)\n\n def get20MostFrequentWords(book):\n words = {}\n f = open(book)\n\n for line in f:\n w = line.lower().split()\n for word in w:\n word = word.strip(\",.\\\"'-;!)(‘’:“”?\")\n if word not in words:\n words[word] = 1\n else:\n words[word] += 1\n\n top20 = list(words.values())\n top20.sort(reverse=True)\n top20 = top20[0:20]\n out = []\n for val in words:\n if words[val] in top20:\n out.append([val, words[val]])\n out.sort(key=lambda count: count[1],reverse=True)\n return out\n\n def get20MostInterestingFrequentWords(book, commonWords):\n words = {}\n f = open(book)\n\n for line in f:\n w = line.lower().split()\n for word in w:\n word = word.strip(\",.\\\"'-;!)(‘’:“”?*\")\n if word == \"\":\n continue\n if word not in words:\n words[word] = 1\n else:\n words[word] += 1\n\n common = {}\n num = 1\n c = open(commonWords)\n for line in c:\n line = line.strip().lower()\n if num > 100:\n break\n common[line] = 1\n num += 1\n for word in common:\n if word in words:\n del words[word]\n top20 = list(words.values())\n top20.sort(reverse=True)\n top20 = top20[0:20]\n out = []\n for val in words:\n if words[val] in top20:\n out.append([val, words[val]])\n out.sort(key=lambda count: count[1],reverse=True)\n return out\n\n def get20LeastFrequentWords(book):\n words = {}\n f = open(book)\n\n for line in f:\n w = line.lower().split()\n for word in w:\n word = word.strip(\",.\\\"'-;!)(‘’:“”?\")\n if word not in words:\n words[word] = 1\n else:\n words[word] += 1\n\n top20 = list(words.values())\n top20.sort()\n top20 = top20[0:20]\n out = []\n for val in words:\n if len(out) < 20:\n if words[val] in top20:\n out.append([val, words[val]])\n else:\n break\n return out\n\n def getFrequencyOfWord(book, findWord):\n words = {}\n f = open(book)\n\n count = 0\n for line in f:\n w = line.lower().split()\n for word in w:\n word = word.strip(\",.\\\"'-;!)(‘’:“”?\")\n if word == \"chapter\":\n count += 1\n words[count] = {}\n continue\n if word not in words[count]:\n words[count][word] = 1\n else:\n words[count][word] += 1\n\n counts = []\n for chapter in words:\n if findWord in words[chapter]:\n counts.append(words[chapter][findWord])\n else:\n counts.append(0)\n return counts\n\n def getChapterQuoteAppears(book, q):\n words = {}\n f = open(book)\n \n count = 0\n for line in f:\n if \"chapter\" in line.lower():\n count += 1\n words[count] = {}\n continue\n quote = line.lower().strip(\",.\\\"'-;!)(‘’:“”?\\n\")\n if quote not in words[count]:\n words[count][quote] = quote\n \n for chapter in words:\n for quote in list(words[chapter].values()):\n if q in quote:\n return chapter\n return -1\n\n def generateSentence(book):\n output = []\n currWord = \"the\"\n output.append(currWord)\n for i in range(19):\n words = {}\n f = open(book)\n prevWord = \"\"\n for line in f:\n w = line.lower().split()\n for word in w:\n word = word.strip(\",.\\\"'-;!)(‘’:“”?\")\n if prevWord == currWord:\n if word not in words:\n words[word] = 1\n else:\n words[word] += 1\n prevWord = word\n chosen = random.sample(list(words),1)\n output.append(chosen[0])\n currWord = chosen[0]\n sentence = \"\"\n for word in output:\n sentence = sentence + word + \" \"\n sentence.strip(\" \")\n return sentence\n\nprint(\"Total Num Words:\", Alice.getTotalNumberOfWords(\"book.txt\"))\nprint()\nprint(\"Total Num Unique Words:\", Alice.getTotalUniqueWords(\"book.txt\"))\nprint()\nprint(\"20 Most Freq Words:\", Alice.get20MostFrequentWords(\"book.txt\"))\nprint()\nprint(\"20 Most Interesting Words:\", Alice.get20MostInterestingFrequentWords(\"book.txt\", \"commonwords.txt\"))\nprint()\nprint(\"20 Least Freq Words:\", Alice.get20LeastFrequentWords(\"book.txt\"))\nprint()\nprint(\"Find word frequency:\", Alice.getFrequencyOfWord(\"book.txt\", \"hatter\"))\nprint()\nprint(\"Find quote chapter:\", Alice.getChapterQuoteAppears(\"book.txt\", \"curiouser and curiouser\"))\nprint()\nprint(\"Generate Sentence:\", Alice.generateSentence(\"book.txt\"))","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"27864170","text":"import fileinput as fi\n\ndef hard_process(ls, k):\n\tls = convert_ls(ls)\n\tif (k == 0) or (0 not in ls):\n\t\treturn longest_ones_seq(ls), convert_ls_back(ls)\n\telse:\n\t\tzeroes_ls = []\n\t\tfor index, element in enumerate(ls):\n\t\t\tif element == 0:\n\t\t\t\tleft = longest_ones_seq(ls[index - 1:index])\n\t\t\t\tright = longest_ones_seq(ls[index + 1:index + 2])\n\t\t\t\tzeroes_ls.append((index, left + right))\n\t\tls[greatest_change(zeroes_ls)] = 1\n\t\treturn hard_process(convert_ls_back(ls), k - 1)\n\n\ndef greatest_change(ls):\n\tindex = 0\n\tmaximum = 0\n\tfor pair in ls:\n\t\tif pair[1] > maximum:\n\t\t\tindex, maximum = pair\n\treturn index\n\n\ndef longest_ones_seq(ls):\n\tmaximum = 0\n\tfor el in ls:\n\t\tmaximum = el if el > maximum else maximum\n\treturn maximum\n\n\ndef convert_ls(ls):\n\tnewls = []\n\tnum_ones = 0\n\tfor el in ls:\n\t\tif el == 1:\n\t\t\tnum_ones += 1\n\t\telse:\n\t\t\tif num_ones != 0:\n\t\t\t\tnewls.append(num_ones)\n\t\t\tnewls.append(0)\n\t\t\tnum_ones = 0\n\tif num_ones != 0:\n\t\tnewls.append(num_ones)\n\treturn newls\n\n\ndef convert_ls_back(ls):\n\tnewls = []\n\tfor el in ls:\n\t\tif el != 0:\n\t\t\tfor i in range(el):\n\t\t\t\tnewls.append(1)\n\t\telse:\n\t\t\tnewls.append(0)\n\treturn newls\n\n\nif __name__ == '__main__':\n\targls = []\n\tfor line in fi.input():\n\t\targls.append(line)\n\n\tk = int(argls[0].split(' ')[1])\n\tls = argls[1].split(' ')\n\tfor index, element in enumerate(ls):\n\t\tls[index] = int(element)\n\n\tlength, ls = hard_process(ls, k)\n\tprint(length)\n\tprint(*ls)","sub_path":"jhoak/hard process/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":1384,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"74973914","text":"'''\nAsk a user for\na value x (float)\na value n (positive integer)\n\nWrite a program, with a while loop, to calculate xn yourself\nwithout using ** or any function from the math module\n'''\n\n# zonder input-validatie maar kan je doen als oefening\n# in deze context wel betekenisvolle waarde\nx = float(input(\"Geef waarde voor x: \"))\nn = int(input(\"Geef waarde voor n: \"))\n\n'''while n < 0:\n print(\"Geen geldige waarde! Voer een positieve waarde in!\")\n n = int(input(\"Geef een waarde waarvan dwe de faculteit gaan berekenen: \"))'''\n\nmacht = 1\ncounter = 0\n\nwhile counter < n:\n counter = counter + 1\n macht = macht * x\n\nprint(\"x tot de macht n is\", macht)","sub_path":"macht_met_while.py","file_name":"macht_met_while.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"94982702","text":"import sys\nimport cv2\nimport os\n\ntarget_resolution = (640, 360)\ncrop_borders = 0.23\nfps = 90.\n\ndef main(root, outPath):\n \n fourcc = cv2.VideoWriter_fourcc(*'DIVX')\n writer = cv2.VideoWriter(outPath, fourcc, fps, target_resolution)\n \n images = sorted([os.path.join(root, img) for img in os.listdir(root)])\n \n size = len(images)\n \n for i, i_path in enumerate(images):\n img = cv2.imread(i_path)\n \n w, h, _ = img.shape\n \n if crop_borders:\n img = img[int(crop_borders*w):int(-crop_borders*w), int(crop_borders*h):int(-crop_borders*h),:]\n \n img = cv2.resize(img, target_resolution)\n \n writer.write(img)\n \n cv2.imshow(\"pp\", img)\n \n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n \n if not i % 1000:\n print(\"Did {}/{} images ({:.2f}%)\".format(i, size, 100 * i/size))\n \n writer.release()\n cv2.destroyAllWindows()\n\nif __name__ == \"__main__\":\n root_path = sys.argv[1]\n out_path = sys.argv[2]\n main(root_path, out_path)\n","sub_path":"tomovie.py","file_name":"tomovie.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"138695770","text":"#! /usr/local/python_anaconda/bin/python3.4\n\nimport pandas as pd\nimport re\nimport math\nimport os\nfrom file_utilities import check_filename\nfrom pbs_runners import script_runner\nfrom Bio import SeqIO\nimport pandas as pd\n\n\n\ndef read_bowtie2_summary_paired_and_unpaired(file, results={}):\n file = check_filename(file)\n summary = open(file, \"r\").read()\n if not \"overall alignment rate\" in summary:\n print(\"no bowtie2 results\")\n return {}\n results[\"file\"] = summary.split(\"reads; of these:\")[0].split(\"\\n\")[-2].split(\">\")[-1].strip()\n base = summary.split(\"reads; of these:\")[0].split(\"\\n\")[-2].split(\">\")[-1].strip().split(\"/\")[-1].split(\".bacteria_output.bam\")[0]\n results[\"base\"] = base.split(\"_L00\")[0]\n results[\"lane\"] = base.split(\"L00\")[1].split(\"_\")[0]\n results[\"patient\"] = results[\"base\"].split(\"_\")[0]\n results[\"genetic_material\"] = results[\"base\"].split(\"_\")[1]\n results[\"S\"] = results[\"base\"].split(\"_\")[2]\n results[\"total_read_count\"] = int(summary.split(\"reads; of these:\")[0].split(\"\\n\")[-1].strip())\n info = summary.split(\"reads; of these:\")[1].strip().split(\"\\n\")\n info = [x.strip() for x in info]\n results[\"pair_count\"] = int(info[0].split(\" \")[0])\n results[\"pair_percent\"] = float(info[0].split(\"(\")[1].split(\"%\")[0])\n results[\"concordantly_0_times_count\"] = int(info[1].split(\" \")[0])\n results[\"concordantly_0_times_percent\"] = float(info[1].split(\"(\")[1].split(\"%\")[0])\n\n results[\"concordantly_1_time_count\"] = int(info[2].split(\" \")[0])\n results[\"concordantly_1_time_percent\"] = float(info[2].split(\"(\")[1].split(\"%\")[0])\n\n results[\"concordantly_more_than_1_time_count\"] = int(info[3].split(\" \")[0])\n results[\"concordantly_more_than_1_time_percent\"] = float(info[3].split(\"(\")[1].split(\"%\")[0])\n\n results[\"discordantly_1_time_count\"] = int(info[6].split(\" \")[0])\n results[\"discordantly_1_time_percent\"] = float(info[6].split(\"(\")[1].split(\"%\")[0])\n\n results[\"unpaired_mates_count\"] = int(info[9].split(\" \")[0])\n\n results[\"mates_0_count\"] = int(info[10].split(\" \")[0])\n results[\"mates_0_percent\"] = float(info[10].split(\"(\")[1].split(\"%\")[0])\n\n results[\"mates_1_count\"] = int(info[11].split(\" \")[0])\n results[\"mates_1_percent\"] = float(info[11].split(\"(\")[1].split(\"%\")[0])\n\n results[\"mates_more_than_1_count\"] = int(info[12].split(\" \")[0])\n results[\"mates_more_than_1_percent\"] = float(info[12].split(\"(\")[1].split(\"%\")[0])\n\n results[\"overall_alignment_rate\"] = float(info[13].split(\" \")[0].split(\"%\")[0])\n results[\"total_reads_mapped\"] = results[\"mates_more_than_1_count\"] + results[\"mates_1_count\"] + 2*results[\"discordantly_1_time_count\"] +2*results[\"concordantly_more_than_1_time_count\"] + 2*results[\"concordantly_1_time_count\"]\n\n\n return (results)\n\n\ndef read_bowtie2_summary(file, results={}):\n file = check_filename(file)\n summary = open(file, \"r\").read()\n if not \"overall alignment rate\" in summary:\n print(\"no bowtie2 results\")\n return {}\n results[\"file\"] = summary.split(\"reads; of these:\")[0].split(\"\\n\")[-2].split(\">\")[-1].strip()\n base = summary.split(\"reads; of these:\")[0].split(\"\\n\")[-2].split(\">\")[-1].strip().split(\"/\")[-1].split(\n \".bacteria_output.bam\")[0]\n results[\"base\"] = base.split(\"_L00\")[0]\n results[\"lane\"] = base.split(\"L00\")[1].split(\"_\")[0]\n results[\"patient\"] = results[\"base\"].split(\"_\")[0]\n results[\"genetic_material\"] = results[\"base\"].split(\"_\")[1]\n results[\"total_read_count\"] = int(summary.split(\"reads; of these:\")[0].split(\"\\n\")[-1].strip())\n info = summary.split(\"reads; of these:\")[1].strip().split(\"\\n\")\n info = [x.strip() for x in info]\n results[\"pair_count\"] = int(info[0].split(\" \")[0])\n results[\"pair_percent\"] = float(info[0].split(\"(\")[1].split(\"%\")[0])\n results[\"concordantly_0_times_count\"] = int(info[1].split(\" \")[0])\n results[\"concordantly_0_times_percent\"] = float(info[1].split(\"(\")[1].split(\"%\")[0])\n results[\"concordantly_1_time_count\"] = int(info[2].split(\" \")[0])\n results[\"concordantly_1_time_percent\"] = float(info[2].split(\"(\")[1].split(\"%\")[0])\n results[\"concordantly_more_than_1_time_count\"] = int(info[3].split(\" \")[0])\n results[\"concordantly_more_than_1_time_percent\"] = float(info[3].split(\"(\")[1].split(\"%\")[0])\n results[\"overall_alignment_rate\"] = float(info[4].split(\" \")[0].split(\"%\")[0])\n results[\"reads_aligned\"] = results[\"concordantly_1_time_count\"] + results[\"concordantly_more_than_1_time_count\"]\n return(results)\n\n\n# TODO - consider chaning to conda samtools instead of using a module from server\ndef sam_2_bam(sam_file, bam_file=None):\n \"\"\"\n convert sam file into bam file\n \"\"\"\n if not bam_file:\n bam_file = sam_file.replace(\".sam\", \".bam\")\n script_runner(f\"module load samtools/samtools-1.6\\nsamtools view -S -b {sam_file} > {bam_file}\")\n return bam_file\n\ndef bam_2_sorted_bam(bam_file, sorted_file=None):\n \"\"\"\n sort bam file\n \"\"\"\n\n if not sorted_file:\n sorted_file = bam_file.replace(\".bam\", \".sorted.bam\")\n script_runner(f\"module load samtools/samtools-1.6\\nsamtools sort {bam_file} -o {sorted_file}\")\n return bam_file \n\ndef bowtie_db_2_taxonomy(db_fasta_path, out=None):\n \"\"\"\n map tax id to identifier in output file\n \"\"\"\n res = []\n for record in SeqIO.parse(db_fasta_path, \"fasta\"):\n name = record.description.split('| ')[-1].split(',')[0]\n identifier = record.id\n res.append(tuple(name, identifier))\n df = pd.DataFrame(res, columns=[\"name\",\"id\"])\n if out:\n df.to_csv(out, index=False)\n\n return df\n\ndef summarize_bam(bam_file, out=None):\n \"\"\"\n run samtools summary on an indexed version of a bam file\n :param bam_file: sorted bam file\n \"\"\"\n if not out:\n out = \"./summerized_bam\"\n script_runner(f\"module load samtools/samtools-1.6\\nsamtools idxstats {bam_file} | awk '$4 != 0' | head -n-1 | sort -k3 -V > {out}\")\n return bam_file \n\ndef merge_bam_and_summary(summary, tax_mapping, out=None):\n \"\"\"\n merged summary file from bam with following tax mapping from db used to map reads\n \"\"\"\n bam_summary = pd.read_table(summary, names=[\"id\", \"seq_len\",\"num_mapped\",\"num_unmapped\"])\n mapping = pd.read_csv(mapping)\n\n merged = bam_summary.merge(mapping, on=\"id\")\n if out:\n merged.to_csv(out, index=False)\n return merged\n\n\n \n\n","sub_path":"bowtie2_utilities.py","file_name":"bowtie2_utilities.py","file_ext":"py","file_size_in_byte":6393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"10009205","text":"from flask_migrate import upgrade, downgrade\nimport pytest\n\nfrom app.app import flask_app\nfrom app.models import db\n\n\ndef truncate_all():\n with db.session.begin(subtransactions=True):\n meta = db.metadata\n for table in reversed(meta.sorted_tables):\n db.session.execute(table.delete())\n\n\ndef bootstrap_database():\n with flask_app.app_context():\n upgrade()\n\n\n@pytest.fixture\ndef app():\n return flask_app\n\n\n@pytest.fixture(autouse=True)\ndef database_cleaner():\n try:\n yield\n finally:\n truncate_all()\n\n\nbootstrap_database()\n","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"54312236","text":"# -*- coding: utf-8 -*-\nimport urllib\nimport urlparse\n\nfrom django.conf.urls import patterns, url\nfrom django.contrib.auth import login, get_user_model, logout\nfrom django.contrib.auth.backends import ModelBackend\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.urlresolvers import reverse\nfrom django.http import HttpResponseRedirect\nfrom django.views.generic import View\nfrom itsdangerous import URLSafeTimedSerializer\nfrom webservices.sync import SyncConsumer\n\n\nclass LoginView(View):\n client = None\n\n def get(self, request):\n next = self.get_next()\n scheme = 'https' if request.is_secure() else 'http'\n query = urllib.urlencode([('next', next)])\n netloc = request.get_host()\n path = reverse('simple-sso-authenticate')\n redirect_to = urlparse.urlunparse((scheme, netloc, path, '', query, ''))\n request_token = self.client.get_request_token(redirect_to)\n host = urlparse.urljoin(self.client.server_url, 'authorize/')\n url = '%s?%s' % (host, urllib.urlencode([('token', request_token)]))\n return HttpResponseRedirect(url)\n\n def get_next(self):\n \"\"\"\n Given a request, returns the URL where a user should be redirected to\n after login. Defaults to '/'\n \"\"\"\n next = self.request.GET.get('next', None)\n if not next:\n return '/'\n netloc = urlparse.urlparse(next)[1]\n # Heavier security check -- don't allow redirection to a different\n # host.\n # Taken from django.contrib.auth.views.login\n if netloc and netloc != self.request.get_host():\n return '/'\n return next\n\n\nclass AuthenticateView(LoginView):\n client = None\n\n def get(self, request):\n raw_access_token = request.GET['access_token']\n access_token = URLSafeTimedSerializer(self.client.private_key).loads(raw_access_token)\n user = self.client.get_user(access_token)\n user.backend = self.client.backend\n login(request, user)\n next = self.get_next()\n return HttpResponseRedirect(next)\n\n\nclass LogoutView(LoginView):\n client = None\n\n def get(self, request):\n access_token = request.user.access_token\n next = self.get_next()\n scheme = 'https' if request.is_secure() else 'http'\n query = urllib.urlencode([('next', next)])\n netloc = request.get_host()\n path = reverse('simple-sso-logout-callback')\n redirect_to = urlparse.urlunparse((scheme, netloc, path, '', query, ''))\n request_token = self.client.get_request_token(redirect_to, access_token)\n\n host = urlparse.urljoin(self.client.server_url, 'logout/')\n url = '%s?%s' % (host, urllib.urlencode([('access_token', access_token), ('request_token', request_token)]))\n return HttpResponseRedirect(url)\n\n\nclass LogoutCallbackView(LoginView):\n client = None\n\n def get(self, request):\n raw_access_token = request.GET['access_token']\n access_token = URLSafeTimedSerializer(self.client.private_key).loads(raw_access_token)\n user = get_user_model().objects.get(access_token=access_token)\n request.user = user\n logout(request)\n user.access_token = None\n user.save()\n return HttpResponseRedirect(reverse('simple-sso-login'))\n\n\nclass Client(object):\n login_view = LoginView\n logout_view = LogoutView\n logout_callback_view = LogoutCallbackView\n authenticate_view = AuthenticateView\n backend = \"%s.%s\" % (ModelBackend.__module__, ModelBackend.__name__)\n user_extra_data = None\n\n def __init__(self, server_url, public_key, private_key,\n user_extra_data=None):\n self.server_url = server_url\n self.public_key = public_key\n self.private_key = private_key\n self.consumer = SyncConsumer(self.server_url, self.public_key, self.private_key)\n if user_extra_data:\n self.user_extra_data = user_extra_data\n\n @classmethod\n def from_dsn(cls, dsn):\n parse_result = urlparse.urlparse(dsn)\n public_key = parse_result.username\n private_key = parse_result.password\n netloc = parse_result.hostname\n if parse_result.port:\n netloc += ':%s' % parse_result.port\n server_url = urlparse.urlunparse((parse_result.scheme, netloc, parse_result.path, parse_result.params, parse_result.query, parse_result.fragment))\n return cls(server_url, public_key, private_key)\n\n def get_request_token(self, redirect_to, access_token=None):\n qs = {'redirect_to': redirect_to}\n if access_token:\n qs['access_token'] = access_token\n return self.consumer.consume('/request-token/', qs)['request_token']\n\n\n def is_authenticated(self, user):\n return self.consumer.consume('/is-authenticated/', {'access_token': user.access_token})['status']\n\n def get_user(self, access_token):\n data = {'access_token': access_token}\n if self.user_extra_data:\n data['extra_data'] = self.user_extra_data\n user_data = self.consumer.consume('/verify/', data)\n user = self.build_user(user_data, access_token)\n return user\n\n def build_user(self, user_data, access_token):\n user_model = get_user_model()\n\n try:\n user = user_model.objects.get(username=user_data['username'])\n except user_model.DoesNotExist:\n user = user_model(**user_data)\n user.access_token = access_token\n user.set_unusable_password()\n user.save()\n return user\n\n def get_urls(self):\n return patterns('',\n url(r'^login/$', self.login_view.as_view(client=self), name='simple-sso-login'),\n url(r'^logout/$', login_required(self.logout_view.as_view(client=self)), name='simple-sso-logout'),\n url(r'^authenticate/$', self.authenticate_view.as_view(client=self), name='simple-sso-authenticate'),\n url(r'^logout_callback/$', self.logout_callback_view.as_view(client=self), name='simple-sso-logout-callback'),\n )\n","sub_path":"simple_sso/sso_client/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":6122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"17007800","text":"\nfrom bson import ObjectId\nfrom main.db.db import MongoDatabase\n\nclass NoteService(object):\n \n def __init__(self, config = {}):\n self.__db_client = MongoDatabase(config)\n self.__db_client.connect()\n self.__db = self.__db_client.get_db()\n self.__collection = self.__db['temp_notes']\n\n def insert(self, username=None, payload=None) -> str:\n if not payload or not username:\n return None\n payload['username'] = username\n response = self.__collection.insert_one(payload)\n if response:\n oid = str(response.inserted_id)\n document = self.__collection.find_one({\"username\": username, \"_id\": ObjectId(oid) })\n if document:\n document['id'] = str(document['_id'])\n del document['_id']\n return document\n return None\n\n def get(self, username=None, noteId=None) -> any:\n if not username or not noteId:\n return None\n document = self.__collection.find_one({\"username\": username, \"_id\": ObjectId(noteId)})\n if document:\n document['id'] = str(document['_id'])\n del document['_id']\n return document\n\n def get_all(self, username=None) -> any:\n documents = list(self.__collection.find({\"username\": username}))\n if documents and isinstance(documents, list):\n for document in documents:\n document['id'] = str(document['_id'])\n del document['_id']\n return documents\n\n def update(self, username=None, noteId=None, payload = None) -> str:\n updated_payload = { \"$set\": payload }\n where_payload = { \"username\": username, \"_id\": ObjectId(noteId) }\n response = self.__collection.update_one(where_payload, updated_payload, upsert=False)\n if response and response.modified_count > 0:\n document = self.__collection.find_one({\"username\": username, \"_id\": ObjectId(noteId) })\n if document:\n document['id'] = str(document['_id'])\n del document['_id']\n return document\n return None\n\n def delete(self, username=None, noteId=None) -> int:\n where_payload = { \"username\": username, \"_id\": ObjectId(noteId)}\n response = self.__collection.delete_one(where_payload)\n return response.deleted_count","sub_path":"ms-manage-notes/main/services/note_service.py","file_name":"note_service.py","file_ext":"py","file_size_in_byte":2370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"560647940","text":"import requests\nfrom easygraphql import GraphQL\n\n\nurl_xdai = \"https://api.thegraph.com/subgraphs/name/odyssy-automaton/daohaus-xdai\"\nurl_mainnet = \"https://api.thegraph.com/subgraphs/name/odyssy-automaton/daohaus\"\nurl_idchain = \"https://graph.idchain.one/subgraphs/name/idchain/daohaus-supergraph\"\nquery = '''\nquery membersList($contractAddr: String!, $skip: Int) {\n daoMembers: members(\n where: {molochAddress: $contractAddr}\n orderBy: shares\n orderDirection: desc\n first: 1000\n skip: $skip\n ) {\n id\n delegateKey\n shares\n loot\n kicked\n jailed\n tokenTribute\n didRagequit\n memberAddress\n exists\n createdAt\n }\n}\n'''\n\n\ndef get_name(address):\n url = \"https://data.daohaus.club/dao/\"\n res = requests.get(url+address)\n return res.json()[0]['name']\n\ndef get_DAO_list():\n with open(\"DAO_list\", \"r\") as f :\n for line in f.readlines():\n para = line.split('/')\n #print(para)\n if para[-2] == '0x64':\n u = url_xdai\n elif para[-2] == '0x1':\n u = url_mainnet\n elif para[-2] == '0x4a':\n u = url_idchain\n get_member(u, para[-1].strip())\n \n\ndef get_member(url, address):\n graphql = GraphQL(url)\n variables = {\n \"contractAddr\": \"0xee629a192374caf2a72cf1695c485c5c89611ef2\",\n \"skip\": 0\n }\n #print(url, address)\n variables['contractAddr'] = address\n data, errors = graphql.execute(query, variables)\n name = get_name(address)\n a = []\n #print(data)\n with open(name+\".csv\", 'w') as f:\n for member in data['daoMembers']:\n if member['exists'] == True and (int(member['shares']) > 0 or int(member['loot']) > 0):\n a.append(member['memberAddress'])\n a = list(set(a))\n f.write(name+str(len(a))+\"\\n\")\n for i in a:\n f.write(i+'\\n')\n\n print(name, len(a))\n\n\nif __name__ == '__main__':\n\n get_DAO_list()\n\n\n","sub_path":"address_list/dao.py","file_name":"dao.py","file_ext":"py","file_size_in_byte":1982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"653670661","text":"from datahandler import data_sampling\nfrom regex.handlers import RegexHandler\n\n\ndef matched_regexes(ids, data, regexes, pwds, preprocess_func):\n found_regexes = []\n handler = RegexHandler(return_ignores=True)\n for single_regex in regexes:\n for i in range(len(ids)):\n matches, total_score = handler.score_and_match_sentences(data[i], [single_regex], pwds, preprocess_func)\n print(matches)\n exit()\n return found_regexes\n\n\ndef n_cross(ids, data, labels, n, regexes, pwds, preprocess_func, train_percent=.6, train_num=None, random_seed=None):\n n_samples = data_sampling.n_cross_validation_samples(ids, data, labels, n, train_percent, train_num, random_seed)\n n_regexes = []\n for each in n_samples:\n n_regexes.append(matched_regexes(each[\"ids\"], each[\"data\"], regexes, pwds, preprocess_func))\n\n return n_regexes, n_samples\n\n\ndef rank_ids(n_samples, n_regexes):\n id_ranks = {}\n for sample in n_samples:\n for i in range(len(sample[\"ids\"])):\n num_matches = 0\n for regexes in n_regexes:\n pass","sub_path":"RegexNLP-py/validate/regexes.py","file_name":"regexes.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"593186165","text":"from django.conf.urls import url, include\nfrom django.contrib import admin\nfrom . import views\n\napp_name = 'openqueueparty'\nurlpatterns = [\n url(r'^create', views.create, name=\"create\"),\n url(r'^addtrack', views.add_track, name=\"add_track\"),\n url(r'^join', views.join_playlist, name=\"join\"),\n url(r'^playlist/(?P[0-9]+)', views.playlist_page, name='playlist_page'),\n url(r'^playlist/created/(?P[0-9]+)', views.playlist_created, name='playlist_created'),\n url(r'^', views.home, name=\"home\")\n]","sub_path":"openqueueparty/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"14522010","text":"import time\nimport numpy as np\nfrom numpy.linalg import norm\nfrom rvm import RVM\nfrom sklearn.datasets import load_boston\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error\nimport statistics as stats\n\n\"\"\" A short demonstration of the RVM on the Boston housing dataset.\n\nIn the follow-up paper from 2001, Tipping reports the following results: \n \n For the RVM:\n average error: 7.46\n # of relevance vectors: 39.0\n For the SVM:\n average error: 8.04\n # of support vectors: 142.8\n\nWith a well-chosen kernel radius of 200, we actually manage \nto outperform the reference values, as the code below results in:\n \n - an average error around 4.8, and \n - an average # of relevance vectors 18.6\n\nwhich is quite a significant improvement over the SVM results. Not bad!\n\"\"\"\n\nkernel_radius = 200\nkernel = lambda x,y : gaussian_kernel(x,y,kernel_radius)\n \ndef gaussian_kernel(x,y,r):\n \"\"\"A gaussian kernel with radius r.\"\"\"\n return np.exp(-(norm(x-y))**2 / (r**2))\n\ndef evaluate_RVM(x_train, y_train, x_test, y_test, max_iterations = 1000):\n \"\"\" Train an RVM and evaluate its performance with the given train/test split.\"\"\"\n regressor = RVM(x_train, y_train,kernel=kernel, use_logging=False)\n regressor.train(maxiter=max_iterations)\n\n predictions = regressor.predictive_gaussian_params(x_test)\n # Root mean squared error\n error = mean_squared_error(predictions, y_test, squared=False)\n rel_vec_count = len(regressor.absolute_kept_idx)\n \n return error, rel_vec_count\n\nif __name__ == \"__main__\":\n num_repeat = 20 # We will average the results across several runs\n X, y = load_boston(return_X_y = True)\n\n errors = [] # History of root mean square errors\n vec_counts = [] # History of relevance vector counts\n\n start_time = time.time()\n for i in range(num_repeat):\n print(\"[Iteration {}]\".format(i), end='\\t')\n \n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 25)\n\n error, num_vec = evaluate_RVM(X_train, y_train, X_test, y_test, max_iterations=2000)\n print(\"Error: {}\\t# of relevance vectors: {}\".format(error, num_vec))\n\n errors.append(error)\n vec_counts.append(num_vec)\n \n print('------- Final results -------')\n print('Elapsed time:', time.time() - start_time, \"s\")\n print('[Error statistics]\\t\\taverage: {}\\tstd. dev.: {}\\tMax: {}\\tMin: {}'\n .format(stats.mean(errors), stats.stdev(errors), max(errors), min(errors)))\n print('[# of relevance vectors]\\taverage: {}\\tstd. dev.: {}\\tMax: {}\\tMin: {}'\n .format(stats.mean(vec_counts), stats.stdev(vec_counts), max(vec_counts), min(vec_counts)))\n\n ","sub_path":"2-relevance-vector-machine/boston_housing_regression.py","file_name":"boston_housing_regression.py","file_ext":"py","file_size_in_byte":2757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"186581352","text":"# Copyright Istio Authors\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Functions for manipulating the Istio environment.\"\"\"\nfrom __future__ import print_function\n\nimport contextlib\nimport logging\nimport os\nimport tarfile\nimport tempfile\nfrom typing import Any, Dict, Generator\n\nimport yaml\nimport time\n\nfrom . import consts, kubectl, resources, sh, wait\n\nDAILY_BUILD_URL = \"https://storage.googleapis.com/istio-prerelease/daily-build\"\n\n\ndef convert_archive(archive_url: str) -> str:\n \"\"\"Convert symbolic archive into archive url\n\n \"\"\"\n if archive_url.startswith(\"http\"):\n return archive_url\n\n full_name = \"{}-09-15\".format(archive_url)\n\n return \"{daily}/{full_name}/istio-{full_name}-linux.tar.gz\".format(\n daily=DAILY_BUILD_URL, full_name=full_name)\n\n\ndef set_up(entrypoint_service_name: str, entrypoint_service_namespace: str,\n archive_url: str, values: str) -> None:\n \"\"\"Installs Istio from the archive URL.\n\n Requires Helm client to be present.\n\n This downloads and extracts the archive in a temporary directory, then\n installs the resources via `helm template` and `kubectl apply`.\n \"\"\"\n archive_url = convert_archive(archive_url)\n\n print((\"Using archive_url\", archive_url))\n\n with tempfile.TemporaryDirectory() as tmp_dir_path:\n archive_path = os.path.join(tmp_dir_path, 'istio.tar.gz')\n _download(archive_url, archive_path)\n\n extracted_dir_path = os.path.join(tmp_dir_path, 'istio')\n extracted_istio_path = _extract(archive_path, extracted_dir_path)\n\n crd_path = os.path.join(extracted_istio_path, 'install',\n 'kubernetes', 'helm', 'istio-init')\n\n chart_path = os.path.join(extracted_istio_path, 'install',\n 'kubernetes', 'helm', 'istio')\n\n _apply_crds(\n crd_path,\n 'istio-init',\n consts.ISTIO_NAMESPACE)\n\n _install(\n chart_path,\n consts.ISTIO_NAMESPACE,\n intermediate_file_path=resources.ISTIO_GEN_YAML_PATH,\n values=values)\n\n _create_ingress_rules(entrypoint_service_name,\n entrypoint_service_namespace)\n\n\ndef get_ingress_gateway_url() -> str:\n ip = wait.until_output([\n 'kubectl', '--namespace', consts.ISTIO_NAMESPACE, 'get', 'service',\n 'istio-ingressgateway', '-o',\n 'jsonpath={.status.loadBalancer.ingress[0].ip}'\n ])\n return 'http://{}:{}'.format(ip, consts.ISTIO_INGRESS_GATEWAY_PORT)\n\n\ndef _download(archive_url: str, path: str) -> None:\n logging.info('downloading %s', archive_url)\n sh.run(['curl', '-L', '--output', path, archive_url])\n\n\ndef _extract(archive_path: str, extracted_dir_path: str) -> str:\n \"\"\"Extracts the .tar.gz at archive_path to extracted_dir_path.\n\n Args:\n archive_path: path to a .tar.gz archive file, containing a single\n directory when extracted\n extracted_dir_path: the destination in which to extract the contents\n of the archive\n\n Returns:\n the path to the single directory the archive contains\n \"\"\"\n with tarfile.open(archive_path) as tar:\n tar.extractall(path=extracted_dir_path)\n extracted_items = os.listdir(extracted_dir_path)\n if len(extracted_items) != 1:\n raise ValueError(\n 'archive at {} did not contain a single directory'.format(\n archive_path))\n return os.path.join(extracted_dir_path, extracted_items[0])\n\n\ndef _apply_crds(path: str, name: str, namespace: str) -> None:\n logging.info('applying crd definitions for Istio')\n sh.run_kubectl(['create', 'namespace', namespace])\n\n istio_yaml = sh.run(\n [\n 'helm',\n 'template',\n path,\n '--name',\n name,\n '--namespace',\n namespace\n ],\n check=True).stdout\n kubectl.apply_text(istio_yaml)\n\n logging.info('sleeping for 30 seconds as an extra buffer')\n time.sleep(30)\n wait.until_deployments_are_ready(namespace)\n\n\ndef _install(chart_path: str, namespace: str,\n intermediate_file_path: str, values: str) -> None:\n logging.info('installing Helm chart for Istio')\n istio_yaml = sh.run(\n [\n 'helm',\n 'template',\n chart_path,\n '--namespace',\n namespace,\n '--values',\n values\n # TODO: Use a values file, specified in the TOML configuration.\n # Consider replacing environments with a list of values files, then\n # each of those values files represents the environment. This code\n # can apply those against the chart.\n # '--set=global.proxy.resources.requests.cpu=1000m',\n # '--set=global.proxy.resources.requests.memory=256Mi',\n # '--set=global.defaultResources.requests.cpu=1000m',\n ],\n check=True).stdout\n kubectl.apply_text(\n istio_yaml, intermediate_file_path=intermediate_file_path)\n wait.until_deployments_are_ready(namespace)\n\n\n@contextlib.contextmanager\ndef _work_dir(path: str) -> Generator[None, None, None]:\n prev_path = os.getcwd()\n if not os.path.exists(path):\n os.makedirs(path)\n os.chdir(path)\n yield\n os.chdir(prev_path)\n\n\ndef _create_ingress_rules(entrypoint_service_name: str,\n entrypoint_service_namespace: str) -> None:\n logging.info('creating istio ingress rules')\n ingress_yaml = _get_ingress_yaml(entrypoint_service_name,\n entrypoint_service_namespace)\n kubectl.apply_text(\n ingress_yaml, intermediate_file_path=resources.ISTIO_INGRESS_YAML_PATH)\n\n\ndef _get_ingress_yaml(entrypoint_service_name: str,\n entrypoint_service_namespace: str) -> str:\n gateway = _get_gateway_dict()\n virtual_service = _get_virtual_service_dict(entrypoint_service_name,\n entrypoint_service_namespace)\n return yaml.dump_all([gateway, virtual_service], default_flow_style=False)\n\n\ndef _get_gateway_dict() -> Dict[str, Any]:\n return {\n 'apiVersion': 'networking.istio.io/v1alpha3',\n 'kind': 'Gateway',\n 'metadata': {\n 'name': 'entrypoint-gateway',\n },\n 'spec': {\n 'selector': {\n 'istio': 'ingressgateway',\n },\n 'servers': [{\n 'hosts': ['*'],\n 'port': {\n 'name': 'http',\n 'number': consts.ISTIO_INGRESS_GATEWAY_PORT,\n 'protocol': 'HTTP',\n },\n }],\n },\n }\n\n\ndef _get_virtual_service_dict(\n entrypoint_service_name: str,\n entrypoint_service_namespace: str) -> Dict[str, Any]:\n return {\n 'apiVersion': 'networking.istio.io/v1alpha3',\n 'kind': 'VirtualService',\n 'metadata': {\n 'name': 'entrypoint',\n },\n 'spec': {\n 'hosts': ['*'],\n 'gateways': ['entrypoint-gateway'],\n 'http': [{\n 'route': [{\n 'destination': {\n 'host':\n '{}.{}.svc.cluster.local'.format(\n entrypoint_service_name,\n entrypoint_service_namespace),\n 'port': {\n 'number': consts.SERVICE_PORT,\n },\n },\n }],\n }],\n },\n }\n\n\ndef tear_down() -> None:\n \"\"\"Deletes the Istio resources and namespace.\"\"\"\n sh.run_kubectl(['delete', '-f', resources.ISTIO_GEN_YAML_PATH])\n sh.run_kubectl(['delete', 'namespace', consts.ISTIO_NAMESPACE])\n wait.until_namespace_is_deleted(consts.SERVICE_GRAPH_NAMESPACE)\n","sub_path":"isotope/runner/istio.py","file_name":"istio.py","file_ext":"py","file_size_in_byte":8377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"383042671","text":"import json\nimport util\nfrom flask import Blueprint, request\nimport tasks\nimport validate\n\ndebug = Blueprint('debug', __name__)\n\n@debug.route('/hash-star-log', methods=['POST'])\ndef hashStarLog():\n\tjsonData = request.get_json()\n\treturn json.dumps(util.hashStarLog(jsonData)), 200\n\n@debug.route(\"/probe-star-log\", methods=['POST'])\ndef probeStarLog():\n\tjsonData = request.get_json()\n\ttid = tasks.probeStarLog.delay(jsonData)\n\treturnObject = {}\n\treturnObject['task_id'] = str(tid)\n\treturn json.dumps(returnObject)\n\n@debug.route(\"/probe-star-log-sync\", methods=['POST'])\ndef probeStarLogSync():\n\tjsonData = request.get_json()\n\ttask = tasks.probeStarLog.delay(jsonData)\n\tresult = task.get()\n\treturn json.dumps(result[1])\n\n@debug.route('/sign', methods=['POST'])\ndef sign():\n\tjsonData = request.get_json()\n\treturn util.rsaSign(jsonData['private_key'], jsonData['message']), 200\n\n@debug.route('/verify-signature', methods=['POST'])\ndef verifySignature():\n\tjsonData = request.get_json()\n\treturn 'valid' if validate.rsa(jsonData['public_key'], jsonData['signature'], jsonData['message']) else 'invalid'\n\n@debug.route('/sign-jump', methods=['POST'])\ndef signJump():\n\tjsonData = request.get_json()\n\tmessage = util.concatJump(jsonData)\n\tsignature = util.rsaSign(jsonData['private_key'], message)\n\treturn json.dumps({\n\t\t'public_key': jsonData['public_key'],\n\t\t'fleet': jsonData['fleet'],\n\t\t'key': jsonData['key'],\n\t\t'origin': jsonData['origin'],\n\t\t'destination': jsonData['destination'],\n\t\t'count': jsonData['count'],\n\t\t'signature': signature\n\t}), 200\n\n@debug.route('/verify-jump', methods=['POST'])\ndef verifyJump():\n\tjsonData = request.get_json()\n\treturn 'valid' if validate.jumpRsa(jsonData) else 'invalid'\n\n@debug.route('/unpack-difficulty', methods=['POST'])\ndef unpackDifficulty():\n\tjsonData = request.get_json()\n\treturn util.unpackBits(int(jsonData['difficulty'])), 200\n\n@debug.route('/unpack-hex-difficulty', methods=['POST'])\ndef unpackHexDifficulty():\n\tjsonData = request.get_json()\n\treturn util.unpackBits(util.difficultyFromHex(jsonData['hex_difficulty'])), 200\n\n@debug.route('/difficulty-change', methods=['POST'])\ndef difficultyChange():\n\tjsonData = request.get_json()\n\treturn str(util.calculateDifficulty(jsonData['difficulty'], jsonData['duration'])), 200\n\n@debug.route('/pack-difficulty', methods=['POST'])\ndef packDifficulty():\n\tjsonData = request.get_json()\n\treturn str(util.difficultyFromTarget(jsonData['target'])), 200","sub_path":"debug.py","file_name":"debug.py","file_ext":"py","file_size_in_byte":2428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"112259934","text":"import os\nimport asyncio\n\nfrom ..vote_content_handler import VoteContentHandler,\\\n Vote,\\\n VoteType,\\\n VoteOptions,\\\n MessageType\n\nfrom .common import TestReactionPayload, TestGame, TestChannelHandler\n\n#from content_handlers.vote_content_handler import\\\n# VoteContentHandler,\\\n# Vote,\\\n# VoteType,\\\n# VoteOptions,\\\n# MessageType\n\nVOTE_PANEL = \"\"\"\n# === Channel ID: {ch_id}============================================\n # === Header message =============================================\n {header}\n # === Emoji message ==============================================\n {emojies}\n # === Reactions message ==========================================\n {reacts}\n#====================================================================\n\"\"\"\n\nclass TestVopePanelHandler:\n name = None\n _msg_content = None\n channel_id = None\n content_handler = None\n \n def __init__(self, name, ch_id):\n self.name = name\n self.channel_id = ch_id\n\n def set_content_handler(self, cth):\n self.content_handler = cth\n\n async def publish(self, content):\n print(VOTE_PANEL.format(\n ch_id = self.channel_id,\n header = content[MessageType.TEXT_MSG],\n emojies = content[MessageType.EMOJI_MSG].text,\n reacts = ''))\n \n self._msg_content = content\n\n def update_and_publish(self, content):\n prev_header = self._msg_content[MessageType.TEXT_MSG]\n prev_emoji = self._msg_content[MessageType.EMOJI_MSG].text\n prev_reacts = self._msg_content[MessageType.EMOJI_MSG].reactions\n\n reacts = list()\n \n if content[MessageType.TEXT_MSG] != None \\\n and content[MessageType.TEXT_MSG] != prev_header:\n pass\n \n else:\n content[MessageType.TEXT_MSG] = prev_header\n\n emoji_content = content[MessageType.EMOJI_MSG]\n if emoji_content != None:\n if emoji_content.text != None and emoji_content.text != prev_emoji:\n pass\n else:\n content[MessageType.EMOJI_MSG].text = prev_emoji\n \n if emoji_content.reactions != None:\n for reaction in emoji_content.reactions:\n reacts.append(reaction)\n elif prev_reacts != None:\n content[MessageType.EMOJI_MSG].reactions = prev_reacts\n for reaction in prev_reacts:\n reacts.append(reaction)\n\n \n print(VOTE_PANEL.format(\n ch_id = self.channel_id,\n header = content[MessageType.TEXT_MSG],\n emojies = content[MessageType.EMOJI_MSG].text,\n reacts = ''.join(reacts)))\n \n self._msg_content = content\n\n def clear_reactions(self):\n self._msg_content[MessageType.EMOJI_MSG].reactions = None\n\ndef init_vch_with_mock_data():\n\n ch_h_1 = TestChannelHandler(id = 1, vph = TestVopePanelHandler('VPH#_1', 1))\n ch_h_2 = TestChannelHandler(id = 2, vph = TestVopePanelHandler('VPH#_2', 2))\n ch_h_3 = TestChannelHandler(id = 3, vph = TestVopePanelHandler('VPH#_3', 3))\n ch_h_4 = TestChannelHandler(id = 4, vph = TestVopePanelHandler('VPH#_4', 4))\n ch_h_5 = TestChannelHandler(id = 5, vph = TestVopePanelHandler('VPH#_5', 5))\n ch_h_6 = TestChannelHandler(id = 6, vph = TestVopePanelHandler('VPH#_6', 6))\n ch_h_7 = TestChannelHandler(id = 7, vph = TestVopePanelHandler('VPH#_7', 7))\n ch_h_8 = TestChannelHandler(id = 8, vph = TestVopePanelHandler('VPH#_8', 8))\n\n os.environ['TEST'] = 'YES'\n\n pid_to_txt_ch_hd_dict ={\n 11 : ch_h_1,\n 12 : ch_h_2,\n 13 : ch_h_3,\n 14 : ch_h_4,\n 15 : ch_h_5,\n 16 : ch_h_6,\n 17 : ch_h_7,\n 18 : ch_h_8 \n }\n \n pid_to_emoji = {\n 11 : ':one:',\n 12 : ':two:',\n 13 : ':three:',\n 14 : ':four:',\n 15 : ':five:',\n 16 : ':six:',\n 17 : ':seven:',\n 18 : ':eight:' \n }\n\n t_game = TestGame(pid_to_txt_ch_hd_dict, pid_to_emoji)\n ch_hndlrs = [ch_h_1,\n ch_h_2,\n ch_h_3,\n ch_h_4,\n ch_h_5,\n ch_h_6,\n ch_h_7,\n ch_h_8\n ] \n\n vch = VoteContentHandler(t_game, ch_hndlrs, 3)\n\n return vch\n\nasync def tc1():\n print ('*************** Executing TC 1 ********')\n\n vch = init_vch_with_mock_data()\n\n vpids_to_vote_opts ={\n 11 : VoteOptions.YES_AND_NO,\n 12 : VoteOptions.YES_AND_NO,\n 13 : VoteOptions.YES_AND_NO,\n 14 : VoteOptions.YES_AND_NO,\n 15 : VoteOptions.YES_AND_NO,\n 16 : VoteOptions.YES_AND_NO,\n 17 : VoteOptions.YES_AND_NO,\n 18 : VoteOptions.YES_AND_NO\n }\n\n await vch.initial_render() \n\n party_emojies = ['⭕', '⛔', '🅰️', 'Ⓜ️']\n\n vch.initiate_vote(5, \n vpids_to_vote_opts, \n VoteType.PARTY_APPROVING,\n party_emojies)\n\n await asyncio.sleep(0.05)\n await vch.handle_reaction(TestReactionPayload(VoteContentHandler.NO_VOTE, 1))\n\n await asyncio.sleep(0.05)\n await vch.handle_reaction(TestReactionPayload(VoteContentHandler.YES_VOTE, 2))\n\n await asyncio.sleep(0.05)\n await vch.handle_reaction(TestReactionPayload(VoteContentHandler.NO_VOTE, 3))\n\n await asyncio.sleep(0.05)\n await vch.handle_reaction(TestReactionPayload(VoteContentHandler.NO_VOTE, 4))\n\n await asyncio.sleep(0.05)\n await vch.handle_reaction(TestReactionPayload(VoteContentHandler.NO_VOTE, 5))\n \n await asyncio.sleep(0.05)\n await vch.handle_reaction(TestReactionPayload(VoteContentHandler.YES_VOTE, 6))\n \n await asyncio.sleep(0.05)\n await vch.handle_reaction(TestReactionPayload(VoteContentHandler.NO_VOTE, 7))\n \n await vch.handle_reaction(TestReactionPayload(VoteContentHandler.YES_VOTE, 8))\n\n\nasync def tc2():\n print ('*************** Executing TC 2 ********')\n\n\n vch = init_vch_with_mock_data()\n\n await vch.initial_render() \n\n vpids_to_vote_opts ={\n 17 : VoteOptions.ONLY_YES,\n }\n\n vch.initiate_vote(3, \n vpids_to_vote_opts, \n VoteType.PARTY_FORMING,\n [])\n\n\n vch.update_vote_pannels(['⭕'])\n vch.update_vote_pannels(['⭕', '⛔'])\n vch.update_vote_pannels(['⭕', '⛔', '🅰️'])\n vch.update_vote_pannels(['⭕', '⛔', '🅰️', 'Ⓜ️'])\n\n vch.start_vote()\n\n vch.update_vote_pannels(['⭕', 'Ⓜ️', '🅰️'])\n\n await vch.handle_reaction(TestReactionPayload(VoteContentHandler.YES_VOTE, 7))\n \nasync def tc3():\n print ('*************** Executing TC 3 ********')\n\n vch = init_vch_with_mock_data()\n\n await vch.initial_render() \n\n vpids_to_vote_opts ={\n 17 : VoteOptions.ONLY_YES,\n 13 : VoteOptions.YES_AND_NO,\n 14 : VoteOptions.ONLY_YES,\n }\n\n vch.initiate_vote(3, \n vpids_to_vote_opts, \n VoteType.MISSION_RESULT,\n [],\n 2)\n\n\n await vch.handle_reaction(TestReactionPayload(VoteContentHandler.YES_VOTE, 7))\n await vch.handle_reaction(TestReactionPayload(VoteContentHandler.NO_VOTE, 3))\n await vch.handle_reaction(TestReactionPayload(VoteContentHandler.YES_VOTE, 4))\n \n\nasync def tc4():\n print ('*************** Executing TC 4 ********')\n\n vch = init_vch_with_mock_data()\n\n await vch.initial_render() \n\n vpids_to_vote_opts ={\n 17 : VoteOptions.ONLY_YES,\n }\n\n vch.initiate_vote(1, \n vpids_to_vote_opts, \n VoteType.MERLIN_HUNT,\n [])\n\n vch.update_vote_pannels(['⭕'])\n vch.update_vote_pannels(['⛔'])\n vch.update_vote_pannels(['🅰️'])\n vch.update_vote_pannels(['Ⓜ️'])\n\n await vch.handle_reaction(TestReactionPayload(VoteContentHandler.YES_VOTE, 7))\n\n\ndef run_test():\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n loop.run_until_complete(tc1())\n loop.run_until_complete(tc2())\n loop.run_until_complete(tc3())\n loop.run_until_complete(tc4())\n ","sub_path":"Avalon_Discord/core/content_handlers/test/vote_test.py","file_name":"vote_test.py","file_ext":"py","file_size_in_byte":8311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"567447722","text":"# Implementation of math and random number functions for rat simulation\n#\n# The random number generator isn't especially good, but it provides\n# control over the seed, and it can be easily translated into other\n# languages.\n\n# CONSTANTS\n# These are the parameters used in the C++ minstd_rand random number\n# generator It is based on a class of linear congruential generators\n# due to D. H. Lehmer The parameters were suggested by Stephen K. Park\n# and Keith W. Miller, and Paul K. Stockmeyer in CACM, July 1988.\n\nGROUPSIZE = 2147483647 # 2^31 - 1, a prime number\nMVAL = 48271\nVVAL = 16807\nINITSEED = 418\nDEFAULTSEED = 618\n\nimport math\n\n# Some installations don't support numpy library.\n# Import them only if needed\n\nspecialImported = False\n\ndef importSpecial():\n global specialImported, np\n if not specialImported:\n import numpy as np\n specialImported = True\n\nclass RNG:\n seed = INITSEED\n\n # Initialize with either single int as seed, or with seqence of ints\n def __init__(self, seeds = []):\n self.reseed(seeds)\n\n def reseed(self, seeds = []):\n self.seed = INITSEED\n for s in seeds:\n self.next(s)\n\n def next(self, x = 0):\n val = ((x+1) * VVAL + self.seed * MVAL) % GROUPSIZE\n self.seed = val\n return val\n\n # Return random float, distributed uniformly in interval [0, upperLimit)\n def randFloat(self, upperLimit = 1.0):\n oldseed = self.seed\n val = self.next()\n rval = (float(val)/GROUPSIZE) * upperLimit\n return rval\n\n # Convert number on scale [0, 1.0] to one between two bounds\n # but exponentially distributed\n def expandExp(self, value, lowerLimit = 0.5, upperLimit = 2.0):\n lrange = math.log(upperLimit / lowerLimit)\n evalue = value * lrange\n return math.exp(evalue) * lowerLimit\n\n # Return random float, distributed exponentially in interval [lowerLimit, upperLimit)\n def randExpFloat(self, lowerLimit = 0.5, upperLimit = 2.0):\n return self.expandExp(self.randFloat(1.0))\n\n # Return random integer, distributed uniformly in interval [lower, upper]\n def randInt(self, lower, upper):\n rval = self.randFloat()\n return lower + int(rval * (upper + 1 - lower))\n\n # Choose maxSample elements (without replacement) from sequence at random\n # and return as list.\n def sample(self, seq, maxSample):\n if len(seq) <= maxSample:\n return seq\n result = [0] * maxSample\n swaps = [0] * maxSample\n for i in range(maxSample):\n w = self.randFloat()\n idx = i + int(w * float(len(seq)-i))\n swaps[i] = idx\n result[i] = seq[idx]\n seq[idx], seq[i] = seq[i], seq[idx]\n # Use list of swaps to restore input sequence\n for i in range(maxSample-1, -1, -1):\n idx = swaps[i]\n seq[idx], seq[i] = seq[i], seq[idx]\n return result\n\n # Return list containing all elements of seq in random order\n # Much faster than using sample()\n def permute(self, seq):\n importSpecial()\n n = len(seq)\n a = np.array(range(n))\n while (n > 1):\n idx = self.randInt(0, n-1)\n a[idx], a[n-1] = a[n-1], a[idx]\n n -= 1\n result = []\n for i in a:\n result.append(seq[i])\n return result\n\n # Given a sequence of non-negative weights, choose\n # number between 0 and n-1 based on those weights\n # where n = len(weights)\n def weightedIndex(self, weights):\n sum = reduce(lambda x, y: x + y, weights)\n cval = self.randFloat(sum)\n psum = 0.0\n for idx in range(len(weights)):\n psum += weights[idx]\n if cval < psum:\n return idx\n\n\n# Parameters for computing the weights that guide next-move selection\nCOEFF = 0.4\nOPTVAL = 1.5\nBLIMIT = 1.0\nL2E = math.log(math.e, 2)\n\ndef mweight(val, optval = OPTVAL):\n\n arg = 1.0 + COEFF * (val - optval)\n if arg <= 0.0:\n return 0.0\n log = math.log(arg) * L2E\n denom = 1.0 + log * log\n return 1.0/denom\n\n# Compute imbalance between local and remote values\n# Result < 0 when lcount > rcount and > 0 when lcount < rcount\ndef imbalance(lcount, rcount):\n if lcount == rcount:\n return 0\n if lcount == 0:\n return BLIMIT\n if rcount == 0:\n return -BLIMIT\n b = math.log10(float(rcount)/float(lcount))\n return max(min(b, BLIMIT), -BLIMIT)\n\n# Given list of values \n# (each of which is the number of rats at a node divided by the load factor)\n# compute weights for nodes and select index of one\ndef chooseMove(rng, vals, optvals):\n weights = [mweight(vals[i],optvals[i]) for i in range(len(vals))]\n return rng.weightedIndex(weights)\n","sub_path":"code/rutil.py","file_name":"rutil.py","file_ext":"py","file_size_in_byte":4754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"538486854","text":"# Time Complexity : Add - O(N)\n# Space Complexity :O(h), h being the height of tree\n# Did this code successfully run on Leetcode : Yes\n# Any problem you faced while coding this : No\n'''\n1. I have used used DFS to calcluate parent and level of both x and y\n2. Then compare the result in main function\n'''\n\nclass Solution:\n \n def __init__(self):\n \n self.xLevel = None\n self.yLevel = None\n \n self.xParent = None\n self.yParent = None\n \n def isCousins(self, root: TreeNode, x: int, y: int) -> bool:\n\n self._helper(root, -1, None, x, y)\n \n if self.xLevel > 0 and self.yLevel > 0:\n if self.xLevel == self.yLevel and self.xParent != self.yParent:\n return True\n \n return False\n \n #DFS\n def _helper(self, root, level, parent, x,y):\n \n if root is None:\n return\n \n level += 1\n \n if root.val == x:\n self.xLevel = level\n self.xParent = parent\n \n elif root.val == y:\n self.yLevel = level\n self.yParent = parent\n \n \n self._helper(root.left, level, root, x, y)\n self._helper(root.right, level, root, x, y)\n ","sub_path":"cousins_BT.py","file_name":"cousins_BT.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"310350920","text":"class Dog:\n def __init__(self, breed, nickname, color, wool_length, age):\n self.breed = breed\n self.nickname = nickname\n self.color = color\n self.wool_length = wool_length\n self.age = age\n\n def general_description(self):\n print(self.breed, self.nickname, self.color, self.wool_length, self.age)\n\n\n def sleep(self):\n print('Snoooring')\n\n def eat(self):\n print('Chafk-Chafk')\n\n def bark(self):\n print('Whoaaff whooaaaff whoaaf')\n\n def hunting(self):\n print('Helps during the hunting activity')\n\n def guard(self):\n print('\"Home guard\" function')\n\n def be_nice(self):\n print('\"Being nice\" function')\n\n\n def tore_wolves_apart(self):\n print('Keeps wolves away')\n\n\nclass Wolfdog(Dog):\n def __init__(self, breed, nickname, color, wool_length, age, scars):\n super().__init__(breed, nickname, color, wool_length, age)\n self.scars = scars\n\n\n\n\n\n\n\ndog1 = Dog('BullDog', 'Fury', 'Black', 'short', '3')\ndog2 = Dog('Spaniel', 'Jessie', 'Brawn', 'medium', '2')\ndog3 = Dog('Beagle', ' Noah', 'Brawn', 'medium', '4')\nwolfdog1 = Wolfdog('Beast', 'Hell', 'Black', 'Short', '3', 'avaliable')\n\n\n\nprint('------------BulldogZ main fuctions------------')\n\nprint(dog1.breed, dog1.color, dog1.wool_length, dog1.age, dog1.nickname)\ndog1.bark()\ndog1.guard()\ndog1.sleep()\ndog1.eat()\n\nprint('------------SpanielZ main fuctions------------')\n\nprint(dog2.breed, dog2.color, dog2.wool_length, dog2.age, dog2.nickname)\ndog2.bark()\ndog2.hunting()\ndog2.sleep()\ndog2.eat()\n\nprint('------------BeagleZ main fuctions------------')\n\nprint(dog3.breed, dog3.color, dog3.wool_length, dog3.age, dog3.nickname)\ndog3.bark()\ndog3.be_nice()\ndog3.sleep()\ndog3.eat()\n\nprint('------------WolfdogZ main fuctions------------')\n\nprint(wolfdog1.breed, wolfdog1.color, wolfdog1.wool_length, wolfdog1.age, wolfdog1.nickname, wolfdog1.scars)\nwolfdog1.bark()\nwolfdog1.eat()\nwolfdog1.tore_wolves_apart()\n\n\n\n\n","sub_path":"Class_dog.py","file_name":"Class_dog.py","file_ext":"py","file_size_in_byte":1985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"190873256","text":"from datetime import datetime\nfrom queue import Queue\n\nimport boto3\nfrom cliff import command\n\nfrom .token_bucket import TokenBucket\n\nlogs = boto3.client(\"logs\")\n\n\nclass StreamPurge(command.Command):\n BUCKETS = {\n \"logs\": TokenBucket(8, 1), # only 9 b/c of paginated describe call\n }\n QUEUE = Queue()\n\n def get_parser(self, prog_name):\n parser = super().get_parser(prog_name)\n parser.add_argument(\"log_group_name\")\n return parser\n\n def take_action(self, parsed_args):\n lgn = parsed_args.log_group_name\n pg = logs.get_paginator(\"describe_log_streams\")\n total_purged = 0\n for batch in pg.paginate(logGroupName=lgn, orderBy=\"LastEventTime\"):\n for ls in batch[\"logStreams\"]:\n self.BUCKETS[\"logs\"].get()\n logs.delete_log_stream(\n logGroupName=lgn, logStreamName=ls[\"logStreamName\"]\n )\n last_dt = datetime.fromtimestamp(ls[\"lastEventTimestamp\"] / 1000)\n count = len(batch[\"logStreams\"])\n total_purged += count\n print(f\"{count} / {total_purged}: {last_dt:%Y-%m-%d %H:%M:%S}\")\n","sub_path":"ardev/cloudwatch_purge.py","file_name":"cloudwatch_purge.py","file_ext":"py","file_size_in_byte":1160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"526943683","text":"#This program reads the parallax, V magnitude, and stellar mass\n#for each star in the RECONS catalogue and,\n#assuming that each star hosts a single planet of Jupiter mass and 5 year period,\n#returns a list of the stars' number in catalogue, mass, distance, V_magnitude,\n#astrometric signal, and signal to noise ratio, sorted in descending order\n#of signal to noise ratio. The RECONS catalogue has no V - Ic data,\n#so for this quantity each star is assigned a random value between -1 and 2\n\nfrom string import *\nfrom numpy import *\nfrom random import uniform\n\nfin = open(\"catalogues/RECONS_cat.txt\",\"r\")\n\n#Get the number of stars\nnum_stars = sum(1 for line in fin)\nfin.seek(0)\n\n#Define planetary parameters\n#The ones most likely to be observed are Jupiter-like\n\nP = 1.0 #Period in years\nMp = 9.5427e-4 #Jupiter mass in solar masses\n\n#Define stellar parameters\n\nd = zeros(num_stars,float) #distance of system from Earth, in pc\nV = zeros(num_stars,float) #V magnitude\nVIc = zeros(num_stars,float) #V - Ic\nG = zeros(num_stars,float) #G magnitude\na = zeros(num_stars,float) #Semi-major axis of planetary orbit, in AU\nalpha = zeros(num_stars,float) #Signal in microarcseconds\nz = zeros(num_stars,float) #Used to calculate noise\nsigma = zeros(num_stars,float) #Noise in microarcseconds\nSN = zeros(num_stars,float)#Signal to noise ratio\nlabel = zeros(num_stars,int) #To identify the star\nparallax = zeros(num_stars,float) #Get distance from parallax\nM_star = zeros(num_stars, float) #Mass of host star\n\n#Initialise the final list of stars\nstar_list = []\n\n#Get the parameters\n\nfor i in range(num_stars):\n x = fin.readline()\n y = x.split()\n\n #The catalogue is missing values - reject such stars (as it is difficult\n #to identify the missing parameters)\n \n if len(y) == 4:\n \n label[i] = y[0]\n parallax[i] = y[1]\n d[i] = 1.0/parallax[i]\n V[i] = y[2]\n M_star[i] = y[3]\n\n #print d[i]\n \n #Give star a random V_Ic between -1 and 2\n VIc[i] = uniform(-1.0, 2.0)\n\n #print M_star[i]\n \n #Compute the G-magnitude for each star\n G[i] = V[i] - 0.0257 - 0.0924*VIc[i] - 0.1623*VIc[i]*VIc[i] + 0.0090*VIc[i]*VIc[i]*VIc[i]\n\n #print G[i]\n \n #GAIA is magnitude limited - 6<=G<=20\n\n if (G[i] > 6.0 and G[i] < 20.0):\n \n #Use Kepler's law to deduce the semi-major axis of the planet's orbit\n a[i] = (P*P*M_star[i])**(1.0/3.0)\n #print a[i]\n\n #Compute and print the expected signal in microarcseconds\n alpha[i] = 1.0e6*Mp*a[i]/(M_star[i]*d[i])\n #print alpha[i]\n\n #Obtain the noise: first, compute the value of z\n if G[i] > 12:\n z = 10**(0.4*(G[i]-15.0))\n else:\n z = 10**(-1.2)\n\n #Then, get the noise\n sigma[i] = ((9.3 + 658.1*z + 4.568*z*z)**0.5)*(0.986 + (1 - 0.986)*VIc[i])\n\n #print sigma[i]\n \n #Calculate the signal to noise ratio\n SN[i] = alpha[i]/sigma[i]\n\n #print SN[i]\n\n #Add the star to a list of the labels, masses, distances,\n #V magnitudes, signals and signal to noise ratios of each star\n star_list.append([label[i], M_star[i], d[i], V[i], alpha[i], SN[i]])\n\nfin.close()\n\n#Sort the list in descending S/N\n\nstar_list.sort(key=lambda x: x[5], reverse=True)\n#print star_list\n\n#Print the list to file\nfout = open(\"sorted_lists/RECONS_sorted_list_period%d.txt\" % int(100*P), \"w\")\nsavetxt(fout, star_list, \"%d %.2f %.6f %.2f %.10f %.10f\", \" \", \"\\n\")\nfout.close()\n\n\n \n","sub_path":"RECONS_signal.py","file_name":"RECONS_signal.py","file_ext":"py","file_size_in_byte":3636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"485347072","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Mar 22 12:11:12 2019\r\n\r\n@author: javie\r\n\"\"\"\r\n# Dash Install \r\n# pip install dash==0.39.0 # The core dash backend\r\n# pip install dash-daq==0.1.0 # DAQ components (newly open-sourced!)\r\n\r\nimport plotly_express as px\r\nimport dash\r\nimport dash_html_components as html\r\nimport dash_core_components as dcc\r\nfrom dash.dependencies import Input, Output\r\n\r\ntips = px.data.tips()\r\ncol_options = [dict(label=x, value=x) for x in tips.columns]\r\ndimensions = [\"x\", \"y\", \"color\", \"facet_col\", \"facet_row\"]\r\n\r\napp = dash.Dash(\r\n __name__, external_stylesheets=[\"https://codepen.io/chriddyp/pen/bWLwgP.css\"]\r\n)\r\n\r\napp.layout = html.Div(\r\n [\r\n html.H1(\"Demo: Plotly Express in Dash with Tips Dataset\"),\r\n html.Div(\r\n [\r\n html.P([d + \":\", dcc.Dropdown(id=d, options=col_options)])\r\n for d in dimensions\r\n ],\r\n style={\"width\": \"25%\", \"float\": \"left\"},\r\n ),\r\n dcc.Graph(id=\"graph\", style={\"width\": \"75%\", \"display\": \"inline-block\"}),\r\n ]\r\n)\r\n\r\n\r\n@app.callback(Output(\"graph\", \"figure\"), [Input(d, \"value\") for d in dimensions])\r\ndef make_figure(x, y, color, facet_col, facet_row):\r\n return px.scatter(\r\n tips,\r\n x=x,\r\n y=y,\r\n color=color,\r\n facet_col=facet_col,\r\n facet_row=facet_row,\r\n height=700,\r\n )\r\n\r\n\r\napp.run_server(debug=True)","sub_path":"businessCycles/exploreData/Python/dash_plotly_express_ex.py","file_name":"dash_plotly_express_ex.py","file_ext":"py","file_size_in_byte":1416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"164426344","text":"# from __future__ import print_function\nimport numpy as np\nimport gym\nfrom scipy.special import entr\nfrom scipy.stats import entropy\nimport gym_gridworlds\n\nfrom scipy import optimize\nimport matplotlib.pyplot as plt \n\n\ndef get_constraints_matrix(total_constraints, total_vars, vars_per_constraint):\n\tconstraints_mat = []\n\tfor i in range(0, total_vars, vars_per_constraint):\n\t\trow = list(np.zeros(total_vars))\n\t\tfor j in range(i, i+4):\n\t\t\trow[j] = 1\n\t\tconstraints_mat.append(row)\n\n\treturn constraints_mat\n\ndef pi_constraints(pi):\n\ttotal_constraints = total_states\n\tvars_per_constraint = total_actions\n\ttotal_vars = total_states * total_actions\n\tb = np.ones(total_constraints)\n\tconstraints_mat = get_constraints_matrix(total_constraints, total_vars, vars_per_constraint)\n\tA = np.array(constraints_mat)\n\tret = A.dot(pi) - b\n\treturn ret\n\ndef get_occupancy_measure(pi_k, gamma):\n\ttotal_states = env.observation_space.n\n\ttotal_actions = env.action_space.n\n\tb0 = np.ones(total_states) / total_states\n\tc = np.zeros(total_states)\n\tt_mat = np.zeros([total_states, total_states])\n\tfor j in range(total_states):\n\t\tfor s in range(total_states):\n\t\t\tval = 0\n\t\t\tfor a in range(total_actions):\n\t\t\t\tval += env.P[a,s,j] * pi_k[s,a]\n\t\t\tc[s] = val\n\t\tt_mat[j] = c\n\tI = np.eye(total_states)\n\tA = I - gamma * t_mat\n\tx = np.linalg.solve(A, b0)\n\n\tx_sa = np.zeros([total_states, total_actions])\n\tfor s in range(total_states):\n\t\tfor a in range(total_actions):\n\t\t\tx_sa[s,a] = x[s] * pi_k[s,a]\n\n\treturn x, x_sa\n\ndef pol_loss(pi, pi_k, q_k, ds_k, dsa_k, alpha):\n\tterm1, term2 = 0.0, 0.0\n\ti, j = 0, 0\n\tfor s in range(total_states):\n\t\tH_k = entr(pi_k[s]).sum()\n\t\tH = entr(pi[j:j+4]).sum()\n\t\tlogH = 0 if (H<=0 or np.isinf(H) or np.isnan(H)) else np.log(H)\n\t\tlogH_k = 0 if (H_k<=0 or np.isinf(H_k) or np.isnan(H_k)) else np.log(H_k)\n\t\tterm2 += alpha * (logH - logH_k) * ds_k[s] * H_k \n\t\tj += 4\n\t\tfor a in range(total_actions):\n\t\t\tlogpi = 0 if pi[i]==0 else np.log(pi[i])\n\t\t\tlogpi_k = 0 if pi_k[s][a]==0 else np.log(pi_k[s][a])\n\t\t\tterm1 += (logpi - logpi_k) * dsa_k[s][a] * (q_k[s][a] + alpha * H_k)\n\t\t\ti += 1\n\t\n\treturn -(term1 + term2)\n\ndef pol_solve(pi_k, q_k, gamma, alpha, disp_val): \t\n\ttotal_vars = total_states * total_actions\n\ttotal_constraints = total_states\n\tvars_per_constraint = total_actions \n\tds_k, dsa_k = get_occupancy_measure(pi_k, gamma)\n\t# pi0 = pi_k\t\n\tpi0 = pi_k.flatten()\n\tcons = {'type':'eq', 'fun':pi_constraints}\n\tbnds = [(0+1e-8, 1+1e-8)] * total_vars\n\t\n\tres = optimize.minimize(pol_loss, x0=pi0, args=(pi_k, q_k, ds_k, dsa_k, alpha), method='SLSQP', constraints=cons, bounds=bnds, options={'disp':disp_val})\n\t\n\tobj_val = pol_loss(res.x, pi_k, q_k, ds_k, dsa_k, alpha)\n\n\tnew_pi = np.reshape(res.x, (total_states, total_actions))\n\n\treturn new_pi, obj_val\n\ndef kl_div(pi, q_k, alpha):\n\tentropy_term = entr(pi).sum()\n\t# p2 = np.exp(q_k) #/ np.sum(np.exp(q_k))\n\t# kl = entropy(pi, p2)\n\tcross_entropy_term = - np.sum(pi * (1./alpha) * q_k)\n\treturn (-entropy_term + cross_entropy_term)\n\ndef sum_pi_cons(pi):\n\treturn (np.sum(pi) - 1.0)\n\ndef soft_pol_impr(pi_k, q_k, gamma, alpha, disp_val):\n\tnew_pi = np.zeros([total_states, total_actions])\n\tbnds = [(0+1e-8, 1+1e-8)] * total_actions\n\tcons = {'type':'eq', 'fun':sum_pi_cons}\n\n\ttotal_kl_obj = 0\n\tfor s in range(total_states):\n\t\tres = optimize.minimize(kl_div, x0=pi_k[s], args=(q_k[s], alpha), method='SLSQP', constraints=cons, bounds=bnds, options={'disp':disp_val})\n\t\tnew_pi[s] = res.x\n\t\ttotal_kl_obj += kl_div(new_pi[s], q_k, alpha) \n\t\n\treturn new_pi, total_kl_obj\n\n######## can be formulated as a LP ###############\ndef soft_pol_eval(pi, gamma, eps, alpha):\n\tv = np.zeros(total_states)\n\tq = np.zeros([total_states, total_actions])\n\n\t# while True:\n\t# \tdelta = 0\n\t# \tfor s in range(total_states):\n\t# \t\tv_val = 0\n\t# \t\tlogpi = np.zeros(total_actions)\n\t# \t\tfor a in range(total_actions):\n\t# \t\t\tlogpi[a] = 0 if pi[s,a]<=0 else np.log(pi[s,a])\n\t# \t\t\tq_val = 0\n\t# \t\t\tfor (s_, p) in enumerate(env.P[a][s]):\n\t# \t\t\t\tr = 1 if s==0 else 0\n\t# \t\t\t\tq_val += p * (r + gamma * v[s_])\n\t# \t\t\tq[s][a] = q_val\n\t\t\t\n\t# \t\tfor a in range(total_actions):\n\t# \t\t\tv_val += pi[s,a] * (q[s][a] - alpha * logpi[a])\n\n\t# \t\tdelta = max(delta, np.abs(v_val - v[s]))\n\n\t# \t\tv[s] = v_val\n\t\t\n\t# \tif delta < eps:\n\t# \t\tbreak\n\n\t# return q, v\n\n\twhile True:\n\t\tdelta = 0\n\t\tfor s in range(total_states):\n\t\t\tfor a in range(total_actions):\n\t\t\t\tq_val = 0\n\t\t\t\tfor (s_, p) in enumerate(env.P[a][s]):\n\t\t\t\t\tr = 1 if s==0 else 0\n\t\t\t\t\tq_val += p * (r + gamma * v[s_])\n\t\t\t\tq[s][a] = q_val\n\t\t\t\t\n\t\tfor s in range(total_states):\n\t\t\tv_val = 0\n\t\t\tfor a in range(total_actions):\n\t\t\t\tlogpi = 0 if pi[s,a]<=0 else np.log(pi[s,a])\n\t\t\t\tv_val += pi[s,a] * (q[s][a] - alpha * logpi)\n\t\t\tdelta = max(delta, np.abs(v_val - v[s]))\n\t\t\tv[s] = v_val\n\n\t\tif delta < eps: \n\t\t\tbreak\n\n\treturn q, v\n\n\ndef get_mdp_obj(pi, gamma, alpha):\n\tmdp_obj = 0\n\tds, dsa = get_occupancy_measure(pi, gamma)\n\tfor s in range(total_states):\n\t\tH = entr(pi[s]).sum()\n\t\tr = 1 if s==0 else 0\n\t\tfor a in range(total_actions):\n\t\t\tmdp_obj += dsa[s][a] * (r + alpha * H)\n\n\treturn mdp_obj\n\ndef solve(gamma, n_itr, tol_norm, tol_obj, alpha, eps, disp_val, soft_pol_impr_flag, print_always):\n\tpi = np.ones([total_states, total_actions])/total_actions \n\tb0 = np.ones(total_states) / total_states\n\tpol_obj_vals = []\n\tmdp_obj_vals = []\n\toldpol_obj_val = 1e5\n\tfor itr in range(n_itr):\n\t\t\n\t\tq, v = soft_pol_eval(pi, gamma, eps, alpha)\n\t\t\n\t\tif soft_pol_impr_flag: #false by default\n\t\t\tnew_pi, pol_obj_val = soft_pol_impr(pi, q, gamma, alpha, disp_val)\n\t\telse:\n\t\t\tnew_pi, pol_obj_val = pol_solve(pi, q, gamma, alpha, disp_val)\n\t\t\n\t\tmdp_obj = get_mdp_obj(pi, gamma, alpha)\n\n\t\tif print_always:\n\t\t\tprint(\"Iteration {}\".format(itr))\n\t\t\t# print(\"pi: \",pi)\n\t\t\t# print(\"new_pi: \", new_pi)\n\t\t\tprint(\"DC objective value: \", pol_obj_val)\n\t\t\tprint(\"MDP objective value (from occupancy measure): \", mdp_obj)\n\t\t\t# print(\"MDP objective value (from value function): \", np.sum(v * b0))\n\t\t\tprint(\"\\n\")\n\n\t\tpol_obj_vals.append(pol_obj_val)\n\t\tmdp_obj_vals.append(mdp_obj)\n\t\t\n\t\tif itr%(n_itr/10)==0 and not print_always:\n\t\t\tprint(\"Iteration {}/{}\".format(itr, n_itr))\n\n\t\tif abs(pol_obj_val - oldpol_obj_val) <= tol_obj:\n\t\t\tprint('Converged at iteration {}'.format(itr))\n\t\t\tbreak\n\t\toldpol_obj_val = pol_obj_val\n\n\t\t# if np.linalg.norm(pi-new_pi) <= tol_norm:\n\t\t# \tprint('Converged at itrsode %d' %(itr+1))\n\t\t# \tbreak\n\t\t\n\t\tpi = np.copy(new_pi)\n\n\treturn pi, q, v, pol_obj_vals, mdp_obj_vals\n\n\ndef best_pol_directions(pi):\n\td = {}\n\td[0] = 'UP'\n\td[1] = 'RIGHT'\n\td[2] = 'DOWN'\n\td[3] = 'LEFT'\n\tdirections = []\n\tfor s in range(total_states):\n\t\ta = np.argmax(pi[s])\n\t\tdirections.append(d[a])\n\n\treturn directions\n\ndef plotting_util(pol_obj_vals, figname, figdir, xlabel, ylabel, dosave):\n\tfig, ax = plt.subplots()\n\tax.plot(pol_obj_vals)\n\tax.set_xlabel(xlabel)\n\tax.set_ylabel(ylabel)\n\tif dosave:\n\t\tplt.savefig(figdir+figname)\n\ndef main(args):\n\topt_pi, opt_q, opt_v, pol_obj_vals, mdp_obj_vals = solve(args.gamma, args.n_itr, args.tol_norm, args.tol_obj, \n\t\tfloat(args.alpha), args.eps, args.disp_val, args.soft_pol_impr, args.print_always)\n\t\n\tprint(\"OPT POLICY: \", opt_pi)\n\tprint(\"OPT Q: \", opt_q)\n\tprint(\"OPT V: \", opt_v)\n\tprint(\"\\n\")\n\n\tbest_directions = best_pol_directions(opt_pi)\n\tfor (s, d) in enumerate(best_directions):\n\t\tprint(\"State {} : {}\".format(s,d))\n\n\tif float(args.alpha) == 0:\n\t\tfigname_dc = \"dc_obj_noH.png\"\n\t\tfigname_mdp = \"mdp_obj_noH.png\"\n\telse:\n\t\tif args.soft_pol_impr:\n\t\t\tfigname_dc = \"kl_obj_H.png\"\n\t\t\tfigname_mdp = \"mdp_obj_H_kl.png\"\n\t\telse:\n\t\t\tfigname_dc = \"dc_obj_H.png\"\n\t\t\tfigname_mdp = \"mdp_obj_H.png\"\n\t\n\tplotting_util(pol_obj_vals, figname_dc, args.figdir, xlabel=\"Number of iterations\", ylabel=\"DC (policy) objective\", dosave=True)\n\tplotting_util(mdp_obj_vals, figname_mdp, args.figdir, xlabel=\"Number of iterations\", ylabel=\"MDP objective\", dosave=True)\n\nif __name__ == '__main__':\n\timport argparse\n\tparser = argparse.ArgumentParser(description=\"DC-Entropy in tabular setting (15 state gridworld)\")\n\tparser.add_argument('--env', help=\"Environment\", type=str, default='Gridworld-v0')\n\tparser.add_argument('--n_itr', help=\"Total number of iterations for the program\", default=100)\n\tparser.add_argument('--gamma', help=\"Discount factor\", default=0.90)\n\tparser.add_argument('--tol_norm', help=\"Stopping criterion for the program using policy norm\", default=1e-3)\n\tparser.add_argument('--tol_obj', help=\"Stopping criterion for the program using DC objective\", default=1e-3)\n\tparser.add_argument('--alpha', help=\"Temperature for entropy\", default=1)\n\tparser.add_argument('--eps', help=\"Stopping criterion for policy evaluation\", default=1e-5)\n\tparser.add_argument('--disp_val', help=\"Display DC objective value/status\", default=False)\n\tparser.add_argument('--soft_pol_impr', help=\"Do soft policy improvement using KL divergence\", default=False)\n\tparser.add_argument('--print_always', help=\"Print objective values at each iteration\", default=True)\n\tparser.add_argument('--figdir', help=\"Directory to save plots (as a string)\", required=True)\n\n\targs = parser.parse_args()\n\tenv = gym.make(args.env)\n\ttotal_states, total_actions = env.observation_space.n, env.action_space.n\n\tnp.random.seed(0)\n\n\tmain(args)\n","sub_path":"tab_dc_entropy3.py","file_name":"tab_dc_entropy3.py","file_ext":"py","file_size_in_byte":9111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"147778493","text":"#!/usr/bin/env python\n\nimport rospy\nimport time\nfrom std_msgs.msg import Float64\nimport pigpio\n\npi = pigpio.pi()\nbus = pi.i2c_open(1, 0x76)\n\nadc_address = 0x76\n\nchannel_multipliers = [1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1]\n\npublishers = []\n\nvref = 5\nmax_reading = 8388608.0\n\ni2c_read_delay = 0.15\ni2c_read_bytes = 0x06\n\ndef read_adc(adc_address, adc_channel):\n time.sleep(i2c_read_delay)\n #print(adc_channel)\n pi.i2c_write_byte(bus, (0b10110000) | (adc_channel & 0x0F))\n \n time.sleep(i2c_read_delay)\n\n (count, reading) = pi.i2c_read_device(bus, i2c_read_bytes)\n valor = ( ((reading[0]&0x3F) << 16) + (reading[1] << 8) + (reading[2]&0xE0) )\n volts = valor * vref / max_reading\n print(volts)\n\n if((reading[0] & 0b11000000) == 0b11000000):\n #handle read error\n pass\n\n return volts\n\n\ndef talker():\n for i in range(0, 16): #supports 16\n pub = rospy.Publisher(\"ADC_Channels/CH\" + str(i), Float64, queue_size=10)\n publishers.append(pub)\n\n rospy.init_node('LTC2497_ADC_HAT')\n\n frequency = rospy.get_param(\"~frequency\", 1/i2c_read_delay);\n\n rate = rospy.Rate(frequency)\n\n while not rospy.is_shutdown():\n for i in range(0, 16): #supports 16\n adc_val = channel_multipliers[i] * read_adc(adc_address, i)\n publishers[i].publish(adc_val)\n\n rate.sleep()\n\n\nif __name__ == '__main__':\n try:\n talker()\n except rospy.ROSInterruptException:\n pass\n","sub_path":"src/mavric/src/LTC2497_ADC_HAT.py","file_name":"LTC2497_ADC_HAT.py","file_ext":"py","file_size_in_byte":1493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"414572750","text":"# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2001-2015 Andreas Lang-Nevyjel\n#\n# this file is part of python-modules-base\n#\n# Send feedback to: \n#\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License Version 2 as\n# published by the Free Software Foundation.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software\n# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA\n#\n\"\"\" logging tools \"\"\"\nimport bz2\nimport datetime\nimport gzip\nimport inspect\nimport logging # @UnusedImport\nimport logging.handlers\nimport os\nimport pickle\nimport re\nimport stat\nimport string\nimport sys\nimport threading\nimport time\nimport traceback\n\nif sys.platform in [\"linux2\", \"linux3\", \"linux\"]:\n import syslog\n\ntry:\n import zmq\nexcept ImportError:\n zmq = None\n\nLOG_LEVEL_OK = 20\nLOG_LEVEL_WARN = 30\nLOG_LEVEL_ERROR = 40\nLOG_LEVEL_CRITICAL = 50\n\n# add the levels to the logging dict\nlogging.addLevelName(LOG_LEVEL_OK, \"ok\")\nlogging.addLevelName(LOG_LEVEL_WARN, \"warn\")\nlogging.addLevelName(LOG_LEVEL_ERROR, \"err\")\nlogging.addLevelName(LOG_LEVEL_CRITICAL, \"crit\")\n\n# default unified name\nUNIFIED_NAME = \"unified\"\n\n\ndef rewrite_log_destination(log_dest):\n if log_dest.startswith(\"uds:\"):\n log_dest = log_dest.replace(\"uds:\", \"ipc://\")\n if log_dest.startswith(\"ipc://\"):\n if not log_dest.endswith(\"_zmq\"):\n log_dest = \"{}_zmq\".format(log_dest)\n return log_dest\n\n\ndef map_old_to_new_level(in_level):\n return {\n 0: LOG_LEVEL_OK,\n 5: LOG_LEVEL_WARN,\n 10: LOG_LEVEL_ERROR,\n 20: LOG_LEVEL_CRITICAL,\n }.get(in_level, in_level)\n\n\ndef map_log_level_to_log_status(log_lev):\n return {\n LOG_LEVEL_OK: \"i\",\n LOG_LEVEL_WARN: \"w\",\n LOG_LEVEL_ERROR: \"e\",\n LOG_LEVEL_CRITICAL: \"c\",\n }.get(log_lev, \"c\")\n\n\ndef get_relative_dt(dt_struct):\n act_time = datetime.datetime.now()\n diff_days = (datetime.date(act_time.year, act_time.month, act_time.day) -\n datetime.date(dt_struct.year, dt_struct.month, dt_struct.day)).days\n if diff_days < 2:\n if diff_days == 1:\n return dt_struct.strftime(\"yesterday %H:%M:%S\")\n elif diff_days == 0:\n return dt_struct.strftime(\"today %H:%M:%S\")\n else:\n return dt_struct.strftime(\"%a, {:d} days ago %H:%M:%S\".format(int(diff_days)))\n else:\n return dt_struct.strftime(\"%a, %d. %b %Y %H:%M:%S\")\n\n\ndef only_printable(in_str):\n return \"\".join([_chr for _chr in in_str if _chr in string.printable])\n\n\ndef get_plural(in_str, num, show_int=1, fstr_len=0, **kwargs):\n if type(num) in [list, set]:\n r_num = len(num)\n else:\n r_num = num\n end_idx = len(in_str)\n if r_num != 1:\n if in_str and in_str[-1].lower() in [\"s\", \"x\", \"h\"]:\n p_str = \"es\"\n elif in_str and in_str[-1].lower() in [\"y\"]:\n if len(in_str) >= 2 and in_str[-2].lower() in [\"a\", \"e\", \"i\", \"o\", \"u\"]:\n p_str = \"s\"\n else:\n p_str = \"ies\"\n end_idx = -1\n else:\n p_str = \"s\"\n else:\n p_str = \"\"\n if fstr_len > 0:\n f_str = u\"{{:{:d}d}} \".format(fstr_len)\n elif fstr_len < 0:\n f_str = u\"{{:0{:d}d}} \".format(abs(fstr_len))\n else:\n f_str = u\"{:d} \"\n return u\"{}{}{}\".format(\n (show_int and f_str.format(int(r_num))) or \"\",\n in_str[0:end_idx],\n p_str\n )\n\n\ndef get_size_str(in_s, long_format=False, divider=1024, strip_spaces=False, long_version=True, per_second=False):\n if isinstance(in_s, basestring):\n _len_in_s = len(in_s)\n else:\n _len_in_s = in_s\n b_str = long_format and \"Byte\" or \"B\"\n if per_second:\n b_str = \"{}/s\".format(b_str)\n pf_f, pf_str = ([\"k\", \"M\", \"G\", \"T\", \"P\", \"E\"], \"\")\n while in_s > divider:\n in_s = in_s / float(divider)\n pf_str = pf_f.pop(0)\n ret_str = \"{} {}{}\".format(\n pf_str and \"{:6.2f}\".format(float(in_s)) or \"{:4d}\".format(int(in_s)),\n pf_str,\n b_str\n )\n if strip_spaces:\n ret_str = \" \".join(ret_str.split())\n return ret_str\n\n\ndef interpret_size_str(in_str, **kwargs):\n size_re = re.compile(\"^(?P\\d+(\\.\\d+)*)\\s*(?P.*?)b*(yte)*s*$\", re.IGNORECASE)\n size_m = size_re.match(in_str)\n if size_m:\n value = float(size_m.group(\"value\"))\n pfix = size_m.group(\"pfix\").lower()\n value = int(value * {\n \"m\": 1024 * 1024,\n \"mi\": 1000 * 1000,\n \"g\": 1024 * 1024 * 1024,\n \"gi\": 1000 * 1000 * 1000,\n \"t\": 1024 * 1024 * 1024 * 1024,\n \"ti\": 1000 * 1000 * 1000 * 1000,\n }.get(pfix, 1))\n return value\n else:\n return 0\n\n\ndef get_diff_time_str(diff_secs):\n if type(diff_secs) == datetime.timedelta:\n diff_secs = diff_secs.total_seconds()\n abs_diffs = abs(diff_secs)\n is_int = type(abs_diffs) in [int, long]\n if abs_diffs < 0.1:\n diff_str = \"{:.2f} mseconds\".format(abs_diffs * 1000)\n else:\n abs_mins, abs_hours = (0, 0)\n if abs_diffs > 60:\n abs_mins = int(abs_diffs / 60)\n abs_secs = int(abs_diffs - 60 * abs_mins)\n if abs_mins > 60:\n abs_hours = int(abs_mins / 60)\n abs_mins -= 60 * abs_hours\n if abs_hours:\n if abs_hours > 24:\n abs_days = int(abs_hours / 24)\n abs_hours -= 24 * abs_days\n if abs_days > 365:\n abs_years = int(abs_days / 365)\n abs_days -= 365 * abs_years\n diff_str = \"{:d}y {:3d}d {:02d}:{:02d}:{:02d}\".format(abs_years, abs_days, abs_hours, abs_mins, abs_secs)\n else:\n diff_str = \"{:d}d {:02d}:{:02d}:{:02d}\".format(abs_days, abs_hours, abs_mins, abs_secs)\n else:\n diff_str = \"{:d}:{:02d}:{:02d}\".format(abs_hours, abs_mins, abs_secs)\n else:\n diff_str = \"{:d}:{:02d}\".format(abs_mins, abs_secs)\n else:\n diff_str = \"{} seconds\".format(\"{:d}\" if is_int else \"{:.2f}\").format(abs_diffs)\n if diff_secs < 0:\n diff_str = \"{} [NEGATIVE TIME]\".format(diff_str)\n return diff_str\n\n\ndef get_time_str(secs):\n parts, left = ([], secs)\n for div in [3600 * 24, 3600, 60]:\n parts.append(int(left / div))\n left -= div * parts[-1]\n parts.append(left)\n days = parts.pop(0)\n out_f = []\n if days:\n out_f.append(get_plural(\"day\", days))\n hms_f, any_written = ([], False)\n for hms in parts:\n if hms:\n if any_written:\n hms_f.append(\"{:02d}\".format(int(hms)))\n else:\n hms_f.append(\"{:d}\".format(int(hms)))\n any_written = True\n else:\n if any_written:\n hms_f.append(\"{:02d}\".format(int(hms)))\n out_f.append(\":\".join(hms_f))\n return \" \".join(out_f)\n\n\nclass twisted_log_observer(object):\n def __init__(self, name, destination, **kwargs):\n kwargs.update({\"init_logger\": True})\n self.__logger = get_logger(name,\n destination,\n **kwargs)\n self.__last_cinfo = 0.0\n\n def __call__(self, in_dict):\n for line in in_dict[\"message\"]:\n self.__logger.log(in_dict.get(\"log_level\", LOG_LEVEL_OK), line)\n if in_dict[\"isError\"]:\n if in_dict.get(\"why\", None):\n self.__logger.log(LOG_LEVEL_CRITICAL, in_dict[\"why\"])\n act_time = time.time()\n if abs(act_time - self.__last_cinfo) > 1:\n self.__last_cinfo = act_time\n for line in in_dict[\"failure\"].getTraceback().split(\"\\n\"):\n self.__logger.log(LOG_LEVEL_CRITICAL, line)\n\n def close(self):\n for handle in self.__logger.logger.handlers:\n handle.close()\n\n\ndef get_logger(name, destination, **kwargs):\n \"\"\" specify init_logger=True to prepend init.at to the logname \"\"\"\n is_linux, cur_pid = (\n sys.platform in [\"linux2\", \"linux3\", \"linux\"],\n os.getpid())\n if kwargs.get(\"init_logger\", False) and is_linux:\n # force init.at logger\n if not name.startswith(\"init.at.\"):\n name = \"init.at.{}\".format(name)\n # get unique logger for 0MQ send\n act_logger = logging.getLogger(\"{}.{:d}\".format(name, cur_pid))\n act_logger.name = name\n act_logger.propagate = 0\n if not hasattr(act_logger, \"handler_strings\"):\n # only initiate once\n act_logger.handler_strings = []\n act_logger.setLevel(kwargs.get(\"base_log_level\", logging.DEBUG))\n if type(destination) != list:\n destination = [destination]\n # hack to make destination unique with respect to pid\n destination = [(cur_pid, cur_dest) for cur_dest in destination]\n for act_dest in destination:\n # print name, act_dest\n if (cur_pid, act_dest) not in act_logger.handler_strings:\n act_dest = act_dest[1]\n act_logger.handler_strings.append((cur_pid, act_dest))\n if \"context\" not in kwargs:\n cur_context = zmq.Context()\n else:\n cur_context = kwargs[\"context\"]\n pub = cur_context.socket(zmq.PUSH)\n pub.setsockopt(zmq.LINGER, -1)\n pub.connect(rewrite_log_destination(act_dest))\n act_logger.addHandler(zmq_handler(pub, act_logger))\n if log_adapter:\n # by using the log_adapter we also add thread-safety to the logger\n act_adapter = log_adapter(act_logger, {})\n else:\n act_adapter = act_logger\n return act_adapter\n\n\nclass log_adapter(logging.LoggerAdapter):\n \"\"\" small adapater which adds host information to logRecords \"\"\"\n def __init__(self, logger, extra):\n self.__lock = threading.Lock()\n self.set_prefix()\n logging.LoggerAdapter.__init__(self, logger, extra)\n\n def process(self, msg, kwargs):\n # add hostname and parent process id (to handle multiprocessing logging)\n if sys.platform in [\"linux2\", \"linux3\", \"linux\"]:\n kwargs.setdefault(\"extra\", {})\n kwargs[\"extra\"].setdefault(\"host\", os.uname()[1].split(\".\")[0])\n kwargs[\"extra\"].setdefault(\"ppid\", os.getppid())\n elif sys.platform in [\"win32\"]:\n kwargs.setdefault(\"extra\", {})\n kwargs[\"extra\"].setdefault(\"host\", os.getenv(\"COMPUTERNAME\").lower())\n kwargs[\"extra\"].setdefault(\"ppid\", os.getppid())\n return msg, kwargs\n\n def set_prefix(self, pfix=\"\"):\n self.__prefix = pfix\n\n def log_command(self, what):\n self.log(LOG_LEVEL_OK, \"{}\".format(what))\n\n def log(self, level=LOG_LEVEL_OK, what=LOG_LEVEL_OK, *args, **kwargs):\n self.__lock.acquire()\n if isinstance(level, basestring):\n # exchange level and what\n _lev = what\n what = level\n level = _lev\n if self.__prefix:\n what = \"{}{}\".format(self.__prefix, what)\n try:\n logging.LoggerAdapter.log(self, level, what, *args, **kwargs)\n except:\n my_syslog(what)\n print(what, self)\n raise\n self.__lock.release()\n\n def close(self):\n self.log_command(\"close\")\n for handle in self.logger.handlers:\n if hasattr(handle, \"close\"):\n handle.close()\n\n\nclass zmq_handler(logging.Handler):\n def __init__(self, t_sock, logger_struct, **kwargs):\n self.set_target(t_sock)\n self._open = True\n logging.Handler.__init__(self)\n self.__logger = logger_struct\n\n def set_target(self, t_sock):\n self.__target = t_sock\n\n def makePickle(self, record):\n \"\"\"\n Pickles the record in binary format with a length prefix, and\n returns it ready for transmission across the socket.\n \"\"\"\n ei = record.exc_info\n if ei:\n dummy = self.format(record) # just to get traceback text into record.exc_text\n record.exc_info = None # to avoid Unpickleable error\n _d = dict(record.__dict__)\n _d[\"msg\"] = record.getMessage()\n _d[\"args\"] = None\n p_str = pickle.dumps(_d, 1)\n if ei:\n record.exc_info = ei # for next handler\n return p_str\n\n def emit(self, record):\n self.__target.send(self.makePickle(record))\n\n def close(self):\n if self._open:\n self._open = False\n # set linger to zero to speed up close process\n self.__target.setsockopt(zmq.LINGER, -1)\n self.__target.close()\n del self.__target\n if self.__logger:\n # remove from handler\n self.__logger.removeHandler(self)\n\n\nclass initat_formatter(object):\n # in fact a dummy formatter\n def format(self, record):\n record.message = record.getMessage()\n if getattr(record, \"exc_info\", None):\n tb_object = record.exc_info[2]\n frame_info = []\n for file_name, line_no, name, line in traceback.extract_tb(tb_object):\n frame_info.append(\"File '{}', line {:d}, in {}\".format(file_name, line_no, name))\n if line:\n frame_info.append(u\" - {:d} : {}\".format(line_no, line))\n frame_info.append(u\"{} ({})\".format(\n unicode(record.exc_info[0]),\n unicode(record.exc_info[1])))\n record.error_str = record.message + \"\\n\" + \"\\n\".join(frame_info)\n var_list, info_lines = ([], [])\n request = inspect.trace()[-1][0].f_locals.get(\"request\", None)\n if request:\n info_lines.extend([\n \"\",\n \"method is {}\".format(request.method),\n \"\",\n ])\n # print get / post variables\n v_dict = getattr(request, request.method, None)\n if v_dict:\n var_list.extend([\n \"\",\n \"{}:\".format(get_plural(\"variable\", len(v_dict))),\n \"\",\n ])\n for s_num, s_key in enumerate(sorted(v_dict.keys())):\n var_list.append(\n \" {:3d} {}: {}\".format(\n s_num + 1,\n s_key,\n v_dict[s_key]\n )\n )\n # print frame_info, var_list\n record.exc_text = \"\\n\".join(frame_info + var_list + info_lines)\n if hasattr(record, \"request\"):\n delattr(record, \"request\")\n\n\nclass init_handler(zmq_handler):\n zmq_context = None\n\n def __init__(self, filename=None):\n if not init_handler.zmq_context:\n self._init_zmq()\n pub = self._socket()\n zmq_handler.__init__(self, pub, None)\n\n def _init_zmq(self):\n init_handler.init_pid = os.getpid()\n init_handler.zmq_context = zmq.Context()\n\n def _socket(self):\n cur_context = init_handler.zmq_context\n pub = cur_context.socket(zmq.PUSH)\n pub.connect(rewrite_log_destination(\"uds:/var/lib/logging-server/py_log_zmq\"))\n return pub\n\n def emit(self, record):\n if not record.name.startswith(\"init.at.\"):\n record.name = \"init.at.{}\".format(record.name)\n self.format(record)\n zmq_handler.emit(self, record)\n\n\nclass init_email_handler(zmq_handler):\n zmq_context = None\n\n def __init__(self, filename=None, *args, **kwargs):\n if not init_handler.zmq_context:\n init_handler.zmq_context = zmq.Context()\n cur_context = init_handler.zmq_context\n pub = cur_context.socket(zmq.PUSH)\n pub.connect(rewrite_log_destination(\"uds:/var/lib/logging-server/py_err_zmq\"))\n zmq_handler.__init__(self, pub, None)\n self.__lens = {\n \"name\": 1,\n \"threadName\": 1,\n \"lineno\": 1\n }\n\n def emit(self, record):\n record.IOS_type = \"error\"\n self.format(record)\n record.uid = os.getuid()\n record.gid = os.getgid()\n record.pid = os.getpid()\n record.ppid = os.getppid()\n zmq_handler.emit(self, record)\n\n\nclass init_handler_unified(zmq_handler):\n zmq_context = None\n\n def __init__(self, filename=None, *args, **kwargs):\n if not init_handler.zmq_context:\n self._init_zmq()\n pub = self._socket()\n zmq_handler.__init__(self, pub, None)\n\n def _init_zmq(self):\n init_handler.init_pid = os.getpid()\n init_handler.zmq_context = zmq.Context()\n\n def _socket(self):\n cur_context = init_handler.zmq_context\n pub = cur_context.socket(zmq.PUSH)\n pub.connect(rewrite_log_destination(\"uds:/var/lib/logging-server/py_log_zmq\"))\n return pub\n\n def emit(self, record):\n if record.name.startswith(\"init.at.\"):\n record.name = record.name[8:]\n if os.getpid() != init_handler.init_pid:\n # dont share zmq-contexts between processes\n self._init_zmq()\n pub = self._socket()\n self.set_target(pub)\n self.format(record)\n form_str = \"{: 1:\n row_lens = [max(x, y) for x, y in zip(row_lens[:l_p_l - 1], [len(unicode(y)) for y in list(l_p[:-1])])] + row_lens[l_p_l - 1:]\n else:\n row_lens = [max(x, y) for x, y in zip(row_lens, [len(unicode(y)) for y in list(l_p)])]\n # body format parts, header format parts\n b_f_parts, h_f_parts = ([], [])\n for idx in range(num_rows):\n tp_str, lf_str, pre_str, post_str, min_len = self.form_dict.get(idx, (\"s\", \"<\", \"\", \"\", 0))\n act_len = max(row_lens[idx], min_len, len(self.header_dict.get(idx, \"\")))\n if tp_str.endswith(\"f\") and tp_str.startswith(\".\"):\n b_f_parts.append((u\"{}{{:{}{:d}{}}}{}\".format(pre_str, lf_str, act_len, tp_str, post_str)))\n else:\n b_f_parts.append((u\"{}{{:{}{:d}{}}}{}\".format(pre_str, lf_str, act_len, tp_str, post_str)))\n h_f_parts.append((u\"{}{{:{}{:d}}}{}\".format(pre_str, lf_str, act_len, post_str)))\n b_form_str_dict = {num_rows: self.col_separator.join(b_f_parts)}\n h_form_str_dict = {num_rows: self.col_separator.join(h_f_parts)}\n for idx in range(1, len(b_f_parts)):\n b_form_str_dict[idx] = self.col_separator.join(b_f_parts[0:idx - 1] + [u\"{}\"])\n h_form_str_dict[idx] = self.col_separator.join(h_f_parts[0:idx - 1] + [u\"{}\"])\n out_lines = []\n if self.header_dict:\n headers = [self.header_dict.get(idx, \"\") for idx in range(len(self.header_dict.keys()))]\n out_lines.append((h_form_str_dict[len(headers)].format(*headers)).rstrip())\n out_lines.append(\"-\" * len(out_lines[-1]))\n for l_p in self.lines:\n # print len(l_p), b_form_str_dict\n # print b_form_str_dict[len(l_p)]\n out_lines.append((b_form_str_dict[len(l_p)].format(*l_p)).rstrip())\n return \"\\n\".join(out_lines)\n\n def __len__(self):\n return len(self.lines)\n\n def __unicode__(self):\n return self.__str__()\n\n\nclass form_entry(object):\n def __init__(self, content, **kwargs):\n self.content = content\n self.left = True\n self.min_width = 0\n self.pre_str = \"\"\n self.post_str = \"\"\n for key, value in kwargs.iteritems():\n setattr(self, key, value)\n setattr(self, \"content_type\", {\n str: \"s\",\n unicode: \"s\",\n type(None): \"s\",\n int: \"d\",\n long: \"d\",\n float: \"f\",\n datetime.date: \"s\",\n datetime.datetime: \"s\"\n }.get(type(self.content), \"s\"))\n\n def has_key(self, key):\n return hasattr(self, key)\n\n def __contains__(self, key):\n return hasattr(self, key)\n\n def __getitem__(self, key):\n return getattr(self, key)\n\n def min_len(self):\n return max(len(str(self)), self.min_width)\n\n def __str__(self):\n return self.form_str().format(self.content)\n\n def form_str(self, max_len=None):\n if self.content_type == \"d\":\n form_str = \"d\"\n elif self.content_type == \"f\":\n form_str = \"f\"\n else:\n form_str = \"s\"\n if max_len is None:\n form_str = \"{{:{}}}\".format(form_str)\n else:\n form_str = \"{{:{}{:d}{}}}\".format(\n \"<\" if self.left else \">\",\n max_len,\n form_str,\n )\n return \"{}{}{}\".format(self.pre_str, form_str, self.post_str)\n\n def format(self, max_len):\n return self.form_str(max_len).format(self.content)\n\n\nclass form_entry_right(form_entry):\n def __init__(self, content, **kwargs):\n form_entry.__init__(self, content, left=False, **kwargs)\n\n\nclass new_form_list(object):\n def __init__(self, **kwargs):\n self.__content = []\n self.__header_dict = {}\n self.__col_sep = kwargs.get(\"column_separator\", \" \")\n self.__strict_mode = kwargs.get(\"strict_mode\", False)\n self.__none_string = kwargs.get(\"none_string\", \"None\")\n # self.__format_dict = {}\n\n def append(self, add_list):\n # add list is a list of dicts\n for row_idx, item in enumerate(add_list):\n if item.content is None:\n item.content = self.__none_string\n if \"header\" in item:\n self.__header_dict[row_idx] = (item[\"left\"], item[\"header\"])\n self.__content.append(add_list)\n\n def extend(self, add_list):\n [self.append(_line) for _line in add_list]\n\n def __str__(self):\n return unicode(self)\n\n def __unicode__(self):\n if not self.__content:\n if self.__strict_mode:\n raise ValueError(\"empty list (no lines)\")\n else:\n return \"\"\n # count number of rows\n row_count = [len(line) for line in self.__content]\n _min_rows, max_rows = (\n min(row_count),\n max(row_count))\n row_lens = [0] * max_rows\n for line in self.__content:\n line_rows = len(line)\n # hack because part has to be casted to a string\n line_lens = []\n for part in line:\n line_lens.append(part.min_len())\n if line_rows < max_rows:\n if line_rows > 1:\n # only count the first (line_rows - 1) rows\n row_lens = [max(old_len, new_len) for old_len, new_len in zip(\n row_lens[:line_rows - 1],\n line_lens[:line_rows - 1])] + row_lens[line_rows - 1:]\n else:\n # count all rows\n row_lens = [max(old_len, new_len) for old_len, new_len in zip(row_lens, line_lens)]\n # take header into account\n row_lens = [max(old_len, len(self.__header_dict.get(idx, (True, \"\"))[1])) for idx, old_len in enumerate(row_lens)]\n out_lines = []\n if self.__header_dict:\n header_list = [self.__header_dict.get(idx, (True, \"\")) for idx in xrange(max_rows)]\n form_str = self.__col_sep.join([\"{{:{}{:d}s}}\".format(\"<\" if header_list[idx][0] else \"\", row_len) for idx, row_len in enumerate(row_lens)])\n out_lines.append(form_str.format(*[_e[1] for _e in header_list]).rstrip())\n out_lines.append(\"-\" * len(out_lines[-1]))\n for line in self.__content:\n out_lines.append(self.__col_sep.join([entry.format(max_len) for entry, max_len in zip(line, row_lens[:len(line)])]))\n return \"\\n\".join(map(lambda line: line.rstrip(), out_lines))\n\n def __len__(self):\n return len(self.__content)\n\n\ndef compress_list(ql, **kwargs):\n # node prefix, postfix, start_string, end_string\n def add_p(np, ap, s_str, e_str):\n if s_str == e_str:\n return \"{}{}{}\".format(np, s_str, ap)\n elif int(s_str) + 1 == int(e_str):\n return \"{}{}{}/{}{}\".format(np, s_str, ap, e_str, ap)\n else:\n return \"{}{}{}-{}{}\".format(np, s_str, ap, e_str, ap)\n pf_re = re.compile(\"^(?P.*?)(?P\\d+)(?P.*)$\")\n nc_dict, unmatch_list = ({}, [])\n for q_e in ql:\n pf_m = pf_re.match(q_e)\n if pf_m:\n # prefix, postfix and index\n pef, pof = (\n pf_m.group(\"pef\"),\n pf_m.group(\"pof\"),\n )\n nc_dict.setdefault(pef, {}).setdefault(pof, {})[int(pf_m.group(\"num\"))] = pf_m.group(\"num\")\n else:\n # no match found\n unmatch_list.append(q_e)\n nc_a = []\n for pef in nc_dict.keys():\n for pof in nc_dict[pef].keys():\n act_l = nc_dict[pef][pof]\n s_idx = None\n for e_idx in sorted(act_l.keys()):\n e_num = act_l[e_idx]\n if s_idx is None:\n s_idx, s_num = (e_idx, e_num)\n l_num, l_idx = (s_num, s_idx)\n else:\n if e_idx == l_idx + 1:\n pass\n else:\n nc_a += [add_p(pef, pof, s_num, l_num)]\n s_num, s_idx = (e_num, e_idx)\n l_num, l_idx = (e_num, e_idx)\n if pef:\n nc_a += [add_p(pef, pof, s_num, l_num)]\n return kwargs.get(\"separator\", \", \").join(sorted(nc_a) + sorted(unmatch_list))\n\n\ndef compress_num_list(ql, excl_list=[]):\n def add_p(s_idx, e_idx):\n if e_idx == s_idx:\n return \"{:d}\".format(s_idx)\n elif e_idx == s_idx + 1:\n return \"{:d}/{:d}\".format(s_idx, e_idx)\n else:\n return \"{:d}-{:d}\".format(s_idx, e_idx)\n if type(ql) == list:\n ql.sort()\n nc_a = []\n s_num = None\n for t_num in ql:\n if t_num not in excl_list:\n e_num = t_num\n if s_num is None:\n s_num, l_num = (e_num, e_num)\n else:\n if e_num == l_num + 1:\n pass\n else:\n nc_a.append(add_p(s_num, l_num))\n s_num = e_num\n l_num = e_num\n if s_num is not None:\n nc_a.append(add_p(s_num, e_num))\n return \", \".join(nc_a)\n\n\ndef my_syslog(out_str, log_lev=LOG_LEVEL_OK, out=False):\n if log_lev >= LOG_LEVEL_WARN:\n log_type = syslog.LOG_WARNING | syslog.LOG_USER\n elif log_lev >= LOG_LEVEL_ERROR:\n log_type = syslog.LOG_ERR | syslog.LOG_USER\n else:\n log_type = syslog.LOG_INFO | syslog.LOG_USER\n try:\n if type(out_str) == str:\n syslog.syslog(log_type, str(out_str))\n else:\n syslog.syslog(log_type, out_str.encode(\"utf-8\"))\n except:\n exc_info = sys.exc_info()\n error_str = \"({}, {})\".format(\n unicode(exc_info[0]),\n unicode(exc_info[1]),\n )\n if type(out_str) == unicode:\n syslog.syslog(\n syslog.LOG_ERR | syslog.LOG_USER,\n \"error logging unicode ({}, len {:d}, log_type {:d})\".format(\n error_str,\n len(out_str),\n log_type)\n )\n else:\n syslog.syslog(\n syslog.LOG_ERR | syslog.LOG_USER,\n \"error logging string ({}, len {:d}, log_type {:d})\".format(\n error_str,\n len(str(out_str)),\n log_type)\n )\n if out:\n print(out_str)\n\n\ndef get_log_level_str(level):\n return {\n LOG_LEVEL_OK: \"ok\",\n LOG_LEVEL_WARN: \"warn\",\n LOG_LEVEL_ERROR: \"err\",\n LOG_LEVEL_CRITICAL: \"crit\"\n }.get(level, \"lev{:d}\".format(level))\n\n\nclass my_formatter(logging.Formatter):\n def __init__(self, *args):\n logging.Formatter.__init__(self, *args)\n self.set_max_line_length(0)\n\n def set_max_line_length(self, max_length):\n self.__max_line_length = max_length\n\n def format(self, message):\n # threshold is 20 bytes longer because of double-formatting\n if self.__max_line_length and len(message.msg) > self.__max_line_length + 20:\n left = len(message.msg) - self.__max_line_length\n if left > 4:\n message.msg = u\"{} ({:d} left)\".format(message.msg[:self.__max_line_length], len(message.msg))\n return logging.Formatter.format(self, message)\n\n\nclass logfile(logging.handlers.BaseRotatingHandler):\n def __init__(self, filename, mode=\"a\", max_bytes=1000000, encoding=None, max_age_days=365):\n # always append if max_size > 0\n if max_bytes > 0:\n mode = \"a\"\n logging.handlers.BaseRotatingHandler.__init__(self, filename, mode, encoding, delay=False)\n self.__last_record = None\n self.set_max_bytes(max_bytes)\n self.max_age = max_age_days\n self._cleanup_old_logfiles()\n\n def set_max_bytes(self, max_bytes):\n self.__max_size = max_bytes\n\n def shouldRollover(self, record):\n do_rollover = False\n if self.__max_size > 0:\n msg = u\"{}\\n\".format(self.format(record))\n try:\n if self.stream.tell() + len(msg) > self.__max_size:\n do_rollover = True\n except ValueError:\n pass\n return do_rollover\n\n def _cleanup_old_logfiles(self):\n cur_dir = os.path.dirname(self.baseFilename)\n base_name = os.path.basename(self.baseFilename)\n file_list = [entry for entry in os.listdir(cur_dir) if entry.startswith(base_name) and entry != base_name]\n for cur_file in file_list:\n f_name = os.path.join(cur_dir, cur_file)\n act_age = int(abs(time.time() - os.stat(f_name)[stat.ST_MTIME]) / (24 * 3600))\n if act_age > self.max_age:\n try:\n os.unlink(f_name)\n except:\n my_syslog(\"cannot remove file '{}' ({:d} > {:d} days)\".format(f_name, act_age, self.max_age), LOG_LEVEL_ERROR)\n else:\n my_syslog(\"removed file '{}' ({:d} > {:d} days)\".format(f_name, act_age, self.max_age))\n\n def doRollover(self):\n self._cleanup_old_logfiles()\n self.stream.close()\n act_time = time.localtime()\n base_postfix = \"{:04d}{:02d}{:02d}\".format(act_time[0], act_time[1], act_time[2])\n if bz2:\n gz_postfix = \"bz2\"\n else:\n gz_postfix = \"gz\"\n act_idx = 0\n while True:\n act_postfix = \"{}.{:d}\".format(base_postfix, act_idx) if act_idx else base_postfix\n gz_file_name = \"{}-{}.{}\".format(\n self.baseFilename,\n act_postfix,\n gz_postfix)\n if os.path.isfile(gz_file_name):\n act_idx += 1\n else:\n break\n try:\n if bz2:\n act_z = bz2.BZ2File(gz_file_name, \"w\")\n else:\n act_z = gzip.open(gz_file_name, \"wb\", 4)\n except:\n exc_info = sys.exc_info()\n my_syslog(\"error opening {}: {} ({})\".format(\n gz_file_name,\n str(exc_info[0]),\n str(exc_info[1])))\n else:\n act_z.write(open(self.baseFilename, \"r\").read())\n act_z.close()\n os.chmod(gz_file_name, stat.S_IRUSR | stat.S_IWUSR | stat.S_IRGRP)\n self.stream.close()\n os.unlink(self.baseFilename)\n self.stream = self._open()\n os.chmod(self.baseFilename, stat.S_IRUSR | stat.S_IWUSR | stat.S_IRGRP)\n self.mode = \"w\"\n self.stream = self._open()\n\n\ndef list_to_struct(in_list, **kwargs):\n # minimum size for matching prefixes\n pf_min_size = kwargs.get(\"prefix_min_size\", 1)\n # reduce list and create a strcuture\n if len(in_list) == 1:\n return [(\"\", in_list)]\n # find longest common prefix\n _len = 0\n while True:\n _pfs = set([_value[:_len + 1] for _value in in_list])\n if len(_pfs) == 1:\n _len += 1\n else:\n break\n if _len:\n _res = list_to_struct(\n [_value[_len:] for _value in in_list],\n **kwargs\n )\n return [(in_list[0][:_len], _res)]\n else:\n _pfs = sorted(list(_pfs))\n # check for integer pfs\n if all([_pf.isdigit() for _pf in _pfs]):\n _dict = {}\n _pfs = set()\n for _value in in_list:\n _pf = _value[0]\n if len(_value) > 2 and _value[1:3].isdigit():\n _pf = _value[:3]\n elif len(_value) > 1 and _value[1].isdigit():\n _pf = _value[:2]\n else:\n _pf = _value[0]\n _pfs.add(int(_pf))\n _dict.setdefault(_pf, []).append(_value[len(_pf):])\n _pfs = sorted(list(_pfs))\n if len(_pfs) > 1 and len(set([\"\".join(_val) for _val in _dict.itervalues()])) == 1:\n # all values are the same, return compressed list\n return [(\"[{}]\".format(compress_num_list(_pfs)), list_to_struct(_dict.values()[0], **kwargs))]\n else:\n _pfs = [\"{:d}\".format(_val) for _val in _pfs]\n return [(_pf, list_to_struct(_dict[_pf], **kwargs)) for _pf in _pfs]\n else:\n if len(_pfs[0]) < pf_min_size and min(len(_v) for _v in in_list) > pf_min_size:\n _pfs = set(sorted([_value[:pf_min_size] for _value in in_list]))\n return [(_pf, list_to_struct([_value[len(_pf):] for _value in in_list if _value[:len(_pf)] == _pf], **kwargs)) for _pf in _pfs]\n\n\ndef struct_to_string(in_struct):\n # recursivly expand a given line_struct\n _pf, _list = in_struct\n _r = []\n for _entry in _list:\n if isinstance(_entry, basestring):\n _r.append(_entry)\n else:\n _r.append(struct_to_string(_entry))\n if len(_r) == 1:\n return \"{}{}\".format(\n _pf,\n _r[0],\n )\n else:\n return \"{}[{}]\".format(\n _pf,\n \"][\".join(_r),\n )\n\n\ndef reduce_list(in_list, **kwargs):\n return \",\".join([struct_to_string(_sub_list) for _sub_list in list_to_struct(in_list, **kwargs)])\n","sub_path":"logging_tools.py","file_name":"logging_tools.py","file_ext":"py","file_size_in_byte":40905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"436355184","text":"from bxcommon.constants import BX_HDR_COMMON_OFF\nfrom bxcommon.messages.bloxroute.broadcast_message import BroadcastMessage\nfrom bxcommon.utils import crypto, convert\nfrom bxcommon.utils.object_hash import Sha256Hash\nfrom bxcommon.utils.stats.statistics_event_service import StatisticsEventService\nfrom bxutils.logging.log_record_type import LogRecordType\nfrom bxutils import logging\n\n\nclass _BlockStatisticsService(StatisticsEventService):\n def __init__(self):\n super(_BlockStatisticsService, self).__init__()\n self.name = \"BlockInfo\"\n self.logger = logging.get_logger(LogRecordType.BlockInfo)\n\n def add_block_event(self, block_msg, block_event_settings, network_num, start_date_time=None, end_date_time=None,\n **kwargs):\n if not self._should_log_stat_event(block_event_settings):\n return\n\n if isinstance(block_msg, BroadcastMessage):\n block_hash = block_msg.block_hash().binary\n elif isinstance(block_msg, memoryview):\n block_hash = block_msg[BX_HDR_COMMON_OFF:BX_HDR_COMMON_OFF + crypto.SHA256_HASH_LEN].tobytes()\n else:\n block_hash = block_msg[BX_HDR_COMMON_OFF:BX_HDR_COMMON_OFF + crypto.SHA256_HASH_LEN]\n\n self.log_event(block_event_settings, convert.bytes_to_hex(block_hash), start_date_time, end_date_time,\n network_num=network_num,\n **kwargs)\n\n def add_block_event_by_block_hash(self, block_hash, block_event_settings, network_num, start_date_time=None, end_date_time=None,\n **kwargs):\n if not self._should_log_stat_event(block_event_settings):\n return\n\n if isinstance(block_hash, Sha256Hash):\n block_hash_str = block_hash.binary\n elif isinstance(block_hash, memoryview):\n block_hash_str = block_hash.tobytes()\n else:\n block_hash_str = block_hash\n\n self.log_event(block_event_settings, convert.bytes_to_hex(block_hash_str), start_date_time, end_date_time,\n network_num=network_num,\n **kwargs)\n\n def _should_log_stat_event(self, event_type_settings):\n return self.node.opts.log_detailed_block_stats or not event_type_settings.detailed_stat_event\n\n\nblock_stats = _BlockStatisticsService()\n","sub_path":"src/bxcommon/utils/stats/block_statistics_service.py","file_name":"block_statistics_service.py","file_ext":"py","file_size_in_byte":2342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"215798307","text":"from flask_restful import reqparse\r\nfrom flask_restful import Api\r\nfrom flask_cors import CORS\r\nfrom osgeo import ogr\r\n\r\nfrom app import NetworkApplication, ElectricNetwork\r\nfrom views import *\r\nfrom config import *\r\n\r\nnetworks = ['normal', 'work', 'chrono']\r\nconnection_strings = {}\r\n\r\nfor network_name in networks:\r\n connection_string = \"PG: host={} dbname={} user={} password={} port={}\".format(\r\n DATABASE_SERVER, DATABASE_NAMES[network_name], DATABASE_USER, DATABASE_PW, DATABASE_PORT)\r\n connection_strings[network_name] = connection_string\r\n\r\nif __name__ == '__main__':\r\n parser = reqparse.RequestParser()\r\n parser.add_argument('x')\r\n parser.add_argument('y')\r\n parser.add_argument('supports_count')\r\n\r\n connection_normal = ogr.Open(connection_strings['normal'])\r\n # connection_work = ogr.Open(connection_strings['work'])\r\n # connection_chrono = ogr.Open(connection_strings['chrono'])\r\n\r\n app = NetworkApplication(__name__)\r\n\r\n network = ElectricNetwork()\r\n network.connection_normal = connection_normal\r\n # network.connection_work = connection_work\r\n # network.connection_chrono = connection_chrono\r\n\r\n network.connection_string = connection_strings['normal']\r\n normal_connection = ogr.Open(connection_strings['normal'])\r\n lines = []\r\n supports = []\r\n for line in normal_connection.GetLayer(LINES_LAYER):\r\n lines.append(line)\r\n for support in normal_connection.GetLayer(SUPPORTS_LAYER):\r\n supports.append(support)\r\n network.lines = lines\r\n network.supports = supports\r\n app.network = network\r\n\r\n CORS(app)\r\n api = Api(app)\r\n\r\n app.network.create_network()\r\n params = {'app': app, 'parser': parser}\r\n\r\n api.add_resource(HasPathHandler, '/', resource_class_kwargs=params)\r\n api.add_resource(SuccessorsHandler, '/successors', resource_class_kwargs=params)\r\n api.add_resource(DisconnectedHandler, '/disconnected', resource_class_kwargs=params)\r\n api.add_resource(SupplyAreaHandler, '/supply', resource_class_kwargs=params)\r\n api.add_resource(FindPerspectiveHandler, '/perspective_consumers', resource_class_kwargs=params)\r\n api.add_resource(FindPointsInRadiusHandler, '/buffer', resource_class_kwargs=params)\r\n api.add_resource(SupportsFromPointHandler, '/supports_from_point', resource_class_kwargs=params)\r\n app.run(host='0.0.0.0')\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"32144153","text":"'''\n Copyright (C) 1997-2017 JDERobot Developers Team\n\n This program is free software; you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation; either version 2 of the License, or\n (at your option) any later version.\n\n This program is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU Library General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with this program; if not, see .\n\n Authors : Okan Asik (asik.okan@gmail.com)\n\n '''\nfrom PyQt5.QtCore import Qt, pyqtSignal\nfrom PyQt5.QtGui import QPainter, QPixmap\nfrom PyQt5.QtWidgets import QMainWindow, QDockWidget, QTreeView, QGraphicsView, \\\n QWidget, QLabel, QVBoxLayout, QPushButton, QGraphicsItem, \\\n QGraphicsScene\nfrom visualstates.gui.tree.treemodel import TreeModel\nfrom visualstates.core.state import State\nfrom visualstates.core.transition import Transition\nfrom visualstates.configs.package_path import get_package_path\n\nfrom threading import Thread\nimport time\nimport sysv_ipc\n\nclass RunTimeGui(QMainWindow):\n\n activeStateChanged = pyqtSignal(int)\n runningStateChanged = pyqtSignal(int)\n loadFromRoot = pyqtSignal(int)\n\n def __init__(self, parent=None):\n super(QMainWindow, self).__init__()\n\n self.setWindowTitle(\"VisualStates RunTime GUI\")\n\n self.rootState = None\n\n # create status bar\n self.statusBar()\n\n self.createTreeView()\n self.createStateCanvas()\n\n self.setGeometry(0, 0, 800, 600)\n self.show()\n\n self.states = {}\n self.transitions = {}\n\n self.activeState = None\n\n self.activeStateChanged.connect(self.activeStateChangedHandle)\n self.runningStateChanged.connect(self.runningStateChangedHandle)\n self.loadFromRoot.connect(self.loadFromRootHandle)\n\n self.memory = None\n self.ipcThread = None\n self.semaphore = None\n\n def createTreeView(self):\n dockWidget = QDockWidget()\n dockWidget.setAllowedAreas(Qt.LeftDockWidgetArea)\n dockWidget.setFeatures(QDockWidget.NoDockWidgetFeatures)\n dockWidget.setTitleBarWidget(QWidget())\n self.treeView = QTreeView()\n self.treeView.clicked.connect(self.treeItemClicked)\n self.treeModel = TreeModel()\n self.treeView.setModel(self.treeModel)\n\n self.logo = QLabel()\n logoPixmap = QPixmap(get_package_path() + '/share/resources/jderobot.png')\n self.logo.setPixmap(logoPixmap)\n\n self.upButton = QPushButton()\n self.upButton.setText('Up')\n self.upButton.clicked.connect(self.upButtonClicked)\n\n leftContainer = QWidget()\n leftLayout = QVBoxLayout()\n leftLayout.addWidget(self.treeView)\n leftLayout.addWidget(self.upButton)\n leftLayout.addWidget(self.logo)\n leftContainer.setLayout(leftLayout)\n\n dockWidget.setWidget(leftContainer)\n self.addDockWidget(Qt.LeftDockWidgetArea, dockWidget)\n\n def createStateCanvas(self):\n self.stateCanvas = QGraphicsView()\n self.scene = QGraphicsScene()\n self.scene.setSceneRect(0, 0, 2000, 2000)\n\n self.setCentralWidget(self.stateCanvas)\n self.stateCanvas.setScene(self.scene)\n self.stateCanvas.setRenderHint(QPainter.Antialiasing)\n\n\n def addState(self, id, name, initial, x, y, parentId):\n if parentId is not None:\n self.states[id] = State(id, name, initial, self.states[parentId])\n self.states[parentId].addChild(self.states[id])\n parentItem = self.treeModel.getByDataId(parentId)\n # print('parent:' + str(parentItem))\n else:\n self.states[id] = State(id, name, initial, None)\n if id == 0:\n self.rootState = self.states[id]\n\n self.states[id].setPos(x, y)\n\n def addTransition(self, id, name, originId, destinationId, x, y):\n self.transitions[id] = Transition(id, name, self.states[originId], self.states[destinationId])\n self.transitions[id].setPos(x, y)\n\n def emitRunningStateById(self, id):\n # print('emit running state:' + str(id))\n self.runningStateChanged.emit(id)\n\n def runningStateChangedHandle(self, id):\n # print('running state:' + str(id))\n if id not in self.states:\n return\n\n runningState = self.states[id]\n\n parentId = None\n if runningState.parent is not None:\n for child in runningState.parent.getChildren():\n child.setRunning(False)\n\n runningState.setRunning(True)\n parentId = runningState.parent.id\n\n self.treeModel.setAllBackgroundByParentId(Qt.white, parentId)\n self.treeModel.setBackgroundById(runningState.id, Qt.green)\n\n def emitActiveStateById(self, id):\n self.activeStateChanged.emit(id)\n\n def activeStateChangedHandle(self, id):\n if self.activeState is not None:\n for child in self.activeState.getChildren():\n child.resetGraphicsItem()\n for tran in child.getOriginTransitions():\n tran.resetGraphicsItem()\n\n self.activeState = self.states[id]\n # print('set active state:' + str(id))\n self.scene.clear()\n for childState in self.activeState.getChildren():\n # print('add child to scene:' + str(childState.id))\n qitem = childState.getGraphicsItem()\n qitem.setAcceptHoverEvents(False)\n qitem.setFlag(QGraphicsItem.ItemIsMovable, False)\n qitem.doubleClicked.connect(self.stateDoubleClicked)\n self.setAcceptDrops(False)\n self.scene.addItem(qitem)\n for tran in childState.getOriginTransitions():\n # print('add transition:' + str(tran.id))\n qitem = tran.getGraphicsItem()\n qitem.disableInteraction()\n self.scene.addItem(qitem)\n\n def emitLoadFromRoot(self):\n self.loadFromRoot.emit(0)\n\n def loadFromRootHandle(self, id):\n self.treeModel.loadFromRoot(self.states[id])\n\n def stateDoubleClicked(self, stateItem):\n if len(stateItem.stateData.getChildren()) > 0:\n self.emitActiveStateById(stateItem.stateData.id)\n\n def upButtonClicked(self):\n if self.activeState is not None:\n if self.activeState.parent is not None:\n self.emitActiveStateById(self.activeState.parent.id)\n\n def getStateById(self,state, id):\n if state.id == id:\n return state\n else:\n result = None\n for child in state.getChildren():\n result = self.getStateById(child, id)\n if result is not None:\n return result\n return result\n\n def treeItemClicked(self, index):\n # print('clicked item.id:' + str(index.internalPointer().id))\n pass\n\n def getStateList(self, state, stateList):\n if len(state.getChildren()) > 0:\n stateList.append(state)\n\n for s in state.getChildren():\n self.getStateList(s, stateList)\n\n def loopIPC(self):\n while True:\n msg = self.getIPCMessage()\n if msg is not None:\n if len(msg) > 0:\n msg = msg.strip()\n methodName = msg.split(' ')[0]\n id = int(msg.split(' ')[1])\n if methodName == 'emitRunningStateById':\n self.emitRunningStateById(id)\n else:\n print('unknown method name')\n\n # time.sleep(1.0/1000)\n\n def activateIPC(self):\n try:\n self.memory = sysv_ipc.SharedMemory(123456, flags=sysv_ipc.IPC_CREAT, size = 1024)\n except:\n print('cannot create or open share mem with id 123456')\n return\n\n # create sempahore\n try:\n self.semaphore = sysv_ipc.Semaphore(9, flags=sysv_ipc.IPC_CREAT, initial_value=0)\n except:\n print('cannot create or open semaphore with id 9')\n self.memory.remove()\n return\n\n self.ipcThread = Thread(target=self.loopIPC)\n self.ipcThread.start()\n\n def getIPCMessage(self):\n self.semaphore.acquire()\n data = self.memory.read().decode()\n return data\n","sub_path":"src/visualstates/codegen/python/runtimegui.py","file_name":"runtimegui.py","file_ext":"py","file_size_in_byte":8544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"643448953","text":"import torch\nimport torch.nn as nn\nimport torch.fft # This import is needed for fourier transform and using nn.fft.fftn would outcome bugs\nfrom torchsummary import summary\n\n\ndef fftshift2d(img):\n bs, ch, h, w = img.shape\n fs11 = img[:, :, h // 2:, w // 2:]\n fs12 = img[:, :, h // 2:, :w // 2]\n fs21 = img[:, :, :h // 2, w // 2:]\n fs22 = img[:, :, :h // 2, :w // 2]\n output = torch.cat([torch.cat([fs11, fs21], axis=2), torch.cat([fs12, fs22], axis=2)], axis=3)\n return output\n\n\nclass FCAB(nn.Module):\n '''\n Fourier channel attention block\n '''\n\n def __init__(self):\n super(FCAB, self).__init__()\n\n self.conv_gelu1 = nn.Sequential(\n nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1), # 缩小两个\n nn.GELU()\n )\n self.conv_gelu2 = nn.Sequential(\n nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),\n nn.GELU()\n )\n self.conv_relu1 = nn.Sequential(\n nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),\n nn.ReLU(inplace=True)\n )\n self.avgpool = nn.AdaptiveAvgPool2d(output_size=(128, 128))\n self.conv_relu2 = nn.Sequential(\n nn.Conv2d(64, 4, kernel_size=1, stride=1, padding=0),\n nn.ReLU()\n )\n self.conv_sig = nn.Sequential(\n nn.Conv2d(4, 64, kernel_size=1, stride=1, padding=0),\n nn.Sigmoid()\n )\n\n def forward(self, x, gamma=0.8):\n x0 = x\n x = self.conv_gelu1(x)\n x = self.conv_gelu2(x)\n x1 = x\n x = torch.fft.fftn(x, dim=(2, 3)) # 这里的dimension要注意\n x = torch.pow(torch.abs(x) + 1e-8, gamma) # abs\n x = fftshift2d(x)\n x = self.conv_relu1(x)\n x = self.avgpool(x)\n x = self.conv_relu2(x)\n x = self.conv_sig(x)\n x = x1 * x\n output = x + x0\n return output\n\n\nclass ResidualGroup(nn.Module):\n '''\n The Residual Group\n '''\n def __init__(self, n_fcab=4):\n super(ResidualGroup, self).__init__()\n FCABs = []\n for _ in range(n_fcab):\n FCABs.append(FCAB())\n self.FCABs = nn.Sequential(*FCABs) # 变成顺序，*会将原来的模型变成一个一个层\n\n def forward(self, x):\n x0 = x\n x = self.FCABs(x)\n output = x + x0\n return output\n\n\nclass DFCAN(nn.Module):\n def __init__(self, input_shape, scale=2, size_psc=128):\n super(DFCAN, self).__init__()\n self.conv_gelu1 = nn.Sequential(\n nn.Conv2d(input_shape, 64, kernel_size=3, stride=1, padding=1),\n nn.GELU()\n )\n\n n_residualgroups = 4\n ResidualGroups = []\n for _ in range(n_residualgroups):\n ResidualGroups.append(ResidualGroup(n_fcab=4))\n self.res = nn.Sequential(*ResidualGroups)\n\n self.conv_gelu2 = nn.Sequential(\n nn.Conv2d(64, 64 * (scale ** 2), kernel_size=3, stride=1, padding=1),\n nn.GELU()\n )\n self.pixelshuffle = nn.PixelShuffle(scale)\n self.conv_sig = nn.Sequential(\n nn.Conv2d(64, 1, kernel_size=3, stride=1, padding=1),\n nn.Sigmoid()\n )\n\n def forward(self, x):\n x = self.conv_gelu1(x)\n x = self.res(x)\n x = self.conv_gelu2(x)\n x = self.pixelshuffle(x) # upsampling\n output = self.conv_sig(x)\n return output\n\n\n# 测试模型 Test the model\nif __name__ == '__main__':\n x = torch.rand(1, 6, 128, 128)\n model = DFCAN(input_shape=x.size()[1])\n y = model(x)\n print('Output shape:', y.shape)\n # Turn to GPU\n # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n # model.to(device)\n if torch.cuda.is_available():\n model.cuda()\n summary(model, input_size=(6, 128, 128))\n","sub_path":"PytorchImplementation/models/DFCAN.py","file_name":"DFCAN.py","file_ext":"py","file_size_in_byte":3811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"493469431","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('core', '0002_auto_20151017_1242'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='ControlWork',\n fields=[\n ('id', models.AutoField(primary_key=True, serialize=False, verbose_name='ID', auto_created=True)),\n ('name', models.CharField(max_length=200)),\n ],\n ),\n migrations.CreateModel(\n name='Ticket',\n fields=[\n ('id', models.AutoField(primary_key=True, serialize=False, verbose_name='ID', auto_created=True)),\n ('control_work', models.ForeignKey(null=True, to='core.ControlWork')),\n ],\n ),\n migrations.AddField(\n model_name='question',\n name='ticket',\n field=models.ForeignKey(null=True, to='core.Ticket'),\n ),\n ]\n","sub_path":"exam/core/migrations/0003_auto_20151023_1539.py","file_name":"0003_auto_20151023_1539.py","file_ext":"py","file_size_in_byte":1008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"439109391","text":"import pandas as pd\nimport numpy as np\nimport os\nimport json\n\nclass results(object):\n def __init__(self, pathResponse):\n self.pathResponse = pathResponse\n self.sdm_dataset = pd.read_csv(self.pathResponse+\"sdm.csv\")\n self.data = {\"responseGraph\":{}, \"responseStatistics\":{}, \"continuousAttributes\":{}, \"categoricalAttributes\":{'WT_SSE':{},'WT_SS':{},'WT_SN':{},'WT_SO':{},'MT_SSE':{},'MT_SS':{},'MT_SN':{},'MT_SO':{},'Class':{}}}\n \n def responseStatistics(self):\n statistics = {'Minimum': 0, 'Maximum': 0, 'Mean': 0, 'Standard deviation': 0}\n attributes = ['WT_RSA','WT_DEPTH','MT_RSA','MT_DEPTH','Predicted_DDG']\n for element in attributes:\n statistics.update({'Minimum':np.min(self.sdm_dataset[element])}) \n statistics.update({'Maximum':np.max(self.sdm_dataset[element])}) \n statistics.update({'Mean':np.mean(self.sdm_dataset[element])}) \n statistics.update({'Standard deviation':np.std(self.sdm_dataset[element])})\n self.data['responseStatistics'].update({element:statistics})\n statistics = {'Minimum': 0, 'Maximum': 0, 'Mean': 0, 'Standard deviation': 0}\n #print(self.data['responseStatistics'])\n \n def continuousAttributes(self):#histograma\n attributes = ['WT_RSA','WT_DEPTH','MT_RSA','MT_DEPTH','Predicted_DDG']\n for element in attributes:\n self.data['continuousAttributes'].update({element:list(self.sdm_dataset[element])})\n #print(self.data['continuousAttributes'])\n\n def categoricalAttributes (self):#pie\n attributes = ['WT_SSE','WT_SS','WT_SN','WT_SO','MT_SSE','MT_SS','MT_SN','MT_SO','Class']\n for element in attributes:\n values = list(self.sdm_dataset[element].value_counts().keys())\n counts = self.sdm_dataset[element].value_counts().tolist()\n k = 0\n for i in values:\n self.data['categoricalAttributes'][element].update({i:counts[k]})\n k = k+1 \n \n #print(self.data['categoricalAttributes'])\n \n def json(self):\n with open(self.pathResponse+\"results.json\", 'w') as fp:\n json.dump(self.data, fp)","sub_path":"model/build/lib/modules/results/results.py","file_name":"results.py","file_ext":"py","file_size_in_byte":2240,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"307754709","text":"# -*- coding: utf-8 -*-\n\"\"\"\n2. \n\nNLP system for NIMH notes\n\n@author: hpan\n\"\"\"\n\nimport re\nimport os\nimport csv\nimport pandas as pd\n#%%\n# Change to working directory\nos.getcwd()\nos.chdir('Z:/project')\n\n# Read Grid file\nstd = pd.read_csv(\"./KB/371-KIOUS-Patient-Grid-Plain-fill_1116.csv\") #, nrows=5\nstd = std.fillna('nav')\n\n# Read csv to dataframe and change to a dictionary with keywords and their score.\nann_target_score = pd.read_csv(\"./KB/NIMH_target_score_1116_su.csv\", sep='$') # to lowercase\nscore= {str(x[0]).lower(): x[1] for x in ann_target_score.itertuples(index=False)}\n\n#%%\n# Sum score for each row and add to the score list\nscore_list=[]\n\nfor index, row in std.iterrows():\n score1=0\n for i in [32,36,39]:\n try:\n score1 = score1 + score[row[i].lower()]\n except:\n #print(\"{} not in dict\" .format(row[i]))\n print(row[i])\n #pass\n score_list.append(score1)\n #print (i)\n \n#%%\n# Add score to the Grid dataframe\nse = pd.Series(score_list)\nstd['r_score_su'] = se.values\n\n#%%\n \nstd.to_csv('./KB/371-KIOUS-Patient-Grid-Plain-fill_1116_score_su1.csv', index=False)\n\n#%%\n# add files size to the dataframe table\n\nstd1 = pd.read_csv(\"./KB/371-KIOUS-Patient-Grid-Plain-fill_1116_score_su1.csv\")\npatvisit = std1['PAT_VISIT']\npath = 'Z:/project/notes'\nfilesizes = []\nfor pid in patvisit:\n fname=pid + \".txt\"\n try:\n filesize = os.path.getsize(os.path.join(path,fname))\n filesize = filesize//1000\n except:\n filesize = 0\n filesizes.append(filesize)\n \nse1 = pd.Series(filesizes)\nstd1['fsize'] = se1.values\n\n#%%\nstd1.to_csv('./KB/371-KIOUS-Patient-Grid-Plain-fill_1116_score_size_su1.csv', index=False)\n","sub_path":"code/2 pat_grid_score1126_su1.py","file_name":"2 pat_grid_score1126_su1.py","file_ext":"py","file_size_in_byte":1711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"24115727","text":"# -*- coding: UTF-8 -*-\n\n# @Date : 2019/12/4\n# @Author : WANG JINGE\n# @Email : wang.j.au@m.titech.ac.jp\n# @Language: python 3.7\n\"\"\"\n\n\"\"\"\n\n# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nfrom collections import deque\n\n\nclass Solution:\n\n def levelOrder(self, root: TreeNode) -> List[List[int]]:\n\n if not root:\n return []\n\n res = []\n q = deque([root])\n\n while q:\n current_layer = []\n for i in range(len(q)):\n node = q.popleft()\n if node:\n q.append(node.left)\n q.append(node.right)\n current_layer.append(node.val)\n if current_layer:\n res.append(current_layer)\n\n return res\n","sub_path":"Queue_Stack/102.Binary_Tree_Level_Order_Traversal.py","file_name":"102.Binary_Tree_Level_Order_Traversal.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"602560687","text":"from __future__ import division\nfrom copy import copy\nfrom ..tools.containers import optnamedtuple\nfrom numpy import prod\n\n#############################################################################################\n############## _TOP\ndef cast(d):\n if d is None or d=='None':\n return None\n else:\n return int(d)\n\ndef parse(line):\n line = line.strip()\n strs = line.split(' ')\n ans = []\n for a in strs:\n if a:\n if a[0]=='[':\n if a[1] == '[':\n # a = [[1,2],[3,4],[4,5]]\n ans.append([])\n for c in a[2:-2].split('],['):\n ans[-1].append([])\n for d in c.split(','):\n try:\n ans[-1][-1].append(cast(d))\n except ValueError:\n ans[-1][-1].append(d)\n else:\n ans.append([])\n for c in a[1:-1].split(','):\n try:\n ans[-1].append(cast(c))\n except ValueError:\n ans[-1].append(c)\n else:\n try:\n ans.append(cast(a))\n except ValueError:\n ans.append(a)\n return ans\n\n_TOP = optnamedtuple('_TOP', ['op', 'in0', 'in1','a0', 'a1','a2', 'out0', 'out1', 'out2'])\n\ndef op(top):\n return top.op\n\ndef inputs(top):\n return top.in0, top.in1\n\ndef arguments(top):\n return top.a0, top.a1, top.a2\n\ndef outputs(top):\n return top.out0, top.out1, top.out2\n\ndef op_matches(op_a, op_b):\n \"\"\"\n\n :param op_a:\n :type op_a: TOP\n :param op_b:\n :type op_b: TOP\n :return:\n \"\"\"\n return inputs(op_a)==inputs(op_b)\\\n and arguments(op_a)==arguments(op_b) \\\n and op_a.op == op_b.op\n\ndef depends_on(need_list, a):\n return any([n in outputs(a) for n in need_list])\n\ndef as_TOP(*args, **kwargs):\n d = dict()\n if len(args)==1:\n try:\n args[0].op\n except AttributeError:\n line = args[0]\n opstr, _, subline = line.partition(' ')\n instr, _, subline = subline.partition(';')\n argstr, _, outstr = subline.partition('->')\n\n _inputs = parse(instr)\n _arguments = parse(argstr)\n _outputs = parse(outstr)\n\n d.update({'op':opstr})\n d.update({'in'+str(j):v for j, v in enumerate(_inputs)})\n d.update({'a'+str(j):v for j, v in enumerate(_arguments)})\n d.update({'out'+str(j):v for j, v in enumerate(_outputs)})\n else:\n d.update(args[0]._asdict())\n d.update(kwargs)\n return _TOP(**d)\n\ndef as_str(top):\n return ' '.join([str(y).replace(' ', '') for y in\n [top.op, top.in0, top.in1, ';', top.a0, top.a1, top.a2, '->', top.out0, top.out1, top.out2] if\n y is not None])\n\n_TOP.__repr__ = as_str\n\n####################################################################################\n#slicing\n\ndef convert_to_slice(v):\n if v is None:\n return slice(None)\n elif type(v) is int:\n return v\n else:\n return slice(v[0], v[1] if 11:\n assert s0[-1]==s1[-2]\n shapes[out0] = list(s0[:-1])+list(s1[:-2])+[s1[-1]]\n elif len(s1)==1:\n assert s0[-1]==s1[0]\n shapes[out0] = s0[:-1]\n else:\n raise ValueError('scalar')\n else:\n raise NotImplementedError\n","sub_path":"SimplePEPS/contract/TensorOperations.py","file_name":"TensorOperations.py","file_ext":"py","file_size_in_byte":9131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"150851428","text":"import os\nimport re\nimport time\nimport torch as t\nfrom torch import nn\n\nfrom torch.utils.data import DataLoader\n\nfrom utils import progress_bar\nfrom utils import Visualizer\n\ndevice = 'cuda' if t.cuda.is_available() else 'cpu'\n\nvis = Visualizer(env='face_attr')\n\nclass BasicModule(nn.Module):\n def __init__(self):\n super(BasicModule, self).__init__()\n class_name = str(type(self))\n pattern = re.compile('\\'(.*)\\'')\n self.model_name = pattern.findall(class_name)[0]\n\n def compile(self, lr, epochs, criterion, optimizer):\n self.lr = lr\n self.epochs = epochs\n self.criterion = criterion\n self.optimizer = optimizer\n\n def __adjust_learning_rate(self, epoch):\n lr = self.lr * (0.1 ** (epoch // 30))\n for param_group in self.optimizer.param_groups:\n param_group['lr'] = lr\n\n def fit(self, train_loader, val_loader):\n # 训练模式\n self.train()\n for epoch in range(self.epochs):\n print('Epoch: {}/{}'.format(epoch+1, self.epochs))\n train_loss = 0\n correct = 0\n total = 0\n for batch_idx, (inputs, targets) in enumerate(train_loader):\n inputs, targets = inputs.to(device), targets.to(device)\n self.optimizer.zero_grad()\n outputs = self(inputs)\n loss = self.criterion(outputs, targets)\n loss.backward()\n self.optimizer.step()\n\n train_loss += loss.item()\n _, predicted = outputs.max(1)\n total += targets.size(0)\n correct += predicted.eq(targets).sum().item()\n\n msg = '- loss: {:.4f} - acc: {:.4f}'.format(train_loss / total, correct / total)\n progress_bar(batch_idx, len(train_loader), msg, end='\\r')\n\n vis.plot('train_loss', train_loss / total)\n vis.plot('train_acc', correct / total)\n\n val_loss, val_acc = self.evaluate(val_loader, prefix='val')\n vis.plot('val_loss', val_loss)\n vis.plot('val_acc', val_acc)\n msg = '- loss: {:.4f} - acc: {:.4f} - val_loss: {:.4f} - val_acc: {:.4f}'.format(train_loss / total,\n correct / total,\n val_loss, val_acc)\n progress_bar(len(train_loader) - 1, len(train_loader), msg, end='\\n')\n if epoch % 10 == 9:\n self.save(epoch, self.epochs)\n self.__adjust_learning_rate(epoch)\n\n pass\n\n def evaluate(self, eval_loader, prefix='eval'):\n # 测试模式\n self.eval()\n loss = 0\n correct = 0\n total = 0\n for batch_idx, (inputs, targets) in enumerate(eval_loader):\n inputs, targets = inputs.to(device), targets.to(device)\n outputs = self(inputs)\n loss += self.criterion(outputs, targets).item()\n total += targets.size(0)\n\n _, predicted = outputs.max(1)\n correct += predicted.eq(targets).sum().item()\n loss = loss / total\n acc = correct / total\n return loss, acc\n\n def predict(self, x):\n pass\n\n def save(self, epoch, epochs):\n checkpoints_dir = 'checkpoints'\n checkpoint_name = '{model_name}_epoch[{epoch}.{epochs}]_{timestamp}.pt'.format(\n model_name=self.model_name,\n epoch=epoch,\n epochs=epochs,\n timestamp=time.strftime('%y%m%d%H%M%S')\n )\n if not os.path.exists(checkpoints_dir):\n os.mkdir(checkpoints_dir)\n checkpoint_fp = os.path.join(checkpoints_dir, checkpoint_name)\n if not os.path.exists(checkpoint_fp):\n os.mknod(checkpoint_fp)\n t.save(self.state_dict(), checkpoint_fp)\n\n def load(self, path):\n self.load_state_dict(t.load(path))\n","sub_path":"models/basic_module.py","file_name":"basic_module.py","file_ext":"py","file_size_in_byte":3979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"146988452","text":"from bs4 import BeautifulSoup\nimport urllib.request\n\nname = \"gridsource.html\"\n\nhtml_doc = open(name, 'r')\n\nhead = \"https://epp.caltech.edu\"\n\nsoup = BeautifulSoup(html_doc, 'html.parser')\n\ngalleries = soup.find_all(\"a\", attrs={\"data-lightbox\": \"manuscripts\"})\ndescriptions = soup.find_all(\"div\",attrs={\"class\": \"desc\"})\nthumbs = soup.find_all(\"img\",attrs={\"data-lightbox\": \"manuscripts\"})\nprint(len(galleries))\n\ndef download(url, name):\n try:\n urllib.request.urlretrieve(url, name)\n except:\n print(\"download: \", url, \"failed. Skipping\")\n\nfor gallery,description, thumb in zip(galleries, descriptions,thumbs):\n url = head + gallery['href']\n thumburl = head + thumb['src']\n name = description.a.getText()\n print(\"downloading page: \", name)\n download(url, \"./imageset/fullsize/\"+name + \".jpg\")\n download(thumburl, \"./imageset/thumbnail/\"+name + \".jpg\")\n\n","sub_path":"html_parse.py","file_name":"html_parse.py","file_ext":"py","file_size_in_byte":888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"429152865","text":"\"\"\"\nCall variants\n\"\"\"\nimport multiprocessing\nimport functools\nimport argparse\nimport vcf\nimport numpy as np\nimport itertools\nimport scipy.special\nfrom scipy.integrate import quad\nfrom scipy.stats.distributions import beta, binom\nimport h5py\n\ndef generate_prior_SNP_frequency_distributions(prior_data, window_size, avg_coverage=20):\n prior_coords = np.uint64(prior_data[:, 0])\n prior_alpha = (prior_data[:, 1] * avg_coverage)\n prior_beta = avg_coverage - prior_alpha\n return prior_coords, prior_alpha, prior_beta\n\ndef generate_prior_mutation_frequency_distribution(prior_data_file):\n bases = {'A', 'T', 'C', 'G'}\n mut_freq_dict = {}\n permutations = itertools.permutations(bases, 2)\n for b1, b2 in permutations:\n if b1 != b2:\n freq = prior_data_file['/freqs/{}/{}'.format(b1, b2)].value\n mut_freq_dict[(b1, b2)] = freq\n # scaling of frequencies\n for b1 in bases:\n total = np.sum([mut_freq_dict[(b1, b2)] for b2 in bases if b2 != b1])\n for b2 in bases:\n if b1 != b2:\n mut_freq_dict[(b1, b2)] /= total\n return mut_freq_dict\n\ndef p_theta_given_X_B1_B2_beta_parameters(prior_coords, prior_alpha, prior_beta, window_size, mutation_frequencies, X, B1, B2):\n window = int(X // window_size)\n if window < prior_coords.shape[0]:\n assert(prior_coords[window] <= X < prior_coords[window] + window_size)\n return ((prior_alpha[window] * mutation_frequencies[(B1, B2)]) + 0.5, prior_beta[window] + (prior_alpha[window] * (1.0 - mutation_frequencies[(B1, B2)]))+ 0.5)\n return (0.5, 0.5)\n\ndef p_theta_given_D_X_B1_B2_beta_parameters(B2_in_D, total_D, prior_coords, prior_alpha, prior_beta, window_size, mutation_frequencies, X, B1, B2):\n prior_alpha, prior_beta = p_theta_given_X_B1_B2_beta_parameters(prior_coords, prior_alpha, prior_beta, window_size, mutation_frequencies, X, B1, B2)\n return (prior_alpha + B2_in_D, prior_beta + total_D - B2_in_D)\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('-i', '---in-file', type=str, help='Input HDF5 file', action='store', required=True)\n parser.add_argument('-o', '---out-file', type=str, help='Output BED File', action='store', required=True)\n parser.add_argument('-v', '--vcf', type=str, help='Input VCF file', action='store', required=True)\n parser.add_argument('--hdf5-contigs', type=str, nargs='+', help='Ordered list of contigs to use prior from in input HDF5 file', action='store', required=False)\n parser.add_argument('--vcf-contigs', type=str, nargs='+', help='Ordered list of contigs to predict on in input VCF file', action='store', required=False)\n parser.add_argument('--coverage', type=int, default=20, help='Average coverage', action='store')\n parser.add_argument('--min-probability', type=float, default=0.0, help='Minimum probability to output', action='store')\n\n args = parser.parse_args()\n\n input = h5py.File(args.in_file, 'r')\n if not args.hdf5_contigs:\n args.hdf5_contigs = list(sorted([k for k in input.keys() if k != 'freqs']))\n if not args.vcf_contigs:\n args.vcf_contigs = args.hdf5_contigs\n\n assert(len(args.vcf_contigs) == len(args.hdf5_contigs))\n\n reader = vcf.Reader(filename=args.vcf)\n\n mutation_type_prior = generate_prior_mutation_frequency_distribution(input)\n\n outfile = open(args.out_file, 'w')\n\n for hdf5_contig, vcf_contig in zip(args.hdf5_contigs, args.vcf_contigs):\n records = reader.fetch(vcf_contig)\n prior_data = input[hdf5_contig][:]\n window_size = input.attrs['/{}/window_size'.format(hdf5_contig)]\n\n prior_coords, prior_alpha, prior_beta = generate_prior_SNP_frequency_distributions(prior_data, window_size, args.coverage)\n\n for rec in records:\n ref = rec.REF\n alts = rec.ALT\n total_depth = rec.INFO['DP']\n base_depths = rec.INFO['AD']\n alt_and_depths = zip(alts, base_depths[1:])\n for alt, dp in alt_and_depths:\n if str(alt) not in {'A', 'T', 'C', 'G'} or str(ref) not in {'A', 'T', 'C', 'G'}:\n continue\n # used 0-based coords for windows\n a, B = p_theta_given_D_X_B1_B2_beta_parameters(dp, total_depth, prior_coords, prior_alpha, prior_beta, window_size, mutation_type_prior, rec.POS - 1, str(ref), str(alt))\n prob = 1.0E-8\n if a + B - 2 > 0:\n prob = (a - 1) / (a + B - 2)\n if prob >= args.min_probability:\n outfile.write('{}\\t{}\\t{}\\t{}\\t{}\\t{}\\n'.format(vcf_contig, rec.POS, rec.POS + 1, ref, alt, prob))\n print('DONE')","sub_path":"call_variants.py","file_name":"call_variants.py","file_ext":"py","file_size_in_byte":4679,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"76342139","text":"# -*- coding: utf-8 -*- 2\nimport tensorflow as tf\n\n#定义神经网络结构相关参数\nINPUT_NODE= 784\nOUTPUT_NODE =10\nLAYER1_NODE=500\n#测试中通过保存的模型来加载这些变量取值\ndef get_weight_variable(shape, regularizer):\n weights=tf.get_variable(\"weights\",shape,initializer=tf.truncated_normal_initializer(stddev=0.1))\n if regularizer != None:\n tf.add_to_collection('losses',regularizer(weights))\n return weights\n#定义神经网络的前向传播过程\ndef inference(input_tensor,regularizer):\n #声明第一层神经网络的变量并完成前向传播过程\n with tf.variable_scope('layer1'):\n weights =get_weight_variable([INPUT_NODE,LAYER1_NODE],regularizer)\n biases =tf.get_variable(\"biases\",[LAYER1_NODE],initializer=tf.constant_initializer(0.0))\n layer1=tf.nn.relu(tf.matmul(input_tensor,weights)+biases)\n #类似的声明第二层神经网络的变量并完成前向传播的过程\n with tf.variable_scope('layer2'):\n weights = get_weight_variable([LAYER1_NODE,OUTPUT_NODE],regularizer)\n biases=tf.get_variable(\"biases\",[OUTPUT_NODE],initializer=tf.constant_initializer(0.0))\n layer2=tf.matmul(layer1,weights)+biases\n #返回最后前向传播结果\n return layer2\n","sub_path":"mnist_inference.py","file_name":"mnist_inference.py","file_ext":"py","file_size_in_byte":1270,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"536185245","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport pickle\nfrom recnn import grnn_predict_gated\nfrom preprocessing import apply_tf_transform\nfrom preprocessing import create_tf_transform\n\ndef compute_roc_curve(y, y_pred,density=1000):\n \"\"\"return the roc curve\"\"\"\n back = np.argwhere(y == 0)\n back = back.reshape((len(back),))\n sign = np.argwhere(y == 1)\n sign = sign.reshape((len(sign),))\n #prediction\n y_pred_sign = y_pred[sign]\n y_pred_back = y_pred[back]\n t = np.linspace(0.,1.,density)\n tpr = np.zeros(density,dtype = float)\n fpr = np.zeros(density,dtype = float)\n for i in range(density):\n tpr[i] = np.sum(y_pred_sign <= t[i])\n fpr[i] = np.sum(y_pred_back <= t[i])\n tpr = 1-tpr/len(y_pred_sign)\n fpr = 1-fpr/len(y_pred_back)\n return(fpr,tpr,t)\n\n# In[]:\ndef predict(X, filename, func=grnn_predict_gated,regression = True):\n \"\"\"make prediction function\"\"\"\n fd = open(filename, \"rb\")\n params = pickle.load(fd)\n fd.close()\n y_pred = func(params, X,regression = regression)\n return(y_pred)\n\n\ndef transform_for_prediction(Xtrain,Xtest):\n tf = create_tf_transform(Xtrain)\n return(apply_tf_transform(Xtest,tf))\n\ndef build_roc(X, y, filename, func=None):\n \"\"\"evaluates a model and build its roc curve\"\"\"\n print(\"Loading \" + filename),\n y_pred = predict(X, filename, func=func)\n fpr, tpr, _ = compute_roc_curve(y, y_pred,density=10000)\n roc = np.trapz(-tpr,fpr)\n print(\"ROC AUC = %.4f\" % roc)\n return(roc, fpr, tpr)\n","sub_path":"recnn/evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":1537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"592353170","text":"import maya.cmds as mc\nimport os\nfrom miraLibs.pipeLibs import pipeFile\nfrom miraLibs.mayaLibs import get_namespace, export_exocortex_abc, get_frame_range\nfrom miraLibs.pipeLibs.pipeMaya.get_assets_under_type_group import get_assets_under_type_group\n\n\ndef export_single_abc(asset):\n obj = pipeFile.PathDetails.parse_path()\n mc.parent(asset, world=1)\n namespace = get_namespace.get_namespace(asset)\n abc_name = \"%s.abc\" % namespace\n abc_path = os.path.join(obj.cache_dir, abc_name).replace(\"\\\\\", \"/\")\n start, end = get_frame_range.get_frame_range()\n meshes = mc.listRelatives(asset, ad=1, type=\"mesh\")\n geo = [mc.listRelatives(mesh, p=1)[0] for mesh in meshes]\n objects = list(set(geo))\n export_exocortex_abc.export_exocortex_abc(abc_path, 1, end, objects)\n\n\ndef export_model_abc():\n assets = get_assets_under_type_group(\"char\")+get_assets_under_type_group(\"prop\")\n if not assets:\n return\n for asset in assets:\n export_single_abc(asset)\n\n\nif __name__ == \"__main__\":\n export_model_abc()\n","sub_path":"miraLibs/pipeLibs/pipeMaya/export_model_abc.py","file_name":"export_model_abc.py","file_ext":"py","file_size_in_byte":1041,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"384824876","text":"import argparse\nimport os\nfrom utils.user_config import UserConfig\nfrom dataloaders.vqvae_dataloader import VQVAE_DataLoader\nfrom trainer.texture_trainer import TextureTrainer\nfrom model.texture_generator import TextureGeneratorSpec, TextureDiscriminatorSpec\nfrom model.vqvae import VQVAE\nfrom base_train import Base_Train\nfrom utils.tools import random_bbox, bbox2mask\n\n\ndef load_checkpoint(model, checkpoint_dir):\n \"\"\"Load checkpoint.\"\"\"\n checkpoint_dir = os.path.join(checkpoint_dir, 'checkpoints')\n files = os.listdir(checkpoint_dir)\n files.sort(key=lambda x: int(x.split('_')[-1].replace('.h5', '')))\n model.load_weights(os.path.join(checkpoint_dir, files[-1]))\n return model\n\n\nclass TextureGeneratorTrain(Base_Train):\n def __init__(self, config):\n super(TextureGeneratorTrain, self).__init__(config=config)\n bbox = random_bbox(config['data_config']['image_size'], config['data_config']['image_size'],\n config['data_config']['margins'], config['data_config']['mask_size'], random_mask=False)\n regular_mask = bbox2mask(bbox, config['data_config']['image_size'], config['data_config']['image_size'],\n config['data_config']['max_delta'], name='mask_c')\n mask = regular_mask\n self.config = config\n self.gen = TextureGeneratorSpec()\n self.dis = TextureDiscriminatorSpec()\n self.data_loader = VQVAE_DataLoader(config, mask=mask)\n self.data_loader.is_mask = True\n self.data_loader.load_state(self.config['learning_config']['running_config']['vqvae_checkpoints_dir'])\n # load vqvae model\n vae_model = VQVAE(config)\n vae_checkpoint_dir = config['learning_config']['running_config']['vqvae_checkpoints_dir']\n vae_model._build([1, 64, 64, 3])\n vae_model = load_checkpoint(vae_model, vae_checkpoint_dir)\n vae_encoder = vae_model.encoder\n self.runner = TextureTrainer(config['learning_config']['running_config'], mask=mask, vae_model=vae_encoder,\n num_embeddings=config['vqvae']['num_embeddings'])\n self.opt = self.load_opt()\n self.runner.set_total_train_steps(\n config['learning_config']['running_config']['max_steps']\n )\n self.runner.compile(gen=self.gen, dis=self.dis, optimizer=self.opt)\n self.data_loader.batch = self.runner.global_batch_size\n self.training_flag = True\n\nif __name__ == '__main__':\n parse = argparse.ArgumentParser()\n parse.add_argument('--data_config', type=str, default='./config/data_config.yaml',\n help='the am data config path')\n parse.add_argument('--model_config', type=str, default='./config/model_config.yaml',\n help='the am model config path')\n args = parse.parse_args()\n config = UserConfig(args.data_config, args.model_config)\n train = TextureGeneratorTrain(config)\n train.train()","sub_path":"texture_generator.py","file_name":"texture_generator.py","file_ext":"py","file_size_in_byte":2957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"433539318","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 28 14:22:30 2021\n\n@author: wuyi1234\n\"\"\"\n# dir_path should contain all the data needed for plotting\ndir_path = r'C:\\Users\\wuyi1234\\Desktop\\DataLogger\\aaaa'\n\nimport datetime\nimport os\n\nimport pandas as pd\n\nstarting_hour = 16 # which is 24-hour format\nexecute_once = True\n\n# first step is to find the time interval which files cover through\nfor file in os.listdir(dir_path):\n CurrFile = pd.read_csv(os.path.join(dir_path, file))\n # drop the last three digits in date (UTC)\n CurrFile[\"date (UTC)\"] = CurrFile[\"date (UTC)\"].map(lambda x: x[:-4])\n # convert your timestamps to datetime and then use matplotlib\n CurrFile[\"date-format\"] = CurrFile[\"date (UTC)\"].map(lambda x: datetime.datetime.strptime(x, \"%d.%m.%Y %H:%M:%S\"))\n # sort timestamp in ascending order\n CurrFile = CurrFile.sort_values(by='date-format', ascending=True)\n # delete the NA rows\n CurrFile = CurrFile.dropna()\n\n if execute_once:\n min_time = min(CurrFile['date-format'])\n max_time = max(CurrFile['date-format'])\n execute_once = False\n\n if min(CurrFile['date-format']) < min_time:\n min_time = min(CurrFile['date-format'])\n if max(CurrFile['date-format']) > max_time:\n max_time = max(CurrFile['date-format'])\n\n# each element in file_list would be one day-data\nfile_list = []\n# each element in date_list would be corresponding to weekday of the 24-hour\n# len(date_list) should be equal to the file_list\ndate_list = []\ntime_list = []\n\nstarting_date = pd.Timestamp(min_time.year, min_time.month, min_time.day,\n starting_hour, 0, 0)\n\nending_date = starting_date + pd.Timedelta(days=1)\n\nwhile ending_date < max_time + pd.Timedelta(days=1):\n temp_df = pd.DataFrame()\n for file in os.listdir(dir_path): # since 24-hour data may be contained in two files or more\n CurrFile = pd.read_csv(os.path.join(dir_path, file))\n # drop the last three digits in date (UTC)\n CurrFile[\"date (UTC)\"] = CurrFile[\"date (UTC)\"].map(lambda x: x[:-4])\n # convert your timestamps to datetime and then use matplotlib\n CurrFile[\"date-format\"] = CurrFile[\"date (UTC)\"].map(\n lambda x: datetime.datetime.strptime(x, \"%d.%m.%Y %H:%M:%S\"))\n # sort timestamp in ascending order\n CurrFile = CurrFile.sort_values(by='date-format', ascending=True)\n # delete the NA rows\n CurrFile = CurrFile.dropna()\n\n filtered_file = CurrFile[(starting_date <= CurrFile[\"date-format\"]) &\n (CurrFile[\"date-format\"] < ending_date)]\n if len(filtered_file) != 0:\n temp_df = temp_df.append(filtered_file)\n\n if len(temp_df) != 0: # there could be some missing day\n temp_df = temp_df.sort_values(by='date-format', ascending=True)\n # remove duplicate rows if there is\n temp_df = temp_df.drop_duplicates()\n temp_df['date-format'] = temp_df['date-format'] - pd.Timedelta(hours=5)\n file_list.append(temp_df)\n\n if starting_date.weekday() in [0, 1, 2, 3]:\n date_list.append('weekday')\n else:\n date_list.append('weekend')\n\n time_list.append([starting_date, ending_date])\n\n starting_date += pd.Timedelta(days=1)\n ending_date += pd.Timedelta(days=1)\n\n print(starting_date)\n print(ending_date)\n\nimport numpy as np\n\n\ndef moving_average(df, window_size, time_threshold):\n # add nan value for the first few values\n new_col = []\n new_col.extend([np.nan] * (window_size - 1))\n\n for i in range(0, len(df) - window_size + 1):\n # time interval check\n delta_seconds = (df.iloc[i + window_size - 1, 7] - df.iloc[i, 7]).total_seconds()\n if 0 < delta_seconds <= time_threshold:\n avg = df.iloc[i:(i + window_size), 6].mean()\n new_col.append(avg)\n else:\n new_col.append(np.nan)\n\n df['ma'] = new_col\n return\n\n\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as mdates\n\n\ndef accumlating_plot(file_list, date_list, unit_name, save_path):\n weekday_lines = []\n weekend_lines = []\n weekday_index = []\n weekend_index = []\n\n for i in range(len(date_list)):\n if date_list[i] == 'weekday':\n weekday_index.append(i)\n if date_list[i] == 'weekend':\n weekend_index.append(i)\n\n for index in weekday_index:\n if len(file_list[index]) > 360: # ensure that we have enough points for calculation\n # calculate the moving average\n moving_average(file_list[index], 360, 1920)\n\n # change year,month,day to the same\n mindate = time_list[index][0].date()\n # maxdate=max(CurrFile['date-format']).date())\n file_list[index]['redefined-timestamp'] = file_list[index]['date-format'].map(\n lambda x: x.replace(year=2000, month=1, day=1)\n if x.date() == mindate else x.replace(year=2000, month=1, day=2))\n weekday_lines.append(file_list[index][['date-format', 'redefined-timestamp', 'ma']])\n\n for index in weekend_index:\n if len(file_list[index]) > 360:\n # calculate the moving average\n moving_average(file_list[index], 360, 1920)\n # change year,month,day to the same\n mindate = time_list[index][0].date()\n # maxdate=max(CurrFile['date-format']).date())\n file_list[index]['redefined-timestamp'] = file_list[index]['date-format'].map(\n lambda x: x.replace(year=2000, month=1, day=1)\n if x.date() == mindate else x.replace(year=2000, month=1, day=2))\n weekend_lines.append(file_list[index][['date-format', 'redefined-timestamp', 'ma']])\n\n fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(19, 9))\n\n for line in weekday_lines:\n ax1.plot(line[\"redefined-timestamp\"], line['ma'], 'm')\n print(1)\n\n for line in weekend_lines:\n ax2.plot(line[\"redefined-timestamp\"], line['ma'], 'm')\n print(2)\n\n hrlocator = mdates.HourLocator()\n majorFmt = mdates.DateFormatter('%H:%M')\n\n ax1.xaxis.set_major_locator(hrlocator)\n ax1.xaxis.set_major_formatter(majorFmt)\n\n ax2.xaxis.set_major_locator(hrlocator)\n ax2.xaxis.set_major_formatter(majorFmt)\n # rotate 90 degrees\n plt.setp(ax1.xaxis.get_majorticklabels(), rotation=90)\n plt.setp(ax2.xaxis.get_majorticklabels(), rotation=90)\n ax1.set_ylabel('weekday-VOC', fontsize=13)\n ax2.set_ylabel('weekend-VOC', fontsize=13)\n\n # save prior to show\n file_name = unit_name + \".svg\"\n plt.savefig(os.path.join(save_path, file_name), format='svg', dpi=1200, bbox_inches='tight')\n\n plt.show()\n\n return\n\n\naccumlating_plot(file_list, date_list, '630094-VOC', r'C:\\Users\\wuyi1234\\Desktop\\New')\n","sub_path":"pyfiles/plot daily date-advanced.py","file_name":"plot daily date-advanced.py","file_ext":"py","file_size_in_byte":6784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"527997959","text":"# Qiuyang Zhang\n# COMP 590, Spring 2020\n# Assignment: Feature Extraction\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ntry:\n # Python 2\n from itertools import izip\nexcept ImportError:\n # Python 3\n izip = zip\n\n#-------------------------------------------------------------------------------\n\n# display keypoints in an image\n#\n# inputs:\n# - image: input image, assumed to be grayscale\n# - keypoints: Nx2 array of keypoint (x,y) pixel locations in the image\n# returns: None\ndef plot_keypoints(image, keypoints):\n plt.imshow(image, cmap=\"gray\")\n plt.scatter(keypoints[:,0], keypoints[:,1], s=8, c=\"r\")\n plt.xlim(0, image.shape[1])\n plt.ylim(image.shape[0], 0)\n\n#-------------------------------------------------------------------------------\n\n# display keypoint matches between a pair of images\n#\n# inputs:\n# - image1: first input image, assumed to be grayscale\n# - keypoints1: N1 x 2 array of keypoint (x,y) pixel locations in the first\n# image, assumed to be integer coordinates\n# - image2: second input image, assumed to be grayscale\n# - keypoints2: N2 x 2 array of keypoint (x,y) pixel locations in the second\n# image, assumed to be integer coordinates\n# - matches: M x 2 array of indices for the matches; the first column\n# provides the index for the keypoint in the first image, and the second\n# column provides the corresponding keypoint index in the second image\n# returns: None\ndef plot_matches(image1, keypoints1, image2, keypoints2, matches):\n keypoints1 = keypoints1[matches[:, 0]]\n keypoints2 = keypoints2[matches[:, 1]]\n\n # show the original images side-by-side\n im = np.column_stack((image1, image2))\n plt.imshow(im, cmap=\"gray\")\n plt.scatter(keypoints1[:, 0], keypoints1[:, 1],\n\t\t facecolors='none', edgecolors='r')\n plt.scatter(keypoints2[:, 0] + image1.shape[1], keypoints2[:, 1],\n\t\t facecolors='none', edgecolors='r')\n\n # draw the matches with random colors\n for kp1, kp2 in izip(keypoints1, keypoints2):\n x = (kp1[0], kp2[0] + image1.shape[1])\n y = (kp1[1], kp2[1])\n c = (2. * np.random.rand(3) + 1.) / 3. # RGB \\in (0.5, 1)\n plt.plot(x, y, color=c, linewidth=1.0)\n\n plt.gca().axis('off')\n plt.tight_layout()\n","sub_path":"myharriscornerdetection/plot_util.py","file_name":"plot_util.py","file_ext":"py","file_size_in_byte":2227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"233661394","text":"# -*- coding: utf-8 -*-\n\nfrom . import utils, Commands\nfrom .tasks import task\nfrom ..utils import print_error\n\n\ndef unpack(*args, **values):\n items = [arg for arg in args\n if isinstance(arg, (tuple, list)) and len(arg) == 2]\n extra_values = {key: value for key, value in items}\n\n values.update(extra_values)\n\n keys = [arg for arg in args if not isinstance(arg, (tuple, list))]\n keys.extend(values.keys())\n\n return keys, values\n\n\nclass EventHandler(task):\n\n def __init__(self, *args, func, prefix=None, **values):\n super().__init__(func)\n\n self.keys, self.values = unpack(*args, **values)\n self.prefix = prefix\n\n if prefix is not None:\n self.command = Commands(prefix=prefix)\n\n def __call__(self, *args, **kwargs):\n if hasattr(self, \"command\"):\n return super().__call__(*args, self.command, **kwargs)\n else:\n return super().__call__(*args, **kwargs)\n\n def __repr__(self):\n return \"<{clsname}: keys:{keys} prefix:{prefix}>\".format(\n clsname=self.__class__.__name__,\n prefix=self.prefix,\n keys=\", \".join(self.keys)\n )\n\n\ndef event_handler(*args, prefix=None, **values):\n def decorator(func):\n event_handler = EventHandler(\n *args,\n func=func,\n prefix=prefix,\n **values\n )\n\n return event_handler\n\n return decorator\n\n\ndef _test(data, keys, values):\n if any(key not in data for key in keys):\n return False\n\n for key, value in values.items():\n if isinstance(data[key], dict):\n if isinstance(value, dict):\n value = value.items()\n elif not isinstance(value, (list, tuple, set)):\n value = (value,)\n\n if _test(data[key], *unpack(value)) is False:\n return False\n else:\n if data[key] != value:\n return False\n\n return True\n\n\nclass EventStream:\n\n def __init__(self, client):\n self._client = client\n\n def __getitem__(self, key):\n return self._client[key]\n\n def __getattr__(self, key):\n return getattr(self._client, key)\n\n @property\n def stream_request(self):\n clsname = self.__class__.__name__\n msg = \"You must overload stream_request property in \" + clsname\n raise RuntimeError(msg)\n\n @utils.restart_on(TimeoutError)\n async def _start(self):\n if callable(self.stream_request):\n stream_request = self.stream_request()\n else:\n stream_request = self.stream_request\n\n async with stream_request as ressource:\n async for data in ressource:\n try:\n await self._run(data)\n except Exception as e:\n msg = \"error in %s._start:\\n\" % self.__class__.__name__\n print_error(msg)\n\n def _check(self, func):\n if not func.startswith(\"_\"):\n return isinstance(getattr(self, func), EventHandler)\n else:\n return False\n\n def _get(self, data):\n try:\n functions = [getattr(self, func)\n for func in dir(self) if self._check(func)]\n\n for event_handler in functions:\n keys, values = event_handler.keys, event_handler.values\n if _test(data, keys, values):\n return event_handler\n\n except:\n msg = \"error in %s._get:\\n\" % self.__class__.__name__\n print_error(msg)\n\n async def _run(self, data):\n event_handler = self._get(data)\n\n try:\n if event_handler:\n coro = event_handler(self, data)\n return await utils.execute(coro)\n\n except Exception as e:\n fmt = \"error occurred while running {classname} {handler}:\\n\"\n msg = fmt.format(classname=self.__class__.__name__,\n handler=event_handler.__name__)\n\n print_error(msg)\n\n\nclass EventStreams(list):\n\n def __init__(self):\n super().__init__()\n self.is_setup = False\n\n def check_setup(func):\n def decorated(self, client):\n if not self.is_setup:\n self.setup(client)\n\n return func(self)\n\n return decorated\n\n @check_setup\n def get_tasks(self):\n return [stream._start() for stream in self]\n\n @check_setup\n def get_task(self):\n if len(self) == 1:\n return self[0]._start()\n elif self:\n raise RuntimeError(\"more than 1 event stream\")\n else:\n raise RuntimeError(\"no event stream\")\n\n def setup(self, client):\n for i in range(len(self)):\n self[i] = self[i](client=client)\n\n self.is_setup = True\n","sub_path":"peony/commands/event_handlers.py","file_name":"event_handlers.py","file_ext":"py","file_size_in_byte":4816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"317493278","text":"#! /usr/bin/env python3\n\nimport torch\nfrom torch.optim import lr_scheduler\nfrom torch.autograd import Variable\nimport argparse\nfrom torchvision import transforms, models, datasets\nimport argparse\nimport sys\nimport time\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-w\", \"--workers\", type=int, default=8)\nparser.add_argument(\"-t\", \"--total-images\", type=int)\nparser.add_argument(\"-g\", \"--gpu\", type=int, default=0)\nparser.add_argument(\"-e\", \"--epoch\", type=int, default=1)\nparser.add_argument(\"-B\", \"--begin\", type=int)\nparser.add_argument(\"-b\", \"--batch-size\", type=int, default=32)\nparser.add_argument(\"-E\", \"--end\", type=int)\nparser.add_argument(\"model_prefix\")\nparser.add_argument(\"testing_dir\")\nargs = parser.parse_args()\n\ntransform = transforms.Compose([\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n ])\n\nclasses = [c.strip(\"\\r\\n\")\n for c in open(f\"{args.model_prefix}_classmap.txt\", \"r\")]\ndataset = datasets.ImageFolder(args.testing_dir, transform)\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size,\n shuffle=False, num_workers=args.workers)\n\ndevice = torch.device(f\"cuda:{args.gpu}\" if torch.cuda.is_available() else \"cpu\")\nprint(f\"Using device {device}\")\n\nif (not args.begin):\n args.begin = args.epoch\n args.end = args.epoch + 1\n\nwith torch.no_grad():\n for epoch in range(args.begin, args.end):\n t1 = time.time()\n print(f'Running epoch {epoch}')\n\n model = f\"{args.model_prefix}_{epoch:05d}.h5f\"\n print(f'Model: {model}')\n \n net = torch.load(model)\n net = net.to(device)\n net.train(False)\n\n total_images = len(dataset) if args.total_images is None else args.total_images\n\n ok = 0\n ok_weak = 0\n for i, (inputs, labels) in enumerate(dataloader):\n print(f\"{i*dataloader.batch_size}/{len(dataset)}\", end=\"\\r\",\n file=sys.stderr)\n\n inputs = inputs.to(device)\n outputs = net(inputs)\n _, preds = torch.max(outputs.data, 1)\n for p, l in zip(preds, labels):\n if classes[p] == dataset.classes[l]:\n ok += 1\n if abs(float(classes[p])-float(dataset.classes[l])) < 16:\n ok_weak += 1\n\n t2 = time.time()\n\n print(file=sys.stderr)\n print(\"Strict: \", ok/total_images*100, \"%\", sep=\"\")\n print(\"Weak: \", ok_weak/total_images*100, \"%\", sep=\"\")\n print(f\"Time: {round(t2 - t1, 2)} seconds\")","sub_path":"mestrado/python/partspose/test_alexnet.py","file_name":"test_alexnet.py","file_ext":"py","file_size_in_byte":2571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"51204190","text":"import numpy as np\r\n\r\ndef compute_confusion_matrix(actual, predicted): # function with arguments\r\n uniqueclass = np.unique(actual) # to check number of unique classes in both input arrays\r\n uniqueclassIterator = uniqueclass # to iterate through unique class\r\n dict = {} # empty dictionary to collect the output\r\n\r\n for k in uniqueclassIterator: #to iterate through class ID\r\n confusionmatrix = np.zeros((len(uniqueclass), len(uniqueclass))) # to create a numpy array of length of unique dimensions\r\n for i in range(len(uniqueclass)): # nested for loop to calculate the each element of uniqueclass\r\n for j in range(len(uniqueclass)):\r\n confusionmatrix[i, j] = np.sum(((actual == uniqueclass[i]) &\r\n (predicted == uniqueclass[j])))\r\n\r\n uniqueclass = np.roll(uniqueclass,-1) # np.roll to iterate through the classID\r\n #dict[k]=confusionmatrix #output of each classID in dictionary\r\n dict[k] = calculate_elements_confusionmatrix(confusionmatrix, k) # output classid, tp,tn,fp,fn\r\n return dict\r\n\r\n\r\ndef calculate_elements_confusionmatrix(confusion_mat, i=0):\r\n # i means which class to choose to do one-vs-the-rest calculation\r\n # rows are actual obs whereas columns are predictions\r\n TP = confusion_mat[i,i]\r\n FP = confusion_mat[:,i].sum() - TP\r\n FN = confusion_mat[i,:].sum() - TP\r\n TN = confusion_mat.sum().sum() - TP - FP - FN\r\n return TP, FP, FN, TN\r\n\r\n\r\nactual = [1,0,1,0,3,1,1,2,1]\r\npredicted = [1,1,0,0,3,0,0,2,2]\r\nresult = compute_confusion_matrix(actual,predicted)\r\nprint(result)\r\n\r\n","sub_path":"cm.py","file_name":"cm.py","file_ext":"py","file_size_in_byte":1733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"408940072","text":"import numpy as np\nfrom utils import *\nfrom sklearn.neighbors import NearestNeighbors\nfrom distances import euclidean_distance, cosine_distance\n\n\nclass KNNClassifier:\n def __init__(\n self, k=1, strategy='brute', metric='euclidean',\n weights=False, test_block_size=None, epsilon=1e-5,\n augment_test_data=False, angle=None, x_shift=None, y_shift=None, sigma=None\n ):\n self.k = k\n self.strategy = strategy\n self.metric = metric\n self.weights = weights\n self.test_block_size = test_block_size\n self.epsilon = epsilon\n\n self.augment_test_data = augment_test_data\n self.angle = angle\n self.x_shift = x_shift\n self.y_shift = y_shift\n self.sigma = sigma\n\n self.model = None\n\n self.x = None\n self.y = None\n self.n_classes = None\n\n if self.metric == 'euclidean':\n self.pairwise_dist = euclidean_distance\n elif self.metric == 'cosine':\n self.pairwise_dist = cosine_distance\n else:\n raise NotImplementedError\n\n self._find_kneighbors_chunk = self._find_kneighbors_chunk\n\n def fit(self, x, y):\n self.y = y.astype(np.int)\n self.n_classes = np.max(y) + 1\n\n if self.strategy != 'my_own':\n self.model = NearestNeighbors(\n n_neighbors=self.k,\n algorithm=self.strategy,\n metric=self.metric,\n )\n self.model.fit(x)\n self._find_kneighbors_chunk = self.model.kneighbors\n else:\n self.x = x\n\n def find_kneighbors(self, x, return_distance):\n distances = np.empty([x.shape[0], self.k], dtype=np.float64) if return_distance else None\n idx_neighbors = np.empty([x.shape[0], self.k], dtype=np.int)\n\n if self.test_block_size is None:\n real_test_block_size = x.shape[0]\n else:\n real_test_block_size = min(self.test_block_size, x.shape[0])\n r = (real_test_block_size - x.shape[0] % real_test_block_size) % real_test_block_size\n n = x.shape[0] // real_test_block_size + (1 if r else 0)\n left_border = 0\n for idx in range(n):\n chunk_size = real_test_block_size - 1 if idx < r else real_test_block_size\n right_border = left_border + chunk_size\n\n if return_distance:\n (distances_chunk, idx_neighbors_chunk) = \\\n self._find_kneighbors_chunk(x[left_border:right_border], return_distance=True)\n else:\n (distances_chunk, idx_neighbors_chunk) = \\\n None, self._find_kneighbors_chunk(x[left_border:right_border], return_distance=False)\n\n idx_neighbors[left_border:right_border] = idx_neighbors_chunk\n if return_distance:\n distances[left_border:right_border] = distances_chunk\n del idx_neighbors_chunk, distances_chunk\n\n left_border += chunk_size\n if return_distance:\n return distances, idx_neighbors\n else:\n return idx_neighbors\n\n def _find_kneighbors_chunk(self, x, return_distance):\n pairwise_dist = self.pairwise_dist(x, self.x)\n idx_neighbors = np.argsort(pairwise_dist, axis=1)[:, :self.k]\n if return_distance:\n return np.sort(pairwise_dist, axis=1)[:, :self.k], idx_neighbors\n else:\n return idx_neighbors\n\n def predict(self, x):\n y_predict = np.empty([x.shape[0]], dtype=np.int)\n\n if self.test_block_size is None:\n real_test_block_size = x.shape[0]\n else:\n real_test_block_size = min(self.test_block_size, x.shape[0])\n r = (real_test_block_size - x.shape[0] % real_test_block_size) % real_test_block_size\n n = x.shape[0] // real_test_block_size + (1 if r else 0)\n\n left_border = 0\n for idx in range(n):\n chunk_size = real_test_block_size - 1 if idx < r else real_test_block_size\n right_border = left_border + chunk_size\n\n y_predict_chunk = self._predict_chunk(x[left_border:right_border])\n y_predict[left_border:right_border] = y_predict_chunk\n del y_predict_chunk\n\n left_border += chunk_size\n return y_predict\n\n def _predict_chunk(self, x):\n if self.weights:\n if self.augment_test_data:\n x_augmented = augment_data(\n x, angle=self.angle, x_shift=self.x_shift, y_shift=self.y_shift, sigma=self.sigma\n )\n\n if self.sigma is None:\n distances_aug = np.empty([x.shape[0], 3 * self.k], dtype=np.float64)\n idx_neighbors_aug = np.empty([x.shape[0], 3 * self.k], dtype=np.int)\n\n (distances_aug[:, 2 * self.k:],\n idx_neighbors_aug[:, 2 * self.k:]) = \\\n self._find_kneighbors_chunk(x_augmented[2 * x.shape[0]:], return_distance=True)\n else:\n distances_aug = np.empty([x.shape[0], 2 * self.k], dtype=np.float64)\n idx_neighbors_aug = np.empty([x.shape[0], 2 * self.k], dtype=np.int)\n\n (distances_aug[:, :self.k],\n idx_neighbors_aug[:, :self.k]) = \\\n self._find_kneighbors_chunk(x_augmented[:x.shape[0]], return_distance=True)\n\n (distances_aug[:, self.k:2 * self.k],\n idx_neighbors_aug[:, self.k:2 * self.k]) = \\\n self._find_kneighbors_chunk(x_augmented[x.shape[0]:2 * x.shape[0]], return_distance=True)\n\n distances = np.sort(distances_aug, axis=1)[:, :self.k]\n idx_neighbors = np.empty([x.shape[0], self.k], dtype=np.int)\n idx_quasi_neighbors = np.argsort(distances_aug, axis=1)\n for idx in range(x.shape[0]):\n idx_neighbors[idx] = idx_neighbors_aug[idx, idx_quasi_neighbors[idx]][:self.k]\n else:\n distances, idx_neighbors = self._find_kneighbors_chunk(x, return_distance=True)\n else:\n idx_neighbors = self._find_kneighbors_chunk(x, return_distance=False)\n distances = None\n\n if self.weights:\n distances += self.epsilon\n np.divide(1., distances, out=distances)\n weights = distances\n else:\n weights = None\n\n neighbor_classes = self.y[idx_neighbors]\n if not self.weights:\n class_scores = np.apply_along_axis(np.bincount, axis=1, arr=neighbor_classes)\n else:\n class_scores = np.empty([x.shape[0], self.n_classes], dtype=np.float64)\n for idx in range(x.shape[0]):\n class_scores[idx] = np.bincount(neighbor_classes[idx], weights=weights[idx], minlength=self.n_classes)\n y_predict = np.argmax(class_scores, axis=1)\n return y_predict\n","sub_path":"Tasks/task1/nearest_neighbors.py","file_name":"nearest_neighbors.py","file_ext":"py","file_size_in_byte":6912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"82043299","text":"# Copyright 2016 Red Hat, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nTest management of KeyboardInterrupt in stratisd.\n\"\"\"\n\n# isort: LOCAL\nimport stratis_cli\n\nfrom .._misc import SimTestCase\n\n\nclass KeyboardInterruptTestCase(SimTestCase):\n \"\"\"\n Test behavior of stratis on KeyboardInterrupt.\n \"\"\"\n\n def test_catch_keyboard_exception(self):\n \"\"\"\n Verify that the KeyboardInterrupt is propagated by the run() method.\n ./bin/stratis contains a try block at the outermost level which\n then catches the KeyboardInterrupt and exits with an error message.\n The KeyboardInterrupt is most likely raised in the dbus-python\n method which is actually communicating on the D-Bus, but it is\n fairly difficult to get at that method. Instead settle for getting\n at the calling method generated by dbus-python-client-gen.\n \"\"\"\n\n def raise_keyboard_interrupt(_):\n \"\"\"\n Just raise the interrupt.\n \"\"\"\n raise KeyboardInterrupt()\n\n # pylint: disable=import-outside-toplevel\n # isort: LOCAL\n from stratis_cli._actions import _data\n\n # pylint: disable=protected-access\n stratis_cli._actions._data.Manager.Properties.Version.Get = (\n raise_keyboard_interrupt\n )\n\n with self.assertRaises(KeyboardInterrupt):\n stratis_cli.run()([\"daemon\", \"version\"])\n","sub_path":"tests/whitebox/monkey_patching/test_keyboard_interrupt.py","file_name":"test_keyboard_interrupt.py","file_ext":"py","file_size_in_byte":1934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"291617014","text":"import json\nimport requests\nfrom requests_oauthlib import OAuth1Session #OAuthのライブラリの読み込み\n\n\napp_id = \"APP_ID\"\nurl_chrono = \"https://labs.goo.ne.jp/api/chrono\"\n\n# See sample response below.\ndef chrono(sentence, class_filter, request_id=\"record002\"):\n\tpayload = {\"app_id\": app_id, \"request_id\": request_id, \"sentence\": sentence, \"class_filter\": class_filter}\n\theaders = {'content-type': 'application/json'}\n\tr = requests.post(url_chrono, data=json.dumps(payload), headers=headers)\n\treturn (r.text)","sub_path":"textanalyze.py","file_name":"textanalyze.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"576929509","text":"import sys\nfrom sklearn import tree\n\n\ndef read_problem(data_path):\n '''\n Read Libsvm format data and return labels y and data instance x.\n '''\n f = open(data_path, \"r\")\n Y,X = [],[]\n for line in f:\n line = line.split(' ', 1)\n label, feature = line\n Y.append(label)\n tmpx = []\n idx = 1\n for e in feature.split():\n e = e.split(':')\n for i in range(idx, int(e[0])):\n tmpx.append(0.0)\n idx = int(e[0])+1\n tmpx.append(float(e[1]))\n X.append(tmpx)\n f.close()\n return Y,X\n\ndef evaluate(teY, prY):\n corr = 0\n total = 0\n for (y1, y2) in zip(teY, prY):\n if y1 == y2:\n corr += 1\n total += 1\n print(\"Accuracy: {0} ({1}/{2})\".format(float(corr)/total, corr, total))\n\ndef main():\n\n if len(sys.argv) != 3:\n print(\"Usage: python destree.py [train-data] [test-data]\")\n exit(0)\n print(sys.argv[1])\n trY, trX = read_problem(sys.argv[1])\n teY, teX = read_problem(sys.argv[2])\n \n ''' \n for (x1, y1) in zip(trX, trY):\n print(y1, x1)\n '''\n clf = tree.DecisionTreeClassifier()\n clf = clf.fit(trX,trY)\n \n prY = clf.predict(teX)\n evaluate(teY, prY)\n \n\nif __name__ == \"__main__\":\n main();\n","sub_path":"code/mltools/dectree.py","file_name":"dectree.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"178634337","text":"import torch\nfrom torch import nn, einsum\nimport torch.nn.functional as F\nfrom einops import rearrange\n\n# helpers\n\ndef exists(val):\n return val is not None\n\ndef default(val, d):\n return val if exists(val) else d\n\n# classes\n\n# main class\n\nclass HourglassTransformer(nn.Module):\n def __init__(\n self,\n *,\n num_tokens,\n dim,\n max_seq_len,\n depth,\n heads = 8,\n dim_head = 64,\n causal = True\n ):\n super().__init__()\n self.max_seq_len = max_seq_len\n\n self.token_emb = nn.Embedding(num_tokens, dim)\n self.pos_emb = nn.Embedding(max_seq_len, dim)\n\n self.to_logits = nn.Sequential(\n nn.LayerNorm(dim),\n nn.Linear(dim, num_tokens)\n )\n\n def forward(self, x):\n device = x.device\n x = self.token_emb(x)\n pos_emb = self.pos_emb(torch.arange(x.shape[-2], device = device))\n x = x + rearrange(pos_emb, 'n d -> () n d')\n\n return self.to_logits(x)\n","sub_path":"hourglass_transformer_pytorch/hourglass_transformer_pytorch.py","file_name":"hourglass_transformer_pytorch.py","file_ext":"py","file_size_in_byte":1007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"187586275","text":"\nimport json\nimport asyncio\nfrom beacontools import BeaconScanner, EddystoneUIDFrame\nfrom web3 import Web3\n\n\n\nlist_a = []\nlist_b= []\n\n\n\ndef callback(bt_addr, rssi, packet, additional_info):\n received_ble = packet.namespace+packet.instance\n print(\"ble: \"+received_ble)\n compare_bles(received_ble)\n\n\ndef compare_bles(received_ble):\n global list_a\n if received_ble in list_a :\n print(\"Access A granted\")\n else:\n print(\"Access A denied\")\n \n global list_b\n if received_ble in list_b:\n print(\"Access B granted\")\n print(\"\")\n else:\n print(\"Access B denied \")\n print(\"\")\n \n\n\ndef getContracts_a(count):\n #get all addresses from smart contract\n for x_add in range(count):\n currentcontract = contract.functions.ble_addresses_a(x_add).call()\n global list_a\n list_a.append(currentcontract)\n list_a = list(set(list_a))\n\n \n\n\ndef getContracts_b(count):\n # get all addresses from smart contract\n for x_add in range(count):\n currentcontract = contract.functions.ble_addresses_b(x_add).call()\n global list_b\n list_b.append(currentcontract)\n list_b = list(set(list_b))\n\ndef printLists():\n print(\"List Access A: \")\n print(*list_a, sep = \", \")\n print(\"\")\n print(\"List Access B: \")\n print(*list_b, sep = \", \")\n print(\"\")\n\n\n#Ethereum Node\ninfura_url = 'https://rinkeby.infura.io/v3/fcbf73400bbb48b28291758dd4e2b8f5'\nweb3 = Web3(Web3.HTTPProvider(infura_url))\nprint(web3.isConnected())\n\n\n\n#Smart Contract \nabi = json.loads('[{\"inputs\":[{\"internalType\":\"address payable\",\"name\":\"_wallet_reciever\",\"type\":\"address\"}],\"stateMutability\":\"nonpayable\",\"type\":\"constructor\"},{\"inputs\":[{\"internalType\":\"string\",\"name\":\"ble_add\",\"type\":\"string\"}],\"name\":\"addAdress\",\"outputs\":[],\"stateMutability\":\"payable\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"uint256\",\"name\":\"\",\"type\":\"uint256\"}],\"name\":\"ble_addresses_a\",\"outputs\":[{\"internalType\":\"string\",\"name\":\"\",\"type\":\"string\"}],\"stateMutability\":\"view\",\"type\":\"function\"},{\"inputs\":[{\"internalType\":\"uint256\",\"name\":\"\",\"type\":\"uint256\"}],\"name\":\"ble_addresses_b\",\"outputs\":[{\"internalType\":\"string\",\"name\":\"\",\"type\":\"string\"}],\"stateMutability\":\"view\",\"type\":\"function\"},{\"inputs\":[],\"name\":\"count_a\",\"outputs\":[{\"internalType\":\"uint256\",\"name\":\"\",\"type\":\"uint256\"}],\"stateMutability\":\"view\",\"type\":\"function\"},{\"inputs\":[],\"name\":\"count_b\",\"outputs\":[{\"internalType\":\"uint256\",\"name\":\"\",\"type\":\"uint256\"}],\"stateMutability\":\"view\",\"type\":\"function\"}]')\naddress = web3.toChecksumAddress(\"0xdf24542177e1e86e016fea56bca8002597147d1e\")\ncontract = web3.eth.contract(address = address, abi=abi)\nprint(contract)\n\n\nscanner = BeaconScanner(\n callback,\n packet_filter=[EddystoneUIDFrame]\n)\n\n#for access A\nasync def ble_sc():\n while True:\n try:\n getContracts_a(contract.functions.count_a().call())\n except Exception as e:\n print(str(e))\n #for access B\n try:\n getContracts_b(contract.functions.count_b().call())\n except Exception as e:\n print(str(e))\n printLists()\n await asyncio.sleep(10)\n\nasync def scanBle():\n while True:\n scanner.start()\n await asyncio.sleep(2)\n \n \nloop = asyncio.get_event_loop()\ncors = asyncio.wait([ble_sc(), scanBle()])\nloop.run_until_complete(cors)\n","sub_path":"com_access.py","file_name":"com_access.py","file_ext":"py","file_size_in_byte":3271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"64594553","text":"\"\"\"\nImplementation of attack methods. Running this file as a program will\napply the attack to the model specified by the config file and store\nthe examples in an .npy file.\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\nimport numpy as np\nfrom scipy.spatial.distance import pdist, squareform\nfrom matplotlib import pyplot as plt\nimport os\n\nimport cifar10_input\n\n\nc = 1.1\nimport time\nclass LinfBLOBAttack:\n def __init__(self, model, epsilon, k, a, random_start, loss_func):\n \"\"\"Attack parameter initialization. The attack performs k steps of\n size a, while always staying within epsilon from the initial\n point.\"\"\"\n self.model = model\n self.epsilon = epsilon\n self.k = k\n self.a = a\n self.rand = random_start\n self.loss_func = loss_func\n\n if loss_func == 'xent':\n loss = model.y_xent\n c = 1.1\n print('c: ', c)\n elif loss_func == 'cw':\n label_mask = tf.one_hot(model.y_input,\n 10,\n on_value=1.0,\n off_value=0.0,\n dtype=tf.float32)\n correct_logit = tf.reduce_sum(label_mask * model.pre_softmax, axis=1)\n wrong_logits = (1-label_mask) * model.pre_softmax - label_mask*1e4\n wrong_logit = tf.reduce_max(wrong_logits, axis=1)\n\n loss = -tf.nn.relu(correct_logit - wrong_logit + 50)\n c = 10.0\n print('c: ', c)\n else:\n print('Unknown loss function. Defaulting to cross-entropy')\n loss = model.y_xent\n c = 1.1\n print('c: ', c)\n \n self.grad = tf.gradients(loss, model.x_input)[0]\n\n def perturb(self, x_nat, x_adv, y, sess):\n \"\"\"Given a set of examples (x_nat, y), returns a set of adversarial\n examples within epsilon of x_nat in l_infinity norm.\"\"\"\n \n batch_size = x_adv.shape[0] \n \n for epoch in range(10):\n\n# x_adv = np.array(x_adv).reshape(x_adv.shape[0], 32,32,3)\n \n grad = sess.run(self.grad, feed_dict={self.model.x_input: np.array(x_adv).reshape(x_adv.shape[0], 32,32,3),\n self.model.y_input: y})\n \n grad = np.array(grad).reshape(grad.shape[0], 32 * 32 * 3)\n kxy, dxkxy = self.svgd_kernel(x_adv)\n# kxy, dxkxy = self.svgd_kernel(np.array(x_adv).reshape(x_adv.shape[0], 32,32,3))\n# print(\"kxy shape\", kxy.shape)\n# print(\"grad shape\", grad.shape)\n x_adv += self.a * np.sign(c*(-(np.matmul(kxy, -grad) + dxkxy)/batch_size) + grad)\n \n \n x_adv = np.clip(x_adv, x_nat - self.epsilon, x_nat + self.epsilon) \n x_adv = np.clip(x_adv, 0, 255) # ensure valid pixel range\n\n\n return x_adv\n\n \n def svgd_kernel(self, theta):\n# print(\"Theta: \", theta.shape)\n sq_dist = pdist(theta)\n pairwise_dists = squareform(sq_dist)**2\n \n h = np.median(pairwise_dists) \n h = np.sqrt(0.5 * h / np.log(theta.shape[0]))\n\n # compute the rbf kernel\n Kxy = np.exp( -pairwise_dists / h**2 / 2)\n\n dxkxy = -np.matmul(Kxy, theta)\n sumkxy = np.sum(Kxy, axis=1)\n for i in range(theta.shape[1]):\n dxkxy[:, i] = dxkxy[:,i] + np.multiply(theta[:,i],sumkxy)\n dxkxy = dxkxy / (h**2)\n return (Kxy, dxkxy)\n \n\nif __name__ == '__main__':\n import json\n import sys\n import math\n\n\n from model import Model\n\n with open('config.json') as config_file:\n config = json.load(config_file)\n\n model_file = tf.train.latest_checkpoint(config['model_dir'])\n if model_file is None:\n print('No model found')\n sys.exit()\n\n model = Model(mode='eval')\n attack = LinfBLOBAttack(model,\n config['epsilon'],\n config['num_steps'],\n config['step_size'],\n config['random_start'],\n config['loss_func'])\n saver = tf.train.Saver()\n\n data_path = config['data_path']\n cifar = cifar10_input.CIFAR10Data(data_path)\n\n with tf.Session() as sess:\n # Restore the checkpoint\n saver.restore(sess, model_file)\n\n # Iterate over the samples batch-by-batch\n num_eval_examples = config['num_eval_examples']\n eval_batch_size = config['eval_batch_size']\n num_batches = int(math.ceil(num_eval_examples / eval_batch_size))\n\n \n\n print('Iterating over {} batches'.format(num_batches))\n \n\n x_cifar = np.copy(cifar.eval_data.xs)\n \n# x_temp = np.zeros((x_cifar.shape[0],x_cifar.shape[1] * x_cifar.shape[2]))\n x_temp = np.zeros((x_cifar.shape[0],x_cifar.shape[1] * x_cifar.shape[2] * x_cifar.shape[3]))\n for i in range(len(x_cifar)) : \n img = x_cifar[i]\n# print(img.shape)\n# img = np.dot(img[...,:3], [0.2989, 0.587, 0.114])\n # x_adv[i] = img\n x_temp[i] = img.flatten()\n \n x_cifar = np.copy(x_temp)\n \n x_adv_final = np.copy(x_cifar)\n print(\"x_adv_final shape:\", x_adv_final.shape)\n \n \n for restart in range(50):\n# for restart in range(1):\n # Initialize permutation\n permutation = np.arange(num_eval_examples)\n idx = np.arange(num_eval_examples)\n # Initialize data\n x_test, y_test = np.copy(x_cifar), np.copy(cifar.eval_data.ys)\n \n \n x_adv = x_test + np.random.uniform(-attack.epsilon, attack.epsilon, x_test.shape)\n \n for epoch in range(int(attack.k/10)):\n np.random.shuffle(idx)\n x_test, x_adv, y_test = x_test[idx], x_adv[idx], y_test[idx]\n permutation = permutation[idx]\n \n for ibatch in range(num_batches):\n bstart = ibatch * eval_batch_size\n bend = min(bstart + eval_batch_size, num_eval_examples)\n \n x_batch = x_test[bstart:bend, :]\n x_batch_adv = x_adv[bstart:bend, :]\n y_batch = y_test[bstart:bend]\n \n x_adv[bstart:bend, :] = attack.perturb(x_batch, x_batch_adv, y_batch, sess)\n \n \n inv_permutation = np.argsort(permutation)\n x_adv = x_adv[inv_permutation]\n \n \n predictions = np.array([None] * x_adv.shape[0])\n \n for ibatch in range(num_batches):\n bstart = ibatch * eval_batch_size\n bend = min(bstart + eval_batch_size, num_eval_examples)\n\n x_batch_adv = x_adv[bstart:bend, :]\n y_batch = np.copy(cifar.eval_data.ys)[bstart:bend]\n\n predictions[bstart:bend] = sess.run(attack.model.correct_prediction, \n feed_dict={\n attack.model.x_input: np.array(x_batch_adv).reshape(x_batch_adv.shape[0], 32,32,3), \n attack.model.y_input: y_batch\n })\n ## Replace with wrong sample\n print(\"Final Prediction\")\n for i in range(predictions.shape[0]):\n if not predictions[i]:\n x_adv_final[i] = x_adv[i] \n\n print('Storing examples')\n path = config['store_adv_path']\n \n np.save(path, np.array(x_adv_final).reshape(x_adv_final.shape[0], 32,32,3))\n print('Examples stored in {}'.format(path))\n","sub_path":"blob_rand_cifar.py","file_name":"blob_rand_cifar.py","file_ext":"py","file_size_in_byte":7112,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"125216797","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Oct 21 12:03:52 2016\r\n\r\n@author: Laila\r\n\"\"\"\r\nfrom pylab import*\r\n\r\ndt = 1e-15 #str. på tidsteg\r\ntime = 1e-12 #total tid\r\nn = time/dt; #antall tidssteg\r\n\r\nt = zeros(n) \r\na = zeros((n,3)) \r\nv = zeros((n,3))\r\nr = zeros((n,3))\r\n\r\nr[0] = array([.0,.0,.0]) \r\nv[0] = array([10000.0,.0,.0]) #10 km/s = 10 000 m/s\r\n\r\nme = 9.11e-31 \r\npe = -1.6e-19 \r\nB = array([.0,.0,2.0])\r\n\r\ndef a_(r):\r\n F0 = pe*(cross(v[0],B)) #ref boka\r\n a0 = F0/me #N2L\r\n return a0\r\n\r\n#Numerical solution: Euler-Cromers\r\nfor i in range(0,int(n-1)):\r\n a[i] = a_(r)\r\n v[i+1] = v[i] + a[i]*dt\r\n r[i+1] = r[i] + (v[i+1])*dt\r\n t[i+1] = t[i] + dt\r\n\r\n\r\n#Analytical solution \r\nr_tx= 0.5*a0[0]*t**2\r\nr_ty= 0.5*a0[1]*t**2\r\nr_tz= 0.5*a0[2]*t**2\r\n\r\n\r\n\r\nplot(t, r[:,0], 'b')\r\nhold('on')\r\nplot(t, r[:,1], 'g')\r\nhold('on')\r\nplot(t, r[:,2], 'o' )\r\n\r\nxlabel('t, time [s]')\r\nylabel('position[m]')\r\nlegend(['x(t)', 'y(t)', 'z(t)'])\r\nshow()\r\n","sub_path":"Oblig2211.py","file_name":"Oblig2211.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"176449800","text":"from random import random\n\nfrom flask import Flask, request, Response\nfrom flask.json import jsonify\nimport json\nimport argparse\nimport requests\nfrom requests import ConnectionError\nimport random\n\n# Definimos los parametros de la linea de comandos\nparser = argparse.ArgumentParser()\nparser.add_argument('--host', default='localhost', help=\"Host del agente\")\nparser.add_argument('--port', type=int, help=\"Puerto de comunicacion del agente\")\nparser.add_argument('--acomm', help='Direccion del agente con el que comunicarse')\nparser.add_argument('--aport', type=int, help='Puerto del agente con el que comunicarse')\nparser.add_argument('--messages', nargs='+', default=[], help=\"mensajes a enviar\")\n\napp = Flask(__name__)\n\narrayActividadesEvento = [\"FESTIVIDAD\", \"FIESTA NOCHE\", \"NIGHT CLUB\"]\narrayActividadesRestaurante = [\"RESTAURANTE PACO\", \"RESTAURANTE DE MARISCO 1\", \"RESTAURANTE 2\" ,\"RESTAURANTE 3\"]\narrayActividadesTuristico = [\"SAGRADA FAMILIA\", \"MACBA\", \"CASTILLO MONTJUIC\", \"PARC GÜELL\", \"MNAC\", \"FUNDACIÓ JOAN MIRÓ\"]\n\n@app.route(\"/\")\ndef isAlive():\n texto = 'Hola soy los centros de actividades. Para obtener las actividades para tu viaje ve a http://localhost:5000/actividades'\n return texto\n\n\n@app.route(\"/actividades\")\ndef actividades():\n \"\"\"\n /place?location=loc&keyword=key&type=type\n :return:\n \"\"\"\n country = request.args[\"pais\"]\n currency = request.args[\"moneda\"]\n date = request.args[\"fecha\"]\n\n respuesta = arrayActividadesTuristico[random.randint(0, len(arrayActividadesTuristico)-1)]\n\n return respuesta\n\nif __name__ == '__main__':\n\n # parsing de los parametros de la linea de comandos\n args = parser.parse_args()\n\n # Ponemos en marcha el servidor\n app.run(host=args.host, port=9101)\n\n print('The End')","sub_path":"AgentesExternos/CentrosDeActividades.py","file_name":"CentrosDeActividades.py","file_ext":"py","file_size_in_byte":1771,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"30154730","text":"# -*- coding:utf-8 -*-\nimport pymysql\n\n\nDB_IP = 'localhost'\nDB_USERNAME = 'root'\nDB_PASSWORD = '123456'\nTABLE = 'fangyuan'\n\n\nclass DB():\n\n def __init__(self):\n # 初始化连接数据库\n self.connect = pymysql.connect(DB_IP, DB_USERNAME, DB_PASSWORD, TABLE, charset='utf8')\n self.cursor = self.connect.cursor()\n\n def create_table(self):\n \"\"\"\n 创建数据库表\n :return:\n \"\"\"\n create_table_sql = \"\"\"CREATE TABLE fangyuan_info(\n id int auto_increment primary key,\n house_title varchar(100),\n house_url varchar(500),\n house_price varchar(10),\n house_zuping varchar(20),\n house_size varchar(30),\n house_xiaoqu varchar(20),\n house_area varchar(20),\n house_detailed_address varchar(60),\n house_phone varchar(20),\n house_man varchar(20))default charset=utf8\"\"\"\n try:\n result = self.cursor.execute(create_table_sql)\n self.connect.commit()\n return result\n except:\n self.connect.rollback()\n\n def clear_table(self):\n try:\n result = self.cursor.execute('delete from fangyuan_info where 1=1')\n result2 = self.cursor.execute('truncate table fangyuan_info')\n self.connect.commit()\n return result, result2\n except Exception as e:\n print('delete fail')\n print(e)\n self.connect.rollback()\n\n def exist_of_table(self, table_name):\n \"\"\"\n 判断数据库中的表是否存在\n :param table_name:\n :return boolean:\n \"\"\"\n self.cursor.execute('show tables')\n tables = self.cursor.fetchall() # tables is tuple:(('fangyuan_info'))\n tables = list(tables) # [('fangyuan_info')]\n for t in tables:\n # 将元组转换为列表 ['fangyuan_info']\n tables[tables.index(t)] = t[0]\n # print(tables)\n if table_name not in tables:\n # print('table not exist')\n return False\n else:\n # print('exist')\n return True\n\n def select(self):\n select_sql = \"\"\"SELECT id,house_title,house_url,house_price,house_zuping,house_size,house_xiaoqu,house_area,\n house_detailed_address,house_phone,house_man FROM fangyuan_info \"\"\"\n\n try:\n self.cursor.execute(select_sql)\n result = self.cursor.fetchall()\n return result\n except Exception as e:\n print('select failed')\n print(e)\n finally:\n self.close()\n\n def insert(self, house_title, house_url, house_price, house_zuping, house_size, house_xiaoqu, house_area,\n house_detailed_address, house_phone, house_man):\n \"\"\"\n 向数据库插入数据\n \"\"\"\n insert_sql = \"\"\"INSERT INTO fangyuan_info(house_title, house_url, house_price, house_zuping, house_size, \n house_xiaoqu, house_area, house_detailed_address, house_phone, house_man)\n VALUES('{house_title}', '{house_url}', '{house_price}', '{house_zuping}', '{house_size}',\n '{house_xiaoqu}', '{house_area}', '{house_detailed_address}', '{house_phone}', '{house_man}')\"\"\"\n insert_sql = insert_sql.format(house_title=house_title, house_url=house_url, house_price=house_price,\n house_zuping=house_zuping, house_size=house_size, house_xiaoqu=house_xiaoqu,\n house_area=house_area, house_detailed_address=house_detailed_address,\n house_phone=house_phone, house_man=house_man)\n try:\n result = self.cursor.execute(insert_sql)\n self.connect.commit()\n print('insert success\\n')\n return result\n except Exception as e:\n self.connect.rollback()\n print('insert failed')\n print(e)\n\n def close(self):\n self.connect.close()\n\n\n","sub_path":"FangyuanHelper/fangyuan/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":4193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"271720684","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.macosx-10.15-x86_64/egg/samcli/commands/local/lib/local_lambda.py\n# Compiled at: 2020-03-21 12:32:11\n# Size of source mod 2**32: 10043 bytes\n\"\"\"\nImplementation of Local Lambda runner\n\"\"\"\nimport os, logging, boto3\nfrom samcli.lib.utils.codeuri import resolve_code_path\nfrom samcli.local.lambdafn.env_vars import EnvironmentVariables\nfrom samcli.local.lambdafn.config import FunctionConfig\nfrom samcli.local.lambdafn.exceptions import FunctionNotFound\nfrom samcli.commands.local.lib.exceptions import OverridesNotWellDefinedError\nLOG = logging.getLogger(__name__)\n\nclass LocalLambdaRunner:\n __doc__ = '\\n Runs Lambda functions locally. This class is a wrapper around the `samcli.local` library which takes care\\n of actually running the function on a Docker container.\\n '\n MAX_DEBUG_TIMEOUT = 36000\n\n def __init__(self, local_runtime, function_provider, cwd, aws_profile=None, aws_region=None, env_vars_values=None, debug_context=None):\n \"\"\"\n Initializes the class\n\n :param samcli.local.lambdafn.runtime.LambdaRuntime local_runtime: Lambda runtime capable of running a function\n :param samcli.commands.local.lib.provider.FunctionProvider function_provider: Provider that can return a\n Lambda function\n :param string cwd: Current working directory. We will resolve all function CodeURIs relative to this directory.\n :param string aws_profile: Optional. Name of the profile to fetch AWS credentials from.\n :param string aws_region: Optional. AWS Region to use.\n :param dict env_vars_values: Optional. Dictionary containing values of environment variables.\n :param DebugContext debug_context: Optional. Debug context for the function (includes port, args, and path).\n \"\"\"\n self.local_runtime = local_runtime\n self.provider = function_provider\n self.cwd = cwd\n self.aws_profile = aws_profile\n self.aws_region = aws_region\n self.env_vars_values = env_vars_values or {}\n self.debug_context = debug_context\n self._boto3_session_creds = None\n self._boto3_region = None\n\n def invoke(self, function_name, event, stdout=None, stderr=None):\n \"\"\"\n Find the Lambda function with given name and invoke it. Pass the given event to the function and return\n response through the given streams.\n\n This function will block until either the function completes or times out.\n\n Parameters\n ----------\n function_name str\n Name of the Lambda function to invoke\n event str\n Event data passed to the function. Must be a valid JSON String.\n stdout samcli.lib.utils.stream_writer.StreamWriter\n Stream writer to write the output of the Lambda function to.\n stderr samcli.lib.utils.stream_writer.StreamWriter\n Stream writer to write the Lambda runtime logs to.\n\n Raises\n ------\n FunctionNotfound\n When we cannot find a function with the given name\n \"\"\"\n function = self.provider.get(function_name)\n if not function:\n all_functions = [f.functionname for f in self.provider.get_all()]\n available_function_message = '{} not found. Possible options in your template: {}'.format(function_name, all_functions)\n LOG.info(available_function_message)\n raise FunctionNotFound(\"Unable to find a Function with name '{}'\".format(function_name))\n LOG.debug(\"Found one Lambda function with name '%s'\", function_name)\n LOG.info('Invoking %s (%s)', function.handler, function.runtime)\n config = self._get_invoke_config(function)\n self.local_runtime.invoke(config, event, debug_context=(self.debug_context), stdout=stdout, stderr=stderr)\n\n def is_debugging(self):\n \"\"\"\n Are we debugging the invoke?\n\n Returns\n -------\n bool\n True, if we are debugging the invoke ie. the Docker container will break into the debugger and wait for\n attach\n \"\"\"\n return bool(self.debug_context)\n\n def _get_invoke_config(self, function):\n \"\"\"\n Returns invoke configuration to pass to Lambda Runtime to invoke the given function\n\n :param samcli.commands.local.lib.provider.Function function: Lambda function to generate the configuration for\n :return samcli.local.lambdafn.config.FunctionConfig: Function configuration to pass to Lambda runtime\n \"\"\"\n env_vars = self._make_env_vars(function)\n code_abs_path = resolve_code_path(self.cwd, function.codeuri)\n LOG.debug('Resolved absolute path to code is %s', code_abs_path)\n function_timeout = function.timeout\n if self.is_debugging():\n function_timeout = self.MAX_DEBUG_TIMEOUT\n return FunctionConfig(name=(function.functionname),\n runtime=(function.runtime),\n handler=(function.handler),\n code_abs_path=code_abs_path,\n layers=(function.layers),\n memory=(function.memory),\n timeout=function_timeout,\n env_vars=env_vars)\n\n def _make_env_vars(self, function):\n \"\"\"Returns the environment variables configuration for this function\n\n Parameters\n ----------\n function : samcli.commands.local.lib.provider.Function\n Lambda function to generate the configuration for\n\n Returns\n -------\n samcli.local.lambdafn.env_vars.EnvironmentVariables\n Environment variable configuration for this function\n\n Raises\n ------\n samcli.commands.local.lib.exceptions.OverridesNotWellDefinedError\n If the environment dict is in the wrong format to process environment vars\n\n \"\"\"\n name = function.functionname\n variables = None\n if function.environment and isinstance(function.environment, dict) and 'Variables' in function.environment:\n variables = function.environment['Variables']\n else:\n LOG.debug(\"No environment variables found for function '%s'\", name)\n for env_var_value in self.env_vars_values.values():\n if not isinstance(env_var_value, dict):\n reason = '\\n Environment variables must be in either CloudFormation parameter file\\n format or in {FunctionName: {key:value}} JSON pairs\\n '\n LOG.debug(reason)\n raise OverridesNotWellDefinedError(reason)\n\n if 'Parameters' in self.env_vars_values:\n LOG.debug('Environment variables overrides data is in CloudFormation parameter file format')\n overrides = self.env_vars_values['Parameters']\n else:\n LOG.debug('Environment variables overrides data is standard format')\n overrides = self.env_vars_values.get(name, None)\n shell_env = os.environ\n aws_creds = self.get_aws_creds()\n return EnvironmentVariables((function.memory),\n (function.timeout),\n (function.handler),\n variables=variables,\n shell_env_values=shell_env,\n override_values=overrides,\n aws_creds=aws_creds)\n\n def _get_session_creds(self):\n if self._boto3_session_creds is None:\n LOG.debug(\"Loading AWS credentials from session with profile '%s'\", self.aws_profile)\n session = boto3.session.Session(profile_name=(self.aws_profile), region_name=(self.aws_region))\n if hasattr(session, 'region_name'):\n if session.region_name:\n self._boto3_region = session.region_name\n if session:\n self._boto3_session_creds = session.get_credentials()\n return self._boto3_session_creds\n\n def get_aws_creds(self):\n \"\"\"\n Returns AWS credentials obtained from the shell environment or given profile\n\n :return dict: A dictionary containing credentials. This dict has the structure\n {\"region\": \"\", \"key\": \"\", \"secret\": \"\", \"sessiontoken\": \"\"}. If credentials could not be resolved,\n this returns None\n \"\"\"\n result = {}\n creds = self._get_session_creds()\n if self._boto3_region:\n result['region'] = self._boto3_region\n else:\n return creds or result\n if hasattr(creds, 'access_key'):\n if creds.access_key:\n result['key'] = creds.access_key\n if hasattr(creds, 'secret_key'):\n if creds.secret_key:\n result['secret'] = creds.secret_key\n if hasattr(creds, 'token'):\n if creds.token:\n result['sessiontoken'] = creds.token\n return result","sub_path":"pycfiles/aws_sam_cli_without_docker-0.48.0-py3.7/local_lambda.cpython-37.py","file_name":"local_lambda.cpython-37.py","file_ext":"py","file_size_in_byte":8930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"101210677","text":"# copy of cat_encoder.ipynb ; required for Azure Notebooks\n\nfrom sklearn import preprocessing\nfrom sklearn.preprocessing import StandardScaler\nimport numpy as np\n\nclass CatEncoder:\n def __init__(self, cat_columns, data, normalize: bool=True):\n self.cat_indexes = [data.columns.get_loc(name) for name in cat_columns]\n self.num_indexes = [idx for idx in range(len(data.columns)) if idx not in self.cat_indexes]\n self.encoder = preprocessing.OneHotEncoder()\n self.encoder.fit(data[cat_columns])\n self.num_columns = list(data.columns[self.num_indexes])\n self.cat_columns = cat_columns\n cat_transformed_names = self.encoder.get_feature_names(input_features=self.cat_columns)\n self._transformed_column_names = self.num_columns + list(cat_transformed_names)\n if normalize:\n self.normalizer = StandardScaler()\n self.normalizer.fit(data.iloc[:, self.num_indexes])\n else:\n self.normalizer = None\n \n def __call__(self, x):\n numeric = x[:, self.num_indexes]\n if self.normalizer is not None:\n numeric = self.normalizer.transform(numeric)\n categorical = self.encoder.transform(x[:, self.cat_indexes]).toarray()\n return np.concatenate((numeric, categorical), axis=1)\n\n @property\n def transformed_features(self):\n return self._transformed_column_names\n","sub_path":"microsoft-responsibleai-5-aug-2020/scripts/cat_encoder.py","file_name":"cat_encoder.py","file_ext":"py","file_size_in_byte":1401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"403119474","text":"# coding=utf-8\n\nfrom Adfunctions import *\n\n\ndef analyse(apk_hash, out, traffic):\n states_dir = os.path.join(out, \"states\")\n # isCrashed通过读取state判断app是否被成功运行，如果所有viewtree里都没有app的activity，就代表Crash了，返回空\n # 如果成功运行，返回刚才读的states_detail\n states_detail = isCrashed(apk_hash, states_dir)\n if states_detail:\n # views_dir = os.path.join(out, \"views\")\n # 读出clicked信息\n clicked = os.path.join(traffic, \"clicked.txt\")\n # 这里要结合states_detail分析，原因是点击广告控件的时候，可能会点击广告控件的子控件。\n # 这个函数主要为了防止点击子控件导致重复计数，细节可以不看...\n clicks = getClickInfo(clicked, states_detail)\n hook_log = os.path.join(out, \"error.log\")\n # 这个是读出hook的流量信息，正常的httpinfos信息在error.log里都是#开头的，所以我按行读了\n # 但有的webview相关的函数我存了它加载的内容，有可能超过一行了，我把它放到unknown里了。这个里面也可能会包含一些广告host\n # 我hook的时候拿了手机里所有的apk的流量信息，但应该是没啥必要。。。\n httpinfos, unknown = readLog(apk_hash, hook_log)\n # getOrigin是把完整的stacks里面可能是第三方库的筛选出来，然后把httpinfos里面的完整stacks也更新为简化的\n # 之前是用了whitelib和blacklib的两个list，whitelib里是已知的一些ad库，blacklist是非ad库\n # 除了这两个list里的，剩下第三方库的我都取出来并当做是ad lib了。当然这样是不对的，这里需要优化\n # 应该是先把ad host和ad lib迭代出来，再去跑之后的实验\n origins, httpinfos = getOrigin(apk_hash, httpinfos)\n # urload = getLoadUrl(httpinfos, origins)\n # 这个就是更新一下lib和host的对应，取的是完整host\n RenewLibHost(httpinfos, LIB_HOST)\n # 把fiddler的流量都读出来\n urlinfos = fiddler(traffic)\n # 分类拿各种traffic\n # adload：明确从第三方库加载出来的流量，因为上面提到，我迭代是后做的，所以有误报\n # webload就是不在adload里面，但仍然是webview加载出来的流量（取这部分是因为很多广告是从webview加载的）。这里面会包含漏报，本来想分析的，最后也没分析..\n # browser：浏览器里的流量，这里面是ad-click的landing page。正常是拿adload的response和browser的url匹配的，但我之前也没匹都拿出来了...\n # page是browser的基础上是html网页的，browser里面的其他流量都是资源文件\n # gplay：google的流量\n # abnormal不用管，x-requested-with的值正常应该是com.android.browser或者app的pkgname，这个就记录了一下不属于这两种的，没啥用\n # rest就是不属于以上所有流量的\n # sids是和广告有关的sessionid，判断的也挺粗糙的...\n # 论文里ad-click有三种，browser, gplay和直接下载apk，但这里就分析了前两种。\n # 直接下载apk是提出来所有文件之后按文件头统一判断的，其他方法都没这个靠谱。这部分国外也用不上，因为之前2w里都几乎没碰见过。\n adload, webload, browser, gplay, rest, abnormal, page, sids = parseTraffic(urlinfos, httpinfos, apk_hash, unknown)\n # 把传播的gapp的pkgname拿出来\n gapps = \"0\"\n if gplay:\n Gapp = getGplayInfo(gplay)\n gapps = (' ').join(Gapp)\n\n if page:\n recordPage(page, apk_hash)\n # 根据click和sid判断广告数量，比较粗糙，属于估算的...\n # 这篇论文实际上没有做一个app中多个ad view, ad view和ad content的结合\n ads = AdNum(clicks, traffic, sids)\n # 按文件格式把所有文件取出来\n getFile(adload, webload, browser, gplay, rest, abnormal, traffic, apk_hash, page)\n # 以下就是简单的统计\n # 另外一提，url.txt是所有page，url-zero.txt是reponsebody大小为0的page\n # lib_host是是lib和host的对应，而lib_host_coarse没什么用...\n # analyzed是分析完的应用，以免中间断掉\n result_list = [apk_hash]\n result_list.append(PKG_MAP[apk_hash])\n webview = 0\n imageview = 0\n viewflipper = 0\n for adview in ads.keys():\n if ads[adview].lower() == 'webview':\n webview = webview + 1\n elif ads[adview].lower() == 'imageview':\n imageview = imageview +1\n elif ads[adview].lower() == 'viewflipper':\n viewflipper = viewflipper + 1\n result_list.append(str(webview))\n result_list.append(str(imageview))\n result_list.append(str(viewflipper))\n result_list.append((' '.join(origins)))\n if browser:\n result_list.append(\"1\")\n else:\n result_list.append('0')\n result_list.append(gapps)\n pass\n with open(RESULT_CSV, \"a+\") as f:\n f.write(','.join(result_list)+'\\n')\n else:\n with open(CRASHED, \"a+\") as f:\n f.write(apk_hash + '\\n')\n\n\nif __name__ == '__main__':\n input = getDirs(INPUT_DIR)\n for dir in input:\n apks = os.listdir(dir)\n for apk in apks:\n apk_hash = apk[:-4]\n PKG_MAP[apk_hash] = os.path.split(dir)[-1]\n\n ANALYZED_FILES = []\n with open(ANALYZED, \"r+\") as f:\n for line in f.readlines():\n ANALYZED_FILES.append(line.strip())\n\n outlog_dirs = getDirs(LOGOUT_DIR)\n\n for device_ip in DEVICES:\n traffic_ip_path = os.path.join(TRAFFIC_DIR, device_ip)\n for logout_apk in outlog_dirs:\n apk_hash = os.path.split(logout_apk)[-1]\n if apk_hash in ANALYZED_FILES:\n continue\n traffic_apk = os.path.join(traffic_ip_path, apk_hash)\n if os.path.exists(traffic_apk):\n # 单个app的分析入口\n analyse(apk_hash, logout_apk, traffic_apk)\n print(apk_hash + \" done.\")\n with open(ANALYZED, \"a+\") as f:\n f.write(apk_hash + \"\\n\")\n\n","sub_path":"Analyzer_main.py","file_name":"Analyzer_main.py","file_ext":"py","file_size_in_byte":6359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"178018015","text":"\n# Program will calculate leap year\n\ndef leapYear():\n year = input(\"Enter a year: \")\n # check year is leap year or not\n if(year.isdigit() == True):\n if( int(year) % 4 == 0 and int(year) %100 != 0 or int(year) % 400 == 0 ):\n # if ( ):\n print (\"This is a leap-year!\")\n\n else:\n print (\"This is not a leap-year!\")\n else :\n print(\"Please Enter valid Year ,digit only!\")\n\nleapYear()\n","sub_path":"leapYear.py","file_name":"leapYear.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"11237731","text":"###This file will save all epoched data as well as \n###evoked data for the events of interest\nimport mne\nimport numpy as np\nimport scipy\nimport scipy.io\nimport matplotlib.pyplot as plt\n\n###import config and helper files\nimport config\nimport helper\n\ntmp_rootdir = config.tmp_rootdir\nraw_dir = config.raw_dir\nresampled_dir = config.resampled_dir\nfiltered_dir = config.filtered_dir\nica_dir = config.ica_dir\nepoch_dir = config.epoch_dir\nevoked_dir = config.evoked_dir\n\n##fig dirs\nfig_dir_mmn = config.fig_dir_mmn\nfig_dir_test = config.fig_dir_test\nfig_dir_exposure = config.fig_dir_exposure\n\nfig_evoked_dir = config.fig_evoked_dir\n\n\n###################################################################\n######################### subject #################################\n###################################################################\n#============================================\n#subject lists, in canonical-reverse order, reverse-canonical order or full list\n\nsubj_list = config.subj_list\n\n###################################################################\n########################## eeg channel list #######################\n###################################################################\neeg_chan = config.eeg_chan\nEOG_list = config.EOG_list\nFC_cluster = config.FC_cluster\nparietal_cluster = config.parietal_cluster\n\ninclude = config.include\n\ndrop_names = config.drop_names\n###################################################################\n################################ events ###########################\n###################################################################\nBlock = config.Block\nevent_id = config.event_id\n\nbiosemi_layout = mne.channels.read_montage(tmp_rootdir + 'biosemi_cap_32_M_2_EOG_3.locs')\nbiosemi_layout.ch_names = eeg_chan\nn_subj = len(subj_list) \n \ndef save_evoked(eType, picks = 'A31', save_epoch = False):\n ####This function saves and plots epoched data for all subjects\n ####picks is the channel name and chan is the channel list\n ####And the averaged ERP across all subjects\n if eType == 'MMN':\n eventType2 = config.MMN\n fig_dir = fig_dir_mmn\n elif eType == 'Test':\n eventType2 = config.Test\n fig_dir = fig_dir_test\n elif eType == 'Exposure':\n eventType2 = config.Exposure\n fig_dir = fig_dir_exposure\n else:\n raise Exception('Event type does not exist')\n\n event_pick, colors, linestyles = helper.pick_event_type(Block, eventType2)\n print(event_pick)\n evoked_subj = dict()\n evoked = dict()\n n_subj = len(subj_list) \n\n ###define the paths to save the plots, depending on which event type to plot\n #============================================ \n #start the plotting by each subject \n for i in range(n_subj):\n subj = subj_list[i]\n print(subj)\n raw_fname = ica_dir + \"%s_after_ica_raw.fif\" %(subj)\n \n raw = mne.io.Raw(raw_fname, preload = True)\n events = mne.find_events(raw, shortest_event = 1)\n raw.set_eeg_reference('average', projection = False)\n raw.pick_types(include = eeg_chan, exclude = raw.info['bads'])\n\n #raw.filter(0.1, 32)\n #raw.set_eeg_reference('average', projection = False)\n \n baseline = (-0.2, 0.0)\n epochs = mne.Epochs(raw, events = events, event_id=event_id, \n tmin = -0.2, tmax = 0.5, baseline=baseline)\n epoch_fname = epoch_dir + '%s_epoch.fif' %(subj)\n if save_epoch == True:\n epochs.save(epoch_fname)\n\n\n for j in range(len(event_pick)):\n evoked_subj[event_pick[j]] = epochs[event_pick[j]].average()\n if i == 0:\n evoked[event_pick[j]] = [evoked_subj[event_pick[j]]]\n else:\n evoked[event_pick[j]].append(evoked_subj[event_pick[j]])\n\n #uncomment if you want plots for each subject\n fig = mne.viz.plot_compare_evokeds(evoked_subj, \n show_sensors = False,\n picks = picks,\n colors = colors,\n linestyles = linestyles,\n title = 'Subject%s_parietal_%s'%(subj, eType),\n show=False)\n fig_savename = fig_dir + '%s_%s_parietal_cluster.png' %(subj, eType)\n fig.savefig(fig_savename)\n\n ##plot an averaged ERP \n block_etype = dict()\n for j in range(len(event_pick)):\n block_etype[event_pick[j]] = mne.grand_average(evoked[event_pick[j]])\n\n fig1 = mne.viz.plot_compare_evokeds(block_etype, \n show_sensors = False,\n picks = picks,\n colors = colors,\n linestyles = linestyles,\n title = 'Average_parietal_cluster_%s'%(eType),\n show = False)\n fig_savename = fig_evoked_dir + 'ave_%s_parietal_cluster.png'%(eType)\n fig1.savefig(fig_savename)\n\nif __name__ == '__main__':\n ###Execute the function and pick the event type and channel that you are interested in\n save_evoked('Exposure', picks = parietal_cluster)\n \n\n","sub_path":"Preprocessing/Preprocessing3_epoching_plotting.py","file_name":"Preprocessing3_epoching_plotting.py","file_ext":"py","file_size_in_byte":5241,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"559762875","text":"from rectangle import Rectangle\n\n\nclass Canvas:\n def __init__(self, width, height):\n \"\"\"\n Initialize new canvas.\n :param width: canvas width.\n :param height: canvas height.\n \"\"\"\n self.width = width\n self.height = height\n self.cells = [[None] * width for i in range(height)]\n\n def add_rectangle(self, corner, size, border='*', inside=' '):\n \"\"\"\n Add rectangle to canvas.\n :param corner: corner coordinates.\n :param size: size of rectangle\n :param border: symbol that is used to display rectangle border.\n :param inside: symbol that is used to display rectangle border.\n :return: recatngle that has been added.\n \"\"\"\n r = Rectangle(corner, size, border=border, inside=inside)\n for i in range(size[0]):\n for j in range(size[1]):\n coordinate = (corner[0] + i, corner[1] + j)\n if 0 <= coordinate[0] < self.height and 0 <= coordinate[1] < self.width:\n if self.cells[coordinate[0]][coordinate[1]]:\n self.cells[coordinate[0]][coordinate[1]].append(r)\n else:\n self.cells[coordinate[0]][coordinate[1]] = [r]\n return r\n\n def __str__(self):\n \"\"\"\n View of canvas.\n :return: view of canvas.\n \"\"\"\n s = ''\n for i in range(self.height):\n for j in range(self.width):\n cell = self.cells[i][j]\n if cell != None:\n s += cell[-1].view((i, j))\n else:\n s += ' '\n s += '\\n'\n return s\n\n\nif __name__ == '__main__':\n print('Example 1:')\n c = Canvas(20, 20)\n r1 = c.add_rectangle((1, 2), (10, 10))\n print(c)\n\n print('Example 2:')\n r1.border = '#'\n r1.inside = '%'\n print(c)\n\n print('Example 3:')\n r2 = c.add_rectangle((7, 7), (10, 10))\n print(c)\n","sub_path":"myPaint/canvas.py","file_name":"canvas.py","file_ext":"py","file_size_in_byte":1969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"628935772","text":"import numpy as np\nimport tensorflow as tf\n\nfrom td3fd.env_manager import EnvManager # manage environments\nfrom td3fd.rollout import RolloutWorker, SerialRolloutWorker # rollout worker for generating experiences and testing\n\n\ndef check_params(params, default_params):\n \"\"\"make sure that the keys match\"\"\"\n assert type(params) == dict\n assert type(default_params) == dict\n for key, value in default_params.items():\n assert key in params.keys(), \"missing key: {} in provided params\".format(key)\n if type(value) == dict:\n check_params(params[key], value)\n for key, value in params.items():\n assert key in default_params.keys(), \"provided params has an extra key: {}\".format(key)\n\n\ndef add_env_params(params):\n \"\"\"\n Add the following environment parameters to params:\n make_env, eps_length, gamma, max_u, dims\n \"\"\"\n env_manager = EnvManager(\n env_name=params[\"env_name\"],\n env_args=params[\"env_args\"],\n r_scale=params[\"r_scale\"],\n r_shift=params[\"r_shift\"],\n eps_length=params[\"eps_length\"],\n )\n params[\"make_env\"] = env_manager.get_env\n tmp_env = params[\"make_env\"]()\n # maximum number of simulation steps per episode\n params[\"eps_length\"] = tmp_env.eps_length\n # calculate discount factor gamma based on episode length\n params[\"gamma\"] = 1.0 - 1.0 / params[\"eps_length\"]\n # limit on the magnitude of actions\n params[\"max_u\"] = np.array(tmp_env.max_u) if isinstance(tmp_env.max_u, list) else tmp_env.max_u\n # get environment observation & action dimensions\n tmp_env.reset()\n obs, _, _, info = tmp_env.step(tmp_env.action_space.sample())\n dims = {\n \"o\": obs[\"observation\"].shape, # observation\n \"g\": obs[\"desired_goal\"].shape, # goal (may be of shape (0,) if not exist)\n \"u\": tmp_env.action_space.shape,\n }\n for key, value in info.items():\n value = np.array(value)\n if value.ndim == 0:\n value = value.reshape(1)\n dims[\"info_{}\".format(key)] = value.shape\n params[\"dims\"] = dims\n return params\n\n\ndef config_rollout(params, policy):\n rollout_params = params[\"rollout\"]\n rollout_params.update({\"dims\": params[\"dims\"], \"eps_length\": params[\"eps_length\"], \"max_u\": params[\"max_u\"]})\n return _config_rollout_worker(params[\"make_env\"], params[\"fix_T\"], params[\"seed\"], policy, rollout_params)\n\n\ndef config_evaluator(params, policy):\n rollout_params = params[\"evaluator\"]\n rollout_params.update({\"dims\": params[\"dims\"], \"eps_length\": params[\"eps_length\"], \"max_u\": params[\"max_u\"]})\n return _config_rollout_worker(params[\"make_env\"], params[\"fix_T\"], params[\"seed\"], policy, rollout_params)\n\n\ndef config_demo(params, policy):\n rollout_params = params[\"demo\"]\n rollout_params.update({\"dims\": params[\"dims\"], \"eps_length\": params[\"eps_length\"], \"max_u\": params[\"max_u\"]})\n return _config_rollout_worker(params[\"make_env\"], params[\"fix_T\"], params[\"seed\"], policy, rollout_params)\n\n\ndef _config_rollout_worker(make_env, fix_T, seed, policy, rollout_params):\n\n if fix_T: # fix the time horizon, so use the parrallel virtual envs\n rollout = RolloutWorker(make_env, policy, **rollout_params)\n else:\n rollout = SerialRolloutWorker(make_env, policy, **rollout_params)\n rollout.seed(seed)\n\n return rollout\n","sub_path":"Package/td3fd/td3fd/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":3348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"502233059","text":"#!/usr/bin/env python\n\nimport vtk\nfrom sys import argv\nimport numpy as np\nfrom vtk.util import numpy_support\nimport math\nimport csv\nimport numpy\n# import matplotlib.pyplot as plt\n# import seaborn\n\n# import pickle\n\nRADIUS_TIMES_AVERAGE_EDGE_LENGTH=1\n\ndef ClipMRIWithCylinder(mri_data,cylinderTransformed):\n implicitPolyDataDistance = vtk.vtkImplicitPolyDataDistance()\n implicitPolyDataDistance.SetInput(cylinderTransformed)\n mri_data_copy=mri_data\n # Create an array to hold distance information\n signedDistances = vtk.vtkFloatArray()\n signedDistances.SetNumberOfComponents(1)\n signedDistances.SetName(\"SignedDistances\")\n\n\n # Evaluate the signed distance function at all of the grid points\n for pointId in range(mri_data.GetNumberOfPoints()):\n p = mri_data.GetPoint(pointId)\n signedDistance = implicitPolyDataDistance.EvaluateFunction(p)\n signedDistances.InsertNextValue(signedDistance)\n \n # add the SignedDistances to the grid\n mriDistanceData=np.zeros([int(mri_data.GetNumberOfPoints()),1])\n\n for i in range(mri_data.GetNumberOfPoints()):\n mriDist=signedDistances.GetTuple(i)[0]\n mriDistanceData[i]=mriDist\n \n \n mriDistVtk=vtk.vtkDoubleArray()\n mriDistVtk.SetArray(mriDistanceData,mri_data.GetNumberOfPoints(),1)\n mri_data_copy.GetPointData().SetScalars(mriDistVtk)\n\n clipper = vtk.vtkClipDataSet()\n clipper.SetInputData(mri_data_copy)\n clipper.InsideOutOn()\n clipper.SetValue(0.0)\n clipper.Update()\n\n return clipper\n\n\n \n \n\ndef euclidean2(v1,v2): \n if type(v1)==list:\n v1=np.array(v1) \n if type(v2)==list:\n v2=np.array(v2)\n diff = v1 - v2\n squareDistance = np.dot(diff.T, diff)\n dist=math.sqrt(squareDistance)\n return dist\n\n#==================================================================\n# MAIN \n#==================================================================\n\n# Read in mesh files\nprint('Reading mesh...')\nsreader = vtk.vtkPolyDataReader()\nsreader.SetFileName(argv[1])\n# sreader.SetFileName(\"target.vtk\")\nsreader.Update()\nprint('Finished reading mesh.')\ntrianglepd= sreader.GetOutput()\n\nprint('Reading lge mesh data...')\nsreader1 = vtk.vtkPolyDataReader()\nsreader1.SetFileName(argv[2])\n# sreader1.SetFileName(\"advanced_all.vtk\")\nsreader1.Update()\nprint('Finished reading mesh.')\nmri_data= sreader1.GetOutput()\n\nmri_inten=numpy_support.vtk_to_numpy(mri_data.GetPointData().GetArray('MetaImage'))\nmax_intn=np.amax(mri_inten)\nprint(max_intn)\ndelaunay3D = vtk.vtkDelaunay3D()\ndelaunay3D.SetInputData(mri_data)\ndelaunay3D.Update()\n\ngeofilter = vtk.vtkGeometryFilter()\ngeofilter.SetInputData(delaunay3D.GetOutput())\ngeofilter.Update()\nMRI_delaunay3D_pd= geofilter.GetOutput()\n\n# # Read in image file\nprint('Reading image')\nireader = vtk.vtkMetaImageReader()\nireader.SetFileName(argv[3])\n# ireader.SetFileName(\"pt_002.mha\")\nireader.Update()\nprint('Finished reading image.')\n\n\nnormFilter = vtk.vtkPolyDataNormals()\nnormFilter.ComputeCellNormalsOn()\nnormFilter.SetInputData(trianglepd)\nnormFilter.SplittingOff() # Prevents us ending up with more points than started with\nnormFilter.ConsistencyOn()\nnormFilter.Update()\ncellNormalsRetrieved = normFilter.GetOutput().GetCellData().GetNormals()\n\ncellcenter=vtk.vtkCellCenters()\ncellcenter.SetInputData(normFilter.GetOutput())\ncellcenter.Update()\ncell_centeroutput=cellcenter.GetOutput()\n\ncell_centeroutput_num=cell_centeroutput.GetNumberOfPoints()\n\ncenter=[0.0,0.0,0.0]\ncenterOfMassFilter =vtk.vtkCenterOfMass()\ncenterOfMassFilter.SetInputData(trianglepd)\ncenterOfMassFilter.SetUseScalarsAsWeights(0)\ncenterOfMassFilter.Update()\nxx=centerOfMassFilter.GetCenter()\n\n\np=[0.0,0.0,0.0]\nepsilon=0.00001\ncN=[0.0,0.0,0.0] \nx=[0.0,0.0,0.0] \npcoords=x=[0.0,0.0,0.0] \nsubId = vtk.mutable(0)\ninf=100000000\nt=vtk.mutable(0)\n\nsize_normals = normFilter.GetOutput().GetNumberOfCells()\nprint(size_normals)\nvalue=[]\nvalue1=[]\nnormalvec=[]\ncell_center=[]\ncell_cent_shift=[]\nfor i in range(0,size_normals):\n cellcenter.GetOutput().GetPoint(i,p)\n cellNormalsRetrieved.GetTuple(i, cN)\n \n vec2=np.subtract(xx,p)\n\n a = np.dot(vec2,cN)\n cN=np.array(cN)\n if a > 0:\n cN=-1*cN\n normalvec.append((list(cN)))\n cell_center.append((list(p)))\n\n cell_center_shiftedontocylin=p+10*cN\n cell_cent_shift.append((list(cell_center_shiftedontocylin)))\n\n \ncellId = vtk.mutable(0)\npointIdList=vtk.vtkIdList()\ndataList=vtk.vtkIdList()\n\n\noutputCellIds = []\noutputPointIds = []\n\n\ncN=[0.0, 0.0, 0.0]\np=[0.0, 0.0, 0.0]\ncellt=vtk.vtkGenericCell()\nnew_point=[0.0, 0.0, 0.0]\ncellarray=vtk.vtkCellArray()\npts=vtk.vtkPoints()\nnumberOfCells = trianglepd.GetNumberOfCells()\np=[0.0,0.0,0.0]\naverag_ed_length=[]\n\ndef averag_length(tri_pts):\n A = np.array(tri_pts[0])\n B = np.array(tri_pts[1])\n C = np.array(tri_pts[2]) \n AB = B - A\n AC = C - A\n BC = C - B\n A_len = np.linalg.norm(AB)\n B_len = np.linalg.norm(AC)\n C_len = np.linalg.norm(BC)\n mean_len=(A_len+B_len+C_len)/3\n\n return mean_len\n\nfor j in range(0, numberOfCells):\n\n sreader.GetOutput().GetCell(j, cellt)\n pts= cellt.GetPoints() \n tri_pts=[]\n avg_len=[]\n p=[0.0,0.0,0.0]\n i=0\n \n for i in range(0,3): \n pts.GetPoint(i,p)\n tri_pts.append((list(p)))\n\n avg_len=averag_length(tri_pts) \n\n averag_ed_length.append(avg_len)\n\ntest=[]\n\n\ncylin_axis=[0, 1, 0]\ndotp=np.matmul(normalvec,cylin_axis)\n\ncrossprod=np.zeros((size_normals,3))\ntheta_deg=np.zeros((size_normals,1))\n\n# crossprod=np.cross(normalvec,cylin_axis)\n# theta=np.arccos(dotp)\n# theta_deg=360-numpy.rad2deg(theta) #subtyract 360 from angle to realign arccos\n\nappend = vtk.vtkAppendPolyData()\naverageIntensities=np.zeros([size_normals,1])\nprop_scar=np.zeros([size_normals,1])\nSIZE_points=np.zeros([size_normals,1])\n\n \n# dataListToSave=[]\n\n\n\nfor ii in range (2525,2526): \n\n print(ii)\n crossprod[ii]=np.cross(normalvec[ii],cylin_axis)\n theta=np.arccos(dotp[ii])\n theta_deg[ii]=360-numpy.rad2deg(theta) #subtyract 360 from angle to realign arccos\n\n \n cylinder = vtk.vtkCylinderSource()\n cylinder.SetRadius(0.4*averag_ed_length[ii])\n cylinder.SetHeight(20)\n cylinder.SetResolution(360)\n cylinder.Update()\n\n transform=vtk.vtkTransform()\n transform.PostMultiply()\n transform.RotateWXYZ(theta_deg[ii],crossprod[ii])\n transform.Translate(cell_cent_shift[ii])\n transform.Update()\n transformPD1 = vtk.vtkTransformPolyDataFilter()\n transformPD1.SetTransform(transform)\n transformPD1.SetInputData(cylinder.GetOutput())\n transformPD1.Update()\n\n\n append.AddInputData(transformPD1.GetOutput())\n append.Update()\n\n \n # clip=ClipMRIWithCylinder(mri_data,transformPD1.GetOutput())\n\n\n # size_points = clip.GetOutput().GetNumberOfPoints()\n # SIZE_points[ii]=size_points\n # print(size_points)\n # if size_points==0:\n # averageIntensities[ii]=np.nan\n # allIntensities=np.nan\n # prop_scar[ii]=np.nan\n # continue\n\n # ptsClipped=vtk.vtkPoints()\n # ptsClipped=clip.GetOutput().GetPoints()\n # ptsClippedPD=vtk.vtkPolyData()\n # ptsClippedPD.SetPoints(ptsClipped)\n \n \n\n\n # probeFilter=vtk.vtkProbeFilter()\n # probeFilter.SetInputData(ptsClippedPD)\n # probeFilter.SetSourceData(ireader.GetOutput())\n # probeFilter.Update()\n \n # allIntensities=numpy_support.vtk_to_numpy(probeFilter.GetOutput().GetPointData().GetArray('MetaImage')) \n\n # # import pdb;pdb.set_trace()\n # # num_true= (np.count_nonzero(allIntensities>(max_intn/2)))\n # # print(num_true)\n # prop_scar[ii]= float((np.count_nonzero(allIntensities>(max_intn/2))))/size_points\n # print(prop_scar[ii])\n \n \n # averageIntensities[ii]=np.mean(allIntensities)\n # print(averageIntensities[ii])\n # # dataListToSave.append(allIntensities)\n\nwriter=vtk.vtkPolyDataWriter()\nwriter.SetInputData(append.GetOutput())\nwriter.SetFileName('cylinder.vtk')\nwriter.Write()\nimport pdb; pdb.set_trace()\n# averageIntensities.tofile(argv[4])\n# prop_scar.tofile(argv[5])\n \n\n# \nallCVs=np.fromfile(argv[4])\n\nfilt=[~np.isinf(allCVs)]\nallCVs=allCVs[filt]\n# import pdb; pdb.set_trace()\nCVs_reshape=allCVs[:size_normals]\nCVs_reshape=CVs_reshape.reshape(averageIntensities.shape)\navgints_filt=averageIntensities[~np.isnan(averageIntensities)]\nprop_scar_filt=prop_scar[~np.isnan(averageIntensities)]\nCVs_reshape_filt=CVs_reshape[~np.isnan(averageIntensities)]\n\nsize_pts_np=np.array(SIZE_points)\nSIZE_points_filt=size_pts_np[~np.isnan(averageIntensities)]\n\n\n\navgints_filt.tofile(argv[5])\nprop_scar_filt.tofile(argv[6])\nCVs_reshape_filt.tofile(argv[7])\nSIZE_points_filt.tofile(argv[8])\n\n# getCorrelationCVIntensity(allCVs,averageIntensities)\n# cvs_filt=allCVs.reshape(averageIntensities.shape)[~np.isnan(averageIntensities)]\n\n\n# ax = seaborn.regplot(x=avgints_filt, y=allCVs)\n# plt.show()\n\n\n# SIZE_points_filt2=SIZE_points_filt[SIZE_points_filt>50]\n# prop_scar_filt2=prop_scar_filt[SIZE_points_filt>50]\n# CVs_reshape_filt2=CVs_reshape_filt[SIZE_points_filt>50]\n# avgints_filt2=avgints_filt[SIZE_points_filt>50] \n \n ","sub_path":"triangle_project.py","file_name":"triangle_project.py","file_ext":"py","file_size_in_byte":9070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"609028420","text":"from django.conf.urls import url\n\nfrom adminApp.views import NewsListView, NewsCreateView, UpdateNewsView, DeleteNewsView, MainView\nfrom mainApp.views import IndexView\n\napp_name = 'adminApp'\n# Main url's:\nurlpatterns = [\n url(r'^$', MainView.as_view(), name='admin_page')\n]\n\n# News editing url's:\nurlpatterns += [\n url(r'^news/$', NewsListView.as_view(), name='news_list'),\n url(r'^news/update/(?P\\d+)$', UpdateNewsView.as_view(), name='update_news'),\n url(r'^news/delete/(?P\\d+)$', DeleteNewsView.as_view(), name='delete_news'),\n url(r'^create/news$', NewsCreateView.as_view(), name='create_news'),\n]\n\n# Category editing url's\nurlpatterns += [\n url(r'^categories/$', IndexView.as_view(), name='category_list'),\n url(r'^categories/create/(\\w+)/$', IndexView.as_view(), name='create_category'),\n url(r'^categories/update/(\\w+)/$', IndexView.as_view(), name='update_category'),\n url(r'^categories/delete/(\\w+)/$', IndexView.as_view(), name='delete_category'),\n]\n\n# User editing url's.\nurlpatterns += [\n url(r'^news/(\\w+)/$', IndexView.as_view(), name='index'),\n url(r'^news/(\\w+)/(\\d+)/$', IndexView.as_view(), name='index'),\n]\n","sub_path":"adminApp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"142604172","text":"class User:\r\n def __init__(self, name, email):\r\n self.name = name\r\n self.email = email\r\n self.account_balance = 0\r\n def make_deposit(self, amount):\r\n self.account_balance += amount\r\n return self\r\n def make_withdrawl(self, amount):\r\n self.account_balance -= amount\r\n return self\r\n def display_user_balance(self):\r\n print(f\"User: {self.name}, Balance: ${self.account_balance}\")\r\n def transfer_money(self, other_user, amount):\r\n other_user.make_deposit(amount)\r\n self.make_withdrawl(amount)\r\n\r\n\r\njason = User(\"Jason\", \"jason@python.com\")\r\nagnes = User(\"Agnes\", \"agnes@python.com\")\r\ndeku = User(\"Deku\", \"deku@python.com\")\r\n\r\njason.make_deposit(1000)\r\njason.make_deposit(500)\r\njason.make_deposit(50)\r\njason.make_withdrawl(200)\r\njason.display_user_balance()\r\n\r\nagnes.make_deposit(2000)\r\nagnes.make_deposit(100)\r\nagnes.make_withdrawl(50)\r\nagnes.make_withdrawl(50)\r\nagnes.display_user_balance()\r\n\r\ndeku.make_deposit(1000)\r\ndeku.make_withdrawl(25)\r\ndeku.make_withdrawl(25)\r\ndeku.make_withdrawl(25)\r\ndeku.display_user_balance()\r\n\r\njason.transfer_money(deku,1000)\r\njason.display_user_balance()\r\ndeku.display_user_balance()\r\n\r\n","sub_path":"usercd.py","file_name":"usercd.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"554916901","text":"import sys\nsys.setrecursionlimit(3000)\n\nT = int(input())\nwhile T > 0:\n M, N, K = map(int, input().split())\n\n graph = [[0]*N for _ in range(M)]\n visited = [[False]*N for _ in range(M)]\n\n for _ in range(K):\n a, b = map(int, input().split())\n graph[a][b] = 1\n\n # for x in graph:\n # print(x)\n\n def dfs(x, y):\n d = [(-1, 0), (1, 0), (0, -1), (0, 1)]\n visited[x][y] = True\n for i in range(4):\n nx = x + d[i][0]\n ny = y + d[i][1]\n\n if 0 <= nx < M and 0 <= ny < N and graph[nx][ny] == 1 and not visited[nx][ny]:\n dfs(nx, ny)\n\n\n count = 0\n\n for a in range(M):\n for b in range(N):\n if graph[a][b] == 1 and not visited[a][b]:\n dfs(a, b); count += 1\n\n print(count)\n\n T -= 1\n","sub_path":"백준/BFS_DFS/1012(유기농 배추).py","file_name":"1012(유기농 배추).py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"465709107","text":"import os\nimport subprocess\n\ndot_dir = os.path.expanduser(\"~/dots\")\nhome_dir = os.path.expanduser(\"~\")\nfilepath = ''\nfor p, _, f in os.walk(dot_dir):\n if \"vimrc\" in f:\n filepath = p+\"/vimrc\"\n\nif not filepath:\n raise Exception(\"Couldn't find vimrc!\")\n\nplugin_names = []\nwith open(filepath) as f:\n for line in f:\n if \"Plug\" in line and line.split()[0] != '\"':\n plugin_names.append(line.split()[1].strip(\",\").strip(\"'\"))\n\nplugin_dir = os.path.join(home_dir, \".vim/pack/plugins/opt\") \nif not os.path.isdir(plugin_dir):\n os.makedirs(plugin_dir)\n\nfor repo in plugin_names:\n print(repo)\n x = subprocess.run([\"git\", \"clone\", \"https://github.com/\"+repo], cwd=plugin_dir)\n if x.returncode != 0:\n subprocess.run([\"git\",\"pull\", \"--ff-only\"], cwd=os.path.join(plugin_dir, repo.split(\"/\")[1]) )\n","sub_path":"install_vim_plugins.py","file_name":"install_vim_plugins.py","file_ext":"py","file_size_in_byte":835,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"221346563","text":"from abc import ABC, abstractmethod\nfrom ..clans import Kuirk, Strolth, Ranger\nfrom ..ships import Ship, SmartShip, HeavyShip, AgileShip\nfrom .sprite_factories import PlayerFactory, ShipFactory, SpriteFactory\n\n\nclass AbstractClanFactory(ABC):\n \"\"\"\n AbstractClanFactory\n \"\"\"\n\n @classmethod\n def get_player_sprite(cls, gender: str):\n player = PlayerFactory.factory_method(gender)\n\n if gender == 'male':\n gender_long = \"Man\"\n else:\n gender_long = \"Woman\"\n\n player.complete_sprite = \"{}{}\".format(gender_long, cls.get_clan_sprite().name.capitalize())\n return player\n\n @classmethod\n @abstractmethod\n def get_clan_sprite(cls):\n pass\n\n @classmethod\n def get_ships(cls):\n clan = cls.get_clan_sprite()\n\n specialized_attributes = clan.specialized_attributes\n agile_attributes = specialized_attributes['agile']\n heavy_attributes = specialized_attributes['heavy']\n smart_attributes = specialized_attributes['smart']\n\n heavy_ship = ShipFactory.factory_method(HeavyShip.name,\n heavy_attributes['speed'],\n heavy_attributes['resistance'],\n heavy_attributes['attack']\n )\n\n agile_ship = ShipFactory.factory_method(AgileShip.name,\n agile_attributes['speed'],\n agile_attributes['resistance'],\n agile_attributes['attack']\n )\n\n smart_ship = ShipFactory.factory_method(SmartShip.name,\n smart_attributes['speed'],\n smart_attributes['resistance'],\n smart_attributes['attack']\n )\n\n return agile_ship, heavy_ship, smart_ship\n\n\nclass ConcreteStrolthFactory(AbstractClanFactory):\n\n @classmethod\n def get_clan_sprite(cls):\n return Strolth()\n\n\nclass ConcreteRangerFactory(AbstractClanFactory):\n\n @classmethod\n def get_clan_sprite(cls):\n return Ranger()\n\n\nclass ConcreteKuirkFactory(AbstractClanFactory):\n\n @classmethod\n def get_clan_sprite(cls):\n return Kuirk()\n\n\nclass FactoryClanAbsFact(SpriteFactory):\n \"\"\"\n class FactoryClanAbsFact\n \"\"\"\n\n @staticmethod\n def factory_method(clan):\n if clan == Kuirk.name:\n return ConcreteKuirkFactory()\n elif clan == Strolth.name:\n return ConcreteStrolthFactory()\n elif clan == Ranger.name:\n return ConcreteRangerFactory()\n else:\n raise Exception(\"{} is not a known clan type\".format(clan))\n","sub_path":"server/models/factories/abstract_clan_factory.py","file_name":"abstract_clan_factory.py","file_ext":"py","file_size_in_byte":2951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"410305609","text":"\n\n# STANDALONE REST API\nimport datetime, json, glob\nfrom flask import Flask, request, jsonify\nfrom flask_cors import CORS\n\n\napp = Flask(__name__)\napp.config[\"SECRET_KEY\"] = \"secret!\"\nCORS(app)\n\n\n@app.route(\"/\", methods=[\"GET\"])\ndef ping():\n\tresponse = { \"data\": None, \"message\": \"API: Alive and kick'n!\" }\n\treturn (jsonify(response), 200)\n\n\n@app.route(\"/device\", methods=[\"POST\"])\ndef add_data():\n\t# receive json document\n\t_doc = request.json\n\tstatus = 200\n\t_result = { \"message\": \"\", \"data\": {} }\n\n\ttry:\n\t\t# add time stamp\n\t\t_doc[\"timestamp\"] = datetime.datetime.now().isoformat()\n\n\t\t# save to file as json with filename using DeviceID\n\t\t# if file already exist, it will be overwritten\n\t\t# else new file will be created\n\t\t_filename = _doc[\"DeviceID\"] + \".json\"\n\t\twith open(_filename, \"w\") as file:\n\t\t\tjson.dump(_doc, file, indent=4, sort_keys=True)\n\t\t\t_result[\"message\"] = \"success\"\n\t\t\t_result[\"data\"] = _filename\n\n\texcept Exception as err:\n\t\t_result[\"message\"] = str(err)\n\t\t_result[\"data\"] = {}\n\t\tstatus = 500\n\n\treturn jsonify(_result), status\n\n\n@app.route(\"/device\", methods=[\"GET\"])\ndef get_data():\n\t_result = { \"message\": \"\", \"data\": {} }\n\tdocs = []\n\n\ttry:\n\t\ttry:\n\t\t\t# open file based on specified deviceId\n\t\t\twith open(\"./\" + request.args.get(\"DeviceID\") + \".json\") as _doc:\n\t\t\t\t_result[\"message\"] = \"success\"\n\t\t\t\t_result[\"data\"] = json.load(_doc)\n\n\t\texcept Exception as findFileErr:\n\n\t\t\t# if deviceId is not specified return all .json files\n\t\t\tfor file in glob.glob(\"./*.json\"):\n\t\t\t\twith open(file) as _doc:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tdocs.append(json.load(_doc))\n\t\t\t\t\texcept Exception as parseErr:\n\t\t\t\t\t\tpass\n\n\t\t\t_result[\"message\"] = \"success\"\n\t\t\t_result[\"data\"] = docs\n\n\texcept Exception as err:\n\t\t_result[\"message\"] = str(err)\n\t\t_result[\"data\"] = {}\n\n\treturn jsonify(_result), 200\n\n\nif __name__ == \"__main__\":\n\tapp.run(host=\"0.0.0.0\", port=8080, debug=True, threaded=True)","sub_path":"backend - exam/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":1873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"14527976","text":"\"\"\"\nA python module for working with songsheets. Songsheets (or leadsheets) are simple sheets\nshowing a song's lyrics, chords, progression patterns and other metadata.\n\n`chordpro `_ is an existing standard serving a similar purpose.\nWhy another implementation? A few reasons:\n\n- chordpro's non-parsed format is a bit hard-to-read. It requires inserting chords in special\n tags before the syllable. `songsheet`'s non parsed text format is just as you'd write it: chords \n *above* the lyrics.\n\n- chordpro's parsed text is the output format and not particularly machine-readable. Its non-parsed\n text is also not machine-readable. In contrast, `songsheet`'s parsed format is JSON. This allows\n developers to easily extract, store and index lyrics, chords and meta data in databases etc.\n\n- `songsheet` is flexible. You can give it JSON and it will deserialize into readable plain-text HTML, PDFs and chordpro format. You can give it readable plain-text or chordpro text and it will serialize it to JSON.\n\n- Chord validation and normalization. Unless you feed it chordpro text, songsheet won't recognize chords that don't exist\n such as B#. Or Hm7. If you give it \"aM7\", it will normalize to \"Am7\" etc.\n\n- It's implemented in Python, on `github `_, has a complete and maintained test suite, \n builds are run on `travis `_ and docs are on \n on `readthedocs `_.\n\"\"\"\n\nimport functools\nimport json\nimport os\nimport re\nimport string\nfrom typing import Any, Callable, List, Pattern, Tuple\n\n\nvalid_chords: List[str] = []\n\"\"\"list: A list of common chords\"\"\"\n\nflat_char = '\\u266D'\nfor chord in string.ascii_uppercase[:7]:\n valid_chords.append(chord)\n if chord not in ['b', 'e']:\n valid_chords.append(f'{chord}#')\n valid_chords.append(f'{chord}m')\n valid_chords.append(f'{chord}#7')\n valid_chords.append(f'{chord}#m7')\n valid_chords.append(f'{chord}#sus')\n if chord not in ['c', 'f']:\n valid_chords.append(f'{chord}{flat_char}')\n valid_chords.append(f'{chord}{flat_char}m')\n valid_chords.append(f'{chord}{flat_char}7')\n valid_chords.append(f'{chord}{flat_char}m7')\n valid_chords.append(f'{chord}{flat_char}sus')\n valid_chords.append(f'{chord}m')\n valid_chords.append(f'{chord}7')\n valid_chords.append(f'{chord}m7')\n valid_chords.append(f'{chord}sus')\n\nvalid_chords_re: Pattern = re.compile(\n '(' + '|'.join([f'\\\\b{chord}\\\\b' for chord in valid_chords]) + ')', re.IGNORECASE)\n\"\"\"A case-insensitive regular expression matching any valid chord.\"\"\"\n\n\ndef compose(*funcs: Any) -> Callable:\n \"\"\" Composes a list of functions into a single composite func.\n\n Args:\n funcs: a list of functions\n\n Returns:\n A function composed of the passed-in functions.\n \"\"\"\n return functools.reduce(lambda f, g: lambda x: f(g(x)), funcs)\n\n\ndef normalize_line_breaks(s: str) -> str:\n \"\"\"Returns a string with line breaks replaced with\n the OS appropriate line break.\n\n Args:\n s: A source string.\n\n Returns:\n A string with line breaks normalized.\n \"\"\"\n return re.sub(r'[\\n\\r]+', os.linesep, s)\n\n\nstrip_spaces_each_line = compose(lambda s: re.sub(f'\\\\s+{os.linesep}', os.linesep, s),\n lambda s: re.sub(f'{os.linesep}\\\\s', os.linesep, s))\n\"\"\"Strips spaces before and after each new line.\n\n Args:\n x (str): An input string.\n\n Returns:\n str: A string with spaces stripped before and after each new-line.\n\"\"\"\n\n\ndef clean(s: str) -> str:\n \"\"\"Cleans an input songsheet in preparation for parsing. A\n composition of the following functions:\n\n * strips spaces before and after each line\n * normalizes line breaks.\n * Strips leading and trailing spaces.\n\n Args:\n x: The input string.\n\n Returns:\n The cleaned string.\n\"\"\"\n return compose(lambda s: s.strip(), normalize_line_breaks,\n strip_spaces_each_line)(s)\n\n\ndef is_chord_line(chord_line: List[str]) -> bool:\n \"\"\"\n Determines whether an array of characters can be considered a line\n of valid chords.\n\n Args:\n chord_line: A list of characters.\n\n\n Returns:\n `True` if all contigious characters are chords.\n\n Example:\n >>> songsheet.is_chord_line(['a', 'B', ' ', 'g#m']) \n True\n\n >>> songsheet.is_chord_line(['a', 'z', ' ', 'g#m']) \n False\n\n \"\"\"\n return all([valid_chords_re.search(chord) for chord in chord_line if chord != ' '])\n\n\ndef compact_chords(line: List[str]) -> List[str]:\n \"\"\"Compacts a list of letters such that a multi letter chord\n is one element in the list.\n\n Args:\n line: A list of characters.\n\n Returns:\n A list of compacted characters.\n\n Example:\n >>> songsheet.compact_chords(['g', 'm', ' ', 'B']\n ['gm', ' ', 'B']\n \"\"\"\n ret = []\n el = ''\n for i, char in enumerate(line):\n if i == len(line) - 1:\n el += char\n ret.append(el)\n elif char == ' ':\n ret.append(el)\n ret.append(' ')\n el = ''\n else:\n el += char\n\n # Hack, why is there an extra empty string.\n return [char for char in ret if char != '']\n\n\ndef normalize_chord_case(word: str) -> str:\n \"\"\"\n Normalizes the case of a chord.\n\n Example:\n\n >>> normalize_chord_case('gM#')\n 'Gm#'\n \"\"\"\n ret = ''\n if len(word) > 1:\n return word[0].upper() + word[1:].lower()\n else:\n return word.upper()\n\n\ndef serialize(songsheet_str: str):\n \"\"\"\n Serializes a songsheet string into a tuple of tuples.\n\n Args:\n songsheet_str: A string representing a songsheet.\n\n Returns:\n A tuple of tuples. Each tuple is a line of chords followed by a line of lyrics.\n\n Example:\n\n >>> songsheet.serialize('am C \\\\nfoo bar')\n ('Am', ' ', ' ', 'C ', ' ', ' ', ' '),\n ('f', 'o', 'o', ' ', 'b', 'a' 'r'))\n \"\"\"\n cleaned_song_str = clean(songsheet_str)\n lines = []\n split_str = list(cleaned_song_str)\n for (i, letter) in enumerate(split_str):\n if i == 0:\n line_list: List[str] = []\n lines.append(line_list)\n if letter == '\\n':\n line_list = []\n lines.append(line_list)\n else:\n line_list.append(letter)\n data = tuple([tuple(line) for line in lines])\n\n # TODO make this into function.\n ret = []\n for line in lines:\n compacted = compact_chords(line)\n if is_chord_line(compacted):\n ret.append(normalize_chord_case(el) for el in compacted)\n else:\n ret.append(line)\n #print( tuple([tuple(line) for line in ret]))\n return tuple([tuple(line) for line in ret])\n\n\ndef to_json(s: str, indent: int = 0) -> str:\n \"\"\"Returns a json string given a songsheet string.\n\n Args:\n s: A songsheet string.\n indent: How many spaces to indent (prettify). Defaults to None.\n\n Returns:\n A string of json\n \"\"\"\n # Curry json.dumps to freeze the indent parameter, leaving a function\n # that only takes a string. Then compose it with serialize.\n indent_dumps = functools.partial(json.dumps, indent=indent)\n serialize_and_dumps = compose(lambda data: indent_dumps(\n data), lambda s: serialize(s))\n\n return serialize_and_dumps(s)\n\n\ndef to_str(lines: List[List[str]]) -> str:\n \"\"\"Converts a parsed songsheet back into a string.\n\n Args:\n lines: A list of parsed songsheet lines\n\n Returns:\n A songsheet string.\n \"\"\"\n ret = ''\n for i, line in enumerate(lines):\n ret += ''.join(line)\n if i != len(lines) - 1:\n ret += '\\n'\n return ret\n","sub_path":"songsheet/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":7849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"496049294","text":"#Write a generic function to compute various scenarios for the following optimization problem: A farmer owns X acres of land. \n# She profits P1 dollars per acre of corn and P2 dollars per acre of oats. Her team has Y hours of labor available. \n# The corn takes H1 hours of labor per acre and oats require H2 hours of labor per acre. \n# How many acres of each can be planted to maximize profits?\n#Test the function for the following cases:\n#a) X = 240, Y = 320, P1 = $40, P2 = $30, H1 = 2, H2 = 1\n#b) X = 300, Y = 380, P1 = $70, P2 = $45, H1 = 3, H2 = 1\n#c) X = 180, Y = 420, P1 = $65, P2 = $55, H1 = 3, H2 = 2\n\nfrom scipy.optimize import linprog\n\ndef createMatrix(X,Y,p1,p2,h1,h2):\n c = [-p1,-p2]\n A = [[1,1],[h1,h2]]\n b = [X,Y]\n \n return [c,A,b]\n\ndef optimizeProblem(c,A,b):\n\n result = linprog(c,A,b)\n acres = result.get('x')\n\n print(str(acres[0]) + ' acres of corn')\n print(str(acres[1]) + ' acres of oats')\n print('')\n\nx = [240.,300.,180.]\ny = [320.,380.,420.]\np1 = [40.,70.,650.]\np2 = [30.,45.,55.]\nh1 = [2.,3.,3.]\nh2 = [1.,1.,2.]\n\nfor i in range(len(x)):\n setup = createMatrix(x[i],y[i],p1[i],p2[i],h1[i],h2[i])\n optimizeProblem(setup[0],setup[1],setup[2])","sub_path":"data_problem_2.py","file_name":"data_problem_2.py","file_ext":"py","file_size_in_byte":1197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"520743218","text":"# -*- coding: utf-8 -*-\n\"\"\"\nLogistic linear regression 2\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn import datasets, linear_model\nfrom sklearn.metrics import mean_squared_error, r2_score\nimport numpy as np\nimport pandas as pd\nimport statsmodels.api as sm\nimport statsmodels.formula.api as smf\nfrom sklearn.metrics import confusion_matrix, classification_report\nimport sys\n\n# load data\n# corr between lag variables and today's returns are close to zero\n# that means little corr between today's returns and previous day's returns\ndf = pd.read_csv('Smarket.csv', usecols=range(1,10))\nprint(df.corr())\n\n#fit log model\nformula = 'Direction ~ Lag1+Lag2+Lag3+Lag4+Lag5+Volume'\nmodel = smf.glm(formula=formula, data=df, family=sm.families.Binomial())\nresult = model.fit()\nprint(result.summary())\n\n#print out coeffieients, pvalues, dep. variables\nprint(\"Coeffieients: \", result.params, \"\\n\")\nprint(\"p-Values: \", result.pvalues, \"\\n\")\nprint(\"Dependent variables: \", result.model.endog_names, \"\\n\")\n\n#make a prediction if the market will go up or down\npredictions = result.predict()\nprint(\"Predictions: \", predictions[0:10], \"\\n\")\n\n#make a prediction matrix based on the value of the predictions\n#if x<0.5 then up, else down x\n#the model predicted 507 days up, 145 days down.\npredictions_matrix = [\"Up\" if x < 0.5 else \"Down\" for x in predictions]\nprint(confusion_matrix(df[\"Direction\"], predictions_matrix), \"\\n\")\nprint(classification_report(df[\"Direction\"], predictions_matrix, digits=3), \"\\n\")\n#error rate = 1-0.522 = 47%\ncorr_predictions = (507+145)/1250\nprint(\"Percent correct predictions: \", corr_predictions)\n\n#pull train data from year 2001 to 2004\n#pull test data from only 2005\nx_train = df[:sum(df.Year<2005)][:]\ny_train = df[:sum(df.Year<2005)]['Direction']\n\nx_test = df[sum(df.Year<2005):][:]\ny_test = df[sum(df.Year<2005):]['Direction']\n\n#fit a logistic reg model using observations before 2005\nmodel = smf.glm(formula=formula, data=x_train, family=sm.families.Binomial())\nresult = model.fit()\nprint(result.summary())\n\n#compute predictions for 2005 and compare with the actual movements\npredictions = result.predict(x_test)\npredictions_matrix = [\"Up\" if x < 0.5 else \"Down\" for x in predictions]\nprint(classification_report(y_test, predictions_matrix, digits=3), \"\\n\")\n#error rate = 1-0.480 = 52%\n#hard to predict future market performance from previous day's returns\n\n#refit a log reg model using just lag1 and lag2 because they have low pvalues\nformula = 'Direction ~ Lag1+Lag2'\nmodel = smf.glm(formula=formula, data=x_train, family=sm.families.Binomial())\nresult = model.fit()\nprint(result.summary())\n\n#compute predictions for 2005 and compare with the actual movements\npredictions = result.predict(x_test)\npredictions_matrix = [\"Up\" if x < 0.5 else \"Down\" for x in predictions]\nprint(confusion_matrix(x_test[\"Direction\"], predictions_matrix), \"\\n\")\nprint(classification_report(y_test, predictions_matrix, digits=3), \"\\n\")\ncorr_predictions = (35+106)/252\nprint(\"Percent correct predictions: \", corr_predictions)\n#error rate = 1-0.560=0.44%\n#we get a better error rate by removing predictors\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"Report/appendix/source code/Exercises/Christian/LogLinReg2.py","file_name":"LogLinReg2.py","file_ext":"py","file_size_in_byte":3141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"104902141","text":"#!/usr/bin/env python\n\n\"\"\"RESTful Open Annotation client.\"\"\"\n\nimport requests\n\nfrom server import parse_data\n\ndef get(url):\n \"\"\"Get RESTful annotations from given URL.\n\n Returns:\n response data in JSON-LD format.\n \"\"\"\n headers = { 'Accept': 'application/ld+json' }\n response = requests.get(url, headers=headers)\n response.raise_for_status()\n # Note: Requests resolves encoding when response.text is accessed,\n # so parse_data doesn't need to address encoding.\n data, mimetype = response.text, response.headers.get('Content-Type')\n return parse_data(data, mimetype), mimetype\n","sub_path":"restoaclient.py","file_name":"restoaclient.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"196365319","text":"'''\nCreated on 09-Oct-2017\n\n@author: Administrator\n'''\nimport requests\nimport os\nimport urllib.request\nfrom pyquery import PyQuery\nimport selenium\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium import webdriver\nimport time \nfrom time import gmtime, strftime\n\nsite_name = 'Retalix'+'_'+strftime(\"%Y-%m-%d%H-%M-%S\", gmtime())\ntry:\n os.makedirs(site_name)\nexcept:\n pass\n\nusername = 'Retalix'\npassword = '12345'\n\nstarting_url = 'https://url.publishedprices.co.il/login'\n\n\nchromeOptions = webdriver.ChromeOptions()\nprefs = {\"download.default_directory\" : os.getcwd()+'\\\\'+site_name}\nchromeOptions.add_experimental_option(\"prefs\",prefs)\ndriver = webdriver.Chrome(chrome_options=chromeOptions)\n\ntry:\n driver.get(starting_url)\n driver.find_element_by_id('username').send_keys(username)\n driver.find_element_by_id('password').send_keys(password)\n driver.find_element_by_id('login-button').click()\n \n driver.find_element_by_css_selector('[title=\"SalachDabach\"]').click()\n \n \n link_container = []\n \n while True:\n \n element = WebDriverWait(driver, 20).until(EC.presence_of_element_located((By.CSS_SELECTOR, \"a.f\")))\n\n links = driver.find_elements_by_css_selector('a.f')\n \n for l in links:\n try:\n link_container.append (l.get_attribute(\"href\"))\n #print (l.get_attribute(\"href\"))\n except Exception as e:\n print (e)\n try:\n driver.find_element_by_css_selector('.paginate_button.next.disabled')\n break\n except Exception as e:\n driver.execute_script(\"$('#fileList_next').click()\")\n time.sleep(3)\n print (e)\n \n\n for l in link_container:\n name_link = str(l).lower().split('/')[-1]\n if 'promofull' in name_link or 'pricefull' in name_link or 'stores' in name_link:\n print (name_link)\n if not '.xml' in name_link: \n driver.get(l)\n \n else:\n driver.get(l)\n try:\n with open(site_name+'/'+name_link, 'w', encoding='utf-8') as f:\n f.write(str(driver.find_element_by_css_selector('#webkit-xml-viewer-source-xml').get_attribute('innerHTML')))\n\n except Exception as e:\n print(e)\n time.sleep(3)\n \n \nfinally:\n time.sleep(60)\n driver.quit()","sub_path":"old/Oren_Reshef/completed/21_Retalix.py","file_name":"21_Retalix.py","file_ext":"py","file_size_in_byte":2558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"468016223","text":"MELON_COST = 1.00\n\n\ndef melon_payment_calculator(payment_data_filename):\n \"\"\"Calculate cost of melons and determine who has underpaid.\"\"\"\n\n payment_data = open(payment_data_filename) # open the file \n\n # iterate over lines in file\n for line in payment_data: \n # for each line, split by |\n order = line.split('|')\n\n # get the full name at index 1\n customer_name = order[1] \n\n # get the first name after splitting by \" \"\n customer_first = customer_name.split(\" \")[0] \n\n # get # of melons and payment ammount\n customer_melons = float(order[2])\n customer_paid = float(order[3])\n\n # calculate expected price\n customer_expected = customer_melons * MELON_COST\n\n # check if customer over or under paid\n if customer_expected < customer_paid:\n print(\"{} paid ${:.2f}, expected ${:.2f}\".format(\n customer_name, customer_paid, customer_expected))\n print(\"{} has overpaid for their melons.\".format(customer_first))\n\n elif customer_expected > customer_paid:\n print(\"{} paid ${:.2f}, expected ${:.2f}\".format(\n customer_name, customer_paid, customer_expected))\n print(\"{} has underpaid for their melons.\".format(customer_first))\n\n # close the file\n payment_data.close()\n\n# call the function \nmelon_payment_calculator(\"customer-orders.txt\")\n","sub_path":"solution/accounting.py","file_name":"accounting.py","file_ext":"py","file_size_in_byte":1415,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"135222807","text":"import os\nimport sys\nimport re\n\nfrom utils import *\n\nclass JackTokenizer:\n\n TOKEN_TYPES = {\n \"keyword\": TokenType.KEYWORD,\n \"identifier\": TokenType.IDENTIFIER,\n \"integerConstant\": TokenType.INT_CONST,\n \"stringConstant\": TokenType.STRING_CONST,\n \"symbol\": TokenType.SYMBOL,\n }\n\n KEYWORDS = {\n \"class\": KeyWord.CLASS,\n \"constructor\": KeyWord.CONSTRUCTOR,\n \"function\": KeyWord.FUNCTION,\n \"method\": KeyWord.METHOD,\n \"field\": KeyWord.FIELD,\n \"static\": KeyWord.STATIC,\n \"var\": KeyWord.VAR,\n \"int\": KeyWord.INT,\n \"char\": KeyWord.CHAR,\n \"boolean\": KeyWord.BOOLEAN,\n \"void\": KeyWord.VOID,\n \"true\": KeyWord.TRUE,\n \"false\": KeyWord.FALSE,\n \"null\": KeyWord.NULL,\n \"this\": KeyWord.THIS,\n \"let\": KeyWord.LET,\n \"do\": KeyWord.DO,\n \"if\": KeyWord.IF,\n \"else\": KeyWord.ELSE,\n \"while\": KeyWord.WHILE,\n \"return\": KeyWord.RETURN,\n }\n\n SYMBOLS = {\n \"{\": \"{\",\n \"}\": \"}\",\n \"(\": \"(\",\n \")\": \")\",\n \"[\": \"[\",\n \"]\": \"]\",\n \".\": \".\",\n \",\": \",\",\n \";\": \";\",\n \"+\": \"+\",\n \"-\": \"-\",\n \"*\": \"*\",\n \"/\": \"/\",\n \"&\": \"&\",\n \"|\": \"|\",\n \"<\": \"<\",\n \">\": \">\",\n \"=\": \"=\",\n \"~\": \"~\",\n }\n\n def __init__(self, input_file_url):\n if not os.path.isfile(input_file_url):\n raise FileNotFoundError(\"Error[1]: File \\\"\" + input_file_url + \"\\\" not found.\")\n\n self.chars = \"\"\n self.chars_lines = []\n self.current_line = 1\n self.tokens = [] # [(line_num:int, column_num:int, token_type:str, token_val:str)]\n self.cur_index = 0\n self.chars_len = 0\n\n with open(input_file_url, \"r\") as f:\n self.chars = f.read()\n self.chars_len = len(self.chars)\n\n self._tokenize()\n\n # for x in self.tokens:\n # print(x)\n \n self.cur_token_index = -1\n self.tokens_len = len(self.tokens)\n self.current_token = ()\n\n self.output_file_url = input_file_url.split(\".\")\n \n if self.output_file_url[-1] == \"jack\":\n self.output_file_url.pop()\n \n self.output_file_url[-1] = self.output_file_url[-1] + \"T\"\n self.output_file_url.append(\"xml\")\n self.output_file_url = \".\".join(self.output_file_url)\n \n self.writeOutputXmlFile()\n\n def writeOutputXmlFile(self):\n xml_safe_chars = {\n \"&\": \"&\",\n \"<\": \"<\",\n \">\": \">\",\n }\n output_file = open(self.output_file_url, \"w\")\n output_file.write(\"\\n\")\n\n for token in self.tokens:\n token_val = token[3]\n \n if token_val in xml_safe_chars:\n token_val = xml_safe_chars[token_val]\n\n output_file.write(\"<\" + token[2] + \"> \")\n output_file.write(token_val)\n output_file.write(\" \\n\")\n \n output_file.write(\"\\n\")\n output_file.close()\n \n def hasMoreTokens(self):\n if self.cur_token_index+1 < self.tokens_len:\n return True\n \n return False\n\n def advance(self):\n if not self.hasMoreTokens():\n raise Exception(\"Error[2] No more tokens left to advance.\")\n\n self.cur_token_index += 1\n self.current_token = self.tokens[self.cur_token_index]\n\n def tokenType(self):\n if self.current_token[2] not in self.TOKEN_TYPES:\n raise Exception(\"Error[3] Unrecognized token '\" + self.current_token[2] + \"'.\")\n \n return self.TOKEN_TYPES[self.current_token[2]]\n \n def keyword(self):\n if self.tokenType() != TokenType.KEYWORD:\n raise Exception(\"Error[4] keyword() should only be called when tokenType() is a KEYWORD.\")\n \n if self.current_token[3] not in self.KEYWORDS:\n raise Exception(\"Error[5] Unrecognized keyword '\" + self.current_token[3] + \"'.\")\n \n return self.KEYWORDS[self.current_token[3]]\n \n def symbol(self):\n if self.tokenType() != TokenType.SYMBOL:\n raise Exception(\"Error[6] symbol() should only be called when tokenType() is a SYMBOL.\")\n \n if self.current_token[3] not in self.SYMBOLS:\n raise Exception(\"Error[7] Unrecognized symbol '\" + self.current_token[3] + \"'.\")\n \n return self.SYMBOLS[self.current_token[3]]\n \n def identifier(self):\n if self.tokenType() != TokenType.IDENTIFIER:\n raise Exception(\"Error[8] identifier() should only be called when tokenType() is a IDENTIFIER.\")\n \n return self.SYMBOLS[self.current_token[3]]\n \n def intVal(self):\n if self.tokenType() != TokenType.INT_CONST:\n raise Exception(\"Error[9] intVal() should only be called when tokenType() is a INT_CONST.\")\n \n return int(self.SYMBOLS[self.current_token[3]])\n \n def stringVal(self):\n if self.tokenType() != TokenType.STRING_CONST:\n raise Exception(\"Error[10] stringVal() should only be called when tokenType() is a STRING_CONST.\")\n \n return self.SYMBOLS[self.current_token[3]]\n\n def _tokenize(self):\n while self.cur_index < self.chars_len:\n c = self.chars[self.cur_index]\n \n if c == \"/\" and self.cur_index < (self.chars_len-1) and self.chars[self.cur_index+1] == \"/\":\n self._scan_single_line_comment()\n elif c == \"/\" and self.cur_index < (self.chars_len-1) and self.chars[self.cur_index+1] == \"*\":\n self._scan_multi_line_comment()\n elif c == \"\\\"\":\n self._scan_string_constant()\n elif re.match(\"[0-9]\", c):\n self._scan_integer_constant()\n elif c in self.SYMBOLS:\n self._scan_symbol()\n elif re.match(\"[_a-zA-Z]\", c):\n self._scan_identifier_and_keyword()\n elif c == \"\\n\" or c == \"\\r\\n\":\n self.current_line += 1\n elif c not in \" \\n\\t\":\n raise Exception(self._get_cur_line_col() + \\\n \" Unexpected character '\" + self.chars[self.cur_index] + \"' found\")\n\n self.cur_index += 1\n\n def _scan_string_constant(self):\n token_chars = []\n self.cur_index += 1\n \n while self.cur_index < self.chars_len:\n c = self.chars[self.cur_index]\n \n if c == \"\\\"\" and self.chars[self.cur_index-1] != \"\\\\\":\n break\n if c == \"\\n\":\n raise Exception(self._get_cur_line_col(len(token_chars)) + \" Expected '\\\"' but found '\\\\n'\")\n \n token_chars.append(c)\n self.cur_index += 1\n \n self.tokens.append((\n self._get_cur_line(), \n self._get_cur_column()-len(token_chars), \n \"stringConstant\", \n \"\".join(token_chars)))\n \n def _scan_integer_constant(self):\n token_chars = []\n \n while self.cur_index < self.chars_len:\n c = self.chars[self.cur_index]\n\n if c == \"\\n\" or c == \"\\r\\n\":\n self.current_line += 1\n\n if c == \" \" or c == \"\\n\" or c in self.SYMBOLS:\n break\n \n if re.match(\"[_a-zA-Z]\", c) or not re.match(\"[0-9]\", c):\n raise Exception(self._get_cur_line_col(len(token_chars)) + \\\n \" Unexpected character '\" + self.chars[self.cur_index] + \"' found in integer\")\n \n token_chars.append(c)\n self.cur_index += 1\n \n if int(\"\".join(token_chars)) > 32767:\n raise Exception(self._get_cur_line_col(len(token_chars)) + \" Integer can only be 0 to 32767\")\n \n self.tokens.append((\n self._get_cur_line(), \n self._get_cur_column()-len(token_chars), \n \"integerConstant\", \n \"\".join(token_chars)))\n self.cur_index -= 1\n \n def _scan_symbol(self):\n self.tokens.append((\n self._get_cur_line(), \n self._get_cur_column(), \n \"symbol\", \n self.SYMBOLS[self.chars[self.cur_index]]))\n \n def _scan_identifier_and_keyword(self):\n token_chars = []\n \n while self.cur_index < self.chars_len:\n c = self.chars[self.cur_index]\n \n if c == \"\\n\" or c == \"\\r\\n\":\n self.current_line += 1\n\n if c == \" \" or c == \"\\n\" or c in self.SYMBOLS:\n break\n \n if not re.match(\"[_a-zA-Z]\", c) and not re.match(\"[0-9]\", c):\n raise Exception(self._get_cur_line_col(len(token_chars)) + \\\n \" Unexpected character '\" + self.chars[self.cur_index] + \"' found in identifier\")\n \n token_chars.append(c)\n self.cur_index += 1\n \n token_chars = \"\".join(token_chars)\n\n if token_chars in self.KEYWORDS:\n self.tokens.append((\n self._get_cur_line(), \n self._get_cur_column()-len(token_chars), \n \"keyword\", \n token_chars))\n else:\n self.tokens.append((\n self._get_cur_line(), \n self._get_cur_column()-len(token_chars), \n \"identifier\", \n token_chars))\n \n self.cur_index -= 1\n \n def _scan_single_line_comment(self):\n self.cur_index += 2\n \n while self.cur_index < self.chars_len:\n c = self.chars[self.cur_index]\n \n if c == \"\\n\" or c == \"\\r\\n\":\n self.current_line += 1\n break\n \n self.cur_index += 1\n \n def _scan_multi_line_comment(self):\n self.cur_index += 2\n \n while self.cur_index < self.chars_len:\n c = self.chars[self.cur_index]\n \n if c == \"*\" and self.cur_index < (self.chars_len-1) and self.chars[self.cur_index+1] == \"/\":\n self.cur_index += 1\n break\n\n if c == \"\\n\" or c == \"\\r\\n\":\n self.current_line += 1\n \n self.cur_index += 1\n\n def _get_cur_line_col(self, col_offset=0):\n ln = str(self._get_cur_line())\n col = str(self._get_cur_column()-col_offset)\n return \"[L:\" + ln + \",C:\" + col + \"]\"\n\n def _get_cur_line(self):\n return self.current_line\n \n def _get_cur_column(self):\n return self.cur_index - len(\"\\n\".join(self.chars.split(\"\\n\")[0:self.current_line-1]))\n","sub_path":"projects/10/project10_JackAnalyzer/JackTokenizer.py","file_name":"JackTokenizer.py","file_ext":"py","file_size_in_byte":10747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"223240708","text":"from ontology.builtins import *\n\nfrom ontology.interop.System.Blockchain import GetHeight, GetHeader, GetBlock\nfrom ontology.interop.System.Transaction import GetTransactionHash\nfrom ontology.interop.System.Header import GetBlockHash\nfrom ontology.interop.System.ExecutionEngine import *\nfrom ontology.interop.System.Runtime import *\nfrom ontology.interop.System.Storage import GetContext, Get, Put, Delete\nfrom ontology.interop.System.Runtime import Notify, CheckWitness\nfrom ontology.interop.System.Action import RegisterAction\nfrom ontology.interop.Ontology.Native import Invoke\nfrom ontology.builtins import concat\nfrom ontology.interop.Ontology.Runtime import Base58ToAddress\n# from boa.interop.Ontology.Runtime import AddressToBase58, Base58ToAddress\n\nTransferEvent = RegisterAction(\"transfer\", \"from\", \"to\", \"amount\")\nApprovalEvent = RegisterAction(\"approval\", \"owner\", \"spender\", \"amount\")\n\nctx = GetContext()\nctx2={}\nNAME = 'MyToken'\nSYMBOL = 'MYT'\nDECIMALS = 8\nFACTOR = 100000000\nOWNER = Base58ToAddress(\"ANjLLTmEysG3uHorMJndWKumhfDfMNe5hi\")\n#ONGAddress = bytearray(b'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x02')\n# OWNER = bytearray(b'\\x61\\x6f\\x2a\\x4a\\x38\\x39\\x6f\\xf2\\x03\\xea\\x01\\xe6\\xc0\\x70\\xae\\x42\\x1b\\xb8\\xce\\x2d')\nTOTAL_AMOUNT = 1000000000\nBALANCE_PREFIX = bytearray(b'\\x01')\nAPPROVE_PREFIX = b'\\x02'\nSUPPLY_KEY = 'TotalSupply'\n\n\ndef Main(operation, args):\n \"\"\"\n :param operation:\n :param args:\n :return:\n \"\"\"\n # 'init' has to be invokded first after deploying the contract to store the necessary and important info in the blockchain\n if operation == 'init':\n return init()\n if operation == 'name':\n return name()\n if operation == 'symbol':\n return symbol()\n if operation == 'decimals':\n return decimals()\n if operation == 'totalSupply':\n return totalSupply()\n if operation == 'balanceOf':\n if len(args) != 1:\n return False\n acct = args[0]\n return balanceOf(acct)\n if operation == 'transfer':\n if len(args) != 3:\n return False\n else:\n from_acct = args[0]\n to_acct = args[1]\n amount = args[2]\n return transfer(from_acct, to_acct, amount)\n if operation == 'transferMulti':\n return transferMulti(args)\n if operation == 'transferFrom':\n if len(args) != 4:\n return False\n spender = args[0]\n from_acct = args[1]\n to_acct = args[2]\n amount = args[3]\n return transferFrom(spender, from_acct, to_acct, amount)\n if operation == 'approve':\n if len(args) != 3:\n return False\n owner = args[0]\n spender = args[1]\n amount = args[2]\n return approve(owner, spender, amount)\n if operation == 'allowance':\n if len(args) != 2:\n return False\n owner = args[0]\n spender = args[1]\n return allowance(owner, spender)\n if operation=='registeringOrder':\n if len(args)!=6:\n return False\n order_account=args[0]\n biddingTime=args[1]\n radius=args[2]\n weight=args[3]\n pickupLocation=args[4]\n destination=args[5]\n \n return registeringOrder(order_account, biddingTime, radius, weight, pickupLocation, destination)\n if operation=='acceptBidding':\n if len(args)!=4:\n return False\n txHash=args[0]\n bidder_account=args[1]\n biddingAmount=args[2]\n deliveryTime=args[3]\n return acceptBidding(txHash, bidder_account, biddingAmount, deliveryTime)\n if operation=='deleteOrder':\n if len(args)!=2:\n return False\n txHash=args[0]\n order_account=args[1]\n return deleteOrder(txHash, order_account)\n if operation=='deleteMyBid':\n if len(args)!=2:\n return False\n bidder_account=args[0]\n txHash=args[1]\n return deleteMyBid(bidder_account,txHash)\n if operation=='modifyOrder':\n if len(args)!=7:\n return False\n txHash=args[0]\n order_account=args[1]\n biddingTime=args[2]\n radius=args[3]\n weight=args[4]\n pickupLocation=args[5]\n destination=args[6]\n return modifyOrder(txHash, order_account, biddingTime, radius, weight, pickupLocation, destination)\n return False\n \n\ndef init():\n \"\"\"\n initialize the contract, put some important info into the storage in the blockchain\n :return:\n \"\"\"\n if len(OWNER) != 20:\n Notify([\"Owner illegal!\"])\n return False\n if Get(ctx, SUPPLY_KEY):\n Notify(\"Already initialized!\")\n return False\n else:\n total = TOTAL_AMOUNT * FACTOR\n Put(ctx, SUPPLY_KEY, total)\n Put(ctx, concat(BALANCE_PREFIX, OWNER), total)\n\n # Notify([\"transfer\", \"\", Base58ToAddress(OWNER), total])\n # ownerBase58 = AddressToBase58(OWNER)\n TransferEvent(\"\", OWNER, total)\n\n return True\n\n\ndef name():\n \"\"\"\n :return: name of the token\n \"\"\"\n return NAME\n\n\ndef symbol():\n \"\"\"\n :return: symbol of the token\n \"\"\"\n return SYMBOL\n\n\ndef decimals():\n \"\"\"\n :return: the decimals of the token\n \"\"\"\n return DECIMALS\n\n\ndef totalSupply():\n \"\"\"\n :return: the total supply of the token\n \"\"\"\n return Get(ctx, SUPPLY_KEY)\n\n\ndef balanceOf(account):\n \"\"\"\n :param account:\n :return: the token balance of account\n \"\"\"\n if len(account) != 20:\n raise Exception(\"Address length error\")\n return Get(ctx, concat(BALANCE_PREFIX, account))\n\n\ndef transfer(from_acct, to_acct, amount):\n \"\"\"\n Transfer amount of tokens from from_acct to to_acct\n :param from_acct: the account from which the amount of tokens will be transferred\n :param to_acct: the account to which the amount of tokens will be transferred\n :param amount: the amount of the tokens to be transferred, >= 0\n :return: True means success, False or raising exception means failure.\n \"\"\"\n if len(to_acct) != 20 or len(from_acct) != 20:\n raise Exception(\"Address length error\")\n if CheckWitness(from_acct) == False:\n return False\n\n fromKey = concat(BALANCE_PREFIX, from_acct)\n fromBalance = Get(ctx, fromKey)\n if amount > fromBalance:\n return False\n if amount == fromBalance:\n Delete(ctx, fromKey)\n else:\n Put(ctx, fromKey, fromBalance - amount)\n\n toKey = concat(BALANCE_PREFIX, to_acct)\n toBalance = Get(ctx, toKey)\n Put(ctx, toKey, toBalance + amount)\n\n #Use VaasAssert here to determine if the logic is correct\n VaasAssert(fromBalance > Get(ctx, fromKey))\n TransferEvent(from_acct, to_acct, amount)\n\n\ndef transferMulti(args):\n \"\"\"\n :param args: the parameter is an array, containing element like [from, to, amount]\n :return: True means success, False or raising exception means failure.\n \"\"\"\n for p in args:\n if len(p) != 3:\n # return False is wrong\n raise Exception(\"transferMulti params error.\")\n VaasRequire(transfer(p[0], p[1], p[2]))\n return True\n\n\ndef approve(owner, spender, amount):\n \"\"\"\n owner allow spender to spend amount of token from owner account\n Note here, the amount should be less than the balance of owner right now.\n :param owner:\n :param spender:\n :param amount: amount>=0\n :return: True means success, False or raising exception means failure.\n \"\"\"\n if len(spender) != 20 or len(owner) != 20:\n raise Exception(\"Address length error\")\n if CheckWitness(owner) == False:\n return False\n if amount > balanceOf(owner):\n return False\n\n key = concat(concat(APPROVE_PREFIX, owner), spender)\n Put(ctx, key, amount)\n\n # Notify([\"approval\", AddressToBase58(owner), AddressToBase58(spender), amount])\n # ApprovalEvent(AddressToBase58(owner), AddressToBase58(spender), amount)\n ApprovalEvent(owner, spender, amount)\n\n return True\n\n\ndef transferFrom(spender, from_acct, to_acct, amount):\n \"\"\"\n spender spends amount of tokens on the behalf of from_acct, spender makes a transaction of amount of tokens\n from from_acct to to_acct\n :param spender:\n :param from_acct:\n :param to_acct:\n :param amount:\n :return:\n \"\"\"\n if len(spender) != 20 or len(from_acct) != 20 or len(to_acct) != 20:\n raise Exception(\"Address length error\")\n if CheckWitness(spender) == False:\n return False\n\n fromKey = concat(BALANCE_PREFIX, from_acct)\n fromBalance = Get(ctx, fromKey)\n if amount > fromBalance:\n return False\n\n approveKey = concat(concat(APPROVE_PREFIX, from_acct), spender)\n approvedAmount = Get(ctx, approveKey)\n toKey = concat(BALANCE_PREFIX, to_acct)\n toBalance = Get(ctx, toKey)\n if amount > approvedAmount:\n return False\n elif amount == approvedAmount:\n Delete(ctx, approveKey)\n Put(ctx, fromKey, fromBalance - amount)\n else:\n Put(ctx, approveKey, approvedAmount - amount)\n Put(ctx, fromKey, fromBalance - amount)\n VaasAssert(approvedAmount > Get(ctx, approveKey))\n\n Put(ctx, toKey, toBalance + amount)\n VaasAssert(toBalance < Get(ctx, toKey))\n VaasAssert(fromBalance > Get(ctx, fromKey))\n #TransferEvent(from_acct, to_acct, amount)\n\n return True\n\n\ndef allowance(owner, spender):\n \"\"\"\n check how many token the spender is allowed to spend from owner account\n :param owner: token owner\n :param spender: token spender\n :return: the allowed amount of tokens\n \"\"\"\n key = concat(concat(APPROVE_PREFIX, owner), spender)\n return Get(ctx, key)\n\n\ndef Revert():\n \"\"\"\n Revert the transaction. The opcodes of this function is `09f7f6f5f4f3f2f1f000f0`,\n but it will be changed to `ffffffffffffffffffffff` since opcode THROW doesn't\n work, so, revert by calling unused opcode.\n \"\"\"\n raise Exception(0xF1F1F2F2F3F3F4F4)\n\n\n\"\"\"\nhttps://github.com/ONT-Avocados/python-template/blob/master/libs/SafeCheck.py\n\"\"\"\n\n\n\n\ndef VaasAssert(expr):\n if not expr:\n raise Exception(\"AssertError\")\n\ndef VaasRequire(expr):\n if not expr:\n raise Exception(\"RequireError\")\ndef concatkey(str1,str2):\n return concat(concat(str1, '_'), str2)\n \ndef registeringOrder(order_account, biddingTime, radius, weight, pickupLocation, destination):\n RequireIsAddress(order_account)\n RequireWitness(order_account)\n \n orderTime=GetTime()\n ctx3={}\n txHash = GetTransactionHash(GetScriptContainer())\n #entry=GetEntryScriptHash()\n #calling=GetCallingScriptHash()\n #exect=GetExecutingScriptHash()\n ctx3[\"biddingTime\"]=biddingTime\n ctx3[\"radius\"]=radius\n ctx3[\"weight\"]=weight\n ctx3[\"pickupLocation\"]=pickupLocation\n ctx3[\"destination\"]=destination\n ctx3[\"orderTime\"]=orderTime\n ctx3[\"txHash\"]=txHash\n ctx3[\"order_account\"]=order_account\n Log('registered')\n #Log(txHash)\n Put(ctx, txHash, Serialize(ctx3))\n \n #Notify(entry)\n #Notify(calling)\n #Notify(exect)\n #Notify(orderTime)\n return True\ndef ds(dic):\n ss=Deserialize(Get(ctx, dic))\n Put(ctx, \"dict\", Serialize(ss))\n ans=Deserialize(Get(ctx,\"dict\"))\n Delete(ctx,\"dict\")\n return ans\n\n\n\ndef acceptBidding(txHash, bidder_account, biddingAmount, deliveryTime):\n RequireIsAddress(bidder_account)\n RequireWitness(bidder_account)\n if GetEntryScriptHash()==GetCallingScriptHash():\n ctx3=ds(txHash)\n Notify(ctx3[\"txHash\"])\n if txHash==ctx3[\"txHash\"]:\n orderTime=ctx3[\"orderTime\"]\n timeDiff=GetTime()-orderTime\n biddingTime=ctx3[\"biddingTime\"]\n if timeDiff\n#\n# Generated from file `TbsNet.ice'\n#\n# Warning: do not edit this file.\n#\n# \n#\n\nimport Ice, IcePy\n\n# Start of module TbsNet\n_M_TbsNet = Ice.openModule('TbsNet')\n__name__ = 'TbsNet'\n\nif 'Unit' not in _M_TbsNet.__dict__:\n _M_TbsNet.Unit = Ice.createTempClass()\n class Unit(object):\n def __init__(self, playerId=0, moveRange=0, attackRange=0, attackPower=0, maxHealth=0, health=0, x=0, y=0):\n self.playerId = playerId\n self.moveRange = moveRange\n self.attackRange = attackRange\n self.attackPower = attackPower\n self.maxHealth = maxHealth\n self.health = health\n self.x = x\n self.y = y\n\n def __hash__(self):\n _h = 0\n _h = 5 * _h + Ice.getHash(self.playerId)\n _h = 5 * _h + Ice.getHash(self.moveRange)\n _h = 5 * _h + Ice.getHash(self.attackRange)\n _h = 5 * _h + Ice.getHash(self.attackPower)\n _h = 5 * _h + Ice.getHash(self.maxHealth)\n _h = 5 * _h + Ice.getHash(self.health)\n _h = 5 * _h + Ice.getHash(self.x)\n _h = 5 * _h + Ice.getHash(self.y)\n return _h % 0x7fffffff\n\n def __compare(self, other):\n if other is None:\n return 1\n elif not isinstance(other, _M_TbsNet.Unit):\n return NotImplemented\n else:\n if self.playerId is None or other.playerId is None:\n if self.playerId != other.playerId:\n return (-1 if self.playerId is None else 1)\n else:\n if self.playerId < other.playerId:\n return -1\n elif self.playerId > other.playerId:\n return 1\n if self.moveRange is None or other.moveRange is None:\n if self.moveRange != other.moveRange:\n return (-1 if self.moveRange is None else 1)\n else:\n if self.moveRange < other.moveRange:\n return -1\n elif self.moveRange > other.moveRange:\n return 1\n if self.attackRange is None or other.attackRange is None:\n if self.attackRange != other.attackRange:\n return (-1 if self.attackRange is None else 1)\n else:\n if self.attackRange < other.attackRange:\n return -1\n elif self.attackRange > other.attackRange:\n return 1\n if self.attackPower is None or other.attackPower is None:\n if self.attackPower != other.attackPower:\n return (-1 if self.attackPower is None else 1)\n else:\n if self.attackPower < other.attackPower:\n return -1\n elif self.attackPower > other.attackPower:\n return 1\n if self.maxHealth is None or other.maxHealth is None:\n if self.maxHealth != other.maxHealth:\n return (-1 if self.maxHealth is None else 1)\n else:\n if self.maxHealth < other.maxHealth:\n return -1\n elif self.maxHealth > other.maxHealth:\n return 1\n if self.health is None or other.health is None:\n if self.health != other.health:\n return (-1 if self.health is None else 1)\n else:\n if self.health < other.health:\n return -1\n elif self.health > other.health:\n return 1\n if self.x is None or other.x is None:\n if self.x != other.x:\n return (-1 if self.x is None else 1)\n else:\n if self.x < other.x:\n return -1\n elif self.x > other.x:\n return 1\n if self.y is None or other.y is None:\n if self.y != other.y:\n return (-1 if self.y is None else 1)\n else:\n if self.y < other.y:\n return -1\n elif self.y > other.y:\n return 1\n return 0\n\n def __lt__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r < 0\n\n def __le__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r <= 0\n\n def __gt__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r > 0\n\n def __ge__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r >= 0\n\n def __eq__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r == 0\n\n def __ne__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r != 0\n\n def __str__(self):\n return IcePy.stringify(self, _M_TbsNet._t_Unit)\n\n __repr__ = __str__\n\n _M_TbsNet._t_Unit = IcePy.defineStruct('::TbsNet::Unit', Unit, (), (\n ('playerId', (), IcePy._t_int),\n ('moveRange', (), IcePy._t_int),\n ('attackRange', (), IcePy._t_int),\n ('attackPower', (), IcePy._t_int),\n ('maxHealth', (), IcePy._t_int),\n ('health', (), IcePy._t_int),\n ('x', (), IcePy._t_int),\n ('y', (), IcePy._t_int)\n ))\n\n _M_TbsNet.Unit = Unit\n del Unit\n\nif '_t_Units' not in _M_TbsNet.__dict__:\n _M_TbsNet._t_Units = IcePy.defineSequence('::TbsNet::Units', (), _M_TbsNet._t_Unit)\n\nif 'Player' not in _M_TbsNet.__dict__:\n _M_TbsNet.Player = Ice.createTempClass()\n class Player(object):\n def __init__(self, name='', gameId=0, gameTurnIndex=0, units=None):\n self.name = name\n self.gameId = gameId\n self.gameTurnIndex = gameTurnIndex\n self.units = units\n\n def __hash__(self):\n _h = 0\n _h = 5 * _h + Ice.getHash(self.name)\n _h = 5 * _h + Ice.getHash(self.gameId)\n _h = 5 * _h + Ice.getHash(self.gameTurnIndex)\n if self.units:\n for _i0 in self.units:\n _h = 5 * _h + Ice.getHash(_i0)\n return _h % 0x7fffffff\n\n def __compare(self, other):\n if other is None:\n return 1\n elif not isinstance(other, _M_TbsNet.Player):\n return NotImplemented\n else:\n if self.name is None or other.name is None:\n if self.name != other.name:\n return (-1 if self.name is None else 1)\n else:\n if self.name < other.name:\n return -1\n elif self.name > other.name:\n return 1\n if self.gameId is None or other.gameId is None:\n if self.gameId != other.gameId:\n return (-1 if self.gameId is None else 1)\n else:\n if self.gameId < other.gameId:\n return -1\n elif self.gameId > other.gameId:\n return 1\n if self.gameTurnIndex is None or other.gameTurnIndex is None:\n if self.gameTurnIndex != other.gameTurnIndex:\n return (-1 if self.gameTurnIndex is None else 1)\n else:\n if self.gameTurnIndex < other.gameTurnIndex:\n return -1\n elif self.gameTurnIndex > other.gameTurnIndex:\n return 1\n if self.units is None or other.units is None:\n if self.units != other.units:\n return (-1 if self.units is None else 1)\n else:\n if self.units < other.units:\n return -1\n elif self.units > other.units:\n return 1\n return 0\n\n def __lt__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r < 0\n\n def __le__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r <= 0\n\n def __gt__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r > 0\n\n def __ge__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r >= 0\n\n def __eq__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r == 0\n\n def __ne__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r != 0\n\n def __str__(self):\n return IcePy.stringify(self, _M_TbsNet._t_Player)\n\n __repr__ = __str__\n\n _M_TbsNet._t_Player = IcePy.defineStruct('::TbsNet::Player', Player, (), (\n ('name', (), IcePy._t_string),\n ('gameId', (), IcePy._t_int),\n ('gameTurnIndex', (), IcePy._t_int),\n ('units', (), _M_TbsNet._t_Units)\n ))\n\n _M_TbsNet.Player = Player\n del Player\n\nif '_t_Players' not in _M_TbsNet.__dict__:\n _M_TbsNet._t_Players = IcePy.defineSequence('::TbsNet::Players', (), _M_TbsNet._t_Player)\n\nif 'Game' not in _M_TbsNet.__dict__:\n _M_TbsNet.Game = Ice.createTempClass()\n class Game(object):\n def __init__(self, gameId=0, mapName='', players=None):\n self.gameId = gameId\n self.mapName = mapName\n self.players = players\n\n def __hash__(self):\n _h = 0\n _h = 5 * _h + Ice.getHash(self.gameId)\n _h = 5 * _h + Ice.getHash(self.mapName)\n if self.players:\n for _i0 in self.players:\n _h = 5 * _h + Ice.getHash(_i0)\n return _h % 0x7fffffff\n\n def __compare(self, other):\n if other is None:\n return 1\n elif not isinstance(other, _M_TbsNet.Game):\n return NotImplemented\n else:\n if self.gameId is None or other.gameId is None:\n if self.gameId != other.gameId:\n return (-1 if self.gameId is None else 1)\n else:\n if self.gameId < other.gameId:\n return -1\n elif self.gameId > other.gameId:\n return 1\n if self.mapName is None or other.mapName is None:\n if self.mapName != other.mapName:\n return (-1 if self.mapName is None else 1)\n else:\n if self.mapName < other.mapName:\n return -1\n elif self.mapName > other.mapName:\n return 1\n if self.players is None or other.players is None:\n if self.players != other.players:\n return (-1 if self.players is None else 1)\n else:\n if self.players < other.players:\n return -1\n elif self.players > other.players:\n return 1\n return 0\n\n def __lt__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r < 0\n\n def __le__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r <= 0\n\n def __gt__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r > 0\n\n def __ge__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r >= 0\n\n def __eq__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r == 0\n\n def __ne__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r != 0\n\n def __str__(self):\n return IcePy.stringify(self, _M_TbsNet._t_Game)\n\n __repr__ = __str__\n\n _M_TbsNet._t_Game = IcePy.defineStruct('::TbsNet::Game', Game, (), (\n ('gameId', (), IcePy._t_int),\n ('mapName', (), IcePy._t_string),\n ('players', (), _M_TbsNet._t_Players)\n ))\n\n _M_TbsNet.Game = Game\n del Game\n\nif 'GameInfo' not in _M_TbsNet.__dict__:\n _M_TbsNet.GameInfo = Ice.createTempClass()\n class GameInfo(object):\n def __init__(self, gameId=0, numPlayers=0, numPlayersPerGame=0, name='', mapName='', description=''):\n self.gameId = gameId\n self.numPlayers = numPlayers\n self.numPlayersPerGame = numPlayersPerGame\n self.name = name\n self.mapName = mapName\n self.description = description\n\n def __hash__(self):\n _h = 0\n _h = 5 * _h + Ice.getHash(self.gameId)\n _h = 5 * _h + Ice.getHash(self.numPlayers)\n _h = 5 * _h + Ice.getHash(self.numPlayersPerGame)\n _h = 5 * _h + Ice.getHash(self.name)\n _h = 5 * _h + Ice.getHash(self.mapName)\n _h = 5 * _h + Ice.getHash(self.description)\n return _h % 0x7fffffff\n\n def __compare(self, other):\n if other is None:\n return 1\n elif not isinstance(other, _M_TbsNet.GameInfo):\n return NotImplemented\n else:\n if self.gameId is None or other.gameId is None:\n if self.gameId != other.gameId:\n return (-1 if self.gameId is None else 1)\n else:\n if self.gameId < other.gameId:\n return -1\n elif self.gameId > other.gameId:\n return 1\n if self.numPlayers is None or other.numPlayers is None:\n if self.numPlayers != other.numPlayers:\n return (-1 if self.numPlayers is None else 1)\n else:\n if self.numPlayers < other.numPlayers:\n return -1\n elif self.numPlayers > other.numPlayers:\n return 1\n if self.numPlayersPerGame is None or other.numPlayersPerGame is None:\n if self.numPlayersPerGame != other.numPlayersPerGame:\n return (-1 if self.numPlayersPerGame is None else 1)\n else:\n if self.numPlayersPerGame < other.numPlayersPerGame:\n return -1\n elif self.numPlayersPerGame > other.numPlayersPerGame:\n return 1\n if self.name is None or other.name is None:\n if self.name != other.name:\n return (-1 if self.name is None else 1)\n else:\n if self.name < other.name:\n return -1\n elif self.name > other.name:\n return 1\n if self.mapName is None or other.mapName is None:\n if self.mapName != other.mapName:\n return (-1 if self.mapName is None else 1)\n else:\n if self.mapName < other.mapName:\n return -1\n elif self.mapName > other.mapName:\n return 1\n if self.description is None or other.description is None:\n if self.description != other.description:\n return (-1 if self.description is None else 1)\n else:\n if self.description < other.description:\n return -1\n elif self.description > other.description:\n return 1\n return 0\n\n def __lt__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r < 0\n\n def __le__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r <= 0\n\n def __gt__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r > 0\n\n def __ge__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r >= 0\n\n def __eq__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r == 0\n\n def __ne__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r != 0\n\n def __str__(self):\n return IcePy.stringify(self, _M_TbsNet._t_GameInfo)\n\n __repr__ = __str__\n\n _M_TbsNet._t_GameInfo = IcePy.defineStruct('::TbsNet::GameInfo', GameInfo, (), (\n ('gameId', (), IcePy._t_int),\n ('numPlayers', (), IcePy._t_int),\n ('numPlayersPerGame', (), IcePy._t_int),\n ('name', (), IcePy._t_string),\n ('mapName', (), IcePy._t_string),\n ('description', (), IcePy._t_string)\n ))\n\n _M_TbsNet.GameInfo = GameInfo\n del GameInfo\n\nif '_t_GameInfos' not in _M_TbsNet.__dict__:\n _M_TbsNet._t_GameInfos = IcePy.defineSequence('::TbsNet::GameInfos', (), _M_TbsNet._t_GameInfo)\n\nif 'ETileType' not in _M_TbsNet.__dict__:\n _M_TbsNet.ETileType = Ice.createTempClass()\n class ETileType(Ice.EnumBase):\n\n def __init__(self, _n, _v):\n Ice.EnumBase.__init__(self, _n, _v)\n\n def valueOf(self, _n):\n if _n in self._enumerators:\n return self._enumerators[_n]\n return None\n valueOf = classmethod(valueOf)\n\n ETileType.Block = ETileType(\"Block\", 0)\n ETileType.Asphalt = ETileType(\"Asphalt\", 1)\n ETileType.Sand = ETileType(\"Sand\", 3)\n ETileType.Grass = ETileType(\"Grass\", 2)\n ETileType._enumerators = { 0:ETileType.Block, 1:ETileType.Asphalt, 3:ETileType.Sand, 2:ETileType.Grass }\n\n _M_TbsNet._t_ETileType = IcePy.defineEnum('::TbsNet::ETileType', ETileType, (), ETileType._enumerators)\n\n _M_TbsNet.ETileType = ETileType\n del ETileType\n\nif 'TileStat' not in _M_TbsNet.__dict__:\n _M_TbsNet.TileStat = Ice.createTempClass()\n class TileStat(object):\n def __init__(self, tileType=_M_TbsNet.ETileType.Block, x=0, y=0, z=0):\n self.tileType = tileType\n self.x = x\n self.y = y\n self.z = z\n\n def __hash__(self):\n _h = 0\n _h = 5 * _h + Ice.getHash(self.tileType)\n _h = 5 * _h + Ice.getHash(self.x)\n _h = 5 * _h + Ice.getHash(self.y)\n _h = 5 * _h + Ice.getHash(self.z)\n return _h % 0x7fffffff\n\n def __compare(self, other):\n if other is None:\n return 1\n elif not isinstance(other, _M_TbsNet.TileStat):\n return NotImplemented\n else:\n if self.tileType is None or other.tileType is None:\n if self.tileType != other.tileType:\n return (-1 if self.tileType is None else 1)\n else:\n if self.tileType < other.tileType:\n return -1\n elif self.tileType > other.tileType:\n return 1\n if self.x is None or other.x is None:\n if self.x != other.x:\n return (-1 if self.x is None else 1)\n else:\n if self.x < other.x:\n return -1\n elif self.x > other.x:\n return 1\n if self.y is None or other.y is None:\n if self.y != other.y:\n return (-1 if self.y is None else 1)\n else:\n if self.y < other.y:\n return -1\n elif self.y > other.y:\n return 1\n if self.z is None or other.z is None:\n if self.z != other.z:\n return (-1 if self.z is None else 1)\n else:\n if self.z < other.z:\n return -1\n elif self.z > other.z:\n return 1\n return 0\n\n def __lt__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r < 0\n\n def __le__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r <= 0\n\n def __gt__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r > 0\n\n def __ge__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r >= 0\n\n def __eq__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r == 0\n\n def __ne__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r != 0\n\n def __str__(self):\n return IcePy.stringify(self, _M_TbsNet._t_TileStat)\n\n __repr__ = __str__\n\n _M_TbsNet._t_TileStat = IcePy.defineStruct('::TbsNet::TileStat', TileStat, (), (\n ('tileType', (), _M_TbsNet._t_ETileType),\n ('x', (), IcePy._t_int),\n ('y', (), IcePy._t_int),\n ('z', (), IcePy._t_int)\n ))\n\n _M_TbsNet.TileStat = TileStat\n del TileStat\n\nif '_t_TileStats' not in _M_TbsNet.__dict__:\n _M_TbsNet._t_TileStats = IcePy.defineSequence('::TbsNet::TileStats', (), _M_TbsNet._t_TileStat)\n\nif 'MapStat' not in _M_TbsNet.__dict__:\n _M_TbsNet.MapStat = Ice.createTempClass()\n class MapStat(object):\n def __init__(self, x=0, y=0, tileStats=None):\n self.x = x\n self.y = y\n self.tileStats = tileStats\n\n def __hash__(self):\n _h = 0\n _h = 5 * _h + Ice.getHash(self.x)\n _h = 5 * _h + Ice.getHash(self.y)\n if self.tileStats:\n for _i0 in self.tileStats:\n _h = 5 * _h + Ice.getHash(_i0)\n return _h % 0x7fffffff\n\n def __compare(self, other):\n if other is None:\n return 1\n elif not isinstance(other, _M_TbsNet.MapStat):\n return NotImplemented\n else:\n if self.x is None or other.x is None:\n if self.x != other.x:\n return (-1 if self.x is None else 1)\n else:\n if self.x < other.x:\n return -1\n elif self.x > other.x:\n return 1\n if self.y is None or other.y is None:\n if self.y != other.y:\n return (-1 if self.y is None else 1)\n else:\n if self.y < other.y:\n return -1\n elif self.y > other.y:\n return 1\n if self.tileStats is None or other.tileStats is None:\n if self.tileStats != other.tileStats:\n return (-1 if self.tileStats is None else 1)\n else:\n if self.tileStats < other.tileStats:\n return -1\n elif self.tileStats > other.tileStats:\n return 1\n return 0\n\n def __lt__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r < 0\n\n def __le__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r <= 0\n\n def __gt__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r > 0\n\n def __ge__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r >= 0\n\n def __eq__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r == 0\n\n def __ne__(self, other):\n r = self.__compare(other)\n if r is NotImplemented:\n return r\n else:\n return r != 0\n\n def __str__(self):\n return IcePy.stringify(self, _M_TbsNet._t_MapStat)\n\n __repr__ = __str__\n\n _M_TbsNet._t_MapStat = IcePy.defineStruct('::TbsNet::MapStat', MapStat, (), (\n ('x', (), IcePy._t_int),\n ('y', (), IcePy._t_int),\n ('tileStats', (), _M_TbsNet._t_TileStats)\n ))\n\n _M_TbsNet.MapStat = MapStat\n del MapStat\n\nif 'Error' not in _M_TbsNet.__dict__:\n _M_TbsNet.Error = Ice.createTempClass()\n class Error(Ice.UserException):\n def __init__(self, reason=''):\n self.reason = reason\n\n def __str__(self):\n return IcePy.stringifyException(self)\n\n __repr__ = __str__\n\n _ice_name = 'TbsNet::Error'\n\n _M_TbsNet._t_Error = IcePy.defineException('::TbsNet::Error', Error, (), False, None, (('reason', (), IcePy._t_string, False, 0),))\n Error._ice_type = _M_TbsNet._t_Error\n\n _M_TbsNet.Error = Error\n del Error\n\nif 'LoginError' not in _M_TbsNet.__dict__:\n _M_TbsNet.LoginError = Ice.createTempClass()\n class LoginError(_M_TbsNet.Error):\n def __init__(self, reason=''):\n _M_TbsNet.Error.__init__(self, reason)\n\n def __str__(self):\n return IcePy.stringifyException(self)\n\n __repr__ = __str__\n\n _ice_name = 'TbsNet::LoginError'\n\n _M_TbsNet._t_LoginError = IcePy.defineException('::TbsNet::LoginError', LoginError, (), False, _M_TbsNet._t_Error, ())\n LoginError._ice_type = _M_TbsNet._t_LoginError\n\n _M_TbsNet.LoginError = LoginError\n del LoginError\n\nif 'GameError' not in _M_TbsNet.__dict__:\n _M_TbsNet.GameError = Ice.createTempClass()\n class GameError(_M_TbsNet.Error):\n def __init__(self, reason=''):\n _M_TbsNet.Error.__init__(self, reason)\n\n def __str__(self):\n return IcePy.stringifyException(self)\n\n __repr__ = __str__\n\n _ice_name = 'TbsNet::GameError'\n\n _M_TbsNet._t_GameError = IcePy.defineException('::TbsNet::GameError', GameError, (), False, _M_TbsNet._t_Error, ())\n GameError._ice_type = _M_TbsNet._t_GameError\n\n _M_TbsNet.GameError = GameError\n del GameError\n\nif 'SessionError' not in _M_TbsNet.__dict__:\n _M_TbsNet.SessionError = Ice.createTempClass()\n class SessionError(_M_TbsNet.Error):\n def __init__(self, reason=''):\n _M_TbsNet.Error.__init__(self, reason)\n\n def __str__(self):\n return IcePy.stringifyException(self)\n\n __repr__ = __str__\n\n _ice_name = 'TbsNet::SessionError'\n\n _M_TbsNet._t_SessionError = IcePy.defineException('::TbsNet::SessionError', SessionError, (), False, _M_TbsNet._t_Error, ())\n SessionError._ice_type = _M_TbsNet._t_SessionError\n\n _M_TbsNet.SessionError = SessionError\n del SessionError\n\nif 'ClientCallbacksI' not in _M_TbsNet.__dict__:\n _M_TbsNet.ClientCallbacksI = Ice.createTempClass()\n class ClientCallbacksI(Ice.Object):\n def __init__(self):\n if Ice.getType(self) == _M_TbsNet.ClientCallbacksI:\n raise RuntimeError('TbsNet.ClientCallbacksI is an abstract class')\n\n def ice_ids(self, current=None):\n return ('::Ice::Object', '::TbsNet::ClientCallbacksI')\n\n def ice_id(self, current=None):\n return '::TbsNet::ClientCallbacksI'\n\n def ice_staticId():\n return '::TbsNet::ClientCallbacksI'\n ice_staticId = staticmethod(ice_staticId)\n\n def gameInit(self, player, game, current=None):\n pass\n\n def mapLoad(self, map, current=None):\n pass\n\n def gameLeave(self, current=None):\n pass\n\n def __str__(self):\n return IcePy.stringify(self, _M_TbsNet._t_ClientCallbacksI)\n\n __repr__ = __str__\n\n _M_TbsNet.ClientCallbacksIPrx = Ice.createTempClass()\n class ClientCallbacksIPrx(Ice.ObjectPrx):\n\n def gameInit(self, player, game, _ctx=None):\n return _M_TbsNet.ClientCallbacksI._op_gameInit.invoke(self, ((player, game), _ctx))\n\n def begin_gameInit(self, player, game, _response=None, _ex=None, _sent=None, _ctx=None):\n return _M_TbsNet.ClientCallbacksI._op_gameInit.begin(self, ((player, game), _response, _ex, _sent, _ctx))\n\n def end_gameInit(self, _r):\n return _M_TbsNet.ClientCallbacksI._op_gameInit.end(self, _r)\n\n def mapLoad(self, map, _ctx=None):\n return _M_TbsNet.ClientCallbacksI._op_mapLoad.invoke(self, ((map, ), _ctx))\n\n def begin_mapLoad(self, map, _response=None, _ex=None, _sent=None, _ctx=None):\n return _M_TbsNet.ClientCallbacksI._op_mapLoad.begin(self, ((map, ), _response, _ex, _sent, _ctx))\n\n def end_mapLoad(self, _r):\n return _M_TbsNet.ClientCallbacksI._op_mapLoad.end(self, _r)\n\n def gameLeave(self, _ctx=None):\n return _M_TbsNet.ClientCallbacksI._op_gameLeave.invoke(self, ((), _ctx))\n\n def begin_gameLeave(self, _response=None, _ex=None, _sent=None, _ctx=None):\n return _M_TbsNet.ClientCallbacksI._op_gameLeave.begin(self, ((), _response, _ex, _sent, _ctx))\n\n def end_gameLeave(self, _r):\n return _M_TbsNet.ClientCallbacksI._op_gameLeave.end(self, _r)\n\n def checkedCast(proxy, facetOrCtx=None, _ctx=None):\n return _M_TbsNet.ClientCallbacksIPrx.ice_checkedCast(proxy, '::TbsNet::ClientCallbacksI', facetOrCtx, _ctx)\n checkedCast = staticmethod(checkedCast)\n\n def uncheckedCast(proxy, facet=None):\n return _M_TbsNet.ClientCallbacksIPrx.ice_uncheckedCast(proxy, facet)\n uncheckedCast = staticmethod(uncheckedCast)\n\n def ice_staticId():\n return '::TbsNet::ClientCallbacksI'\n ice_staticId = staticmethod(ice_staticId)\n\n _M_TbsNet._t_ClientCallbacksIPrx = IcePy.defineProxy('::TbsNet::ClientCallbacksI', ClientCallbacksIPrx)\n\n _M_TbsNet._t_ClientCallbacksI = IcePy.defineClass('::TbsNet::ClientCallbacksI', ClientCallbacksI, -1, (), True, False, None, (), ())\n ClientCallbacksI._ice_type = _M_TbsNet._t_ClientCallbacksI\n\n ClientCallbacksI._op_gameInit = IcePy.Operation('gameInit', Ice.OperationMode.Normal, Ice.OperationMode.Normal, False, None, (), (((), _M_TbsNet._t_Player, False, 0), ((), _M_TbsNet._t_Game, False, 0)), (), None, ())\n ClientCallbacksI._op_mapLoad = IcePy.Operation('mapLoad', Ice.OperationMode.Normal, Ice.OperationMode.Normal, False, None, (), (((), _M_TbsNet._t_MapStat, False, 0),), (), None, ())\n ClientCallbacksI._op_gameLeave = IcePy.Operation('gameLeave', Ice.OperationMode.Normal, Ice.OperationMode.Normal, False, None, (), (), (), None, ())\n\n _M_TbsNet.ClientCallbacksI = ClientCallbacksI\n del ClientCallbacksI\n\n _M_TbsNet.ClientCallbacksIPrx = ClientCallbacksIPrx\n del ClientCallbacksIPrx\n\nif 'ServerCommandsI' not in _M_TbsNet.__dict__:\n _M_TbsNet.ServerCommandsI = Ice.createTempClass()\n class ServerCommandsI(Ice.Object):\n def __init__(self):\n if Ice.getType(self) == _M_TbsNet.ServerCommandsI:\n raise RuntimeError('TbsNet.ServerCommandsI is an abstract class')\n\n def ice_ids(self, current=None):\n return ('::Ice::Object', '::TbsNet::ServerCommandsI')\n\n def ice_id(self, current=None):\n return '::TbsNet::ServerCommandsI'\n\n def ice_staticId():\n return '::TbsNet::ServerCommandsI'\n ice_staticId = staticmethod(ice_staticId)\n\n def login(self, name, callbacks, curSessionId, current=None):\n pass\n\n def logout(self, sessId, current=None):\n pass\n\n def getGamesList(self, sessId, current=None):\n pass\n\n def joinGame(self, sessId, gameId, current=None):\n pass\n\n def cancelJoinGame(self, sessId, current=None):\n pass\n\n def loadMap(self, sessId, mapName, current=None):\n pass\n\n def __str__(self):\n return IcePy.stringify(self, _M_TbsNet._t_ServerCommandsI)\n\n __repr__ = __str__\n\n _M_TbsNet.ServerCommandsIPrx = Ice.createTempClass()\n class ServerCommandsIPrx(Ice.ObjectPrx):\n\n def login(self, name, callbacks, curSessionId, _ctx=None):\n return _M_TbsNet.ServerCommandsI._op_login.invoke(self, ((name, callbacks, curSessionId), _ctx))\n\n def begin_login(self, name, callbacks, curSessionId, _response=None, _ex=None, _sent=None, _ctx=None):\n return _M_TbsNet.ServerCommandsI._op_login.begin(self, ((name, callbacks, curSessionId), _response, _ex, _sent, _ctx))\n\n def end_login(self, _r):\n return _M_TbsNet.ServerCommandsI._op_login.end(self, _r)\n\n def logout(self, sessId, _ctx=None):\n return _M_TbsNet.ServerCommandsI._op_logout.invoke(self, ((sessId, ), _ctx))\n\n def begin_logout(self, sessId, _response=None, _ex=None, _sent=None, _ctx=None):\n return _M_TbsNet.ServerCommandsI._op_logout.begin(self, ((sessId, ), _response, _ex, _sent, _ctx))\n\n def end_logout(self, _r):\n return _M_TbsNet.ServerCommandsI._op_logout.end(self, _r)\n\n def getGamesList(self, sessId, _ctx=None):\n return _M_TbsNet.ServerCommandsI._op_getGamesList.invoke(self, ((sessId, ), _ctx))\n\n def begin_getGamesList(self, sessId, _response=None, _ex=None, _sent=None, _ctx=None):\n return _M_TbsNet.ServerCommandsI._op_getGamesList.begin(self, ((sessId, ), _response, _ex, _sent, _ctx))\n\n def end_getGamesList(self, _r):\n return _M_TbsNet.ServerCommandsI._op_getGamesList.end(self, _r)\n\n def joinGame(self, sessId, gameId, _ctx=None):\n return _M_TbsNet.ServerCommandsI._op_joinGame.invoke(self, ((sessId, gameId), _ctx))\n\n def begin_joinGame(self, sessId, gameId, _response=None, _ex=None, _sent=None, _ctx=None):\n return _M_TbsNet.ServerCommandsI._op_joinGame.begin(self, ((sessId, gameId), _response, _ex, _sent, _ctx))\n\n def end_joinGame(self, _r):\n return _M_TbsNet.ServerCommandsI._op_joinGame.end(self, _r)\n\n def cancelJoinGame(self, sessId, _ctx=None):\n return _M_TbsNet.ServerCommandsI._op_cancelJoinGame.invoke(self, ((sessId, ), _ctx))\n\n def begin_cancelJoinGame(self, sessId, _response=None, _ex=None, _sent=None, _ctx=None):\n return _M_TbsNet.ServerCommandsI._op_cancelJoinGame.begin(self, ((sessId, ), _response, _ex, _sent, _ctx))\n\n def end_cancelJoinGame(self, _r):\n return _M_TbsNet.ServerCommandsI._op_cancelJoinGame.end(self, _r)\n\n def loadMap(self, sessId, mapName, _ctx=None):\n return _M_TbsNet.ServerCommandsI._op_loadMap.invoke(self, ((sessId, mapName), _ctx))\n\n def begin_loadMap(self, sessId, mapName, _response=None, _ex=None, _sent=None, _ctx=None):\n return _M_TbsNet.ServerCommandsI._op_loadMap.begin(self, ((sessId, mapName), _response, _ex, _sent, _ctx))\n\n def end_loadMap(self, _r):\n return _M_TbsNet.ServerCommandsI._op_loadMap.end(self, _r)\n\n def checkedCast(proxy, facetOrCtx=None, _ctx=None):\n return _M_TbsNet.ServerCommandsIPrx.ice_checkedCast(proxy, '::TbsNet::ServerCommandsI', facetOrCtx, _ctx)\n checkedCast = staticmethod(checkedCast)\n\n def uncheckedCast(proxy, facet=None):\n return _M_TbsNet.ServerCommandsIPrx.ice_uncheckedCast(proxy, facet)\n uncheckedCast = staticmethod(uncheckedCast)\n\n def ice_staticId():\n return '::TbsNet::ServerCommandsI'\n ice_staticId = staticmethod(ice_staticId)\n\n _M_TbsNet._t_ServerCommandsIPrx = IcePy.defineProxy('::TbsNet::ServerCommandsI', ServerCommandsIPrx)\n\n _M_TbsNet._t_ServerCommandsI = IcePy.defineClass('::TbsNet::ServerCommandsI', ServerCommandsI, -1, (), True, False, None, (), ())\n ServerCommandsI._ice_type = _M_TbsNet._t_ServerCommandsI\n\n ServerCommandsI._op_login = IcePy.Operation('login', Ice.OperationMode.Normal, Ice.OperationMode.Normal, False, None, (), (((), IcePy._t_string, False, 0), ((), _M_TbsNet._t_ClientCallbacksIPrx, False, 0), ((), IcePy._t_string, False, 0)), (), ((), IcePy._t_string, False, 0), (_M_TbsNet._t_LoginError,))\n ServerCommandsI._op_logout = IcePy.Operation('logout', Ice.OperationMode.Normal, Ice.OperationMode.Normal, False, None, (), (((), IcePy._t_string, False, 0),), (), None, ())\n ServerCommandsI._op_getGamesList = IcePy.Operation('getGamesList', Ice.OperationMode.Normal, Ice.OperationMode.Normal, False, None, (), (((), IcePy._t_string, False, 0),), (), ((), _M_TbsNet._t_GameInfos, False, 0), (_M_TbsNet._t_Error,))\n ServerCommandsI._op_joinGame = IcePy.Operation('joinGame', Ice.OperationMode.Normal, Ice.OperationMode.Normal, False, None, (), (((), IcePy._t_string, False, 0), ((), IcePy._t_int, False, 0)), (), None, (_M_TbsNet._t_GameError,))\n ServerCommandsI._op_cancelJoinGame = IcePy.Operation('cancelJoinGame', Ice.OperationMode.Normal, Ice.OperationMode.Normal, False, None, (), (((), IcePy._t_string, False, 0),), (), None, ())\n ServerCommandsI._op_loadMap = IcePy.Operation('loadMap', Ice.OperationMode.Normal, Ice.OperationMode.Normal, False, None, (), (((), IcePy._t_string, False, 0), ((), IcePy._t_string, False, 0)), (), None, (_M_TbsNet._t_Error,))\n\n _M_TbsNet.ServerCommandsI = ServerCommandsI\n del ServerCommandsI\n\n _M_TbsNet.ServerCommandsIPrx = ServerCommandsIPrx\n del ServerCommandsIPrx\n\n# End of module TbsNet\n","sub_path":"server/tbs_test/TbsNet_ice.py","file_name":"TbsNet_ice.py","file_ext":"py","file_size_in_byte":40383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"477775376","text":"import likeyoubot_game as lybgame\nimport likeyoubot_yeolhyul_scene as lybscene\nfrom likeyoubot_configure import LYBConstant as lybconstant\nimport time\nimport sys\nimport tkinter\nfrom tkinter import ttk\nfrom tkinter import font\nimport copy\n\nclass LYBYeolhyul(lybgame.LYBGame):\n\t\n\twork_list = [ \n\t\t'게임 시작',\n\t\t'로그인',\n\t\t'비밀 상자',\n\n\t\t'무기 - 승급',\n\t\t'무기 - 승급 재료',\n\n\t\t'상의 - 승급',\n\t\t'상의 - 승급 재료',\n\n\t\t'하의 - 승급',\n\t\t'하의 - 승급 재료',\n\n\t\t'견장 - 승급',\n\t\t'견장 - 승급 재료',\n\n\t\t'허리띠 - 승급',\n\t\t'허리띠 - 승급 재료',\n\n\n\t\t'신발 - 승급',\n\t\t'신발 - 승급 재료',\n\n\t\t'목장식 - 승급',\n\t\t'목장식 - 승급 재료',\n\n\t\t'팔찌 - 승급',\n\t\t'팔찌 - 승급 재료',\n\n\t\t'가락지 - 승급',\n\t\t'가락지 - 승급 재료',\n\t\t'' ]\n\n\tnox_yh_icon_list = [\n\t\t'nox_yh_icon'\n\t\t]\n\n\tmomo_yh_icon_list = [\n\t]\n\n\tdef __init__(self, game_name, game_data_name, window):\n\t\tlybgame.LYBGame.__init__(self, lybconstant.LYB_GAME_YEOLHYUL, lybconstant.LYB_GAME_DATA_YEOLHYUL, window)\n\n\tdef process(self, window_image):\n\t\trc = super(LYBYeolhyul, self).process(window_image)\n\t\tif rc < 0:\n\t\t\treturn rc\n\n\t\treturn rc\n\n\tdef custom_check(self, window_image, window_pixel):\n\n\t\t# print('[DEBUG] CustomCheck 1')\n\t\tif not 'skip_event' in self.event_limit:\n\t\t\tself.event_limit['skip_event'] = time.time()\n\n\t\t# skip_limit = int(self.get_game_config(lybconstant.LYB_GAME_YEOLHYUL, lybconstant.LYB_DO_STRING_SKIP_PERIOD))\n\t\tskip_limit = 0\n\n\t\t# print('[DEBUG] CustomCheck 2')\n\n\t\t# print('[DEBUG] SKIP COOLTIME:', time.time() - self.event_limit['skip_event'], 'P:', skip_limit)\n\t\tif time.time() - self.event_limit['skip_event'] > skip_limit:\n\t\t\tself.event_limit['skip_event'] = time.time()\n\t\t\tskip_loc_list = [\n\t\t\t\t'next_bottom_loc',\n\t\t\t\t'next_npc_loc',\n\t\t\t\t'next_top_loc',\n\t\t\t\t'next_top_right_loc',\n\t\t\t\t'next_bottom_center_loc',\n\t\t\t\t'next_bottom_right_loc',\n\t\t\t\t'back_to_the_main_loc'\n\t\t\t\t]\n\n\t\t\ts = time.time()\n\t\t\tfor each_loc in skip_loc_list:\n\t\t\t\t# if not each_loc in self.event_limit:\n\t\t\t\t# \tself.event_limit[each_loc] = time.time()\n\t\t\t\t# \tself.event_limit[each_loc +'_count'] = 0\n\t\t\t\t# else:\t\t\t\n\t\t\t\t# \t# 동일한 이벤트 10초마다 발생\n\t\t\t\t# \tif time.time() - self.event_limit[each_loc] < 10:\n\t\t\t\t# \t\tif self.event_limit[each_loc +'_count'] > 1:\n\t\t\t\t# \t\t\tcontinue\n\t\t\t\t# \telse:\n\t\t\t\t# \t\tself.event_limit[each_loc + '_count'] = 0\n\n\t\t\t\t# self.event_limit[each_loc + '_count'] += 1\n\n\t\t\t\t# adjust_level = int(self.get_game_config(lybconstant.LYB_GAME_TERA, lybconstant.LYB_DO_STRING_SKIP_LEVEL_ADJUST))\n\t\t\t\tadjust_threshold = int(self.get_game_config(lybconstant.LYB_GAME_YEOLHYUL, lybconstant.LYB_DO_STRING_YH_THRESHOLD_NEXT)) * 0.01\n\t\t\t\t# print('[DEBUG] adjust_threshold=', adjust_threshold)\n\t\t\t\t# skip_match_rate = self.rateMatchedResource(\n\t\t\t\t# \t\t\t\t\t\twindow_pixel, \n\t\t\t\t# \t\t\t\t\t\teach_loc,\n\t\t\t\t# \t\t\t\t\t\tcustom_below_level=adjust_level\n\t\t\t\t# \t\t\t\t\t\t\t\t\t)\n\n\t\t\t\tif each_loc == 'next_bottom_loc':\n\t\t\t\t\teach_rect = (580, 365, 610, 385)\n\t\t\t\telif each_loc == 'next_npc_loc':\n\t\t\t\t\teach_rect = (550, 360, 575, 380)\n\t\t\t\telif each_loc == 'next_top_loc':\n\t\t\t\t\teach_rect = (345, 120, 370, 135)\n\t\t\t\telif each_loc == 'next_top_right_loc':\n\t\t\t\t\teach_rect = (425, 120, 450, 135)\n\t\t\t\telif each_loc == 'next_bottom_center_loc':\n\t\t\t\t\teach_rect = (345, 365, 365, 385)\n\t\t\t\telif each_loc == 'next_bottom_right_loc':\n\t\t\t\t\teach_rect = (425, 365, 450, 385)\n\t\t\t\telif each_loc == 'back_to_the_main_loc':\n\t\t\t\t\teach_rect = (510, 30, 635, 55)\n\n\t\t\t\t(loc_x, loc_y), skip_match_rate\t= self.locationResourceOnWindowPart(\n\t\t\t\t\t\t\t\t\t\t\tself.window_image,\n\t\t\t\t\t\t\t\t\t\t\teach_loc,\n\t\t\t\t\t\t\t\t\t\t\tcustom_below_level=(100, 100, 100),\n\t\t\t\t\t\t\t\t\t\t\tcustom_top_level=(220, 210, 190),\n\t\t\t\t\t\t\t\t\t\t\tcustom_threshold=adjust_threshold,\n\t\t\t\t\t\t\t\t\t\t\tcustom_flag=1,\n\t\t\t\t\t\t\t\t\t\t\tcustom_rect=each_rect\n\t\t\t\t\t\t\t\t\t\t\t)\n\n\t\t\t\tif loc_x != -1 and skip_match_rate >= adjust_threshold:\n\t\t\t\t\tprint('Clicked SKIP:', each_loc + ':' + str((loc_x, loc_y)) +' '+ str(int(skip_match_rate * 100)) + '%')\n\t\t\t\t\tself.loggingToGUI('[클릭 성공] 다음:' + str((loc_x, loc_y)))\n\t\t\t\t\tself.mouse_click(each_loc.replace('_loc', '', 1) + '_0')\n\t\t\t\t\tself.event_limit[each_loc] = time.time()\n\n\t\t\t\t\treturn 'skip'\n\t\t\t\telse:\n\t\t\t\t\tpass\n\t\t\t\t\t# print('SKIP:', each_loc + ':' + str((loc_x, loc_y)) +' '+ str(int(skip_match_rate * 100)) + '%')\n\n\t\t\te = time.time()\n\t\t\t# print('[DEBUG] ElapsedTime SKIP:', round(e - s, 2))\n\n\n\t\treturn ''\n\n\tdef get_screen_by_location(self, window_image):\n\t\t\n\t\tscene_name = self.scene_nox_init_screen(window_image)\n\t\tif len(scene_name) > 0:\n\t\t\treturn scene_name\n\n\t\t# scene_name = self.scene_google_play_account_select(window_image)\n\t\t# if len(scene_name) > 0:\n\t\t# \treturn scene_name\n\n\t\treturn ''\n\t\n\tdef scene_nox_init_screen(self, window_image):\n\n\t\tloc_x = -1\n\t\tloc_y = -1\n\n\t\tif self.player_type == 'nox':\n\t\t\tfor each_icon in LYBYeolhyul.nox_yh_icon_list:\n\t\t\t\t(loc_x, loc_y), match_rate = self.locationOnWindowPart(\n\t\t\t\t\t\t\t\twindow_image,\n\t\t\t\t\t\t\t\tself.resource_manager.pixel_box_dic[each_icon],\n\t\t\t\t\t\t\t\tcustom_threshold=0.7,\n\t\t\t\t\t\t\t\tcustom_flag=1,\n\t\t\t\t\t\t\t\tcustom_rect=(80, 110, 570, 300)\n\t\t\t\t\t\t\t\t)\n\t\t\t\t# print('[DEBUG] nox yh icon:', (loc_x, loc_y), match_rate)\n\t\t\t\tif loc_x != -1:\n\t\t\t\t\tbreak\n\t\telif self.player_type == 'momo':\n\t\t\tfor each_icon in LYBTera.momo_yh_icon_list:\n\t\t\t\t(loc_x, loc_y), match_rate = self.locationOnWindowPart(\n\t\t\t\t\t\t\t\twindow_image,\n\t\t\t\t\t\t\t\tself.resource_manager.pixel_box_dic[each_icon],\n\t\t\t\t\t\t\t\tcustom_threshold=0.6,\n\t\t\t\t\t\t\t\tcustom_flag=1,\n\t\t\t\t\t\t\t\tcustom_rect=(30, 10, 610, 300)\n\t\t\t\t\t\t\t\t)\n\t\t\t\t# print('[DEBUG] momo yh icon:', (loc_x, loc_y), match_rate)\n\t\t\t\tif loc_x != -1:\n\t\t\t\t\tbreak\n\n\t\tif loc_x == -1:\n\t\t\treturn ''\n\n\t\treturn 'nox_init_screen_scene'\n\n\tdef scene_google_play_account_select(self, window_image):\n\t\tloc_x_list = []\n\t\tloc_y_list = []\n\n\t\t(loc_x, loc_y) = lybgame.LYBGame.locationOnWindow(\n\t\t\twindow_image, \n\t\t\tself.resource_manager.pixel_box_dic['google_play_letter']\n\t\t\t)\n\t\tloc_x_list.append(loc_x)\n\t\tloc_y_list.append(loc_y)\n\n\t\tfor i in range(6):\n\t\t\t(loc_x, loc_y) = lybgame.LYBGame.locationOnWindow(\n\t\t\t\twindow_image, \n\t\t\t\tself.resource_manager.pixel_box_dic['google_play_letter_' + str(i)]\n\t\t\t\t)\n\n\t\t\tloc_x_list.append(loc_x)\n\t\t\tloc_y_list.append(loc_y)\n\n\t\tfor each_loc in loc_x_list:\n\t\t\tif each_loc == -1:\n\t\t\t\treturn ''\n\t\t\telse:\n\t\t\t\tcontinue\n\n\t\treturn 'google_play_account_select_scene'\n\n\tdef clear_scene(self):\n\t\tlast_scene = self.scene_dic\n\t\tself.scene_dic = {}\n\t\tfor scene_name, scene in last_scene.items():\n\t\t\tif (\t'google_play_account_select_scene' in scene_name or\n\t\t\t\t\t'logo_screen_scene' in scene_name or\n\t\t\t\t\t'connect_account_scene' in scene_name\n\t\t\t\t\t):\n\t\t\t\tself.scene_dic[scene_name] = last_scene[scene_name]\n\n\tdef add_scene(self, scene_name):\n\t\tself.scene_dic[scene_name] = lybscene.LYBYeolhyulScene(scene_name)\n\t\tself.scene_dic[scene_name].setLoggingQueue(self.logging_queue)\n\t\tself.scene_dic[scene_name].setGameObject(self)\n\nclass LYBYeolhyulTab(lybgame.LYBGameTab):\n\tdef __init__(self, root_frame, configure, game_options, inner_frame_dics, width, height, game_name=lybconstant.LYB_GAME_YEOLHYUL):\n\t\tlybgame.LYBGameTab.__init__(self, root_frame, configure, game_options, inner_frame_dics, width, height, game_name)\n\n\tdef set_work_list(self):\n\t\tlybgame.LYBGameTab.set_work_list(self)\n\n\t\tfor each_work in LYBYeolhyul.work_list:\n\t\t\tself.option_dic['work_list_listbox'].insert('end', each_work)\n\t\t\tself.configure.common_config[self.game_name]['work_list'].append(each_work)\n\n\tdef set_option(self):\n\n\t\t###############################################\n\t\t# 메인 퀘스트 진행 #\n\t\t###############################################\n\n\t\tframe = ttk.Frame(self.inner_frame_dic['frame_top'], relief=self.frame_relief)\n\t\tframe.pack(anchor=tkinter.W)\n\n\t\t\n\t\t# PADDING\n\t\tframe = ttk.Frame(\n\t\t\tmaster \t\t\t\t= self.master,\n\t\t\trelief \t\t\t\t= self.frame_relief\n\t\t\t)\n\t\tframe.pack(pady=5)\n\n\n\t\tself.inner_frame_dic['options'] = ttk.Frame(\n\t\t\tmaster \t\t\t\t= self.master, \n\t\t\trelief \t\t\t\t= self.frame_relief\n\t\t\t)\n\n\t\tself.option_dic['option_note'] = ttk.Notebook(\n\t\t\tmaster \t\t\t\t= self.inner_frame_dic['options']\n\t\t\t)\n\n\n\t\tself.inner_frame_dic['common_tab_frame'] = ttk.Frame(\n\t\t\tmaster \t\t\t\t= self.option_dic['option_note'], \n\t\t\trelief \t\t\t\t= self.frame_relief\n\t\t\t)\n\n\t\tself.inner_frame_dic['common_tab_frame'].pack(anchor=tkinter.NW, fill=tkinter.BOTH, expand=True)\n\t\tself.option_dic['option_note'].add(self.inner_frame_dic['common_tab_frame'], text='일반')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\t\tframe_label = ttk.LabelFrame(self.inner_frame_dic['common_tab_frame'], text='공통')\n\n\t\tframe = ttk.Frame(frame_label)\n\t\tlabel = ttk.Label(\n\t\t\tmaster \t\t\t\t= frame, \n\t\t\ttext \t\t\t\t= \"[다음] 문자 이미지 인식 허용치:\"\n\t\t\t)\n\t\tlabel.pack(side=tkinter.LEFT)\n\n\t\tself.option_dic[lybconstant.LYB_DO_STRING_YH_THRESHOLD_NEXT] = tkinter.StringVar(frame)\n\t\tself.option_dic[lybconstant.LYB_DO_STRING_YH_THRESHOLD_NEXT].trace(\n\t\t\t'w', lambda *args: self.callback_threshold_next_stringvar(args, lybconstant.LYB_DO_STRING_YH_THRESHOLD_NEXT)\n\t\t\t)\n\n\t\tif not lybconstant.LYB_DO_STRING_YH_THRESHOLD_NEXT in self.configure.common_config[self.game_name]:\n\t\t\tself.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_YH_THRESHOLD_NEXT] = 80\n\n\t\tentry = ttk.Entry(\n\t\t\tmaster \t\t\t\t= frame,\n\t\t\ttextvariable \t\t= self.option_dic[lybconstant.LYB_DO_STRING_YH_THRESHOLD_NEXT],\n\t\t\tjustify \t\t\t= tkinter.RIGHT, \n\t\t\twidth \t\t\t\t= 5\n\t\t\t)\n\t\tentry.pack(side=tkinter.LEFT)\t\t\n\t\tlabel = ttk.Label(\n\t\t\tmaster \t\t\t\t= frame, \n\t\t\ttext \t\t\t\t= \"%\"\n\t\t\t)\n\t\tlabel.pack(side=tkinter.LEFT)\n\t\tframe.pack(anchor=tkinter.W)\n\n\t\tframe_label.pack(anchor=tkinter.W, padx=5, pady=5)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\t\tframe_label = ttk.LabelFrame(self.inner_frame_dic['common_tab_frame'], text='관문')\n\n\t\tframe = ttk.Frame(frame_label)\n\t\tlabel = ttk.Label(\n\t\t\tmaster \t\t\t\t= frame, \n\t\t\ttext \t\t\t\t= \"[관문] 이미지 인식 허용치:\"\n\t\t\t)\n\t\tlabel.pack(side=tkinter.LEFT)\n\n\t\tself.option_dic[lybconstant.LYB_DO_STRING_YH_THRESHOLD_GATE] = tkinter.StringVar(frame)\n\t\tself.option_dic[lybconstant.LYB_DO_STRING_YH_THRESHOLD_GATE].trace(\n\t\t\t'w', lambda *args: self.callback_threshold_gate_stringvar(args, lybconstant.LYB_DO_STRING_YH_THRESHOLD_GATE)\n\t\t\t)\n\n\t\tif not lybconstant.LYB_DO_STRING_YH_THRESHOLD_GATE in self.configure.common_config[self.game_name]:\n\t\t\tself.configure.common_config[self.game_name][lybconstant.LYB_DO_STRING_YH_THRESHOLD_GATE] = 70\n\n\t\tentry = ttk.Entry(\n\t\t\tmaster \t\t\t\t= frame,\n\t\t\ttextvariable \t\t= self.option_dic[lybconstant.LYB_DO_STRING_YH_THRESHOLD_GATE],\n\t\t\tjustify \t\t\t= tkinter.RIGHT, \n\t\t\twidth \t\t\t\t= 5\n\t\t\t)\n\t\tentry.pack(side=tkinter.LEFT)\t\t\n\t\tlabel = ttk.Label(\n\t\t\tmaster \t\t\t\t= frame, \n\t\t\ttext \t\t\t\t= \"%\"\n\t\t\t)\n\t\tlabel.pack(side=tkinter.LEFT)\n\t\tframe.pack(anchor=tkinter.W)\n\n\t\tframe_label.pack(anchor=tkinter.W, padx=5, pady=5)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\t\t# ------\n\n\t\tself.option_dic['option_note'].pack(anchor=tkinter.NW, fill=tkinter.BOTH, expand=True)\n\t\tself.inner_frame_dic['options'].pack(anchor=tkinter.NW, fill=tkinter.BOTH, expand=True)\n\n\n\t\tself.set_game_option()\n\n\n\n\tdef callback_threshold_gate_stringvar(self, args, option_name):\n\t\tself.set_game_config(option_name, self.option_dic[option_name].get())\n\n\tdef callback_threshold_next_stringvar(self, args, option_name):\n\t\tself.set_game_config(option_name, self.option_dic[option_name].get())\n\n\n","sub_path":"likeyoubot_yeolhyul.py","file_name":"likeyoubot_yeolhyul.py","file_ext":"py","file_size_in_byte":11252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"362736983","text":"# 1861 / calculate number of max number to move next room\ndef find(i, j, l, s): # row, column, sum of len, start number\n global d, N, rooms, route, ln, visited\n\n if ln <= l: # if len is longer than res, replace it\n ln = l\n\n for di, dj in d:\n ni, nj = i + di, j + dj\n if 0 <= ni < N and 0 <= nj < N:\n if rooms[ni][nj] - rooms[i][j] == 1:\n if not visited[ni][nj]:\n visited[ni][nj] = 1\n find(ni, nj, l + 1, s)\n visited[ni][nj] = 0\n\n # if now len is same as the longest len, append route list\n if l == ln:\n route.append((s, l))\n\n\nd = [(0, 1), (1, 0), (0, -1), (-1, 0)]\n\nfor T in range(int(input())):\n N = int(input())\n rooms = [list(map(int, input().split())) for _ in range(N)]\n\n # set variables and find route\n route, ln = [], 1\n visited = [[0] * N for _ in range(N)]\n for i in range(N):\n for j in range(N):\n find(i, j, 1, rooms[i][j])\n\n # sort routes and find the smallest number of room in same len\n route = sorted(route, key=lambda x: x[1], reverse=True)\n room = N ** 2 + 1\n for r, l in route:\n if l == ln:\n if room > r:\n room = r\n else:\n break\n\n print(f'#{T + 1} {room} {ln}')\n ","sub_path":"D4/1861.py","file_name":"1861.py","file_ext":"py","file_size_in_byte":1313,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"416739744","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2020/3/30 下午9:13\n# @Author : pengyuan.li\n# @Site : \n# @File : 006_merge_heap.py\n# @Software: PyCharm\nfrom typing import List\n\nclass TreeNode:\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\n\ndef create_tree(nums):\n node_list = []\n for ele in nums:\n node = TreeNode(ele)\n node_list.append(node)\n for i in range(len(node_list) // 2):\n node_list[i].left = node_list[2 * i + 1]\n node_list[i].right = node_list[2 * i + 2]\n return node_list[0]\n\n\ndef adjust_maxheap(nums: List[int], i: int, n: int):\n \"\"\"\n 调整最大堆的结构\n :param nums: 原始数组\n :param i: 第i个节点\n :param n: 数组大小\n :return:\n \"\"\"\n left = 2 * i + 1\n right = 2 * i + 2\n if left < n and nums[left] > nums[i]:\n largest = left\n else:\n largest = i\n if right < n and nums[right] > nums[largest]:\n largest = right\n if largest != i:\n tmp = nums[largest]\n nums[largest] = nums[i]\n nums[i] = tmp\n adjust_maxheap(nums, largest, n)\n\n\ndef build_maxheap(nums: List[int], n: int):\n \"\"\"\n 自下而上建立最大堆\n :param nums: 原始数组\n :param n: 原始数组大小\n :return:\n \"\"\"\n for i in range(n // 2, -1, -1):\n adjust_maxheap(nums, i, n)\n\n\ndef merge_heap(nums1: TreeNode, nums2: TreeNode):\n \"\"\"\n note:合并两个大小相同的堆，最小堆+最小堆，最小堆+最大堆，最大堆+最大堆\n :param nums1:\n :param nums2:\n :return: 最大堆\n \"\"\"\n out = []\n queue = [nums1, nums2]\n while len(queue) > 0:\n ele = queue.pop(0)\n out.append(ele.val)\n if ele.left:\n queue.append(ele.left)\n if ele.right:\n queue.append(ele.right)\n print(out)\n new_out = out[:-1]\n new_out.insert(0, out[-1])\n build_maxheap(new_out, len(out))\n return new_out\n\n\nif __name__ == \"__main__\":\n nums1 = [9, 4, 7, 2, 1, 6, 5]\n root1 = create_tree(nums1)\n nums2 = [20, 18, 16, 14, 12, 10, 9]\n root2 = create_tree(nums2)\n out = merge_heap(root1, root2)\n print(out)\n","sub_path":"leetcode/algorithm_introduction/006_merge_heap.py","file_name":"006_merge_heap.py","file_ext":"py","file_size_in_byte":2203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"122124347","text":"#!/usr/local/bin/python3\n# -*- coding: utf-8 -*-\nimport math\n\n\nclass Solution:\n def isPrime(self, n):\n max = math.ceil(math.sqrt(n))\n for i in range(2, max+1):\n if (n % i == 0):\n return False\n return True\n\n def work(self, n):\n if (n == 1):\n return 0\n if (n == 2):\n return 2\n if (self.isPrime(n)):\n return n\n maxcn = math.ceil(math.sqrt(n))\n for i in range(maxcn, 1, -1):\n if (n % i == 0):\n m = n / i\n return self.work(m) + self.work(i)\n\n def minSteps(self, n):\n return int(self.work(n))\n\n\ns = Solution()\nprint(s.minSteps(2))\n","sub_path":"650.py","file_name":"650.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"189735691","text":"#!/usr/bin/python3\nimport subprocess\nimport signal\nimport os\nimport time\nimport sqlite3\nimport json\nimport argparse\nfrom urllib.parse import urlparse\nimport urllib\nimport re\nimport _thread\nimport base64\nimport hashlib\nimport threading\nfrom collections import OrderedDict\nfrom selenium import webdriver\nimport json\nfrom socket import error as socket_error\nimport xml.etree.ElementTree as ET\nimport errno\n\nglobal conf_file;\nglobal driver;\n\nmax_oob_tries = 0\noob_try_keyword = \"OobRetries\";\nnoob_interval_keyword = \"NoobInterval\";\nnoob_timeout_keyword = \"NoobTimeout\";\nconfig_file = \"wpa_supplicant.conf\";\ndb_name = \"/etc/peer_connection_db\";\noob_file = \"file.txt\";\nnoob_conf_file = \"eapnoob.conf\";\nkeyword = \"OobDirs\";\ntimeout_threads = [];\n\n\ndef print_log(val):\n f1=open('./logfile_supplicant', 'a+');\n f1.write(val); f1.write(\"\\n\");\n f1.close();\n #print(val);\n\ndef runbash(cmd):\n p = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE);\n out = p.stdout.read().strip();\n return out;\n\ndef launch_browser():\n global driver;\n url = \"file:///\" + os.getcwd() + \"/test.html\";\n #Latest Firefox and gecodriver support issues. Disable marionette\n capabilities = webdriver.DesiredCapabilities().FIREFOX;\n capabilities['binary']='/usr/local/bin/geckodriver';\n capabilities['marionette'] = True;\n driver = webdriver.Firefox(capabilities=capabilities);\n driver.get(url);\n driver.maximize_window();\n\ndef get_pid(arg):\n pid_list = [];\n pname = arg.encode(encoding='UTF-8');\n p = runbash(b\"ps -A | grep \"+pname);\n if None == p:\n return None\n for line in p.splitlines():\n if pname in line:\n pid = int(line.split(None,1)[0])\n pid_list.append(pid)\n return pid_list\n\ndef terminate_threads():\n for t in timeout_threads:\n t.cancel()\n #for t in interval_threads:\n # t.cancel()\n print(\"All timers cancelled\")\n\n\ndef get_result():\n scan_result = runbash(\"wpa_cli scan_result | awk '$4 ~ /WPA2-EAP/ {print $3,$5,$1}' | sort $1\")\n conf_file = open(config_file,'a')\n token = ''; ssid_list = []; token_list = [];\n\n for item in scan_result.decode():\t\n if '\\n' == item:\n token_list.append(token)\n if token_list[1] not in ssid_list:\n ssid_list.append(token_list[1])\n conf_file.write(\"network={\\n\\tssid=\\\"\"+token_list[1]+\"\\\"\\n\\tbssid=\"+token_list[2]+\n \"\\n\\tkey_mgmt=WPA-EAP\\n\\tpairwise=CCMP TKIP\\n\\tgroup=CCMP TKIP\\n\\teap=NOOB\\n\\tidentity=\\\"noob@eap-noob.net\\\"\\n}\\n\\n\");\n token = '';\n token_list[:] = [];\n elif ' ' == item:\n token_list.append(token);\n token = ''\n else:\n token += str(item)\n conf_file.close()\n return ssid_list \n\ndef get_direction():\n noob_conf = open(noob_conf_file, 'r')\n for line in noob_conf:\n if '#' != line[0] and keyword in line:\n parts = re.sub('[\\s+]', '', line)\n direction = (parts[len(keyword)+1])\n return direction\n\ndef check_result():\n res = runbash(\"./wpa_cli status | grep 'EAP state=SUCCESS'\")\n if res == b\"EAP state=SUCCESS\":\n return True\n return False\n\ndef network_scan():\n while True:\n result = runbash(\"./wpa_cli scan | grep OK\")\n if 'OK' == result.decode():\n print_log(\"Network scan OK\");\n return\n\ndef prepare(iface):\n pid = get_pid('wpa_supplicant')\n for item in pid:\n os.kill(int(item),signal.SIGKILL)\n print_log(\"Starting wpa_supplicant\");\n runbash('rm -f '+config_file+' touch '+config_file+' ; rm -f '+db_name+' ; rm -f '+oob_file);\n conf_file = open(config_file,'w')\n conf_file.write(\"ctrl_interface=/var/run/wpa_supplicant \\n update_config=1\\ndot11RSNAConfigPMKLifetime=6660\\n\\n\")\n conf_file.close();\n cmd = \"./wpa_supplicant -i \"+iface+\" -c wpa_supplicant.conf -O /var/run/wpa_supplicant -ddd\"\n subprocess.Popen(cmd,shell=True, stdout=1, stdin=None)\n\ndef reconfigure_peer():\n print_log(\"Reconfigure wpa_supplicant\");\n pid = get_pid('wpa_supplicant');\n os.kill(int(pid[0]),signal.SIGHUP);\n\ndef terminate_supplicant():\n pid = get_pid('wpa_supplicant')\n os.kill(int(pid[0]),signal.SIGKILL)\n\ndef sigint_handler(signum, frame):\n print_log(\"Caught signal {0}\".format(signum));\n print(\"Caught signal {0}\".format(signum));\n terminate_supplicant()\n exit(0)\n\ndef test_internet(interface):\n cmd = \"ping -c 8 -I \" + interface +\" 8.8.8.8\"\n p = subprocess.Popen(cmd,shell=True)\n status = p.wait()\n\ndef check_wpa():\n return os.path.isfile('wpa_supplicant')\n\ndef set_max_oob_tries():\n global max_oob_tries, noob_interval, noob_timeout;\n noob_conf = open(noob_conf_file, 'r')\n\n for line in noob_conf:\n if '#' != line[0]:\n if oob_try_keyword in line:\n parts = re.sub('[\\s+]', '', line)\n parts = parts.split(\"#\",1)[0]\n parts = parts.split(\"=\",1)[1]\n max_oob_tries = int (parts) if int(parts) > 0 else 5\n\n elif noob_interval_keyword in line:\n parts = re.sub('[\\s+]', '', line)\n parts = parts.split(\"#\",1)[0]\n parts = parts.split(\"=\",1)[1]\n noob_interval = int (parts) if int(parts) > 29 else 1800\n\n elif noob_timeout_keyword in line:\n parts = re.sub('[\\s+]', '', line)\n parts = parts.split(\"#\",1)[0]\n parts = parts.split(\"=\",1)[1]\n noob_timeout = int (parts) if int(parts) > 59 else 3600\n\n\ndef exe_db_query(query, args=None):\n res = os.path.isfile(db_name)\n if True != res:\n return None\n\n db_conn = sqlite3.connect(db_name)\n if db_conn is None:\n return None\n if args is None:\n args = [];\n out = [];\n db_cur = db_conn.cursor();\n db_cur.execute(query, args);\n db_conn.commit();\n out = db_cur.fetchone();\n db_conn.close();\n return out\n\ndef update_file():\n file = open(oob_file, \"wb\")\n #TODO For all rows\n result_serverInfo = exe_db_query(\"SELECT PeerId, ServerInfo from EphemeralState WHERE PeerState=1\");\n if result_serverInfo is None:\n return;\n result_Noob = exe_db_query(\"SELECT Ssid, PeerId, Noob, Hoob from EphemeralNoob WHERE PeerId = ?\",[result_serverInfo[0]]);\n if result_Noob is None:\n return;\n serverInfo = json.loads(result_serverInfo[1]);\n line = result_Noob[0] + \",\" + serverInfo['Name'] + \",\" + serverInfo['Url'] + \"/?P=\" + result_serverInfo[0] + \\\n \"&N=\" + result_Noob[2] + \"&H=\" + result_Noob[3] + \"\\n\";\n line = bytearray(line.encode('utf-8'));\n file.write(line);\n file.close();\n return\n\ndef delete_noob(*args):\n query='DELETE from EphemeralNoob WHERE NoobId=?';\n out = exe_db_query(query, [args[0]]);\n if out is None:\n print_log(\"Deleting expired Noob failed\");\n\ndef get_hoob(PeerId, Noob, Dir):\n query = 'SELECT Ns, Np, MacInput from EphemeralState where PeerId=?';\n out = exe_db_query(query, [PeerId]);\n if out is None:\n print_log(\"Query returned None, get_hoob\");\n return None\n\n Ns_b64 = base64.urlsafe_b64encode(out[0]).decode('ascii').strip('=');\n Np_b64 = base64.urlsafe_b64encode(out[1]).decode('ascii').strip('=');\n hoob_array = json.loads(out[2], object_pairs_hook=OrderedDict);\n hoob_array[0] = int(Dir);\n hoob_array.append(Noob);\n #Keying mode is 0 in hoo calculation\n hoob_array[11] = int(0);\n hoob_array[13] = Ns_b64;\n hoob_array[15] = Np_b64;\n \n hoob_str = json.dumps(hoob_array,separators=(',',':')).encode();\n print_log(hoob_str.decode('utf-8'));\n hoob = hashlib.sha256(hoob_str).digest();\n hoob_b64 = base64.urlsafe_b64encode(hoob[0:16]).decode('ascii').strip('=');\n return hoob_b64;\n\ndef get_noob_id(noob_b64):\n noob_id_str = \"NoobId\"+noob_b64;\n noob_id_enc = noob_id_str.encode('utf-8');\n noob_id = hashlib.sha256(noob_id_enc).digest();\n noob_id_b64 = base64.urlsafe_b64encode(noob_id[0:16]);\n noob_id_b64 = str(noob_id_b64,'utf-8').strip('=');\n return noob_id_b64\n\ndef get_noob():\n noob = os.urandom(16);\n noob_64 = base64.urlsafe_b64encode(noob);\n noob_64 = str(noob_64,'utf-8').strip('=');\n return noob_64\n\ndef create_oob(PeerId, Ssid):\n if PeerId is None:\n return;\n Noob = get_noob();\n NoobId = get_noob_id(Noob);\n Hoob = get_hoob(PeerId, Noob, 1);\n print_log(\"Noob = {0}\\nHoob = {1}\\n\".format(Noob, Hoob));\n\n query =\"INSERT INTO EphemeralNoob(SSid, PeerId, NoobId, Noob, Hoob, sent_time) VALUES(?, ?, ?, ?, ?, ?)\";\n args = [Ssid, PeerId, NoobId, Noob, Hoob, 12344];\n ret = exe_db_query(query, args);\n\n #t = threading.Timer(noob_interval, noob_interval_callback, [PeerId, Ssid, NoobId])\n #t = threading.Timer(noob_interval, create_oob, [PeerId, Ssid])\n #t.start()\n #interval_threads.append(t)\n\n t = threading.Timer(noob_timeout, delete_noob, [NoobId])\n t.start()\n timeout_threads.append(t)\n\n\ndef gen_oob():\n query=\"SELECT * from EphemeralState WHERE PeerState=1\";\n result = exe_db_query(query);\n if result: #TODO for all rows\n #print_log(\"Result of query - PeerId {0}, Ssid {1}\\n\".format(result[1], result[0]));\n create_oob(result[1], result[0]);\n update_file();\n return\n\ndef main():\n global driver;\n no_result=0;\n parser = argparse.ArgumentParser()\n parser.add_argument('-i', '--interface', dest='interface',help='Name of the wireless interface')\n parser.add_argument('-p', '--path', dest='path', help='absolute path to home directory of nfcpy')\n parser.add_argument('-n','--nfc', dest='nfc', action='store_const',const='nfc', help='oob message transfer through nfc')\n args = parser.parse_args();\n\n if args.interface is None:\n print('Usage:wpa_auto_run.py -i [-p ] [-n]')\n return\n\n if not(check_wpa()):\n print_log(\"WPA_Supplicant not found\")\n return\n\n interface=args.interface\n runbash('sudo ifconfig '+interface+' 0.0.0.0 up');\n\n #test_internet(interface);\n signal.signal(signal.SIGINT, sigint_handler);\n prepare(interface); time.sleep(2); network_scan();\n while True:\n ssid_list = get_result();\n if len(ssid_list) > 0:\n print(ssid_list)\n break\n time.sleep(2)\n reconfigure_peer();\n direction = get_direction();\n set_max_oob_tries();\n if direction is '2':\n print_log(\"Server to peer direction\")\n if args.nfc == 'nfc':\n _thread.start_new_thread(read_nfc_card,(None,))\n else:\n _thread.start_new_thread(read_qr_code,(None,))\n elif direction is '1':\n print_log(\"Peer to server direction\")\n if args.path is None:\n gen_oob();\n # launch_browser()\n else:\n _thread.start_new_thread(send_via_NFC,(args.path,))\n else:\n print_log(\"No direction specified\")\n terminate_supplicant()\n exit(0)\n\n while no_result == 0:\n if check_result():\n no_result =1\n time.sleep(5)\n if direction is '1':\n gen_oob();\n time.sleep(5)\n\n print_log(\"EAP AUTH SUCCESSFUL\");\n if direction is '1':\n terminate_threads();\n\n runbash('sudo ifconfig '+interface+' 0.0.0.0 up ; dhclient '+interface);\n\n #if direction is '1':\n # driver.close()\n\n #url = \"https://www.youtube.com/watch?v=YlHHTmIkdis\"\n #capabilities = webdriver.DesiredCapabilities().FIREFOX;\n #capabilities['binary']='/usr/local/bin/geckodriver';\n #capabilities['marionette'] = True;\n #driver = webdriver.Firefox(capabilities=capabilities);\n #driver.get(url)\n #fullscreen = driver.find_elements_by_class_name('ytp-fullscreen-button')[0]\n #fullscreen.click();\n\nif __name__=='__main__':\n main();\n","sub_path":"wpa_supplicant-2.9/wpa_supplicant/wpa_auto_run.py","file_name":"wpa_auto_run.py","file_ext":"py","file_size_in_byte":11817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"477698251","text":"from sys import argv\nimport math\nfrom tensorflow.contrib import rnn\nimport tensorflow as tf\nimport numpy as np\nfrom contexttimer import Timer\n\nfrom synth import prepare\n\nslice_size = 612\nfft_size = slice_size // 2 + 1\nsteps_seconds = 2.0\nn_steps = math.ceil(steps_seconds * prepare.samples_per_second / slice_size)\nn_inputs = 2 * fft_size\nn_neurons = 20\nn_outputs = 2 * fft_size\n\n\nX = tf.placeholder(tf.float32, [None, n_steps, n_inputs], name='X')\ny = tf.placeholder(tf.float32, [None, n_steps, n_outputs], name='y')\n\ncell = rnn.OutputProjectionWrapper(\n rnn.DropoutWrapper(\n rnn.LSTMCell(num_units=n_neurons,\n initializer=tf.variance_scaling_initializer(),\n activation=tf.nn.elu,\n ),\n input_keep_prob=0.7\n ),\n output_size=n_outputs,\n)\n\noutputs, states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)\n\nloss = tf.reduce_mean(tf.square(outputs - y))\noptimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\ntraining_op = optimizer.minimize(loss)\n\ninit = tf.global_variables_initializer()\n\nn_epochs = 2000\nn_iterations = 150\nbatch_size = 1000\n\nt_min, t_max = 0, 30\nresolution = 1 / prepare.samples_per_second\n\n\ndef normalise(x):\n mean = np.mean(x)\n std = np.std(x)\n print(mean, std)\n x = (x - mean) / (std + 1e-3)\n clipped_size = x.shape[0] - x.shape[0] % slice_size\n x = x[0:clipped_size]\n return x\n\n\ndef waveform_to_features(x):\n x = x.reshape((-1, slice_size))\n x = np.fft.rfft(x, axis=1)\n re = np.real(x)\n im = np.imag(x)\n x = np.concatenate((re, im), axis=1)\n return x\n\n\ndef features_to_waveform(x):\n re = x[:, :fft_size]\n im = x[:, fft_size:]\n x = re + 1j * im\n slices = np.fft.irfft(x, axis=1)\n waveform = slices.reshape((-1,))\n return waveform\n\n\ndef next_batch(X_all, batch_size, n_steps):\n start_indices = np.random.randint(0, X_all.shape[0] - n_steps,\n size=(batch_size, 1))\n indices = start_indices + np.arange(0, n_steps + 1)\n return (\n X_all[indices[:, :-1], :].reshape(batch_size, n_steps, n_inputs),\n X_all[indices[:, 1:], :].reshape(batch_size, n_steps, n_inputs)\n )\n\n\ndef generate(X_all):\n batch = np.random.randn(1, n_steps, n_inputs)\n wave_form = np.zeros((0, n_inputs))\n\n saver = tf.train.Saver()\n with tf.Session() as sess:\n saver.restore(sess, './sess')\n for i in range(10 * prepare.samples_per_second // slice_size):\n new_point = sess.run(outputs,\n feed_dict={X: batch})\n batch = np.concatenate((batch[:, 1:, :], new_point[:, -1:, :]),\n axis=1)\n new_point = new_point[0, -1, :].reshape((1, n_inputs))\n wave_form = np.concatenate((wave_form, new_point))\n\n print(f'{i * slice_size / prepare.samples_per_second}s')\n\n wave_form = features_to_waveform(wave_form)\n print(wave_form.shape)\n return wave_form\n\n\nclass EarlyTermination(Exception):\n pass\n\n\ndef main():\n T_all = np.arange(0, t_max, resolution)\n theta = 220 * T_all + 110 * np.cos(T_all * 2 * np.pi) / (2 * np.pi)\n X_all = np.sin(theta * 2 * np.pi)\n # X_all = np.random.randn(prepare.samples_per_second * 10)\n # X_all = np.fromfile(\n # '/home/tahsmith/src/audio-synth/data/bensound-goinghigher.raw',\n # dtype=' 1:\n with Timer() as t:\n with tf.Session() as sess:\n init.run()\n saver.restore(sess, './sess')\n try:\n for epoc in range(n_epochs):\n for iteration in range(n_iterations):\n X_batch, y_batch = next_batch(X_all, batch_size,\n n_steps)\n sess.run(training_op,\n feed_dict={X: X_batch, y: y_batch})\n if iteration % 10 == 0:\n loss_eval = loss.eval(\n feed_dict={X: X_batch, y: y_batch})\n print(iteration, \"t = \", t.elapsed, \"Loss: \",\n loss_eval)\n saver.save(sess, './sess')\n if loss_eval < 10:\n raise EarlyTermination\n if t.elapsed > 2500:\n raise EarlyTermination\n except EarlyTermination:\n pass\n else:\n waveform = generate(X_all)\n (waveform * 2 ** 14).astype('int16').tofile('generated.raw',\n format='/', methods=['GET', 'POST'])\ndef update_user(id):\n update_user_form = CreateUserForm(request.form)\n\n if request.method == 'POST' and update_user_form.validate():\n users_dict = {}\n db = shelve.open('storage.db', 'w')\n users_dict = db['Users']\n\n user = users_dict.get(id)\n user.set_first_name(update_user_form.first_name.data)\n user.set_last_name(update_user_form.last_name.data)\n user.set_gender(update_user_form.gender.data)\n user.set_phone(update_user_form.phone.data)\n user.set_address(update_user_form.address.data)\n user.set_email(update_user_form.email.data)\n user.set_password(update_user_form.password.data)\n\n db['Users'] = users_dict\n db.close()\n\n session['user_updated'] = user.get_first_name() + ' ' + user.get_last_name()\n\n return redirect(url_for('retrieve_users'))\n else:\n users_dict = {}\n db = shelve.open('storage.db', 'r')\n users_dict = db['Users']\n db.close()\n\n user = users_dict.get(id)\n update_user_form.first_name.data = user.get_first_name()\n update_user_form.last_name.data = user.get_last_name()\n update_user_form.gender.data = user.get_gender()\n update_user_form.email.data = user.get_email()\n update_user_form.phone.data = user.get_phone()\n update_user_form.address.data = user.get_address()\n update_user_form.password.data = user.get_password()\n\n return render_template('updateUser.html', form=update_user_form)\n\n\n@app.route('/deleteUser/', methods=['POST'])\ndef delete_user(id):\n users_dict = {}\n db = shelve.open('storage.db', 'w')\n users_dict = db['Users']\n\n user = users_dict.pop(id)\n\n db['Users'] = users_dict\n db.close()\n\n session['user_deleted'] = user.get_first_name() + ' ' + user.get_last_name()\n\n return redirect(url_for('retrieve_users'))\n\n\n@app.errorhandler(404)\ndef page_not_found(e):\n return render_template('error404.html'), 404\n\n\nif __name__ == '__main__':\n app.run()\n","sub_path":"choon/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"536740305","text":"\"\"\"\nGiven a list of numbers and a number k, return a new array such that each element\nat index i of the new array is the product of all the numbers in the original\narray except the one at i.\n\nFor example, if our input was [1, 2, 3, 4, 5], the expected output would be\n[120, 60, 40, 30, 24]. If our input was [3, 2, 1], the expected output would\nbe [2, 3, 6].\n\nFollow-up: what if you can't use division?\n\"\"\"\nfrom functools import reduce\n\ninput_case_1, expected_output_case_1 = [1, 2, 3, 4, 5], [120, 60, 40, 30, 24]\ninput_case_2, expected_output_case_2 = [3, 2, 1], [2, 3, 6]\n\ndef product_together_with_division(numbers):\n total = reduce(lambda x, y: x*y, numbers)\n out = [total / number for number in numbers]\n return out\n\ndef product_together_without_division(numbers):\n # numers[3]*numbers[4]=a[0]=20, numbers[2]*a[0]=a[1]=60, numbers[1]*a[1]=120 (2, 3, 4, 5)\n # -> (5, 20, 60, 120)\n # i=0: numbers[0]=b[0]=1, b[0]*a[2]=60 (1, 3, 4, 5)\n # i=1: b[0]*numbers[1]=b[1]=2, b[1]*a[1]=40 (1, 2, 4, 5)\n # i=2: b[1]*numbers[2]=b[2]=6, b[2]*a[0]=30 (1, 2, 3, 5)\n # i=3: b[2]*numbers[3]=24\n\n # Case2\n # number[1]*number[2]=a[0]=2\n # number[0]*number[1]=3\n # number[0]*number[2]=6\n length = len(numbers) #5\n half = int((length + 1) / 2) #3\n out = []\n\n # 1, 2, 3 -> 6 [6, 6]\n buffer = []\n def product(numbers):\n if len(numbers) == 2:\n value = numbers[0] * numbers[1]\n buffer.append(value)\n return value\n value = product(numbers[1:])\n buffer.append(value)\n return numbers[0] * value\n \n out.append(product(numbers[1:] + [1]))\n a = buffer[1:]\n\n b = [0 for _ in range(half+1)]\n b[0] = numbers[0]\n\n if length == 3:\n return[out[0], numbers[0]*numbers[2], numbers[0]*numbers[1]]\n\n for i in range(0, length):\n if i == half + 1:\n return out\n elif i != 0:\n b[i] = b[i-1] * numbers[i]\n\n if half == i:\n result = b[i]\n else:\n result = b[i]* a[half-i-1]\n \n out.append(result)\n \nprint(product_together_without_division(input_case_1))\nprint(product_together_without_division(input_case_2))\n ","sub_path":"daily_coding_problem/04/04.28.py","file_name":"04.28.py","file_ext":"py","file_size_in_byte":2206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"227032413","text":"#!/usr/bin/python3\n\"\"\"\nState Class from Models Module\n\"\"\"\n\nfrom os import environ\nfrom models.base_model import BaseModel, Base, Column, String, relationship\nimport models\n\n\nclass State(BaseModel, Base):\n \"\"\"State class handles all application states\"\"\"\n\n if environ.get('HBNB_TYPE_STORAGE') == 'db':\n __tablename__ = 'states'\n name = Column(String(128), nullable=False)\n cities = relationship('City',\n cascade='all, delete-orphan',\n backref='state')\n else:\n name = ''\n\n @property\n def cities(self):\n \"\"\"\n Returns list of City objects from storage\n \"\"\"\n all_cities = []\n for city_obj in models.storage.all('City').values():\n if city_obj.state_id == self.id:\n all_cities.append(city_obj)\n return (all_cities)\n","sub_path":"models/state.py","file_name":"state.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"253365047","text":"# 5-242016\n\nfrom __future__ import print_function\n\n\ndef lambda_handler(event, context):\n \"\"\" Route the incoming request based on type (LaunchRequest, IntentRequest,\n etc.) The JSON body of the request is provided in the event parameter.\n \"\"\"\n print(\"event.session.application.applicationId=\" +\n event['session']['application']['applicationId'])\n\n \"\"\"\n Uncomment this if statement and populate with your skill's application ID to\n prevent someone else from configuring a skill that sends requests to this\n function.\n \"\"\"\n # if (event['session']['application']['applicationId'] !=\n # \"amzn1.echo-sdk-ams.app.[unique-value-here]\"):\n # raise ValueError(\"Invalid Application ID\")\n\n if event['session']['new']:\n on_session_started({'requestId': event['request']['requestId']},\n event['session'])\n\n if event['request']['type'] == \"LaunchRequest\":\n return on_launch(event['request'], event['session'])\n elif event['request']['type'] == \"IntentRequest\":\n return on_intent(event['request'], event['session'])\n elif event['request']['type'] == \"SessionEndedRequest\":\n return on_session_ended(event['request'], event['session'])\n\n\n# ------------------------- on_session_started()\n\ndef on_session_started(session_started_request, session):\n \"\"\" Called when the session starts \"\"\"\n\n print(\"on_session_started requestId=\" + session_started_request['requestId']\n + \", sessionId=\" + session['sessionId'])\n\n\n# ------------------------- on_launch()\n\ndef on_launch(launch_request, session):\n \"\"\" Called when the user launches the skill without specifying what they\n want\n \"\"\"\n\n print(\"on_launch requestId=\" + launch_request['requestId'] +\n \", sessionId=\" + session['sessionId'])\n # Dispatch to your skill's launch\n return get_welcome_response()\n\n\n# ------------------------- on_intent()\n\ndef on_intent(intent_request, session):\n \"\"\" Called when the user specifies an intent for this skill \"\"\"\n\n print(\"on_intent requestId=\" + intent_request['requestId'] +\n \", sessionId=\" + session['sessionId'])\n\n # intent_request is a Python dict object\n intent = intent_request['intent']\n intent_name = intent_request['intent']['name']\n\n print(\"*** on_intent: I received intent=\" + str(intent));\n print(\"*** on_intent: I received intent_name=\" + str(intent_name));\n\n # Dispatch to your skill's intent handlers\n if intent_name == \"VerifyPIN\":\n return verifyPIN(intent, session)\n elif intent_name == \"MainMenu\":\n return mainMenu(intent, session)\n elif intent_name == \"GetAccount\":\n return getAccount(intent, session)\n elif intent_name == \"AccountCommand\":\n return getAccountCommand(intent, session)\n elif intent_name == \"MyColorIsIntent\":\n return set_color_in_session(intent, session)\n elif intent_name == \"WhatsMyColorIntent\":\n return get_color_from_session(intent, session)\n elif intent_name == \"AMAZON.HelpIntent\":\n return get_welcome_response()\n elif intent_name == \"AMAZON.CancelIntent\" or intent_name == \"AMAZON.StopIntent\":\n return handle_session_end_request()\n else:\n raise ValueError(\"Invalid intent\")\n\n\n# ------------------------- verifyPIN()\n\ndef verifyPIN(intent, session):\n # We hardcode the matching PIN for now\n intCorrectPIN = 9876\n\n print(\"*** verifyPIN: I received intent \" + str(intent));\n\n # Grab the PIN out of the intent and cast it to an integer\n PIN = intent['slots']['PIN']['value']\n intReceivedPIN = int(PIN)\n\n print(\"*** verifyPIN: I received PIN \" + str(PIN))\n\n card_title = \"Welcome\"\n\n # Compare the PIN we received with the correct PIN\n if (intReceivedPIN == intCorrectPIN):\n return mainMenu()\n\n elif (intReceivedPIN != intCorrectPIN):\n speech_output = \"Hmmm. That PIN code doesn't match my records\";\n\n # Setting this to true ends the session and exits the skill.\n should_end_session = True\n\n return build_response({}, build_speechlet_response(\n card_title, speech_output, None, should_end_session))\n\n\n# ------------------------- getAccount()\n\ndef getAccount(intent, session):\n print(\"*** getAccount: I received intent\" + str(intent));\n strAccountNumber = \"2 0 1 3 4 dash 0 9 4\"\n\n card_title = \"Welcome\"\n\n speech_output = \"Here are some details for that account... account number \" + strAccountNumber + \",,,\" \\\n \"Total amount due for the month of May: $94.12\" + \",,,,\" \\\n \"Due date: June 16, 2016\" + \",,,,\" \\\n \"Consumption: 748 kilowatt hours.\" + \",,,,\" \\\n \"Say 'stats' if you want me to analyze your consumption.\";\n\n # Setting this to true ends the session and exits the skill.\n should_end_session = False\n return build_response({}, build_speechlet_response(\n card_title, speech_output, None, should_end_session))\n\n\n# ------------------------- getAccountCommand()\n\ndef getAccountCommand(intent, session):\n print(\"*** getAccountCommand: I received intent\" + str(intent));\n strAccountNumber = \"2 0 1 3 4 dash 0 9 4\"\n\n card_title = \"Welcome\"\n\n speech_output = \"Here is the analysis of your bill for the month of May,,,\" \\\n \"Consumption: 748 kilowatt hours.\" + \",,,,\" \\\n \"Which was 119% of April's consumption of 629 kilowatt hours,,,\" \\\n \"Last year, you consumed 792 kilowatt hours versus 748 this year,,,\" \\\n \"Your consumption is about average, households of similar type in\" \\\n \"your zipcode consume between 512 \" \\\n \"and 1,298 kilowatt hours of energy in the summer months,,,\" \\\n \"I sent a PDF report to your inbox j p dixon@gmail.com\";\n\n # Setting this to true ends the session and exits the skill.\n should_end_session = True\n return build_response({}, build_speechlet_response(\n card_title, speech_output, None, should_end_session))\n\n\n# ------------------------- onSessionEnded()\n\ndef on_session_ended(session_ended_request, session):\n \"\"\" Called when the user ends the session.\n\n Is not called when the skill returns should_end_session=true\n \"\"\"\n print(\"on_session_ended requestId=\" + session_ended_request['requestId'] +\n \", sessionId=\" + session['sessionId'])\n # add cleanup logic here\n\n\n# --------------- Functions that control the skill's behavior ------------------\n\ndef get_welcome_response():\n \"\"\" If we wanted to initialize the session to have some attributes we could\n add those here\n \"\"\"\n\n session_attributes = {}\n card_title = \"Welcome\"\n speech_output = \"Hi there! You're on line with PowerCo! To verify it's you, please say \" \\\n \"the 4 digit code that you created when you enabled the skill for the first time.\"\n\n # If the user either does not reply to the welcome message or says something\n # that is not understood, they will be prompted again with this text.\n reprompt_text = \"You created a 4 digit pin code the first time you enabled the\" \\\n \"PowerCo skill. If you remember it, go ahead and say it now.\"\n\n should_end_session = False\n\n return build_response(session_attributes, build_speechlet_response(\n card_title, speech_output, reprompt_text, should_end_session))\n\n\n# ------------------------- mainMenu()\n\ndef mainMenu():\n session_attributes = {}\n card_title = \"Welcome\"\n speech_output = \"Great! You're all set. I found three accounts in your profile. Which account can I \" \\\n \"help you with? 1. 3 3 3 3 Piedmont Rd? 2. 1 0 4 6 Peachtree Road? 3. 2 1 2 1 Jamieson Avenue?\"\n\n # If the user either does not reply to the welcome message or says something\n # that is not understood, they will be prompted again with this text.\n reprompt_text = \"I'm here to help. You can ask me about your current bill, your energy usage, or \" \\\n \"energy usage in your state. How can I help?\"\n\n should_end_session = False\n\n return build_response(session_attributes, build_speechlet_response(\n card_title, speech_output, reprompt_text, should_end_session))\n\n\n# ------------------------- handle_session_end_request()\n\ndef handle_session_end_request():\n card_title = \"Session Ended\"\n speech_output = \"Thank you for being a PowerCo customer.\" \\\n \"Have a nice day! \"\n\n # Setting this to true ends the session and exits the skill.\n should_end_session = True\n return build_response({}, build_speechlet_response(\n card_title, speech_output, None, should_end_session))\n\n\n# ------------------------- handle_session_end_request()\n\ndef set_color_in_session(intent, session):\n \"\"\" Sets the color in the session and prepares the speech to reply to the\n user.\n \"\"\"\n\n card_title = intent['name']\n session_attributes = {}\n should_end_session = False\n\n if 'Color' in intent['slots']:\n favorite_color = intent['slots']['Color']['value']\n session_attributes = create_favorite_color_attributes(favorite_color)\n speech_output = \"I now know your favorite color is \" + \\\n favorite_color + \\\n \". You can ask me your favorite color by saying, \" \\\n \"what's my favorite color?\"\n reprompt_text = \"You can ask me your favorite color by saying, \" \\\n \"what's my favorite color?\"\n else:\n speech_output = \"I'm not sure what your favorite color is. \" \\\n \"Please try again.\"\n reprompt_text = \"I'm not sure what your favorite color is. \" \\\n \"You can tell me your favorite color by saying, \" \\\n \"my favorite color is red.\"\n return build_response(session_attributes, build_speechlet_response(\n card_title, speech_output, reprompt_text, should_end_session))\n\n\n# ------------------------- handle_session_end_request()\n\ndef create_favorite_color_attributes(favorite_color):\n return {\"favoriteColor\": favorite_color}\n\n\n# ------------------------- handle_session_end_request()\n\ndef get_color_from_session(intent, session):\n session_attributes = {}\n reprompt_text = None\n\n if session.get('attributes', {}) and \"favoriteColor\" in session.get('attributes', {}):\n favorite_color = session['attributes']['favoriteColor']\n speech_output = \"Your favorite color is \" + favorite_color + \\\n \". Goodbye.\"\n should_end_session = True\n else:\n speech_output = \"I'm not sure what your favorite color is. \" \\\n \"You can say, my favorite color is red.\"\n should_end_session = False\n\n # Setting reprompt_text to None signifies that we do not want to reprompt\n # the user. If the user does not respond or says something that is not\n # understood, the session will end.\n return build_response(session_attributes, build_speechlet_response(\n intent['name'], speech_output, reprompt_text, should_end_session))\n\n\n# --------------- Helpers that build all of the responses ----------------------\n\ndef build_speechlet_response(title, output, reprompt_text, should_end_session):\n return {\n 'outputSpeech': {\n 'type': 'PlainText',\n 'text': output\n },\n 'card': {\n 'type': 'Simple',\n 'title': 'SessionSpeechlet - ' + title,\n 'content': 'SessionSpeechlet - ' + output\n },\n 'reprompt': {\n 'outputSpeech': {\n 'type': 'PlainText',\n 'text': reprompt_text\n }\n },\n 'shouldEndSession': should_end_session\n }\n\n\ndef build_response(session_attributes, speechlet_response):\n return {\n 'version': '1.0',\n 'sessionAttributes': session_attributes,\n 'response': speechlet_response\n }","sub_path":"PowerCo-old.py","file_name":"PowerCo-old.py","file_ext":"py","file_size_in_byte":12743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"240079583","text":"import cutil\n\nLI = \"list\"\nID = \"id\"\n\nclass LISTREGISTRY(cutil.RegistryFile):\n\n def __init__(self) :\n map = {ID : cutil.LONG, \"pnumber\" : cutil.LONG, \"pname\" : cutil.STRING }\n cutil.RegistryFile.__init__(self,None,\"LIST-EDIT-SPINNER-REGISTRY\",None,map, LI,ID)\n\nF = LISTREGISTRY()\n\ndef dialogaction(action,var) :\n cutil.printVar(\"edit list spinner\",action,var)\n \n if action == \"before\" :\n F.readList(var)\n cutil.setAddEditMode(var,LI,[\"pnumber\",\"pname\"])\n \n if action == \"editlistrowaction\" :\n var[\"JLIST_EDIT_ACTIONOK_\" + LI] = True\n if var[\"JLIST_EDIT_ACTION_\"+LI] == \"ADDBEFORE\" or var[\"JLIST_EDIT_ACTION_\" + LI ] == \"ADD\" :\n key = F.nextKey()\n var[ID] = key\n var[\"pnumber\"] = 2\n cutil.setCopy(var,[ID,\"pnumber\"],LI)\n \n if var[\"JLIST_EDIT_ACTION_\"+LI] == \"REMOVE\" :\n F.removeMap(var)\n \n if action == \"aftereditrow\" :\n F.addMap(var) \n var[\"JEDIT_ROW_OK_\" + LI] = True\n \n \n \n\n\n \n ","sub_path":"hotel/sample/resources/resources/packages/testpack/listwithspinner.py","file_name":"listwithspinner.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"567466948","text":"\nimport win32com.client\nimport sys\n\nclass file(object):\n def __init__(self):\n self.obj = win32com.client.Dispatch(\"Scripting.FileSystemObject\")\n self.ForReading = 1\n self.ForWriting = 2\n self.ForAppending = 8\n self.Retorno = None\n self.Erro = None\n\n def ListFiles(self,pasta,Filter):\n self.Erro = None\n self.Retorno = None\n Filter = [] if Filter == None else Filter.split(\";\")\n try:\n files=[]\n pst = self.obj.GetFolder(pasta).files\n for file in pst:\n if len(Filter) > 0:\n for ftr in Filter:\n if ftr in file.name:\n files.append(file.name)\n else:\n files.append(file.name)\n self.Retorno = files\n return True\n except:\n self.Erro = sys.exc_info()[1]\n return False\n\n def FileExists(self,file):\n\t return self.obj.FileExists(file)\n\n def FileRead(self,address):\n try:\n self.Erro = None\n self.Retorno = None\n file=self.obj.OpenTextFile(address, self.ForReading, False)\n retorno = file.readAll()\n file.close()\n self.Retorno = retorno\n return True\n except:\n self.Erro = \"Erro abrindo arquivo \" + address + \": \" + sys.exc_info()[1]\n return False\n \n def Csv2Obj(self,file,delimitador):\n self.Erro = None\n self.Retorno = None\n retorno = []\n if self.FileExists(file):\n if self.FileRead(file):\n text = self.Retorno\n lines = text.split(\"\\r\\n\")\n colunas = lines[0].split(delimitador)\n lines.remove(lines[0])\n if lines[-1] == \"\": lines.remove(lines[-1])\n for idx,line in enumerate(lines):\n registro = {}\n linhas = line.split(delimitador)\n for index,col in enumerate(colunas):\n registro[col] = linhas[index]\n retorno.append(registro)\n else:\n return False\n return retorno\n\n def WriteLn(self,address,value):\n self.Erro = None\n self.Retorno = None\n file = self.obj.OpenTextFile(address,self.ForAppending,True)\n file.Write(value + \"\\r\\n\")\n file.Close()\n\n def Write(self,address,value):\n self.Erro = None\n self.Retorno = None\n file = self.obj.OpenTextFile(address,self.ForAppending,True)\n file.Write(value)\n file.Close()\n\n def DeleteFile(self,address):\n try:\n self.Erro = None\n self.Retorno = None\n self.obj.DeleteFile(address)\n return True\n except:\n self.Retorno = sys.exc_info()[1]\n return False\n\n def DeleteFiles(self,pasta,filter):\n try:\n self.Erro = None\n self.Retorno = None\n if self.ListFiles(pasta,filter):\n for file in self.Retorno:\n self.DeleteFile(pasta + \"\\\\\" + file)\n return True\n else:\n return False\n except:\n print(sys.exc_info()[1])\n return False\n\n def Createfolder(self,folder):\n self.Erro = None\n self.Retorno = None\n if not self.obj.FolderExists(folder):\n self.obj.CreateFolder(folder)","sub_path":"Selenium/BBB/Libs/FileLib.py","file_name":"FileLib.py","file_ext":"py","file_size_in_byte":3510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"299062994","text":"def setup():\n size(400, 400)\n \n global txtS\n textSize(txtS)\n \nmessage = \"Tickle me\"\nx = 100\ny = 200\ntxtS = 50\n\ndef draw():\n global message, x, y, txtS\n \n background(255)\n \n fill(200, 100, 150)\n text(message, x, y)\n \n if mouseX > x and mouseX < x + textWidth(message) and mouseY < y and mouseY > y - txtS:\n x += round(random(-5, 5))\n y += round(random(-5, 5))\n","sub_path":"Tickle Text/challenge1.py","file_name":"challenge1.py","file_ext":"py","file_size_in_byte":412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"517703433","text":"from tkinter import Tk, BooleanVar\nimport tkinter.ttk as ttk\nracine = Tk() # Creation de la fenetre\nvar = BooleanVar()\nvar.set(False)\ncheck = ttk.Checkbutton(racine, text=\"Exemple\", \n\t\t\t\t\t\tvariable=var, width=30,\n\t\t\t\t\t\tcommand=\n\t\t\t\t\t\t\tlambda: print(\"quelquechose\"))\ncheck.pack()\nracine.mainloop() # Affichage de la fenetre\n","sub_path":"Exemples/IG/checkbox.py","file_name":"checkbox.py","file_ext":"py","file_size_in_byte":325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"202080080","text":"\nimport math\nINFINITY = math.inf # float(\"inf\")\n\nclass myqueue(list):\n def __init__(self,a=[]):\n list.__init__(self,a)\n\n def dequeue(self):\n return self.pop(0)\n\n def enqueue(self,x):\n self.append(x)\n\nclass Vertex:\n def __init__(self, data):\n self.data = data\n\n def __repr__(self): # voor afdrukken\n return str(self.data)\n\n def __lt__(self, other): # voor sorteren\n return self.data < other.data\n\n\ndef vertices(G):\n return sorted(G)\n\n\n\ndef find_cycle(G,s):\n V = vertices(G)\n s.predecessor = None\n s.distance = 0\n for v in V:\n if v != s:\n v.distance = INFINITY # v krijgt het attribuut 'distance'\n v.predecessor = None\n q = myqueue()\n q.enqueue(s)\n\n while q:\n u = q.dequeue()\n for v in G[u]:\n if v.distance == INFINITY: # v is nog niet bezocht\n v.distance = u.distance + 1\n v.predecessor = u # v krijgt het attribuut 'predecessor'\n q.enqueue(v)\n if u.predecessor == v.predecessor:\n return True\n return False\n\ndef no_cycles(G):\n for vertex in G:\n if find_cycle(G, vertex):\n return False\n return True\n\nv = [Vertex(i) for i in range(8)]\n\nG = {v[0]: [v[4], v[5]],\n v[1]: [v[4], v[6]],\n v[2]: [v[5]],\n v[3]: [v[7]],\n v[4]: [v[0], v[1]],\n v[5]: [v[0], v[2]],\n v[6]: [v[1]],\n v[7]: [v[3]]\n}\n\nprint(no_cycles(G)) #True\n\n\nG = {v[0]: [v[4], v[5]],\n v[1]: [v[4], v[5], v[6]],\n v[2]: [v[4], v[5], v[6]],\n v[4]: [v[0], v[1], v[2], v[5]],\n v[5]: [v[0], v[1], v[2], v[4]],\n v[6]: [v[1], v[2]]\n}\nprint(no_cycles(G)) #False\n\nProeftentamenG = {\n v[0]: [v[1]],\n v[1]: [v[0], v[2]],\n v[2]: [v[1], v[3], v[4]],\n v[3]: [v[2]],\n v[4]: [v[1], v[2]],\n}\nprint(no_cycles(ProeftentamenG)) #False\n","sub_path":"5.2.py","file_name":"5.2.py","file_ext":"py","file_size_in_byte":1878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"454326755","text":"#5.29\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ng=9.81\nrho=1000\nh=50\ns=7.9e-2\n\ndef c(lamb):\n C=np.sqrt((g*lamb/2*np.pi)*(1+s*(4*np.pi**2)/rho*g*lamb**2)*np.tanh(2*np.pi*h/lamb))\n return C\n\nclist1=np.linspace(0.001,0.1,1000)\nclist2=np.linspace(1,2000,1000)\nplt.plot(clist1,c(clist1))\nplt.show()\nplt.plot(clist2,c(clist2))\nplt.show()\n","sub_path":"INF1100/water_wave_velocity.py","file_name":"water_wave_velocity.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"48797302","text":"def main(_):\n hvd.init()\n\n if FLAGS.pipeline_config_path:\n model_config, train_config, input_config = get_configs_from_pipeline_file()\n else:\n model_config, train_config, input_config = get_configs_from_multiple_files()\n\n model_fn = functools.partial(\n model_builder.build,\n model_config=model_config,\n is_training=True)\n\n create_input_dict_fn = functools.partial(\n input_reader_builder.build, input_config)\n\n env = json.loads(os.environ.get('TF_CONFIG', '{}'))\n cluster_data = env.get('cluster', None)\n cluster = tf.train.ClusterSpec(cluster_data) if cluster_data else None\n task_data = env.get('task', None) or {'type': 'master', 'index': 0}\n task_info = type('TaskSpec', (object,), task_data)\n\n ps_tasks = 0\n worker_replicas = 1\n worker_job_name = 'lonely_worker'\n task = 0\n is_chief = True\n master = ''\n\n if cluster_data and 'worker' in cluster_data:\n worker_replicas = len(cluster_data['worker']) + 1\n if cluster_data and 'ps' in cluster_data:\n ps_tasks = len(cluster_data['ps'])\n\n if worker_replicas > 1 and ps_tasks < 1:\n raise ValueError('At least 1 ps task is needed for distributed training.')\n\n if worker_replicas >= 1 and ps_tasks > 0:\n server = tf.train.Server(tf.train.ClusterSpec(cluster), protocol='grpc',\n job_name=task_info.type,\n task_index=task_info.index)\n if task_info.type == 'ps':\n server.join()\n return\n\n worker_job_name = '%s/task:%d' % (task_info.type, task_info.index)\n task = task_info.index\n is_chief = (task_info.type == 'master')\n master = server.target\n\n trainer.train(create_input_dict_fn, model_fn, train_config, master, task,\n FLAGS.num_clones, worker_replicas, FLAGS.clone_on_cpu, ps_tasks,\n worker_job_name, is_chief, FLAGS.train_dir)\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1843,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"156791176","text":"class TreeNode(object):\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n'''\n自己写的算法其实思想已经和最优解法差不多\n如果每次找index的时候用一个hashmap维护可能会更好\n'''\nclass Solution(object):\n def buildTree(self, preorder, inorder):\n if len(preorder) == 0:\n return None\n if len(preorder) == 1:\n return TreeNode(preorder[0])\n def getTree(preorder, inorder, preStart, inStart, inEnd, map):\n if preStart >= len(preorder):\n return None\n if inStart == inEnd:\n return TreeNode(inorder[inStart])\n root = TreeNode(preorder[preStart])\n k = map[preorder[preStart]]\n leftEnd = k-1\n leftNum = leftEnd-inStart+1\n rightStart = k+1\n rightNum = inEnd - rightStart+1\n if leftNum>0:\n root.left = getTree(preorder, inorder, preStart+1, inStart, leftEnd, map)\n if rightNum>0:\n root.right = getTree(preorder, inorder, preStart+max(leftNum,0)+1, rightStart, inEnd, map)\n return root\n n = len(preorder)\n '''\n 此处将循环换成hashmap,否则最差时间复杂度为O(n^2)\n enumerate函数是index, val对，先出index再出val，然后字典里的形式是val:index\n '''\n map = {val:index for index, val in enumerate(inorder, start=0) }\n root = getTree(preorder, inorder, 0,0,n-1, map)\n return root\n\na = Solution()\ntree = a.buildTree([3,9,20,15,7] ,[9,3,15,20,7])\nprint(\"Done\")\n","sub_path":"Tree/Leetcode_105/Mycode.py","file_name":"Mycode.py","file_ext":"py","file_size_in_byte":1633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"14341070","text":"# Copyright 2001-2019 Crytek GmbH / Crytek Group. All rights reserved.\nfrom PyQt5.QtCore import Qt, QObject, QTimer, QRect, QPoint, QByteArray, pyqtSignal, qWarning, QEvent, QSize\nfrom PyQt5.QtGui import QIcon, QMouseEvent\nfrom PyQt5.QtWidgets import QLabel, QTabBar, QRubberBand, QVBoxLayout, QSplitter, QWidget, QSizePolicy, QApplication, QMainWindow, \\\n\tQPushButton\n\nfrom .QToolWindowManagerCommon import *\nfrom .QCustomWindowFrame import QCustomTitleBar, QCustomWindowFrame\nfrom .QToolWindowArea import QToolWindowArea\nfrom .QToolWindowRollupBarArea import QToolWindowRollupBarArea\nfrom .QToolWindowWrapper import QToolWindowWrapper\nfrom .QToolWindowDragHandlerDropTargets import QToolWindowDragHandlerDropTargets\n\n\nclass QToolWindowAreaReference:\n\tTop = 0\n\tBottom = 1\n\tLeft = 2\n\tRight = 3\n\tHSplitTop = 4\n\tHSplitBottom = 5\n\tVSplitLeft = 6\n\tVSplitRight = 7\n\tCombine = 8\n\tFloating = 9\n\tDrag = 10\n\tHidden = 11\n\n\n\tdef __init__ ( self, eType = None ):\n\t\tself.type = eType or QToolWindowAreaReference.Combine\n\n\t@staticmethod\n\tdef isOuter ( eType ) -> bool:\n\t\treturn eType <= QToolWindowAreaReference.Right\n\n\t@staticmethod\n\tdef requiresSplit ( eType ) -> bool:\n\t\treturn eType < QToolWindowAreaReference.Combine\n\n\t@staticmethod\n\tdef splitOrientation ( eType ):\n\t\tif eType == QToolWindowAreaReference.Top or \\\n\t\t\t\teType == QToolWindowAreaReference.Bottom or \\\n\t\t\t\teType == QToolWindowAreaReference.HSplitTop or \\\n\t\t\t\teType == QToolWindowAreaReference.HSplitBottom:\n\t\t\treturn QtCore.Qt.Vertical\n\t\tif eType == QToolWindowAreaReference.Left or \\\n\t\t\t\teType == QToolWindowAreaReference.Right or \\\n\t\t\t\teType == QToolWindowAreaReference.VSplitLeft or \\\n\t\t\t\teType == QToolWindowAreaReference.VSplitRight:\n\t\t\treturn QtCore.Qt.Horizontal\n\t\tif eType == QToolWindowAreaReference.Combine or \\\n\t\t\t\teType == QToolWindowAreaReference.Floating or \\\n\t\t\t\teType == QToolWindowAreaReference.Hidden:\n\t\t\treturn 0\n\n\nclass QToolWindowAreaTarget:\n\n\tdef __init__ ( self, reference = QToolWindowAreaReference.Combine, index = -1, geometry = QRect () ):\n\t\tself.area = None\n\t\tself.reference = reference\n\t\tself.index = index\n\t\tself.geometry = geometry\n\n\t@staticmethod\n\tdef createByArea ( area, reference, index = -1, geometry = QRect () ):\n\t\ttarget = QToolWindowAreaTarget ( reference, index, geometry )\n\t\ttarget.area = area\n\t\treturn target\n\n\nclass QToolWindowManagerClassFactory ( QObject ):\n\n\tdef createArea ( self, manager, parent, areaType ) -> QToolWindowArea:\n\t\t\"\"\"Summary line.\n\n\t\tArgs:\n\t\t\tmanager (QToolWindowManager): Description of param1\n\t\t\tparent (QWidget): Description of param2\n\t\t\tareaType (QTWMWrapperAreaType): Description of param2\n\n\t Returns:\n\t IToolWindowArea: Description of return value\n\t \"\"\"\n\t\tif manager.config.setdefault ( QTWM_SUPPORT_SIMPLE_TOOLS, False ) and areaType == QTWMWrapperAreaType.watRollups:\n\t\t\treturn QToolWindowRollupBarArea ( manager, parent )\n\t\telse:\n\t\t\treturn QToolWindowArea ( manager, parent )\n\n\tdef createWrapper ( self, manager ):\n\t\t\"\"\"Summary line.\n\n\t\tArgs:\n\t\t\tmanager (QToolWindowManager): Description of param1\n\n\t Returns:\n\t QToolWindowWrapper: Description of return value\n\t \"\"\"\n\t\treturn QToolWindowWrapper ( manager, QtCore.Qt.Tool )\n\t\t# return QToolWindowWrapper ( manager, Qt.CustomizeWindowHint | Qt.FramelessWindowHint )\n\n\tdef createDragHandler ( self, manager ):\n\t\t\"\"\"Summary line.\n\n\t\tArgs:\n\t\t\tmanager (QToolWindowManager): Description of param1\n\n\t Returns:\n\t QToolWindowDragHandlerDropTargets: Description of return value\n\t \"\"\"\n\t\treturn QToolWindowDragHandlerDropTargets ( manager )\n\n\tdef createSplitter ( self, manager ):\n\t\t\"\"\"Summary line.\n\n\t\tArgs:\n\t\t\tmanager (QToolWindowManager): Description of param1\n\n\t Returns:\n\t QSplitter: Description of return value\n\t \"\"\"\n\t\tsplitter = None\n\t\tif manager.config.setdefault ( QTWM_PRESERVE_SPLITTER_SIZES, True ):\n\t\t\tsplitter = QSizePreservingSplitter ()\n\t\telse:\n\t\t\tsplitter = QSplitter ()\n\t\tsplitter.setChildrenCollapsible ( False )\n\t\treturn splitter\n\n\nclass QTWMNotifyLock:\n\n\tdef __init__ ( self, parent, allowNotify = True ):\n\t\tself.parent = parent\n\t\tself.notify = allowNotify\n\t\tself.parent.suspendLayoutNotifications ()\n\n\tdef __del__ ( self ):\n\t\tself.parent.resumeLayoutNotifications ()\n\t\tif self.notify:\n\t\t\tself.parent.notifyLayoutChange ()\n\n\nclass QToolWindowManager ( QWidget ):\n\ttoolWindowVisibilityChanged = pyqtSignal ( QWidget, bool )\n\tlayoutChanged = pyqtSignal ()\n\tupdateTrackingTooltip = pyqtSignal ( str, QPoint )\n\n\tdef __init__ ( self, parent, config, factory = None ):\n\t\tsuper ( QToolWindowManager, self ).__init__ ( parent )\n\t\tself.factory = factory if factory != None else QToolWindowManagerClassFactory ()\n\t\tself.dragHandler = None\n\t\tself.config = config\n\t\tself.closingWindow = 0\n\n\t\tself.areas = []\n\t\tself.wrappers = []\n\t\tself.toolWindows = []\n\t\tself.toolWindowsTypes = {}\n\t\tself.draggedToolWindows = []\n\t\tself.layoutChangeNotifyLocks = 0\n\n\t\tif self.factory.parent () == None:\n\t\t\tself.factory.setParent ( self )\n\n\t\tself.mainWrapper = QToolWindowWrapper ( self, QtCore.Qt.FramelessWindowHint )\n\t\tself.mainWrapper.getWidget ().setObjectName ( 'mainWrapper' )\n\t\tself.setLayout ( QVBoxLayout ( self ) )\n\t\tself.layout ().setContentsMargins ( 2, 2, 2, 2 )\n\t\tself.layout ().setSpacing ( 0 )\n\t\tself.layout ().addWidget ( self.mainWrapper.getWidget () )\n\t\tself.lastArea = self.createArea ()\n\t\tself.draggedWrapper = None\n\t\tself.draggedArea = None\n\t\tself.resizedWrapper = None\n\t\tself.mainWrapper.setContents ( self.lastArea.getWidget () )\n\n\t\tself.dragHandler = self.createDragHandler ()\n\t\tself.dragIndicator = QLabel ( None, Qt.ToolTip ) # 使用QLabel来显示拖拽的提示\n\t\tself.dragIndicator.setAttribute ( Qt.WA_ShowWithoutActivating )\n\n\t\tself.preview = QRubberBand ( QRubberBand.Rectangle )\n\t\tself.preview.hide ()\n\n\t\tself.raiseTimer = QTimer ( self )\n\t\tself.raiseTimer.timeout.connect ( self.raiseCurrentArea )\n\t\tself.setSizePolicy ( QSizePolicy.Expanding, QSizePolicy.Expanding )\n\n\t\tqApp.installEventFilter ( self )\n\n\tdef __del__ ( self ):\n\t\tself.suspendLayoutNotifications ()\n\t\twhile len ( self.areas ) != 0:\n\t\t\ta = self.areas[ 0 ]\n\t\t\ta.setParent ( None )\n\t\t\tself.areas.remove ( a )\n\n\t\twhile len ( self.wrappers ) != 0:\n\t\t\tw = self.wrappers[ 0 ]\n\t\t\tw.setParent ( None )\n\t\t\tself.areas.remove ( w )\n\n\t\tdel self.dragHandler\n\t\tdel self.mainWrapper\n\n\tdef empty ( self ) -> bool:\n\t\treturn len ( self.areas ) == 0\n\n\tdef removeArea ( self, area ):\n\t\t\"\"\"Summary line.\n\n\t\tArgs:\n\t\t\tarea (QToolWindowArea): Description of param1\n\n\t Returns:\n\t QToolWindowArea: Description of return value\n\t \"\"\"\n\t\t# print ( \"[QToolWindowManager] removeArea %s\" % area )\n\t\tif area in self.areas:\n\t\t\tself.areas.remove ( area )\n\t\tif self.lastArea == area:\n\t\t\tself.lastArea = None\n\n\tdef removeWrapper ( self, wrapper ):\n\t\t\"\"\"Summary line.\n\n\t\tArgs:\n\t\t\twrapper (QToolWindowWrapper): Description of param1\n\t \"\"\"\n\t\tif self.ownsWrapper ( wrapper ):\n\t\t\tself.wrappers.remove ( wrapper )\n\n\tdef ownsArea ( self, area ) -> bool:\n\t\t\"\"\"Summary line.\n\n\t\tArgs:\n\t\t\tarea (QToolWindowArea): Description of param1\n\n\t Returns:\n\t bool: Description of return value\n\t \"\"\"\n\t\treturn area in self.areas\n\n\tdef ownsWrapper ( self, wrapper ) -> bool:\n\t\t\"\"\"Summary line.\n\n\t\tArgs:\n\t\t\twrapper (QToolWindowWrapper): Description of param1\n\n\t Returns:\n\t bool: Description of return value\n\t \"\"\"\n\t\treturn wrapper in self.wrappers\n\n\tdef ownsToolWindow ( self, toolWindow ) -> bool:\n\t\t\"\"\"Summary line.\n\n\t\tArgs:\n\t\t\twrapper (QToolWindowWrapper): Description of param1\n\n\t Returns:\n\t bool: Description of return value\n\t \"\"\"\n\t\treturn toolWindow in self.toolWindows\n\n\tdef startDrag ( self, toolWindows, area ):\n\t\tif self.dragInProgress ():\n\t\t\tqWarning ( 'ToolWindowManager::execDrag: drag is already in progress' )\n\t\t\treturn\n\t\tif len ( toolWindows ) == 0:\n\t\t\treturn\n\t\t# print ( \"[QToolWindowManager] startDrag\", toolWindows )\n\t\tself.dragHandler.startDrag ()\n\t\tself.draggedToolWindows = toolWindows\n\t\tself.draggedArea = area\n\t\tself.lastArea = None\n\n\t\t# floatingGeometry = QRect ( QCursor.pos (), area.size () )\n\t\t# self.moveToolWindows ( toolWindows, area, QToolWindowAreaReference.Drag, -1, floatingGeometry )\n\n\t\tself.dragIndicator.setPixmap ( self.generateDragPixmap ( toolWindows ) )\n\t\tself.updateDragPosition ()\n\t\tself.dragIndicator.show ()\n\n\tdef startDragWrapper ( self, wrapper ):\n\t\tself.dragHandler.startDrag ()\n\t\tself.draggedWrapper = wrapper\n\t\tself.lastArea = None\n\t\tself.updateDragPosition ()\n\n\tdef startResize ( self, wrapper ):\n\t\tself.resizedWrapper = wrapper\n\n\tdef generateDragPixmap ( self, toolWindows ):\n\t\t'''\n\t\t生成一个QTabBar的快照\n\t\t'''\n\t\twidget = QTabBar ()\n\t\twidget.setDocumentMode ( True )\n\t\tfor toolWindow in toolWindows:\n\t\t\twidget.addTab ( toolWindow.windowIcon (), toolWindow.windowTitle () )\n\t\t#if QT_VERSION >= 0x050000 # Qt5\n\t\t\treturn widget.grab ()\n\t\t#else #Qt4\n\t\t# return QtGui.QPixmap.grabWidget( widget )\n\t\t#endif\n\n\tdef addToolWindowTarget ( self, toolWindow, target, toolType = QTWMToolType.ttStandard ):\n\t\tself.insertToToolTypes ( toolWindow, toolType )\n\t\tself.addToolWindow ( toolWindow, target.area, target.reference, target.index, target.geometry )\n\n\tdef addToolWindow ( self, toolWindow, area = None, reference = QToolWindowAreaReference.Combine, index = -1,\n\t geometry = QRect () ):\n\t\tself.addToolWindows ( [ toolWindow ], area, reference, index, geometry )\n\n\tdef addToolWindowsTarget ( self, toolWindows, target, toolType ):\n\t\tfor toolWindow in toolWindows:\n\t\t\tself.insertToToolTypes ( toolWindow, toolType )\n\t\tself.addToolWindows ( toolWindows, target.area, target.reference, target.index, target.geometry )\n\n\tdef addToolWindows ( self, toolWindows, area = None, reference = QToolWindowAreaReference.Combine, index = -1,\n\t geometry = QRect () ):\n\t\tfor toolWindow in toolWindows:\n\t\t\tif not self.ownsToolWindow ( toolWindow ):\n\t\t\t\ttoolWindow.hide ()\n\t\t\t\ttoolWindow.setParent ( None )\n\t\t\t\ttoolWindow.installEventFilter ( self )\n\t\t\t\tself.insertToToolTypes ( toolWindow, QTWMToolType.ttStandard )\n\t\t\t\tself.toolWindows.append ( toolWindow )\n\n\t\t\t\t# from qt.controls.QToolWindowManager.QToolTabManager import QTabPane\n\t\t\t\t#\n\t\t\t\t# if isinstance ( toolWindow, QTabPane ):\n\t\t\t\t# \tprint ( \"[QToolWindowManager] addToolWindow: QTabPane %s\" % toolWindow.pane )\n\t\t\t\t# else:\n\t\t\t\t# \tprint ( \"[QToolWindowManager] addToolWindow: %s\" % toolWindow )\n\t\tself.moveToolWindows ( toolWindows, area, reference, index, geometry )\n\n\tdef moveToolWindowTarget ( self, toolWindow, target, toolType = QTWMToolType.ttStandard ):\n\t\tself.insertToToolTypes ( toolWindow, toolType )\n\t\tself.addToolWindow ( toolWindow, target.area, target.reference, target.index, target.geometry )\n\n\tdef moveToolWindow ( self, toolWindow, area = None, reference = QToolWindowAreaReference.Combine, index = -1,\n\t geometry = QRect () ):\n\t\tself.moveToolWindows ( [ toolWindow ], area, reference, index, geometry )\n\n\tdef moveToolWindowsTarget ( self, toolWindows, target, toolType = QTWMToolType.ttStandard ):\n\t\tfor toolWindow in toolWindows:\n\t\t\tself.insertToToolTypes ( toolWindow, toolType )\n\t\tself.addToolWindows ( toolWindows, target.area, target.reference, target.index, target.geometry )\n\n\tdef moveToolWindows ( self, toolWindows, area = None, reference = QToolWindowAreaReference.Combine, index = -1,\n\t geometry = None ):\n\n\t\t# qWarning ( \"moveToolWindows %s\" % toolWindows )\n\n\t\t# If no area find one\n\t\tif area == None:\n\t\t\tif self.lastArea != None:\n\t\t\t\tarea = self.lastArea\n\t\t\telif len ( self.areas ) != 0:\n\t\t\t\tarea = self.areas[ 0 ]\n\t\t\telse:\n\t\t\t\tqWarning ( \"lastArea is None, and areas is 0, self.createArea ()\" )\n\t\t\t\tarea = self.createArea ()\n\t\t\t\tself.lastArea = area\n\t\t\t\tself.mainWrapper.setContents ( self.lastArea.getWidget () )\n\n\t\tdragOffset = QPoint ()\n\t\t# Get the current mouse position and offset from the area before we remove the tool windows from it\n\t\tif area != None and reference == QToolWindowAreaReference.Drag:\n\t\t\twidgetPos = area.mapToGlobal ( area.rect ().topLeft () )\n\t\t\tdragOffset = widgetPos - QCursor.pos ()\n\n\t\twrapper = None\n\t\tcurrentAreaIsSimple = True\n\n\t\tfor toolWindow in toolWindows:\n\t\t\t# when iterating over the tool windows, we will figure out if the current one is actually roll-ups and not tabs\n\t\t\tcurrentArea = findClosestParent ( toolWindow, [ QToolWindowArea, QToolWindowRollupBarArea ] )\n\t\t\tif currentAreaIsSimple and currentArea != None and currentArea.areaType () == QTWMWrapperAreaType.watTabs:\n\t\t\t\tcurrentAreaIsSimple = False\n\t\t\tself.releaseToolWindow ( toolWindow, False )\n\n\t\tif reference == QToolWindowAreaReference.Top or \\\n\t\t\t\treference == QToolWindowAreaReference.Bottom or \\\n\t\t\t\treference == QToolWindowAreaReference.Left or \\\n\t\t\t\treference == QToolWindowAreaReference.Right:\n\t\t\tarea = cast ( self.splitArea ( self.getFurthestParentArea ( area.getWidget () ).getWidget (), reference ), [ QToolWindowArea, QToolWindowRollupBarArea ] )\n\t\telif reference == QToolWindowAreaReference.HSplitTop or \\\n\t\t\t\treference == QToolWindowAreaReference.HSplitBottom or \\\n\t\t\t\treference == QToolWindowAreaReference.VSplitLeft or \\\n\t\t\t\treference == QToolWindowAreaReference.VSplitRight:\n\t\t\tarea = cast ( self.splitArea ( area.getWidget (), reference ), [ QToolWindowArea, QToolWindowRollupBarArea ] )\n\t\telif reference == QToolWindowAreaReference.Floating or \\\n\t\t\t\treference == QToolWindowAreaReference.Drag:\n\t\t\t# when dragging we will try to determine target are type, from window types.\n\t\t\tareaType = QTWMWrapperAreaType.watTabs\n\t\t\tif len ( toolWindows ) > 1:\n\t\t\t\tif currentAreaIsSimple:\n\t\t\t\t\tareaType = QTWMWrapperAreaType.watRollups\n\t\t\telif self.toolWindowsTypes[ toolWindows[ 0 ] ] == QTWMToolType.ttSimple:\n\t\t\t\tareaType = QTWMWrapperAreaType.watRollups\n\n\t\t\t# create new window\n\t\t\tarea = self.createArea ( areaType )\n\t\t\twrapper = self.createWrapper ()\n\t\t\twrapper.setContents ( area.getWidget () )\n\t\t\t# wrapper.move ( QCursor.pos () )\n\t\t\twrapper.getWidget ().show ()\n\t\t\t# wrapper.getWidget ().grabMouse ()\n\n\t\t\t# qWarning ( \"[QToolWindowManager] moveToolWindows create new window reference: %s area's wrapper: %s\" % (\n\t\t\t# self.getAreaReferenceString ( reference ), area.wrapper ()) )\\\\\\\n\n\t\t\tif geometry != None: # we have geometry, apply the mouse offset\n\t\t\t\t# If we have a title bar we want to move the mouse to half the height of it\n\t\t\t\ttitleBar = wrapper.getWidget ().findChild ( QCustomTitleBar )\n\t\t\t\tif titleBar:\n\t\t\t\t\tdragOffset.setY ( -titleBar.height () / 2 )\n\t\t\t\t# apply the mouse offset to the current rect\n\t\t\t\tgeometry.moveTopLeft ( geometry.topLeft () + dragOffset )\n\t\t\t\twrapper.getWidget ().setGeometry ( geometry )\n\t\t\t\twrapper.getWidget ().move ( QCursor.pos () )\n\t\t\t\tif titleBar:\n\t\t\t\t\tcurrentTitle = titleBar.caption.text ()\n\t\t\t\t\tif len ( toolWindows ) > 0:\n\t\t\t\t\t\ttitleBar.caption.setText ( toolWindows[ 0 ].windowTitle () )\n\t\t\t\t\twrapper.getWidget ().move (\n\t\t\t\t\t\t- ( titleBar.caption.mapToGlobal ( titleBar.caption.rect ().topLeft () ) - wrapper.mapToGlobal ( wrapper.rect ().topLeft () ) )\n\t\t\t\t\t\t+ QPoint ( -titleBar.caption.fontMetrics ().boundingRect ( titleBar.caption.text () ).width () / 2, -titleBar.caption.height () / 2 )\n\t\t\t\t\t\t+ wrapper.pos ()\n\t\t\t\t\t)\n\t\t\t\t\ttitleBar.caption.setText ( currentTitle )\n\t\t\telse: # with no present geometry we just create a new one\n\t\t\t\twrapper.getWidget ().setGeometry ( QRect ( QPoint ( 0, 0 ), toolWindows[ 0 ].sizeHint () ) )\n\t\t\t\twrapper.getWidget ().move ( QCursor.pos () )\n\n\t\t\t# when create new wrapper, we should transfer mouse-event to wrapper's titleBar for we can continue move.\n\t\t\tif reference == QToolWindowAreaReference.Drag:\n\t\t\t\ttitleBar = wrapper.getWidget ().findChild ( QCustomTitleBar )\n\t\t\t\tif titleBar:\n\t\t\t\t\t# set flag for titleBar releaseMouse\n\t\t\t\t\ttitleBar.createFromDraging = True\n\t\t\t\t\tstartDragEvent = QMouseEvent ( QEvent.MouseButtonPress, QCursor.pos (), QtCore.Qt.LeftButton,\n\t\t\t\t\t QtCore.Qt.LeftButton, QtCore.Qt.NoModifier )\n\t\t\t\t\tqApp.sendEvent ( titleBar, startDragEvent )\n\t\t\t\t\ttitleBar.grabMouse ()\n\n\t\tif reference != QToolWindowAreaReference.Hidden:\n\t\t\tarea.addToolWindows ( toolWindows, index )\n\t\t\tself.lastArea = area\n\n\t\t# This will remove the previous area the tool windows where attached to\n\t\tself.simplifyLayout ()\n\t\tfor toolWindow in toolWindows:\n\t\t\tself.toolWindowVisibilityChanged.emit ( toolWindow, toolWindow.parent () != None )\n\t\tself.notifyLayoutChange ()\n\n\t\tif reference == QToolWindowAreaReference.Drag and wrapper != None:\n\t\t\tself.draggedWrapper = wrapper\n\t\t\t# start the drag on the new wrapper, will end up calling QToolWindowManager::startDrag(IToolWindowWrapper* wrapper)\n\t\t\twrapper.startDrag ()\n\n\tdef getAreaReferenceString ( self, reference ):\n\t\t# Top = 0\n\t\t# Bottom = 1\n\t\t# Left = 2\n\t\t# Right = 3\n\t\t# HSplitTop = 4\n\t\t# HSplitBottom = 5\n\t\t# VSplitLeft = 6\n\t\t# VSplitRight = 7\n\t\t# Combine = 8\n\t\t# Floating = 9\n\t\t# Drag = 10\n\t\t# Hidden = 11\n\t\tif reference == QToolWindowAreaReference.Top:\n\t\t\treturn \"Top\"\n\t\telif reference == QToolWindowAreaReference.Bottom:\n\t\t\treturn \"Bottom\"\n\t\telif reference == QToolWindowAreaReference.Left:\n\t\t\treturn \"Left\"\n\t\telif reference == QToolWindowAreaReference.Right:\n\t\t\treturn \"Right\"\n\t\telif reference == QToolWindowAreaReference.HSplitTop:\n\t\t\treturn \"HSplitTop\"\n\t\telif reference == QToolWindowAreaReference.HSplitBottom:\n\t\t\treturn \"HSplitBottom\"\n\t\telif reference == QToolWindowAreaReference.VSplitLeft:\n\t\t\treturn \"VSplitLeft\"\n\t\telif reference == QToolWindowAreaReference.VSplitRight:\n\t\t\treturn \"VSplitRight\"\n\t\telif reference == QToolWindowAreaReference.Combine:\n\t\t\treturn \"Combine\"\n\t\telif reference == QToolWindowAreaReference.Floating:\n\t\t\treturn \"Floating\"\n\t\telif reference == QToolWindowAreaReference.Drag:\n\t\t\treturn \"Drag\"\n\t\telif reference == QToolWindowAreaReference.Hidden:\n\t\t\treturn \"Hidden\"\n\n\tdef releaseToolWindow ( self, toolWindow, allowClose = False ) -> bool:\n\t\t# No parent, so can't possibly be inside an IToolWindowArea\n\t\tif toolWindow.parentWidget () == None:\n\t\t\t# qWarning ( \"[QToolWindowManager] releaseToolWindow %s, but toolWindow.parentWidget () == None\" % toolWindow )\n\t\t\treturn False\n\n\t\tif not self.ownsToolWindow ( toolWindow ):\n\t\t\tqWarning ( \"Unknown tool window %s\" % toolWindow )\n\t\t\treturn False\n\n\t\tpreviousTabWidget = findClosestParent ( toolWindow, [ QToolWindowArea, QToolWindowRollupBarArea ] )\n\t\tif previousTabWidget is None:\n\t\t\tqWarning ( \"[QToolWindowManager] cannot find area for tool window %s\" % toolWindow )\n\t\t\treturn False\n\n\t\tif allowClose:\n\t\t\treleasePolicy = self.config.setdefault ( QTWM_RELEASE_POLICY, QTWMReleaseCachingPolicy.rcpWidget )\n\t\t\tif releasePolicy == QTWMReleaseCachingPolicy.rcpKeep:\n\t\t\t\tself.moveToolWindow ( toolWindow, None, QToolWindowAreaReference.Hidden )\n\t\t\t\tif self.config.setdefault ( QTWM_ALWAYS_CLOSE_WIDGETS, True ) and not self.tryCloseToolWindow ( toolWindow ):\n\t\t\t\t\treturn False\n\t\t\telif releasePolicy == QTWMReleaseCachingPolicy.rcpWidget:\n\t\t\t\tif not toolWindow.testAttribute ( QtCore.Qt.WA_DeleteOnClose ):\n\t\t\t\t\tif self.config.setdefault ( QTWM_ALWAYS_CLOSE_WIDGETS, True ) and not self.tryCloseToolWindow (\n\t\t\t\t\t\t\ttoolWindow ):\n\t\t\t\t\t\treturn False\n\t\t\t\t\tself.moveToolWindow ( toolWindow, None, QToolWindowAreaReference.Hidden )\n\t\t\telif releasePolicy == QTWMReleaseCachingPolicy.rcpForget or releasePolicy == QTWMReleaseCachingPolicy.rcpDelete:\n\t\t\t\tif not self.tryCloseToolWindow ( toolWindow ):\n\t\t\t\t\treturn False\n\t\t\t\tself.moveToolWindow ( toolWindow, None, QToolWindowAreaReference.Hidden )\n\t\t\t\tself.toolWindows.remove ( toolWindow )\n\n\t\t\t\tif releasePolicy == QTWMReleaseCachingPolicy.rcpDelete:\n\t\t\t\t\ttoolWindow.deleteLater ()\n\t\t\t\treturn True\n\n\t\tpreviousTabWidget.removeToolWindow ( toolWindow )\n\n\t\tif allowClose:\n\t\t\tself.simplifyLayout ()\n\t\telse:\n\t\t\tpreviousTabWidget.adjustDragVisuals ()\n\n\t\ttoolWindow.hide ()\n\t\ttoolWindow.setParent ( None )\n\t\treturn True\n\n\tdef releaseToolWindows ( self, toolWindows, allowClose = False ) -> bool:\n\t\t# print ( \"[QToolWindowManager] releaseToolWindows\", toolWindows )\n\t\tresult = True\n\t\tfor i in range ( len ( toolWindows ) - 1, -1, -1 ):\n\t\t# for toolWindow in toolWindows:\n\t\t\t# if i >= 0 and i < len ( toolWindows ):\n\t\t\tresult &= self.releaseToolWindow ( toolWindows[ i ], allowClose )\n\t\t# while len ( toolWindows ) != 0:\n\t\t# \tresult &= self.releaseToolWindow ( toolWindows[ 0 ], allowClose )\n\t\treturn result\n\n\tdef areaOf ( self, toolWindow ) -> QToolWindowArea:\n\t\tarea = findClosestParent ( toolWindow, [ QToolWindowArea, QToolWindowRollupBarArea ] )\n\t\t# print ( \"[QToolWindowManager] areaOf %s is %s\" % ( toolWindow, area ) )\n\t\treturn area\n\n\tdef swapAreaType ( self, oldArea, areaType = QTWMWrapperAreaType.watTabs ):\n\t\tfrom QToolWindowManager.QToolWindowWrapper import QToolWindowWrapper\n\t\tfrom QToolWindowManager.QToolWindowCustomWrapper import QToolWindowCustomWrapper\n\t\ttargetWrapper = findClosestParent ( oldArea.getWidget (), [ QToolWindowWrapper, QToolWindowCustomWrapper ] )\n\t\tparentSplitter = cast ( oldArea.parentWidget (), QSplitter )\n\t\tnewArea = self.createArea ( areaType )\n\n\t\tif parentSplitter is None and targetWrapper is None:\n\t\t\tqWarning ( \"[QToolWindowManager] Could not determine area parent\" )\n\t\t\treturn\n\n\t\tnewArea.addToolWindow ( oldArea.toolWindows () )\n\t\tif parentSplitter != None:\n\t\t\ttargetIndex = parentSplitter.indexOf ( oldArea.getWidget () )\n\t\t\tparentSplitter.insertWidget ( targetIndex, newArea.getWidget () )\n\t\telse:\n\t\t\ttargetWrapper.setContents ( newArea.getWidget () )\n\n\t\tif self.lastArea == oldArea:\n\t\t\tself.lastArea = newArea\n\n\t\toldAreaIndex = self.areas.index ( oldArea )\n\t\tself.areas.remove ( oldArea )\n\t\tself.areas.insert ( oldAreaIndex, newArea )\n\t\toldArea.getWidget ().setParent ( None )\n\t\tnewArea.adjustDragVisuals ()\n\n\tdef isWrapper ( self, w ) -> bool:\n\t\tif w is None:\n\t\t\treturn False\n\t\tfrom .QToolWindowWrapper import QToolWindowWrapper\n\t\tfrom .QToolWindowCustomWrapper import QToolWindowCustomWrapper\n\t\treturn cast ( w, [ QToolWindowWrapper, QToolWindowCustomWrapper ] ) or (\n\t\t\t\tnot cast ( w, QSplitter ) and cast ( w.parentWidget (), [ QToolWindowWrapper, QToolWindowCustomWrapper ] ) )\n\n\tdef isMainWrapper ( self, w ) -> bool:\n\t\tfrom .QToolWindowWrapper import QToolWindowWrapper\n\t\tfrom .QToolWindowCustomWrapper import QToolWindowCustomWrapper\n\t\treturn self.isWrapper ( w ) and ( cast ( w, [ QToolWindowWrapper, QToolWindowCustomWrapper ] ) == self.mainWrapper or cast ( w.parentWidget (), [ QToolWindowWrapper, QToolWindowCustomWrapper ] ) == self.mainWrapper )\n\n\tdef isFloatingWrapper ( self, w ) -> bool:\n\t\tif not self.isWrapper ( w ):\n\t\t\treturn False\n\t\tfrom .QToolWindowWrapper import QToolWindowWrapper\n\t\tfrom .QToolWindowCustomWrapper import QToolWindowCustomWrapper\n\t\tif cast ( w, [ QToolWindowWrapper, QToolWindowCustomWrapper ] ) and cast ( w, [ QToolWindowWrapper, QToolWindowCustomWrapper ] ) != self.mainWrapper:\n\t\t\treturn True\n\t\treturn not cast ( w, QSplitter ) and cast ( w.parentWidget (),\n\t\t [ QToolWindowWrapper, QToolWindowCustomWrapper ] ) and cast ( w.parentWidget (), [ QToolWindowWrapper, QToolWindowCustomWrapper ] ) != self.mainWrapper\n\n\tdef saveState ( self ):\n\t\tresult = {}\n\t\tresult[ \"toolWindowManagerStateFormat\" ] = 2\n\t\tif self.mainWrapper.getContents () and self.mainWrapper.getContents ().metaObject ():\n\t\t\tresult[ \"mainWrapper\" ] = self.saveWrapperState ( self.mainWrapper )\n\n\t\tfloatingWindowsData = []\n\t\tfor wrapper in self.wrappers:\n\t\t\tif wrapper.getWidget ().isWindow () and wrapper.getContents () and wrapper.getContents ().metaObject ():\n\t\t\t\tm = self.saveWrapperState ( wrapper )\n\t\t\t\tif len ( m ) != 0:\n\t\t\t\t\tfloatingWindowsData.append ( m )\n\n\t\tresult[ \"floatingWindows\" ] = floatingWindowsData\n\t\treturn result\n\n\tdef restoreState ( self, data ):\n\t\tif data is None:\n\t\t\treturn\n\t\tstateFormat = data[ \"toolWindowManagerStateFormat\" ]\n\t\tif stateFormat != 1 and stateFormat != 2:\n\t\t\tqWarning ( \"state format is not recognized\" )\n\t\t\treturn\n\n\t\tself.suspendLayoutNotifications ()\n\t\tself.mainWrapper.getWidget ().hide ()\n\t\tfor wrapper in self.wrappers:\n\t\t\twrapper.getWidget ().hide ()\n\t\tself.moveToolWindow ( self.toolWindows, None, QToolWindowAreaReference.Hidden )\n\t\tself.simplifyLayout ( True )\n\t\tself.mainWrapper.setContents ( None )\n\t\tself.restoreWrapperState ( data[ \"mainWrapper\" ], stateFormat, self.mainWrapper )\n\t\tfor windowData in data[ \"floatingWindows\" ]:\n\t\t\tself.restoreWrapperState ( windowData, stateFormat )\n\t\tself.simplifyLayout ()\n\t\tfor toolWindow in self.toolWindows:\n\t\t\tself.toolWindowVisibilityChanged.emit ( toolWindow, toolWindow.parentWidget () != None )\n\t\tself.resumeLayoutNotifications ()\n\t\tself.notifyLayoutChange ()\n\n\tdef isAnyWindowActive ( self ) -> bool:\n\t\tfor tool in self.toolWindows:\n\t\t\tif tool.isAnyWindowActive ():\n\t\t\t\treturn True\n\t\treturn False\n\n\tdef updateDragPosition ( self ):\n\t\t# if self.draggedWrapper == None:\n\t\t# \treturn\n\t\tif not self.dragInProgress (): return\n\t\tif not QApplication.mouseButtons () & Qt.LeftButton :\n\t\t\tself.finishDrag ()\n\t\t\treturn\n\n\t\t# print ( \"[QToolWindowManager] updateDragPosition\" )\n\n\t\t# self.draggedWrapper.getWidget ().raise_ ()\n\t\tpos = QCursor.pos ()\n\t\tself.dragIndicator.move ( pos + QPoint ( 1, 1 ) )\n\n\t\t# hoveredWindow = windowBelow ( self.draggedWrapper.getWidget () )\n\t\thoveredWindow = QApplication.topLevelAt( pos )\n\t\t# if hoveredWindow != None and hoveredWindow != self.draggedWrapper.window ():\n\t\tif hoveredWindow != None:\n\t\t\thandlerWidget = hoveredWindow.childAt ( hoveredWindow.mapFromGlobal ( QCursor.pos () ) )\n\t\t\tif handlerWidget != None and not self.dragHandler.isHandlerWidget ( handlerWidget ):\n\t\t\t\tarea = self.getClosestParentArea ( handlerWidget )\n\t\t\t\tif area != None and self.lastArea != area:\n\t\t\t\t\tself.dragHandler.switchedArea ( self.lastArea, area )\n\t\t\t\t\tself.lastArea = area\n\t\t\t\t\tdelayTime = self.config.setdefault ( QTWM_RAISE_DELAY, 500 )\n\t\t\t\t\tself.raiseTimer.stop ()\n\t\t\t\t\tif delayTime > 0:\n\t\t\t\t\t\tself.raiseTimer.start ( delayTime )\n\n\t\ttarget = self.dragHandler.getTargetFromPosition ( self.lastArea )\n\t\tif self.lastArea != None and target.reference != QToolWindowAreaReference.Floating:\n\t\t\tif QToolWindowAreaReference.isOuter ( target.reference ):\n\t\t\t\tfrom QToolWindowManager.QToolWindowCustomWrapper import QToolWindowCustomWrapper\n\t\t\t\tpreviewArea = findClosestParent ( self.lastArea.getWidget (), [ QToolWindowWrapper, QToolWindowCustomWrapper ] )\n\t\t\t\tpreviewAreaContents = previewArea.getContents ()\n\t\t\t\tself.preview.setParent ( previewArea.getWidget () )\n\t\t\t\tself.preview.setGeometry (\n\t\t\t\t\tself.dragHandler.getRectFromCursorPos ( previewAreaContents, self.lastArea ).translated (\n\t\t\t\t\t\tpreviewAreaContents.pos () ) )\n\t\t\telse:\n\t\t\t\tpreviewArea = self.lastArea.getWidget ()\n\t\t\t\tself.preview.setParent ( previewArea )\n\t\t\t\tself.preview.setGeometry ( self.dragHandler.getRectFromCursorPos ( previewArea, self.lastArea ) )\n\n\t\t\tself.preview.show ()\n\t\telse:\n\t\t\tself.preview.hide ()\n\n\t\tself.updateTrackingTooltip.emit ( self.textForPosition ( target.reference ),\n\t\t QCursor.pos () + self.config.setdefault ( QTWM_TOOLTIP_OFFSET, QPoint ( 1, 20 ) ) )\n\n\tdef finishDrag ( self ):\n\t\tif not self.dragInProgress ():\n\t\t\tqWarning( 'unexpected finishDrag' )\n\t\t\treturn\n\n\t\ttarget = self.dragHandler.finishDrag ( self.draggedToolWindows, None, self.lastArea )\n\t\tself.lastArea = None\n\n\t\tarea = self.draggedArea\n\t\tself.draggedArea = None\n\t\tself.raiseTimer.stop ()\n\t\tself.preview.setParent ( None )\n\t\tself.preview.hide ()\n\n\t\tcontents = area\n\t\ttoolWindows = self.draggedToolWindows\n\n\t\tif target.reference != QToolWindowAreaReference.Floating:\n\t\t\tcontents = area\n\n\t\t\tif target.reference == QToolWindowAreaReference.Top or \\\n\t\t\t\t\ttarget.reference == QToolWindowAreaReference.Bottom or \\\n\t\t\t\t\ttarget.reference == QToolWindowAreaReference.Left or \\\n\t\t\t\t\ttarget.reference == QToolWindowAreaReference.Right:\n\t\t\t\tself.splitArea ( self.getFurthestParentArea ( target.area.getWidget () ).getWidget (), target.reference, contents )\n\t\t\telif target.reference == QToolWindowAreaReference.HSplitTop or \\\n\t\t\t\t\ttarget.reference == QToolWindowAreaReference.HSplitBottom or \\\n\t\t\t\t\ttarget.reference == QToolWindowAreaReference.VSplitLeft or \\\n\t\t\t\t\ttarget.reference == QToolWindowAreaReference.VSplitRight:\n\t\t\t\tself.splitArea ( target.area.getWidget (), target.reference, contents )\n\t\t\telif target.reference == QToolWindowAreaReference.Combine:\n\t\t\t\tself.moveToolWindowsTarget ( toolWindows, target )\n\n\t\t\t# wrapper.getWidget ().close ()\n\t\t\tself.simplifyLayout ()\n\n\t\tif target.reference != QToolWindowAreaReference.Combine:\n\t\t\tfor w in toolWindows:\n\t\t\t\tself.toolWindowVisibilityChanged.emit ( w, True )\n\n\t\tif target.reference != QToolWindowAreaReference.Floating:\n\t\t\tself.notifyLayoutChange ()\n\n\t\tif target.reference == QToolWindowAreaReference.Floating:\n\t\t\tself.moveToolWindowsTarget ( toolWindows, target )\n\n\t\tself.updateTrackingTooltip.emit ( \"\", QPoint () )\n\n\t\tself.dragIndicator.hide ()\n\t\tself.draggedToolWindows = []\n\n\tdef finishWrapperDrag ( self ):\n\t\ttarget = self.dragHandler.finishDrag ( self.draggedToolWindows, None, self.lastArea )\n\t\tself.lastArea = None\n\n\t\twrapper = self.draggedWrapper\n\t\tself.draggedWrapper = None\n\t\tself.raiseTimer.stop ()\n\t\tself.preview.setParent ( None )\n\t\tself.preview.hide ()\n\n\t\tif wrapper is None:\n\t\t\tqWarning ( \"[QToolWindowManager] finishWrapperDrag wrapper == None.\" )\n\t\t\treturn\n\n\t\t# print ( \"[QToolWindowManager] finishWrapperDrag %s %s\" % ( self.draggedWrapper, self.draggedToolWindows ) )\n\n\t\tcontents = wrapper.getContents ()\n\t\ttoolWindows = []\n\t\tcontentsWidgets = contents.findChildren ( QWidget )\n\t\tcontentsWidgets.append ( contents )\n\n\t\tfor w in contentsWidgets:\n\t\t\tarea = cast ( w, [ QToolWindowArea, QToolWindowRollupBarArea ] )\n\t\t\tif area != None and self.ownsArea ( area ):\n\t\t\t\ttoolWindows += area.toolWindows ()\n\n\t\tif target.reference != QToolWindowAreaReference.Floating:\n\t\t\tcontents = wrapper.getContents ()\n\t\t\twrapper.setContents ( None )\n\n\t\t\tif target.reference == QToolWindowAreaReference.Top or \\\n\t\t\t\t\ttarget.reference == QToolWindowAreaReference.Bottom or \\\n\t\t\t\t\ttarget.reference == QToolWindowAreaReference.Left or \\\n\t\t\t\t\ttarget.reference == QToolWindowAreaReference.Right:\n\t\t\t\tself.splitArea ( self.getFurthestParentArea ( target.area.getWidget () ).getWidget (), target.reference,\n\t\t\t\t contents )\n\t\t\telif target.reference == QToolWindowAreaReference.HSplitTop or \\\n\t\t\t\t\ttarget.reference == QToolWindowAreaReference.HSplitBottom or \\\n\t\t\t\t\ttarget.reference == QToolWindowAreaReference.VSplitLeft or \\\n\t\t\t\t\ttarget.reference == QToolWindowAreaReference.VSplitRight:\n\t\t\t\tself.splitArea ( target.area.getWidget (), target.reference, contents )\n\t\t\telif target.reference == QToolWindowAreaReference.Combine:\n\t\t\t\tself.moveToolWindowsTarget ( toolWindows, target )\n\n\t\t\twrapper.getWidget ().close ()\n\t\t\tself.simplifyLayout ()\n\n\t\tif target.reference != QToolWindowAreaReference.Combine:\n\t\t\tfor w in toolWindows:\n\t\t\t\tself.toolWindowVisibilityChanged.emit ( w, True )\n\n\t\tif target.reference != QToolWindowAreaReference.Floating:\n\t\t\tself.notifyLayoutChange ()\n\n\t\tself.updateTrackingTooltip.emit ( \"\", QPoint () )\n\n\tdef finishWrapperResize ( self ):\n\t\ttoolWindows = []\n\t\tcontents = self.resizedWrapper.getContents ()\n\t\tcontentsWidgets = contents.findChildren ( QWidget )\n\t\tcontentsWidgets.append ( contents )\n\n\t\tfor w in contentsWidgets:\n\t\t\tarea = cast ( w, [ QToolWindowArea, QToolWindowRollupBarArea ] )\n\t\t\tif area and self.ownsArea ( area ):\n\t\t\t\ttoolWindows.append ( area.toolWindows () )\n\n\t\tfor w in toolWindows:\n\t\t\tself.toolWindowVisibilityChanged.emit ( w, True )\n\n\t\tself.resizedWrapper = None\n\n\tdef saveWrapperState ( self, wrapper ):\n\t\tif wrapper.getContents () == None:\n\t\t\tqWarning ( \"[QToolWindowManager] saveWrapperState Empty top level wrapper.\" )\n\t\t\treturn {}\n\n\t\tresult = {}\n\t\tresult[ \"geometry\" ] = wrapper.getWidget ().saveGeometry ().toBase64 ()\n\t\tresult[ \"name\" ] = wrapper.getWidget ().objectName ()\n\n\t\tobj = wrapper.getContents ()\n\t\tif cast ( obj, QSplitter ):\n\t\t\tresult[ \"splitter\" ] = self.saveSplitterState ( obj )\n\t\t\treturn result\n\n\t\tif isinstance ( obj, QToolWindowArea ):\n\t\t\tresult[ \"area\" ] = obj.saveState ()\n\t\t\treturn result\n\n\t\tqWarning ( \"[QToolWindowManager] saveWrapperState Couldn't find valid child widget\" )\n\t\treturn {}\n\n\tdef restoreWrapperState ( self, data, stateFormat, wrapper = None ):\n\t\tnewContents = None\n\n\t\tif wrapper and data[ \"name\" ]:\n\t\t\twrapper.getWidget ().setObjectName ( data[ \"name\" ] )\n\n\t\tif data[ \"splitter\" ]:\n\t\t\tnewContents = self.restoreSplitterState ( data[ \"splitter\" ], stateFormat )\n\t\telif data[ \"area\" ]:\n\t\t\tareaType = QTWMWrapperAreaType.watTabs\n\t\t\tif data[ \"area\" ][ \"type\" ] and data[ \"area\" ][ \"type\" ] == \"rollup\":\n\t\t\t\tareaType = QTWMWrapperAreaType.watRollups\n\n\t\t\tarea = self.createArea ( areaType )\n\t\t\tarea.restoreState ( data[ \"area\" ], stateFormat )\n\t\t\tif area.count () > 0:\n\t\t\t\tnewContents = area.getWidget ()\n\t\t\telse:\n\t\t\t\tarea.deleteLater ()\n\n\t\tif not wrapper:\n\t\t\tif newContents:\n\t\t\t\twrapper = self.createWrapper ()\n\t\t\t\tif data[ \"name\" ]:\n\t\t\t\t\twrapper.getWidget ().setObjectName ( data[ \"name\" ] )\n\t\t\telse:\n\t\t\t\treturn None\n\n\t\twrapper.setContents ( newContents )\n\n\t\tif stateFormat == 1:\n\t\t\tif data[ \"geometry\" ]:\n\t\t\t\tif not wrapper.getWidget ().restoreGeometry ( data[ \"geometry\" ] ):\n\t\t\t\t\tprint ( \"Failed to restore wrapper geometry\" )\n\t\telif stateFormat == 2:\n\t\t\tif data[ \"geometry\" ]:\n\t\t\t\tif not wrapper.getWidget ().restoreGeometry ( QByteArray.fromBase64 ( data[ \"geometry\" ] ) ):\n\t\t\t\t\tprint ( \"Failed to restore wrapper geometry\" )\n\t\telse:\n\t\t\tprint ( \"Unknown state format\" )\n\n\t\tif data[ \"geometry\" ]:\n\t\t\twrapper.getWidget ().show ()\n\n\t\treturn wrapper\n\n\tdef saveSplitterState ( self, splitter ):\n\t\tresult = {}\n\t\tresult[ \"state\" ] = splitter.saveState ().toBase64 ()\n\t\tresult[ \"type\" ] = \"splitter\"\n\t\titems = []\n\t\tfor i in range ( splitter.count () ):\n\t\t\titem = splitter.widget ( i )\n\t\t\titemValue = {}\n\t\t\tarea = item\n\t\t\tif area != None:\n\t\t\t\titemValue = area.saveState ()\n\t\t\telse:\n\t\t\t\tchildSplitter = item\n\t\t\t\tif childSplitter:\n\t\t\t\t\titemValue = self.saveSplitterState ( childSplitter )\n\t\t\t\telse:\n\t\t\t\t\tqWarning ( \"[QToolWindowManager] saveSplitterState Unknown splitter item\" )\n\t\t\titems.append ( itemValue )\n\t\tresult[ \"items\" ] = items\n\t\treturn result\n\n\tdef restoreSplitterState ( self, data, stateFormat ):\n\t\tif len ( data[ \"items\" ] ) < 2:\n\t\t\tprint ( \"Invalid splitter encountered\" )\n\t\t\tif len ( data[ \"items\" ] ) == 0:\n\t\t\t\treturn None\n\n\t\tsplitter = self.createSplitter ()\n\n\t\tfor itemData in data[ \"items\" ]:\n\t\t\titemValue = itemData\n\t\t\titemType = itemValue[ \"type\" ]\n\t\t\tif itemType == \"splitter\":\n\t\t\t\tw = self.restoreSplitterState ( itemValue, stateFormat )\n\t\t\t\tif w:\n\t\t\t\t\tsplitter.addWidget ( w )\n\t\t\telif itemType == \"area\":\n\t\t\t\tarea = self.createArea ()\n\t\t\t\tarea.restoreState ( itemValue, stateFormat )\n\t\t\t\tsplitter.addWidget ( area.getWidget () )\n\t\t\telif itemType == \"rollup\":\n\t\t\t\tarea = self.createArea ()\n\t\t\t\tarea.restoreState ( itemValue, stateFormat )\n\t\t\t\tsplitter.addWidget ( area.getWidget () )\n\t\t\telse:\n\t\t\t\tprint ( \"Unknown item type\" )\n\n\t\tif stateFormat == 1:\n\t\t\tif data[ \"state\" ]:\n\t\t\t\tif not splitter.restoreState ( data[ \"state\" ] ):\n\t\t\t\t\tprint ( \"Failed to restore splitter state\" )\n\t\telif stateFormat == 2:\n\t\t\tif data[ \"state\" ]:\n\t\t\t\tif not splitter.restoreState ( QByteArray.fromBase64 ( data[ \"state\" ] ) ):\n\t\t\t\t\tprint ( \"Failed to restore splitter state\" )\n\n\t\telse:\n\t\t\tprint ( \"Unknown state format\" )\n\n\t\treturn splitter\n\n\tdef resizeSplitter ( self, widget, sizes ):\n\t\ts = cast ( widget, QSplitter )\n\t\tif s == None:\n\t\t\ts = findClosestParent ( widget, QSplitter )\n\t\tif s == None:\n\t\t\tqWarning ( \"Could not find a matching splitter!\" )\n\t\t\treturn\n\n\t\tscaleFactor = s.width () if s.orientation () == QtCore.Qt.Horizontal else s.height ()\n\n\t\tfor i in range ( 0, len ( sizes ) ):\n\t\t\tsizes[ i ] *= scaleFactor\n\n\t\ts.setSizes ( sizes )\n\n\tdef createArea ( self, areaType = QTWMWrapperAreaType.watTabs ):\n\t\t# qWarning ( \"[QToolWindowManager] createArea %s\" % areaType )\n\t\ta = self.factory.createArea ( self, None, areaType )\n\t\tself.lastArea = a\n\t\tself.areas.append ( a )\n\t\treturn a\n\n\tdef createWrapper ( self ):\n\t\tw = self.factory.createWrapper ( self )\n\t\tname = None\n\t\twhile True:\n\t\t\ti = QtCore.qrand ()\n\t\t\tname = \"wrapper#%s\" % i\n\t\t\tfor w2 in self.wrappers:\n\t\t\t\tif name == w2.getWidget ().objectName ():\n\t\t\t\t\tcontinue\n\t\t\tbreak\n\t\tw.getWidget ().setObjectName ( name )\n\t\tself.wrappers.append ( w )\n\t\treturn w\n\n\tdef getNotifyLock ( self, allowNotify = True ):\n\t\treturn QTWMNotifyLock ( self, allowNotify )\n\n\tdef hide ( self ):\n\t\tself.mainWrapper.getWidget ().hide ()\n\n\tdef show ( self ):\n\t\tself.mainWrapper.getWidget ().show ()\n\n\tdef clear ( self ):\n\t\tself.releaseToolWindows ( self.toolWindows, True )\n\t\tif len ( self.areas ) != 0:\n\t\t\tself.lastArea = self.areas[ 0 ]\n\t\telse:\n\t\t\tself.lastArea = None\n\n\tdef bringAllToFront ( self ):\n\t\t# TODO: Win64 / OSX\n\t\tfrom QToolWindowManager.QToolWindowCustomWrapper import QToolWindowCustomWrapper\n\t\tlist = qApp.topLevelWidgets ()\n\t\tfor w in list:\n\t\t\tif w != None and not w.windowState ().testFlag ( QtCore.Qt.WindowMinimized ) and ( isinstance ( w, QToolWindowWrapper ) or isinstance ( w, QToolWindowCustomWrapper ) or isinstance ( w, QCustomWindowFrame ) ):\n\t\t\t\tw.show ()\n\t\t\t\tw.raise_ ()\n\n\tdef bringToFront ( self, toolWindow ):\n\t\tarea = self.areaOf ( toolWindow )\n\t\tif not area:\n\t\t\treturn\n\t\twhile area.indexOf ( toolWindow ) == -1:\n\t\t\ttoolWindow = toolWindow.parentWidget ()\n\t\tarea.setCurrentWidget ( toolWindow )\n\n\t\twindow = area.getWidget ().window ()\n\t\twindow.setWindowState ( window.windowState () & ~QtCore.Qt.WindowMinimized )\n\t\twindow.show ()\n\t\twindow.raise_ ()\n\t\twindow.activateWindow ()\n\n\t\ttoolWindow.setFocus ()\n\n\tdef getToolPath ( self, toolWindow ):\n\t\tw = toolWindow\n\t\tresult = ''\n\t\tpw = w.parentWidget ()\n\t\twhile pw:\n\t\t\tif isinstance ( pw, QSplitter ):\n\t\t\t\torientation = None\n\t\t\t\tif pw.orientation () == QtCore.Qt.Horizontal:\n\t\t\t\t\torientation = 'h'\n\t\t\t\telse:\n\t\t\t\t\torientation = 'v'\n\t\t\t\tresult += \"/%c/%d\" % (orientation, pw.indexOf ( w ))\n\t\t\telif isinstance ( pw, QToolWindowWrapper ):\n\t\t\t\tresult += toolWindow.window ().objectName ()\n\t\t\t\tbreak\n\t\t\tw = pw\n\t\t\tpw = w.parentWidget ()\n\t\treturn result\n\n\tdef targetFromPath ( self, toolPath ) -> QToolWindowAreaTarget:\n\t\tfor w in self.areas:\n\t\t\tif w.count () < 0 and self.getToolPath ( w.widget ( 0 ) ) == toolPath:\n\t\t\t\treturn QToolWindowAreaTarget.createByArea ( w, QToolWindowAreaReference.Combine )\n\t\treturn QToolWindowAreaTarget ( QToolWindowAreaReference.Floating )\n\n\tdef eventFilter ( self, o, e ):\n\t\t\"\"\"Summary line.\n\n\t\tArgs:\n\t\t\to (QObject): Description of param1\n\t\t\te (QEvent): Description of param2\n\t \"\"\"\n\t\tif o == qApp:\n\t\t\tif e.type () == QEvent.ApplicationActivate and (\n\t\t\t\t\tself.config.setdefault ( QTWM_WRAPPERS_ARE_CHILDREN, False )) and (\n\t\t\t\t\tself.config.setdefault ( QTWM_BRING_ALL_TO_FRONT, True )):\n\t\t\t\tself.bringAllToFront ()\n\t\t\treturn False\n\n\t\tif e.type () == 16: # event Destroy\n\t\t\tw = cast ( o, QWidget )\n\t\t\tif not self.closingWindow and self.ownsToolWindow ( w ) and w.isVisible ():\n\t\t\t\tself.releaseToolWindow ( w, True )\n\t\t\t\treturn False\n\n\t\t# if self.draggedWrapper and o == self.draggedWrapper:\n\t\t# \tif e.type () == QEvent.MouseMove:\n\t\t# \t\tqWarning ( \"Manager eventFilter: send MouseMove\" )\n\t\t# \t\tqApp.sendEvent ( self.draggedWrapper, e )\n\n\t\t# print ( \"QToolWindowManager::eventFilter\", type ( o ), EventTypes ().as_string ( e.type () ) )\n\n\t\t# if self.draggedWrapper and o == self.draggedWrapper:\n\t\t# \tif e.type () == QEvent.MouseButtonRelease:\n\t\t# \t\tqWarning ( f\"Manager eventFilter: send MouseButtonRelease {self.draggedWrapper}\" )\n\n\t\t# if e.type () == Qt.QEvent.Destroy:\n\t\t# \tif not self.closingWindow and self.ownsToolWindow ( w ) and w.isVisible ():\n\t\t# \t\tself.releaseToolWindow ( w, True )\n\t\t# \t\treturn False\n\n\t\treturn super ().eventFilter ( o, e )\n\n\tdef event ( self, e ):\n\t\tif e.type () == QEvent.StyleChange:\n\t\t\tif self.parentWidget () and self.parentWidget ().styleSheet () != self.styleSheet ():\n\t\t\t\tself.setStyleSheet ( self.parentWidget ().styleSheet () )\n\n\t\treturn super ().event ( e )\n\n\tdef tryCloseToolWindow ( self, toolWindow ):\n\t\tself.closingWindow += 1\n\t\tresult = True\n\n\t\tif not toolWindow.close ():\n\t\t\tqWarning ( \"Widget could not be closed\" )\n\t\t\tresult = False\n\n\t\tself.closingWindow -= 1\n\t\treturn result\n\n\tdef createSplitter ( self ) -> QSplitter:\n\t\treturn self.factory.createSplitter ( self )\n\n\tdef splitArea ( self, area, reference, insertWidget = None ):\n\t\tfrom .QToolWindowWrapper import QToolWindowWrapper\n\t\tfrom .QToolWindowCustomWrapper import QToolWindowCustomWrapper\n\n\t\tresidingWidget = insertWidget\n\t\tif residingWidget == None:\n\t\t\tresidingWidget = self.createArea ().getWidget ()\n\n\t\tif not QToolWindowAreaReference.requiresSplit ( reference ):\n\t\t\tqWarning ( \"Invalid reference for area split\" )\n\t\t\treturn None\n\n\t\tforceOuter = QToolWindowAreaReference.isOuter ( reference )\n\t\treference = reference & 0x3\n\t\tparentSplitter = None\n\t\ttargetWrapper = findClosestParent ( area, [ QToolWindowWrapper, QToolWindowCustomWrapper ] )\n\t\tif not forceOuter:\n\t\t\tparentSplitter = cast ( area.parentWidget (), QSplitter )\n\t\tif parentSplitter == None and targetWrapper == None:\n\t\t\tqWarning ( \"Could not determine area parent\" )\n\t\t\treturn None\n\t\tuseParentSplitter = False\n\t\ttargetIndex = 0\n\t\tparentSizes = [ QSize () ]\n\t\tif parentSplitter != None:\n\t\t\tparentSizes = parentSplitter.sizes ()\n\t\t\ttargetIndex += parentSplitter.indexOf ( area )\n\t\t\tuseParentSplitter = parentSplitter.orientation () == QToolWindowAreaReference.splitOrientation ( reference )\n\n\t\tif useParentSplitter:\n\t\t\torigIndex = targetIndex\n\t\t\ttargetIndex += reference & 0x1\n\t\t\tnewSizes = self.dragHandler.getSplitSizes ( parentSizes[ origIndex ] )\n\t\t\tparentSizes[ origIndex ] = newSizes.oldSize\n\t\t\tparentSizes.insert ( targetIndex, newSizes.newSize )\n\t\t\tparentSplitter.insertWidget ( targetIndex, residingWidget )\n\t\t\tparentSplitter.setSizes ( parentSizes )\n\t\t\treturn residingWidget\n\n\t\tsplitter = self.createSplitter ()\n\t\tsplitter.setOrientation ( QToolWindowAreaReference.splitOrientation ( reference ) )\n\n\t\tif forceOuter or area == targetWrapper.getContents ():\n\t\t\tfirstChild = targetWrapper.getContents ()\n\t\t\ttargetWrapper.setContents ( None )\n\t\t\tsplitter.addWidget ( firstChild )\n\t\telse:\n\t\t\tarea.hide ()\n\t\t\tarea.setParent ( None )\n\t\t\tsplitter.addWidget ( area )\n\t\t\tarea.show ()\n\n\t\tsplitter.insertWidget ( reference & 0x1, residingWidget )\n\t\tif parentSplitter != None:\n\t\t\tparentSplitter.insertWidget ( targetIndex, splitter )\n\t\t\tparentSplitter.setSizes ( parentSizes )\n\t\telse:\n\t\t\ttargetWrapper.setContents ( splitter )\n\n\t\tsizes = []\n\t\tbaseSize = splitter.height () if splitter.orientation () == QtCore.Qt.Vertical else splitter.width ()\n\t\tnewSizes = self.dragHandler.getSplitSizes ( baseSize )\n\t\tsizes.append ( newSizes.oldSize )\n\t\tsizes.insert ( reference & 0x1, newSizes.newSize )\n\t\tsplitter.setSizes ( sizes )\n\n\t\tcontentsWidgets = residingWidget.findChildren ( QWidget )\n\t\tfor w in contentsWidgets:\n\t\t\tqApp.sendEvent ( w, QEvent ( QEvent.ParentChange ) )\n\n\t\treturn residingWidget\n\n\tdef getClosestParentArea ( self, widget ):\n\t\tarea = findClosestParent ( widget, [ QToolWindowArea, QToolWindowRollupBarArea ] )\n\t\twhile area != None and not self.ownsArea ( area ):\n\t\t\tarea = findClosestParent ( area.getWidget ().parentWidget (), [ QToolWindowArea, QToolWindowRollupBarArea ] )\n\n\t\t# qWarning ( \"[QToolWindowManager] getClosestParentArea %s is %s\" % ( widget, area ) )\n\n\t\treturn area\n\n\tdef getFurthestParentArea ( self, widget ):\n\t\tarea = findClosestParent ( widget, [ QToolWindowArea, QToolWindowRollupBarArea ] )\n\t\tpreviousArea = area\n\t\twhile area != None and not self.ownsArea ( area ):\n\t\t\tarea = findClosestParent ( area.getWidget ().parentWidget (), [ QToolWindowArea, QToolWindowRollupBarArea ] )\n\n\t\t\tif area == None:\n\t\t\t\treturn previousArea\n\t\t\telse:\n\t\t\t\tpreviousArea = area\n\n\t\t# qWarning ( \"getFurthestParentArea %s is %s\" % ( widget, area ) )\n\n\t\treturn area\n\n\tdef textForPosition ( self, reference ):\n\t\ttexts = [\n\t\t\t\"Place at top of window\",\n\t\t\t\"Place at bottom of window\",\n\t\t\t\"Place on left side of window\",\n\t\t\t\"Place on right side of window\",\n\t\t\t\"Split horizontally, place above\",\n\t\t\t\"Split horizontally, place below\",\n\t\t\t\"Split vertically, place left\",\n\t\t\t\"Split vertically, place right\",\n\t\t\t\"Add to tab list\",\n\t\t\t\"\"\n\t\t]\n\t\treturn texts[ reference ]\n\n\tdef simplifyLayout ( self, clearMain = False ):\n\t\tfrom .QToolWindowWrapper import QToolWindowWrapper\n\t\tfrom .QToolWindowCustomWrapper import QToolWindowCustomWrapper\n\n\t\tself.suspendLayoutNotifications ()\n\t\tmadeChanges = True # Some layout changes may require multiple iterations to fully simplify.\n\n\t\twhile madeChanges:\n\t\t\tmadeChanges = False\n\t\t\tareasToRemove = []\n\t\t\tfor area in self.areas:\n\t\t\t\t# remove empty areas (if this is only area in main wrapper, only remove it when we explicitly ask for that)\n\t\t\t\tif area.count () == 0 and ( clearMain or cast ( area.parent (), [ QToolWindowWrapper, QToolWindowCustomWrapper ] ) != self.mainWrapper ):\n\t\t\t\t\tareasToRemove.append ( area )\n\t\t\t\t\tmadeChanges = True\n\n\t\t\t\ts = cast ( area.parentWidget (), QSplitter )\n\t\t\t\twhile s != None and s.parentWidget () != None:\n\t\t\t\t\tsp_s = cast ( s.parentWidget (), QSplitter )\n\t\t\t\t\tsp_w = cast ( s.parentWidget (), [ QToolWindowWrapper, QToolWindowCustomWrapper ] )\n\t\t\t\t\tsw = findClosestParent ( s, [ QToolWindowWrapper, QToolWindowCustomWrapper ] )\n\n\t\t\t\t\t# If splitter only contains one object, replace the splitter with the contained object\n\t\t\t\t\tif s.count () == 1:\n\t\t\t\t\t\tif sp_s != None:\n\t\t\t\t\t\t\tindex = sp_s.indexOf ( s )\n\t\t\t\t\t\t\tsizes = sp_s.sizes ()\n\t\t\t\t\t\t\tsp_s.insertWidget ( index, s.widget ( 0 ) )\n\t\t\t\t\t\t\ts.hide ()\n\t\t\t\t\t\t\ts.setParent ( None )\n\t\t\t\t\t\t\ts.deleteLater ()\n\t\t\t\t\t\t\tsp_s.setSizes ( sizes )\n\t\t\t\t\t\t\tmadeChanges = True\n\t\t\t\t\t\telif sp_w != None:\n\t\t\t\t\t\t\tsp_w.setContents ( s.widget ( 0 ) )\n\t\t\t\t\t\t\ts.setParent ( None )\n\t\t\t\t\t\t\ts.deleteLater ()\n\t\t\t\t\t\t\tmadeChanges = True\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tqWarning ( \"Unexpected splitter parent\" )\n\n\t\t\t\t\t# If splitter's parent is also a splitter, and both have same orientation, replace splitter with contents\n\t\t\t\t\telif sp_s != None and s.orientation () == sp_s.orientation ():\n\t\t\t\t\t\tindex = sp_s.indexOf ( s )\n\t\t\t\t\t\tnewSizes = sp_s.sizes ()\n\t\t\t\t\t\toldSizes = s.sizes ()\n\t\t\t\t\t\tnewSum = newSizes[ index ]\n\t\t\t\t\t\toldSum = 0\n\t\t\t\t\t\tfor i in oldSizes:\n\t\t\t\t\t\t\toldSum += i\n\t\t\t\t\t\tfor i in range ( len ( oldSizes ) - 1, -1, -1 ):\n\t\t\t\t\t\t\tsp_s.insertWidget ( index, s.widget ( i ) )\n\t\t\t\t\t\t\tnewSizes.insert ( index, oldSizes[ i ] / oldSum * newSum )\n\t\t\t\t\t\ts.hide ()\n\t\t\t\t\t\ts.setParent ( None )\n\t\t\t\t\t\ts.deleteLater ()\n\t\t\t\t\t\tsp_s.setSizes ( newSizes )\n\t\t\t\t\t\tmadeChanges = True\n\n\t\t\t\t\ts = sp_s\n\n\t\t\tfor area in areasToRemove:\n\t\t\t\tarea.hide ()\n\t\t\t\taw = cast ( area.parentWidget (), [ QToolWindowWrapper, QToolWindowCustomWrapper ] )\n\t\t\t\tif aw != None:\n\t\t\t\t\taw.setContents ( None )\n\n\t\t\t\tarea.setParent ( None )\n\t\t\t\tself.areas.remove ( area )\n\t\t\t\tarea.deleteLater ()\n\n\t\twrappersToRemove = []\n\t\tfor wrapper in self.wrappers:\n\t\t\tif wrapper.getWidget ().isWindow () and wrapper.getContents () == None:\n\t\t\t\twrappersToRemove.append ( wrapper )\n\n\t\tfor wrapper in wrappersToRemove:\n\t\t\tself.wrappers.remove ( wrapper )\n\t\t\twrapper.hide ()\n\t\t\twrapper.deferDeletion ()\n\n\t\tfor area in self.areas:\n\t\t\tarea.adjustDragVisuals ()\n\n\t\tself.resumeLayoutNotifications ()\n\t\tself.notifyLayoutChange ()\n\n\tdef dragInProgress( self ):\n\t\t'''\n\t\t指示当前是否正处于拖拽状态\n\t\t'''\n\t\treturn len ( self.draggedToolWindows ) > 0\n\n\tdef createDragHandler ( self ):\n\t\tif self.dragHandler != None:\n\t\t\tdel self.dragHandler\n\t\treturn self.factory.createDragHandler ( self )\n\n\tdef suspendLayoutNotifications ( self ):\n\t\tself.layoutChangeNotifyLocks += 1\n\n\tdef resumeLayoutNotifications ( self ):\n\t\tif self.layoutChangeNotifyLocks > 0:\n\t\t\tself.layoutChangeNotifyLocks -= 1\n\n\tdef notifyLayoutChange ( self ):\n\t\tif self.layoutChangeNotifyLocks == 0:\n\t\t\tQtCore.QMetaObject.invokeMethod ( self, 'layoutChanged', QtCore.Qt.QueuedConnection )\n\n\tdef insertToToolTypes ( self, tool, toolType ):\n\t\tif tool not in self.toolWindowsTypes:\n\t\t\tself.toolWindowsTypes[ tool ] = toolType\n\n\tdef raiseCurrentArea ( self ):\n\t\tif self.lastArea:\n\t\t\tw = self.lastArea.getWidget ()\n\t\t\twhile w.parentWidget () and not w.isWindow ():\n\t\t\t\tw = w.parentWidget ()\n\t\t\tw.raise_ ()\n\t\t\tself.updateDragPosition ()\n\t\tself.raiseTimer.stop ()\n\n\nclass QSizePreservingSplitter ( QSplitter ):\n\n\tdef __init__ ( self, parent = None ):\n\t\tsuper ( QSizePreservingSplitter, self ).__init__ ( parent )\n\n\tdef childEvent ( self, c ):\n\t\t\"\"\"Summary line.\n\t\tArgs:\n\t\t\tc (QChildEvent): Description of param1\n\t \"\"\"\n\t\tif c.type () == QEvent.ChildRemoved:\n\t\t\tl = self.sizes ()\n\t\t\ti = self.indexOf ( cast ( c.child (), QWidget ) )\n\t\t\tif i != -1 and len ( l ) > 1:\n\t\t\t\ts = l[ i ] + self.handleWidth ()\n\t\t\t\tif i == 0:\n\t\t\t\t\tl[ 1 ] = l[ 1 ] + s\n\t\t\t\telse:\n\t\t\t\t\tl[ i - 1 ] = l[ i - 1 ] + s\n\t\t\t\tl.pop ( i )\n\t\t\t\tsuper ().childEvent ( c )\n\t\t\t\tself.setSizes ( l )\n\t\t\telse:\n\t\t\t\tsuper ( QSizePreservingSplitter, self ).childEvent ( c )\n\t\telse:\n\t\t\tsuper ( QSizePreservingSplitter, self ).childEvent ( c )\n\n\n#TEST\nif __name__ == '__main__':\n\timport sys\n\tapp = QApplication ( sys.argv )\n\tapp.setStyleSheet (\n\t\topen ( '/Users/Kanbaru/GitWorkspace/CandyEditor/resources/theme/CryEngineVStyleSheet.qss' ).read ()\n\t)\n\n\tclass Test ( QMainWindow ):\n\t\tdef __init__ ( self, *args ):\n\t\t\tsuper ( Test, self ).__init__ ( *args )\n\n\t\t\ttoolConfig = {}\n\t\t\ttoolConfig[ QTWM_AREA_DOCUMENT_MODE ] = True\n\t\t\ttoolConfig[ QTWM_AREA_IMAGE_HANDLE ] = False\n\t\t\ttoolConfig[ QTWM_AREA_SHOW_DRAG_HANDLE ] = False\n\t\t\ttoolConfig[ QTWM_AREA_TABS_CLOSABLE ] = False\n\t\t\ttoolConfig[ QTWM_AREA_EMPTY_SPACE_DRAG ] = True\n\t\t\ttoolConfig[ QTWM_THUMBNAIL_TIMER_INTERVAL ] = 1000\n\t\t\ttoolConfig[ QTWM_TOOLTIP_OFFSET ] = QPoint ( 1, 20 )\n\t\t\ttoolConfig[ QTWM_AREA_TAB_ICONS ] = True\n\t\t\ttoolConfig[ QTWM_RELEASE_POLICY ] = QTWMReleaseCachingPolicy.rcpWidget\n\t\t\ttoolConfig[ QTWM_WRAPPERS_ARE_CHILDREN ] = False\n\t\t\ttoolConfig[ QTWM_RAISE_DELAY ] = 750\n\t\t\ttoolConfig[ QTWM_RETITLE_WRAPPER ] = True\n\t\t\ttoolConfig[ QTWM_SINGLE_TAB_FRAME ] = False\n\t\t\ttoolConfig[ QTWM_BRING_ALL_TO_FRONT ] = True\n\t\t\ttoolConfig[ \"sandboxMinimizeIcon\" ] = QIcon ( \"./resources/icons/window_minimize.ico\" )\n\t\t\ttoolConfig[ \"sandboxMaximizeIcon\" ] = QIcon ( \"./resources/icons/window_maximize.ico\" )\n\t\t\ttoolConfig[ \"sandboxRestoreIcon\" ] = QIcon ( \"./resources/icons/window_restore.ico\" )\n\t\t\ttoolConfig[ \"sandboxWindowCloseIcon\" ] = QIcon ( \"./resources/icons/window_close.ico\" )\n\t\t\ttoolConfig[ QTWM_TAB_CLOSE_ICON ] = QIcon ( \"./resources/icons/window_close.ico\" )\n\t\t\ttoolConfig[ QTWM_SINGLE_TAB_FRAME_CLOSE_ICON ] = QIcon ( \"./resources/icons/window_close.ico\" )\n\n\t\t\tfrom QToolWindowManager.QMainFrame import CToolWindowManagerClassFactory\n\t\t\tmgr = QToolWindowManager ( self, toolConfig, CToolWindowManagerClassFactory () )\n\t\t\tself.setCentralWidget ( mgr )\n\n\t\t\twidget = QPushButton ( 'hello' )\n\t\t\twidget.setWindowTitle ( 'hello' )\n\t\t\twidget.setObjectName ( 'hello' )\n\t\t\tmgr.addToolWindow ( widget, None, QToolWindowAreaReference.Floating )\n\n\t\t\twidget = QPushButton( 'world' )\n\t\t\twidget.setWindowTitle( 'world' )\n\t\t\twidget.setObjectName( 'world' )\n\t\t\tmgr.addToolWindow ( widget, None, QToolWindowAreaReference.Top )\n\n\t\t\twidget = QPushButton( 'happy' )\n\t\t\twidget.setWindowTitle( 'happy' )\n\t\t\twidget.setObjectName( 'happy' )\n\t\t\tmgr.addToolWindow ( widget, None, QToolWindowAreaReference.Floating )\n\n\t\t\twidget = QPushButton( 'goodness' )\n\t\t\twidget.setWindowTitle( 'goodness' )\n\t\t\twidget.setObjectName( 'goodness' )\n\t\t\tmgr.addToolWindow ( widget, None, QToolWindowAreaReference.Floating )\n\n\t\t\t# result = mgr.saveState ()\n\t\t\t# for w in mgr.toolWindows:\n\t\t\t# \tmgr.moveToolWindow ( w, None, QToolWindowAreaReference.Combine )\n\t\t\t# mgr.restoreState ( result )\n\t\t\t# area = mgr.areaOf ( widget )\n\t\t\t# mgr.hideToolWindow ( widget )\n\t\t\t# area.addToolWindow ( widget )\n\n\twindow = Test ()\n\twindow.show ()\n\twindow.raise_ ()\n\tapp.exec_ ()\n","sub_path":"lib/candy_editor/qt/controls/QToolWindowManager/QToolWindowManager.py","file_name":"QToolWindowManager.py","file_ext":"py","file_size_in_byte":51554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"293578261","text":"# -*- coding: utf-8 -*-\n'''\nЗадание 15.3b\n\nПроверить работу функции parse_cfg из задания 15.3a на конфигурации config_r2.txt.\n\nОбратите внимание, что на интерфейсе e0/1 назначены два IP-адреса:\ninterface Ethernet0/1\n ip address 10.255.2.2 255.255.255.0\n ip address 10.254.2.2 255.255.255.0 secondary\n\nА в словаре, который возвращает функция parse_cfg, интерфейсу Ethernet0/1\nсоответствует только один из них (второй).\n\nПеределайте функцию parse_cfg из задания 15.3a таким образом,\nчтобы она возвращала список кортежей для каждого интерфейса.\nЕсли на интерфейсе назначен только один адрес, в списке будет один кортеж.\nЕсли же на интерфейсе настроены несколько IP-адресов, то в списке будет несколько кортежей.\n\nПроверьте функцию на конфигурации config_r2.txt и убедитесь, что интерфейсу\nEthernet0/1 соответствует список из двух кортежей.\n\nОбратите внимание, что в данном случае, можно не проверять корректность IP-адреса,\nдиапазоны адресов и так далее, так как обрабатывается вывод команды, а не ввод пользователя.\n\n'''\nimport re\nimport argparse\n\ndef incl(f_name):\n regex = ' ip address (\\S+) (\\S+)'\n res_dict = {}\n fl1 = False\n with open(f_name) as f:\n for line in f:\n if line.startswith('interface'):\n fl1 = True\n match = re.match('(interface \\S+)',line)\n elif line.startswith(' ip address '):\n if fl1:\n res_dict[match.group()] = tuple((re.match(regex, line).group(1),\n re.match(regex, line).group(2)))\n temp = res_dict[match.group()]\n fl1 = False\n else:\n res_dict[match.group()] = [(temp),(re.match(regex, line).group(1),\n re.match(regex, line).group(2))]\n print(res_dict)\n\nparser = argparse.ArgumentParser(description='script likes include cisco command and a little more)')\nparser.add_argument('filename', action = \"store\", help = 'File name')\n\nargs = parser.parse_args()\nincl(args.filename)","sub_path":"exercises/15_module_re/task_15_3b.py","file_name":"task_15_3b.py","file_ext":"py","file_size_in_byte":2713,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"20"} +{"seq_id":"9481623","text":"from tkinter import *\nfrom Captura import Captura\nfrom functionalitati_aplicatie import *\nfrom scapy.layers.inet import IP, UDP, TCP, ICMP\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\nfrom matplotlib.figure import Figure\n\n\n## FEREASTRA SECUNDARA\nclass Fereastra_noua(Toplevel):\n\n def __init__(self, master=None):\n super().__init__(master=master)\n self.title(\"Statistics\")\n self.geometry(\"200x200\")\n label = Label(self, text=\"Statistics\")\n label.pack()\n frameChartsLT = Frame(self)\n frameChartsLT.pack()\n pachete = [\"TCP\", \"UDP\"]\n udp = captura.numara_udp()\n tcp = captura.numara_tcp()\n print(tcp, udp)\n slices = [tcp, udp]\n fig = Figure()\n ax = fig.add_subplot(111)\n ax.pie(slices, radius=1, labels=pachete, autopct='%0.2f%%', shadow=True, )\n chart1 = FigureCanvasTkAgg(fig, frameChartsLT)\n chart1.get_tk_widget().pack()\n\n# FACEM FEREASTRA PRINCIPALA\naplicatie_root_window = Tk()\naplicatie_root_window.title(\"Aplicatie Proiect Python\")\naplicatie_root_window.geometry(\"600x400\")\naplicatie_root_window.config(bg=\"#84dbd2\")\n\n# FACEM MENIU BAR-UL\n\nmenu_bar = Menu(aplicatie_root_window)\nmenu_bar.add_command(label=\"EXIT\", command=aplicatie_root_window.quit)\n\nlabel_introducere = Label(aplicatie_root_window,\n text=\"Aplicatie proiect python\",\n bg=\"black\",\n fg=\"white\",\n height=2,\n width=40,\n font=(\"Arial\", 20))\n\nlabel_introducere.place(x=0, y=0)\n\nbutton_captura = Button(aplicatie_root_window,\n command=button_click,\n text=\"CAPTURA\",\n font=(\"Arial\", 18),\n bg=\"#a81919\",\n fg=\"black\",\n width=20,\n height=1\n )\n\nbutton_captura.place(x=0, y=100)\n\nlabel = Label(aplicatie_root_window,\n text=\"Filtru\",\n bg=\"black\",\n fg=\"white\",\n font=(\"Arial\", 17),\n height=1\n )\nlabel.place(x=0, y=200)\n\nentry = Entry(aplicatie_root_window,\n bg=\"black\",\n fg=\"white\",\n font=(\"Arial\", 19)\n )\nentry.place(x=56, y=200)\n\nbutton_captura_filtru = Button(aplicatie_root_window,\n command=lambda: evaluare_entry(entry),\n text=\"OK\",\n font=(\"Arial\", 18),\n bg=\"#a81919\",\n fg=\"black\",\n width=10,\n height=1\n )\nbutton_captura_filtru.place(x=341, y=200)\n\nshow_statistics = Button(aplicatie_root_window,\n text=\"Show Statistics\",\n font=(\"Arial\", 18),\n bg=\"white\",\n fg=\"black\",\n )\n\nshow_statistics.bind(\"